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Exam Code: Salesforce-Consumer-Goods-Cloud Salesforce Certified Consumer Goods Cloud Accredited Professional syllabus 2023 by team
Salesforce Certified Consumer Goods Cloud Accredited Professional
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Killexams : Salesforce Professional syllabus - BingNews Search results Killexams : Salesforce Professional syllabus - BingNews Killexams : Salesforce stops reporting individual revenue figures for Slack and Tableau Salesforce CEO Marc Benioff speaks at CES in Las Vegas. (GeekWire Photo / Kevin Lisota) © Provided by Geekwire Salesforce CEO Marc Benioff speaks at CES in Las Vegas. (GeekWire Photo / Kevin Lisota)

Salesforce said it would no longer divulge the individual revenue numbers of acquired companies including Slack and Tableau in its quarterly earnings filings and instead focus on percentage changes.

Initial signs of the new reporting standard came Wednesday when the customer-relationship giant released earnings for the quarter ended April 30.

The change reflects a “standard approach for acquired companies at this stage,” a Salesforce spokesperson told GeekWire in an email Thursday.

In the past, Salesforce provided separate financial figures for Slack, MuleSoft, and Tableau in its regular financial reports. However, in the most exact filings, the revenue from each company was consolidated under Salesforce’s professional services, subscription, and support revenue categories. The company also combines Tableau and MuleSoft revenue under the “data” category.

Salesforce this week presented financial results for its divisions and acquired companies as percentage increases in this chart for its first fiscal quarter. The company previously gave specific dollar amounts for Slack, Tableau, and MuleSoft revenue. (Salesforce Graphic) © Provided by Geekwire Salesforce this week presented financial results for its divisions and acquired companies as percentage increases in this chart for its first fiscal quarter. The company previously gave specific dollar amounts for Slack, Tableau, and MuleSoft revenue. (Salesforce Graphic)

Earlier this year, activist investors criticized Salesforce executives for the company’s string of pricey acquisitions. Salesforce bought Tableau for $15.7 billion, whereas Slack was acquired for $28 billion. The company disbanded the board’s M&A committee in March, in part to signal its effort to increase profitability.

Salesforce has conducted a number of cost-cutting measures in response to activist pressure and a broader market slowdown. The company announced in January a plan to lay off 10% of its employees.

In February, Fortune reported that Slack is ditching its headquarters and moving into Salesforce’s office tower in San Francisco. Salesforce confirmed last week that it would put Tableau’s headquarters building in Seattle on the sublease market. 

Fri, 02 Jun 2023 06:04:00 -0500 en-US text/html
Killexams : Salesforce Shares Fall as Customers Watch Spending

Stephen Lam / Stringer / Getty Images © Provided by Investopedia Stephen Lam / Stringer / Getty Images

Key Takeaways

  • Salesforce shares dropped as CapEx spending rises and customer behavior changes.
  • Salesforce's quarterly earnings, revenue, and current quarter outlook exceeded forecasts.
  • Despite the stock price's decline on Thursday, shares were up about 60% for 2023.

Salesforce (CRM) was the worst-performing stock in the Dow in early trading on Thursday after the cloud-based enterprise software provider reported higher-than-expected spending and warned of changes in customer buying behavior.

Shares dropped even though Salesforce posted strong fiscal 2024 first quarter results, with earnings per share (EPS) of $1.69 and revenue up 11.3% to $8.25 billion. Both were better than forecasts. The company also raised its profit outlook for the full year and gave current quarter guidance that exceeded analysts’ estimates.

However, Salesforce said capital expenditures (CapEx) jumped 35.8% to $243 million, almost $40 million more than anticipated. In addition, COO Brian Millham noted that clients were continuing to look closely at deals, and are taking more time to close them than in the past. He noted that the company’s professional services business began to see less demand for multiyear transformations, and in some cases, they delayed projects.

CFO Amy Weaver added that along with pressure on professional services, more customers are choosing to contract on the time and material basis.

Salesforce shares were down 4.5% as of 11:16 a.m. ET on Thursday, though they were still up about 60% year-to-date. 

YCharts © Provided by Investopedia YCharts
Thu, 01 Jun 2023 03:28:22 -0500 en-US text/html
Killexams : Inactive Salesforce Communities could leak sensitive data

Threat actors could gain access to improperly deactivated or unmaintained Salesforce sites by changing the host header, thereby gaining access to sensitive personal and business data.

In a Wednesday blog post by Varonis Threat Labs, researcher Nitay Bachrach wrote so-called “ghost sites” are Salesforce communities that are no longer being used. The abandoned sites were originally designed to allow partners and customers to collaborate within a company’s Salesforce environment. Ghost sites are simply forgotten or unused collaborative sites that instead of being deactivated create a liability, researchers said.

However, the Salesforce sites still pull new data and can be easily found on the public internet and can be exploited by attackers. 

“Because these unused sites are not maintained, they aren’t tested against vulnerabilities, and admins fail to update the site’s security measures according to newer guidelines,” Bachrach, the author of the Varonis post, wrote.

Ghost sites start when custom domain names are created and point to the Salesforce Community Site by configuring the DNS record. Risk is introduced when companies move to a different vendor, Bachrach explained. Varonis Threat Labs researchers discovered, many companies only changed the DNS records and did not remove the custom domain or deactivate the Salesforce Site.

Since the Salesforce site is still active, attackers can access them by simply changing the host header. Tools that index and archive DNS records, such as SecurityTrails, make identifying ghost sites easier for attackers, Bachrach noted. 

“Our research found many such sites with confidential data, including PII and sensitive business data that were not otherwise accessible,” he wrote. “The exposed data is not restricted to only old data from when the site was in use; it also includes new records that were shared with the guest user due to the sharing configuration in their Salesforce environment.”

To avoid the issue, Varonis researchers said that Salesforce Communities should be deactivated.

Fri, 02 Jun 2023 10:35:00 -0500 en text/html
Killexams :

All Articles for

Salesforce. com Inc. is a global enterprise software company headquartered in San Francisco, California. Though best known for its customer relationship management (CRM) product, Salesforce has also expanded into the "social enterprise arena" through acquisitions. It is currently ranked the most innovative company in America by Forbes magazine, as well as number 27 in Fortune's magazine's 100 Best Companies to Work For in 2012.

Tue, 01 Dec 2020 01:49:00 -0600 en text/html
Killexams : Salesforce (CRM) Q1 2024 Earnings Call Transcript

Salesforce (NYSE: CRM)

Q1 2024 Earnings Call

May 31, 2023, 5:00 p.m. ET


  • Prepared Remarks
  • Questions and Answers
  • Call Participants

Prepared Remarks:


Welcome to Salesforce fiscal 2024 first quarter results conference call. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question-and-answer session. [Operator instructions] I would like to hand over the conference to your speaker, Mike Spencer, executive vice president and investor relations.

Sir, you may begin.

Mike Spencer -- Executive Vice President, Investor Relations

Good afternoon, and thanks for joining us today on our fiscal 2024 first quarter results conference call. Our press release, SEC filings, and a replay of today's call can be found on our website. With me on the call today is Marc Benioff, chair and CEO; Amy Weaver, president and chief financial officer; and Brian Millham, president and chief operating officer. As a reminder, our commentary today will include non-GAAP measures.

Reconciliations between our GAAP and non-GAAP results and guidance can be found in our earnings and press release. Some of our comments today may contain forward-looking statements that are subject to risks, uncertainties, and assumptions which could change. Should any of these risks materialize or should our assumptions prove to be incorrect, actual company results could differ materially from these forward-looking statements. A description of these risks, uncertainties, and assumptions and other factors that could affect our financial results is included in our SEC filings, including our most exact report on forms 10-K, 10-Q, and any other SEC filings.


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This article is a transcript of this conference call produced for The Motley Fool. While we strive for our Foolish Best, there may be errors, omissions, or inaccuracies in this transcript. As with all our articles, The Motley Fool does not assume any responsibility for your use of this content, and we strongly encourage you to do your own research, including listening to the call yourself and practicing the company's SEC filings. Please see our Terms and Conditions for additional details, including our Obligatory Capitalized Disclaimers of Liability.

The Motley Fool has positions in and recommends Salesforce. The Motley Fool has a disclosure policy.

Except as required by law, we do not undertake any responsibility to update these forward-looking statements. And with that, let me hand the call to Marc.

Marc Benioff -- Chairman and Co-Chief Executive Officer

Thanks, Mike, and thank you all for being on the call. On our last call in March, we told you about how Salesforce had radically accelerated our transformation to profitable growth. We shared with you how we hit the hyperspace button across the key areas of our transformation, restructuring for the short and long term, reigniting our performance culture by focusing on productivity, operational excellence, and profitability; prioritizing our core innovations that drive customer success; building even stronger relationships with you, our investors. Our Q1 results show that we continue to make great progress.

As I said in March, we're just getting started with this incredible transformation. We continue to scrutinize every dollar of investment, every resource, and every spend. And we're transforming every corner of our company. Our progress over the last five months, well, it's very impressive, and I cannot be more grateful to our entire team for their leadership.

In fact, you may hear me say that several times on this call. Our transformation drove our Q1 financial results. As I said on our last call, well, improving profitability is our highest priority. As a result, we significantly exceeded our margin target for the quarter, delivering a non-GAAP operating margin of 27.6%, up 1,000 basis points year over year.

Incredible. And there's no greater point of evidence to our transformation than this amazing result following the tremendous operating margin Q4. In Q1, we delivered 8.2 billion in revenue, up 11% year over year and 13% in constant currency. We had some amazing wins in the quarter with Northwell Health, Paramount, Siemens, Spotify, NASA, and the U.S.

Department of Agriculture, among others. We delivered 4.5 billion in operating cash flow, up 22% year over year. Our remaining performance obligation ended the quarter at 46.7 billion, an increase of 11% year over year. And through Q1, we've now returned more than $6 billion in share repurchases.

As a result, for the third quarter in a row, we ended the quarter with fewer shares year over year, another amazing point of evidence on this incredible transformation. Now, turning to our financial guidance. While the economy is not in our control, our margins are, which is why we're raising our margin target for the full fiscal year. For FY '24, we're raising our non-GAAP operating margin to 28%, an improvement of 550 basis points year over year.

And we remain confident that we'll hit 30% non-GAAP operating margins in the first quarter of fiscal year '25. We could not be more excited about our progress. We're maintaining our fiscal year '24 revenue guidance of approximately 34.5 billion to 34.7 billion, over 10% projected growth year over year. I couldn't be more proud of how our team has come together, stepped up, and delivered these results.

I've also been asked numerous times this quarter by our investors and our customers how we're able to make so much progress so fast and deliver these incredible numbers. It's very simple. It's our Ohana culture. It's our superpower.

And again, I'd like to thank our amazing team for this incredible accomplishment. Last quarter, I told you about how our AI team is Getting ready to launch Einstein GPT, the world's first generative AI for CRM. At Trailhead DX in March in front of thousands of trailblazers here in San Francisco, that's exactly what we did. At its foundation, Einstein GPT is open and extensible.

Customers can connect to multiple large language models, including from partners like OpenAI, Anthropic, and others, this is a whole new way to work for our customers, users, and trailblazers. Users on Salesforce are seeing new AI generative features across all of their most common workflows. And while many of these will be created by Salesforce developers, far more will be created by our incredible trailblazer ecosystem. For low-code trailblazers, Einstein GPT will provide a tool set to design generative AI apps built on reasonable prompts.

For pro-code trailblazers, Einstein GPT will offer an extensible ecosystem of LLM providers with configurable grounding. And Einstein GPT is the culmination of tremendous research and engineering by our world-class AI team. And I'd like to congratulate them on this amazing result. And one more amazing result, this week, Einstein -- Salesforce Einstein that we've been talking about for so many years on these calls will generate an incredible 1 trillion predictions for our customers, an incredible milestone on our AI journey.

We saw more of the incredible work of our AI team at our New York City World Tour this month when we demonstrated Slack GPT. Slack is a secure treasure trove of company data that generative AI can use to deliver every company and every employee their own powerful AI assistant, helping every employee be more productive and transforming the future of work. Slack GPT can leverage the power of generative AI to deliver instant conversation summaries, research tools, and writing assistance directly in Slack. And you may never need to leave Slack to get a question answered.

Slack is the perfect conversational interface for working with LLMs, which is why so many AI companies are Slack-first and why OpenAI, ChatGPT, and Anthropic squad can now use Slack as a native interface. Slack is also delivering integrated sales and service experiences powered by native GPT to be the best interface for all of our Salesforce customers. And there's a lot more magic to come with Slack and generative AI. And this month, we also announced Tableau GPT at our Tableau conference, where we had over 8,000 in-person attendees.

Tableau GPT simplifies data analysis for all of our users, enabling anyone to inquire about their data using Einstein GPT and obtain AI-driven insights at scale. The intelligence and automation that Tableau GPT provides is tremendously important in this area of hyperscale data that we're all entering. The coming wave of generative AI will be more revolutionary than any technology innovation that's come before in our lifetime, or maybe any lifetime. Like Netscape Navigator, which opened the door to a greater internet, a new door has opened with generative AI.

And it is reshaping our world in ways that we've never imagined. Every CEO realizes they're going to have to invest in AI aggressively to remain competitive, and Salesforce is going to be their trusted partner to get them to do just that. Every CEO I've spoken with sees AI as a revolution, beginning and ending with the customer. And every CIO I've spoken with wants more productivity, more automation, and more intelligence through using AI.

A great example already deploying this technology is Gucci. We're working with them to augment their client advisors by building AI chat technology that creates a Gucci-fied tone of service. Well, incredible new voice, amplifying brand storytelling, and incremental sales as well. It's an incredibly exciting vision for generative AI to transform what was customer service into now customer service, marketing, and sales, all through augmenting Gucci employee capabilities using this amazing generative AI.

But we can only do all of this with trust. Our customers need to understand where their data is going, and they must be able to maintain data integrity and access and privacy controls. Large customers must maintain data compliance as a critical part of their governance while using generative AI and LLMs. This is not true in the consumer environment.

But it is true for our customers, our enterprise customers who demand the highest levels of this capability. For customers who are, for years, have used relational databases as the secure mechanism of their trusted data, they already have that high level of security to the row and cell level. We all understand that. And that is why we have built our GPT Trust Layer into Einstein GPT.

The GPT Trust Layer gives connected LLMs secure, real-time access to data without the need to move all of your data into the LLM itself. It's an incredible breakthrough for our customers in working with LLMs in a secure and trusted way. While they're using the LLMs, the data itself is not moving and being stored in the LLM. That is what our customers want.

They can be sure that the customer data is where they know it is, where they can be assured that it is for their compliance and for their governance. And I could not be more excited about our AI CRM and delivering on this future of trusted AI through our new Salesforce GPT Trust Layer. Finally, I can't talk about AI without talking about the success of our Data Cloud. Data Cloud is the heart of Customer 360 and now our fastest-growing cloud ever.

Data Cloud creates a real-time intelligent data lake that brings together and harmonizes all of our customers' data in one place. In Q1, we closed one of our largest healthcare industry deals ever with Northwell Health, New York's largest private employer. They have 21 hospitals, 900 outpatient facility or ambulatory facilities, and their own medical school all in New York. By integrating Data Cloud with Health Cloud, Tableau, MuleSoft, well, our entire Customer 360 vision, Northwell is improving patient care by bringing together its vast data resources to create a single source of truth and using AI to govern data, use, and maintain regulatory compliance.

This is the future of our customers and our industry. It's AI plus data plus CRM. And, of course, this AI revolution is just getting started, which is why we've invested 250 million in our new AI venture fund to fuel start-ups developing our trusted generative AI vision. We'll be talking more about this at our AI Day event on June 12th in New York City, and I hope that you'll join me there.

To wrap up, we're transforming every corner of our company. We're laser focused on our short-term and long-term restructuring, improving productivity and performance, prioritizing our core innovations, and delivering for our shareholders. As a result, productivity is up, profitability is up, revenue is up, cash flow is up, and we've dramatically increased our margin guidance. And just like the cloud, mobile, and social, well, AI, this revolution is a new innovation cycle.

It's going to be a new spending cycle as well, which is going to spark a massive new tech buying cycle. And we've led the industry through each of these cycles, and I couldn't be more excited for our future as we continue on a path to our long-term goal to make Salesforce the largest, most profitable enterprise software company in the world, and the No. 1 safest and most trusted AI CRM. With that, Brian, I'll turn it over to you.

Brian Millham -- President and Chief Operating Officer

Thanks, Marc. As Marc said, we're continuing our transformation across every part of our company. Our focus on performance, culture, and operational excellence contributed to our strong first quarter results. Since our last call, we've removed layers to get closer to our customers and took complexity out of our business to help us accelerate through the rest of the year.

We clearly defined our return in remote office guidelines for employees, and it's been great to get together even more in our offices and with our customers around the globe. I had the chance to visit many of our offices this quarter, and the energy is incredible. As you heard from Marc, our transformation plant continues to deliver top- and bottom-line growth as we help our customers increase productivity, drive efficiency, and become AI-first companies. But we're still operating in an uncertain macro environment.

Customers continue to scrutinize every deal, and we see elongated deal cycles and deal compression, particularly in our more transactional revenue streams like SMB, create and close, and self-serve. Also, in Q1, our professional service business started to see less demand for multiyear transformations, and, in some cases, delayed projects as customers focused on quick wins and fast time-to-value. But for this reason, we saw strong performance from some of our fast time-to-value efficiency-focused products with sales performance management, sales productivity, and digital service all growing annual recurring revenue above 40% in the quarter. As customers look to reduce complexity and achieve faster time-to-value, they're expanding their adoption of Salesforce clouds, a key growth strategy for us.

The world's most recognized companies are relying on Salesforce. More than 90% of the Fortune 100 use Salesforce, and they average more than five of our clouds. This is why we're so excited about our AI plus data plus CRM strategy. As Marc explained, we're building Einstein GPT and Data Cloud into every cloud in our Customer 360, and we're perfectly positioned to help our customers harness the phenomenal power of AI.

Our core offerings remain resilient. In Q1, nine of our top 10 deals included sales, service, and platform. Industry clouds continue to be a tailwind to our growth, and we saw momentum with great customers like Northwell, USDA Rural Development, and NASA, who we showcased at World Tour D.C. in April.

Once again, eight of our industry clouds grew ARR above 50%. I met with hundreds of customers in the quarter, and we hosted 700 meetings in our innovation centers with our top customers and prospects. Generative AI is top of mind for all of them as they look to benefit from the intelligence, automation, and cost savings that Salesforce is uniquely positioned to deliver. We're seeing tremendous appetite for our new generative AI products, starting with Einstein GPT, Slack GPT, and Data Cloud.

Our generative AI products will be catalysts for our future growth. As Marc mentioned, Data Cloud continues to be one of our fastest growing products, and we had great wins in the corner with companies like Major League Soccer and Giorgio Armani. Armani uses Data Cloud to deliver hyper-personalized online and in-store experiences, real-time engagement, and curated shopping recommendations. We can see how Data Cloud and Einstein GPT are going to create experiences that weren't possible before and really drive growth.

In an environment where customers are optimizing their current tech stacks, integration and automation continue to be efficiency drivers. MuleSoft again delivered strong results with wins at Siemens [Inaudible] and Vodafone. For the first time, Salesforce was ranked No. 1 in integration by market share in the latest IDC Software Tracker, a great testament to our MuleSoft team.

Tableau is unleashing the power of our data cloud, unlocking customer data, and delivering actionable real-time insights. In the quarter, we had great wins at customers like Union Bank of the Philippines, Discovery Financial Service, Moderna, ADT Solar, and Alaska Air. We've made great investments to reaccelerate Tableau, including new leadership, along with product innovations like Tableau GPT and revenue intelligence, now one of our fastest growing add-ons. I'm really encouraged by the Slack team who has created an ambitious product roadmap with generative AI at the center.

In Q1, we saw amazing momentum with customers, like the California Office of System Integration, Paramount Global, Revel, and OpenAI, and rolled out an AI-ready platform, Slack canvas, and app integrations with ChatGPT and Anthropic squad. Overall, I could not be more thrilled with our offerings and the market position, especially as it relates to delivering on the promise of AI. We're looking forward to continuing the energy and momentum at our AI day in just a couple of weeks. I'm very proud of the teams and of our partners.

Their focus on customer success continues to be outstanding. As Marc said, our productivity is up, profitability is up, revenue is up, cash flow is up. We're increasing our margin guidance. And Salesforce is leading the way as the No.

1 AI CRM. Now, over to you, Amy.

Amy Weaver -- Chief Financial Officer

Thank you, Brian. As Marc said, a key part of our transformation to profitable growth is short- and long-term restructuring of the company. We have now largely completed the restructuring announced in January, and we're completing our comprehensive operating and go-to-market review. As we shift to the implementation phase, we're executing against three key pillars: optimization of resources and organization structure, product investment prioritization, and operational rigor.

We continue to view sales and marketing and G&A as the primary drivers of leverage, while R&D remains an important investment area. Our profitable growth framework, disciplined capital allocation strategy, and opportunity to drive shareholder value are represented in our actions and in our results. Now, turning to our results for Q1 fiscal year '24, beginning with top-line commentary. For the first quarter, revenue was 8.2 billion, up 11% year over year or 13% in constant currency, with the beat primarily driven by strong momentum in Neilsoft and more resilient core performance.

Geographically, we saw strong new business growth in parts of EMEA and Latam, specifically Switzerland, Italy, and Brazil, while we experienced continued pressure in the United States. In Q1, the Americas revenue grew 10%, EMEA grew 12% or 17% in constant currency, and APAC grew 16% or 24% in constant currency. From an industry perspective, manufacturing, automotive, and energy all performed well, while high tech and financial services remained under pressure. Q1 revenue attrition ended the quarter at approximately 8%.

As expected, we saw a modest increase in Q1, partially attributed to the inclusion of Tableau in the metric. We also noted some incremental weakness in our marketing and commerce attrition. As Marc said, non-GAAP operating margin finished strong in Q1 at 27.6%, driven by our discipline investment strategy and accelerating our restructuring efforts. Q1 operating cash flow was 4.5 billion, up 22% year over year.

This includes a 910 basis points headwind from restructuring. Q1 free cash flow was 4.2 billion, up 21% year over year. Turning to remaining performance obligation, or RPO, which represents all future revenue under contract, this ended Q1 at 46.7 billion, up 11% year over year. Current remaining performance obligation, or CRPO, ended at 24.1 billion, up 12% year over year in both nominal and constant currency, ahead of expectations and driven by strong core performance partially offset by continued create and close softness.

And finally, we continue to deliver on our capital return commitment. In Q1, we returned 2.1 billion in the form of share repurchases, bringing the total returned to more than 6 billion since the program was initiated last August, representing more than 38 million shares. Before moving to guidance, I wanted to briefly touch on the current macro environment that Brian discussed. The more measured buying behavior persisted in Q1.

And as Brian noted, in Q1, we started to see weakness in our professional services business. We expect these factors to persist, which is incorporated in our guidance. Let's start with fiscal year '24. On revenue, we are holding our guidance of 34.5 billion to 34.7 billion, representing over 10% growth year over year in both nominal and constant currency.

The strength in our Q1 performance is offset by the pressure in our professional services business previously discussed. For fiscal year '24, we are raising non-GAAP operating margin guidance to 28%, representing at 550 basis points improvement year over year. This guidance increases driven by the acceleration of our restructuring efforts and also includes reinvestment in targeted areas, namely in R&D. I'm proud of our progress and remain confident in our trajectory as we progress toward our 30% non-GAAP operating margin target in Q1 '25.

We also remain focused on stock-based compensation and continue to expect it to Excellerate this year to below 9% as a percent of revenue. Before moving to EPS, on restructuring, we now expect the charges in FY '24 to come in toward the higher end of the range previously provided in our last earnings release. As a result of these updates, we now expect fiscal year '24 GAAP EPS of $2.67 to $2.69, including estimated charges for the restructuring of $1.11. Non-GAAP EPS is now expected to be $7.41 to $7.43.

And we are raising our fiscal year '24 operating cash flow growth to be approximately 16% to 17%, which now includes a 14- to 16-point headwind from restructuring. As a reminder, we will see an increase in our cash taxes in fiscal '24 as we draw down our remaining net operating losses. Capex for the fiscal year is expected to be slightly below 2.5% of revenue. This results in free cash flow growth of approximately 17% to 18% for the fiscal year.

Now, to guidance for Q2. On revenue, we expect 8.51 billion to 8.53 billion, growth of approximately 10% in both nominal and constant currency. CRPO growth for Q2 is expected to be approximately 10% year over year in nominal and constant currency. Our guidance incorporates the momentum of our execution in Q1, offset by the persistent measured buying behavior and a decline in professional services fixed fees contribution.

The professional services impact represents approximately a 1 point headwind in growth. For Q2, we expect GAAP EPS of $0.79 to $0.80 and non-gap EPS of $1.89 to $1.90. And as we focus on shareholder return and discipline capital allocation, we continue to expect to fully offset our stock-based compensation dilution through our share repurchases in fiscal year '24. In closing, we continue to transform every corner of the company.

We are hyper focused on delivering the next wave of innovation led by Data Cloud and Einstein GPT. And Salesforce is well positioned to remain the market leader in this new AI-first world. We are committed to delivering long-term shareholder value. And I personally want to thank our shareholders for their continued support.

Now, Mike, let's open up the call for questions.

Mike Spencer -- Executive Vice President, Investor Relations

Thanks, Amy. Operator, we'll move to questions now. I ask everyone only ask one question in respect for others on the call. In addition, I'd like to introduce Srini Tallapragada, our head of engineering, who will be joining us for Q&A today.

With that, Emma, let's move to the questions.

Questions & Answers:


[Operator instructions] Your first question today comes from the line of Kirk Materne with Evercore. Your line is open.

Kirk Materne -- Evercore ISI -- Analyst

Oh, yeah. Thanks very much, and congrats on the good start to the year. You know, Marc, you've been through a number of cycles from a technology perspective. I was just kind of curious where you think we are in terms of people investigating AI versus when the spending cycle around it might kick in.

Just deliver us an idea of, you know, sort of your thoughts on that and really just the opportunity for you all to monetize AI within your product base. Thanks.

Marc Benioff -- Chairman and Co-Chief Executive Officer

Well, I think this is the absolute question of the day, which is we are about to enter an unbelievable super cycle for tech, and everyone can see that. This is an incredible opportunity for not only Salesforce, but our entire industry. I mean, perhaps, only a year ago or less than a year ago, no one on this call even knew what GPT was. Today, ChatGPT is the fastest-growing consumer product of all time and has transformed many, many lives.

It's definitely not just the technology of this lifetime, but maybe any lifetime. It's an incredible technology. And every company is going to have to transform because every company is going to have to become more productive, more automated, more intelligent through this technology to be competitive with other companies. And just yesterday, I'm in a room here at the top of Salesforce Tower on the 60th floor, and we have the CEO of a very large bank here.

And like every other sales call I've made in the last quarter, there's only one thing that customers want to talk about, and that's artificial intelligence, and specifically, generative AI. Of course, we have been a leader in this area with Einstein, more than a trillion transactions delivered this week. But these are primarily predictive transactions built on machine intelligence, machine learning, and deep learning. But in 2018, deep learning evolved and became much more sophisticated and became generative, as these neural networks expanded their capabilities.

And, also, the hardware went to another level as well. So, now we have this incredible new capability. It's a new platform for growth. And I couldn't be more excited.

But yesterday, there were many questions from my friend, who I'm not going deliver you his name because he's one of the CEO of one of the largest and most important banks in the world. And I'll just say that, of course, his primary focus is on productivity. He knows that he wants to make his bankers a lot more successful. He wants every banker to be able to rewrite a mortgage.

But not every banker can because writing a mortgage takes a lot of technical expertise. But as we showed him in the meeting through a combination of Tableau, which we demonstrated, and Slack, which we demonstrated, and Salesforce's financial services cloud, which he has tens of thousands of users on, that banker understood that this would be incredible. But I also emphasized to him that LLMs, or large language models, they have a voracious appetite for data. They want every piece of data that they can consume.

But through his regulatory standards, he cannot deliver all that data into the LLM because it becomes amalgamated. Today, he runs on Salesforce, and his data is secured down to the row and cell level. He knows that readers don't block writers, that there's all types of security provisions regarding who can see what data about what account or what customer. And when you put it into an LLM, those permissions are not understood.

So, that is a very powerful moment to realize that the way that LLMs operate is in a wait state where they're kind of consuming all this data and then giving us that information back out. Well, that's Salesforce's opportunity. That's why we built this GPT Trust Layer. And through the GPT Trust Layer and rebuilding all of our apps, including Slack and Tableau, but as we demonstrated to him yesterday, a new sales cloud, a new service cloud, a new marketing cloud, and what we'll show on June 12th in New York City, a complete reconceptualization of our product line.

What that means for this customer and for every customer is that they have an opportunity to transform their business. And for Salesforce, that also means an opportunity to transform ourselves; and for our industry, a new super cycle, where every company will have to transform to be AI-first.


Your next question comes from the line of Keith Weiss with Morgan Stanley. Your line is open.

Elizabeth Porter -- Morgan Stanley -- Analyst

Great. This is Elizabeth Porter on for Keith Weiss. Thanks for the question. I wanted to ask on the potential disruption from rebooting the sales enablement process.

Are we past the point of seeing disruption, or could that be a future risk? And if so, how is it included in guidance? The CRPO guidance for 10% looks like a bit of a slowdown despite the easier comp. And, Amy, you called out pro services at 1 point headwind. So, just any other factors we should keep in mind that may create a challenge over the next couple months. Thank you.

Marc Benioff -- Chairman and Co-Chief Executive Officer

Well, I'll tell you that I think that as you know, in Q1 we went through tremendous disruption with human resources in our company, and it was very disruptive to all of our Ohana. And, you know, I'm so grateful to them for how they supported the whole company, all the customers, and themselves during what was probably one of the most disruptive quarters that I've seen. And yet, we delivered these incredible numbers and this incredible technology vision going forward. In terms of enablement of the sales organization, its ability to kind of move forward, that is not, I would say, a material part of what happened in the quarter or what's going to happen for the year.

Our sales organization remains with a very high level of productivity. But let me turn it over to Brian to speak directly to his strategy on delivering the year.

Brian Millham -- President and Chief Operating Officer

Yeah, Marc, thank you. I appreciate it. And, Elizabeth, thank you for the question. I think you're referencing some comments we made on previous calls about enablement being an important strategy for us as we saw during the pandemic.

Not as many of our AEs, NSCs, and leaders were as enabled as we would like. We've made those changes, and we've really invested in the time to make sure our AEs understand our product portfolio, the entire Customer 360. And we're on sort of the next generation of enablement. As Marc just talked about, this new AI wave is going to create a huge opportunity for us.

And we need to make sure that we're investing in the enablement to bring our teams along. It's been a very short window around this innovation, and we've got some work to do on this. But we're very, very excited with our path for a position in the market. All that we're doing with our customers, the demand we're feeling from our customers, Marc mentioned it.

And I had the same experience. Every CEO in the world is talking to us about generative AI right now. And we are investing heavily to make sure our account executives, our sales teams, in fact, the entire company is able to articulate our value proposition to our customers. So, Amy, I don't know if you have any further comments there.

Amy Weaver -- Chief Financial Officer

Sure, Elizabeth, you mentioned CRPO and professional services, so let me jump in on that. For our guide for this next quarter, we are seeing some pressures from the macro situation, and then, also, specifically from professional services. And there's a bit of a nuance with ProServ. I want to make sure people understand.

So, if you back up, our customers can contract for professional services in two ways, either on a time and materials basis, which is typically used for smaller projects, or on a fixed fee kind of milestone basis. For purposes of CRPO, we only include projected revenue from fixed fee deals. One of the things that we are seeing right now is not only professional services, as a whole, seeing pressure, but more customers are choosing to contract on a time and materials basis, which is not included in our CRPO. So, as a result, we're seeing kind of a double pressure there.

And I'm expecting, you know, a full 1 point headwind to CRPO for the quarter from professional services.

Mike Spencer -- Executive Vice President, Investor Relations

Thanks, Elizabeth. Emma, let's move to the next question, please.


Your next question comes from the line of Brad Sills with Bank of America. Your line is open.

Brad Sills -- Bank of America Merrill Lynch -- Analyst

Oh, wonderful. Thanks. I wanted to ask a question to Brian, I think, here on the efforts here to Excellerate productivity. You mentioned removing some layers here.

My question is, we think of all these actions that you're taking as drivers of margin expansion, but are you starting to see some early traction here on the sales productivity front such that, perhaps, that's driving some upside here across the business, you know, perhaps, larger deals now that you're seeing coming out of the field and pipeline and some of the deal closure? Thank you so much.

Brian Millham -- President and Chief Operating Officer

Thanks, Brad, for the question. I really appreciate it. As you know, we're operating in a constrained environment right now. And so, we are really focused on this productivity measure and metric for our organization right now, investing heavily, as I mentioned earlier, in the enablement part of our organization.

Also, looking at other ways to drive productivity. And one of the things that we're talking quite a bit about right now is pricing and packaging, bringing together logical products that we can be selling in a single motion versus our go-to-market, which is in largely aligned by product. How do we focus on a larger average deal size for every transaction? And so, big investments on that front. Really, a strong focus on productivity as it relates to moving people upmarket as well.

We're thinking about self-serve in the bottom end of our market. How do we drive a self-serve motion, an automated motion at the low end of our market to bring our account executives upmarket to drive higher productivity in the sales organization? So, clearly, a big motion for us right now. Feel very good about our big deal motion. Actually, in Q4, we saw some -- sorry, Q1, we saw some very good big-deal execution from the team.

That is not really an area that has held us back. We feel very good about our ability to transform companies and transact these large businesses. It really is the velocity business that has held us back a bit on our create and close, some of the SMB transactions. So, we have a clear focus in this area to drive the productivity with our plans going to Q2 and beyond into Q4.

Mike Spencer -- Executive Vice President, Investor Relations

Thanks, Brad. Emma, next question, please.


Your next question comes from the line of Brent Thill with Jefferies. Your line is open.

Brent Thill -- Jefferies -- Analyst

Amy, regarding Americas, that was a pretty large decel, one of your slow growth quarters, I think, ever in Americas. The rest of the world did decel, but maybe not quite as the magnitude of the Americas. Can you just speak to what happened there in that region?

Amy Weaver -- Chief Financial Officer

Sure. So, thanks, Brent, for the question. The Americas did see a deceleration at 10% year-on-year revenue growth, you know, compared to 17% in EMEA and about 24% in nominal and APAC. We are continuing to see most of the pressure in North America.

There were some real pockets of acceleration in EMEA and in Latam, particularly in Switzerland, I think Brazil, Italy. So, we are seeing some good things. But North America has taken the brunt of the deceleration. Brian, do you want to come in and see if you can address that in more detail?

Brian Millham -- President and Chief Operating Officer

Sure. Yeah, I think when we think about our business from an industry perspective, we have a very nice footprint of our great technology companies and financial services company. Both of which were a bit slower than we would have liked in the Americas in Q1. And so, as we think about the all-in size of Americas business, those industries felt a little bit more of the economic headwind in the quarter in Q1.

And so, I think a bit of a slowdown from that perspective is a result you're seeing in the Americas business.

Mike Spencer -- Executive Vice President, Investor Relations

Thanks, Brent. Emma, next question, please.


Your next question comes from the line of Mark Murphy with JPMorgan. Your line is open.

Mark Murphy -- JPMorgan Chase and Company -- Analyst

Thank you very much, and I'll add my congrats. So, Marc, it feels like the tech and software industry has had a recession without the broader economy being in a recession quite yet. And that's very unusual. Do you think with all the purging and optimizing of IT budgets, which is already taking place, plus Salesforce's headcount optimization already being underway, that, perhaps, the next recession might actually be more manageable or easier to navigate than what you had seen in some of the prior cycles?

Marc Benioff -- Chairman and Co-Chief Executive Officer

Well, I think that this is a great question. I tried to address it on the last call. I just really think you have to look at 2020, 2021 was just this massive super cycle called the pandemic. I don't know if you remember, but we had a pandemic a couple years ago.

And during that, we saw tech buying like we never saw. It was incredible. And everybody surged on tech buying. So, you're really looking at comparisons against that huge mega cycle.

And that is what I think is extremely important to understand, the relative comparisons. And that is where my head is at, which is I am constantly comparing against, what happened in 2021, but also looking at 2020 and 2019. That's a little bit different than '08, and that's a little bit different than '01. We didn't exactly have these huge mega cycles that kind of we were exiting.

And that's also what gives me tremendous confidence going forward in that what we're really seeing is that customers are absorbing the huge amounts of technology that they bought. And that is about to come, I believe, to a close. I can't deliver you the exact date. And it's going to be accelerated by this AI super cycle.

Mark Murphy -- JPMorgan Chase and Company -- Analyst

Thank you.

Mike Spencer -- Executive Vice President, Investor Relations

Thanks, Marc. Emma, next question, please.


Your next question comes from the line of Brent Bracelin with Piper Sandler. Your line is open.

Brent Bracelin -- Piper Sandler -- Analyst

Good afternoon. I wanted to circle back to the generative AI discussion if we could. I totally understand how large enterprises are turning to Microsoft, given the productivity tools and suite that they have. But as you start to engage with customers, what's resonating relative to the Salesforce gen AI journey? Is it the data layer and Customer 360 message that's resonating? Is it the app layer around sales automation functionality that you're going to offer? Just double click on what customers are coming to Salesforce and engaging with you around some of the new things that we'll hear about sounds like in June.

Marc Benioff -- Chairman and Co-Chief Executive Officer

Well, I think that when you look at our artificial intelligence strategy, which we're talking of largest most important companies and governments in the world, it has to be architected around security. It has to be architected around compliance, around trust. It has to be architected around governance. And this is very important.

And, of course, we're also architecting it around being open. That is, we're working with many AI companies to provide the best solutions for our company. Of course, we have a tremendous relationship with OpenAI. We also just invested in Anthropic, Cohere, many of these companies.

But I think, ultimately, this is going to be a solution that enterprise customers are going to come in and make sure that their data is protected. And it's also protected down at the user level. And, Srini, do you want to come in and talk about exactly what we're doing to make sure that we're delivering the best possible solutions for our customers for AI?

Srini Tallapragada -- Chief Engineering Officer

Yes, Marc. So, I think I've met about 70 customers in the last quarter. And like Marc was saying, the only conversation everybody is interested is on AI. And while everybody understands the use cases, they're really worried about trust.

And what they are looking for us is guidance on how to solve that. For example, so we are doing a lot of things. At the basic security level, like, you know, we are really doing tenant-level isolation, coupled with zero-retention architecture at the LLM level so that LLM doesn't remember any of the data. Along with that, for them to use these use cases, they want to have -- they have a lot of these compliances, like, you know, GDPR, ISO, SOC, FedRAMP, they want to ensure that those compliances are still valid.

And we're going to solve it for that. In addition, the big worry everybody has is, you know, people have heard about hallucinations, you know, toxicity, bias. This is what we call model trust. We have a lot of innovation around how to ground the data on 360 data, which is a huge advantage we have.

And we are able to do a lot of things at that level. And then, the thing which I think Marc hinted at, which is, you know, LLMs are not like a database. These intra-enterprise trust, even once you have an LLM, you can't open the data to everybody in the company. So, you need ability to do this, who can access this data, how is it doing both before the query and after the query.

We have to build that. And then, we have to be not only open, but also optimized. We are running an open -- the way we'll run is we'll run like a model document because one of the things everybody has to watch out is, it's great, but what about the cost-to-serve? Not all models are equal. So, we are going to run this and pick very -- we are going to pick a very cost-optimized curve so the value is very high.

And our Salesforce AI research has a lot of Salesforce state-of-the-art models and industry cases, which we are optimizing to run at very low cost and high value. Add to that, we've got the Trailblazer platform, which allows low-code, high-code, and many other things. And we're going to optimize for the jobs to be done for each industry and job course. That's really what they're looking for because they have been using our AI platform.

Like Marc mentioned, we already do a trillion transactions per day. And by the way, the Data Cloud, just in a month, we are importing more than 7 trillion records into the data layer, which is a very powerful asset we have. Coupled with all of this is what they are looking for guidance and how we think we can deliver significant value to our customers.

Marc Benioff -- Chairman and Co-Chief Executive Officer

Srini, I want to ask you a question. In January, you published a paper in Nature from your research team, which was called Large Language Models Generating Functional Protein Sequences Across Diverse Families. And you really showed something amazing, which was that deep learning language models have shown this incredible promise that you just articulated in various biotechnological applications, including protein design, engineering. And you also described very well one of our models that we've created internally, ProGen, which was a language model that can generate protein sequences with predictable function across large protein families.

I was very impressed with that. And the entire research team deserves a huge amount of congratulations. So, when you look at that, especially a kid that generated grammatically and semantically correct natural language sentences for diverse syllabus or how you're going to use that inside our platform against other models that you're seeing like LLaMA, OpenAI's model, Anthropic, and others, when will Salesforce use our own models like CodeGen, ProGen, TCode, our BLIP model, when will we use an outside commercial model like an OpenAI or an anthropic? And when will we go to an open-source model like we've seen emerge in so many of those, including like LLaMA.

Srini Tallapragada -- Chief Engineering Officer

Yeah, I think you hinted something very important. I think as you know, Marc, we have our AI research team as one of the best-in-class models, state-of-the-art models on different areas. The way we are thinking of it is like anything else. Where the world is going to go, which we strongly believe is going to be multiple models, and depending on the use case, you will pick the right models, which will provide you the value at the lowest cost.

Where we have to run with highly regulated industries, where the data cannot leave the trust boundary, or where there's significant advantage where we can train on industry-specific data or Salesforce-specific, 360-specific data, like, for example, FX model, helping our customers implement our flow, we will use our internal model. Where we need more generated image models or something where you need public image databases, we may use a coherent Anthropic or OpenAI. It depends on the use case and which is why in a given request, a secure trusted gateway will decide smartly which is the best use case, which is the model. And we always keep running the tournament, which is what I mean.

So, today, one particular model may be good. Tomorrow something else will come. And we'll behind the scenes flip it, but our customers don't need to know that. We will handle all of it.

We'll handle the model trust. We'll handle all the compliances and all behind the scenes. And this is always what we promise to our customers. We'll always feature proof.

That's the Salesforce promise to our customers so that they can focus on the business use cases.

Marc Benioff -- Chairman and Co-Chief Executive Officer

So, just one last follow-up question. You've described very well this GPT Trust Layer, which I think is going to be a significant amount of value-added that we're going to provide to our customers. It's going to be quite amazing. And then, you developed these specific grounding techniques, which are going to allow us to keep our customers' data safe and not be consumed by these voracious large language models, which are so hungry for all of our customers' data.

What is going to be the key to actually delivering this now across regulated industries?

Srini Tallapragada -- Chief Engineering Officer

I think the key is innovations we are doing, which people will see starting next month, is around what we call from generation and grounding. These are techniques which we'll have to do, but it will work only because we have all of this based on underlying data. We have the Data Cloud, where we have all the 360 data which is there. So, we're able to ground these models and do it.

So, there are a lot of other techniques which are very technical which we put it on our blog, but that's the innovation that we're doing. And you have to remember that Salesforce also is a metadata model. So, we have a semantic understanding of what our customers are trying to do. They're going to leverage the metadata platform and do this grounding automatically for our customers.

Of course, while keeping the trust. That's the baseline.

Marc Benioff -- Chairman and Co-Chief Executive Officer

Absolutely. Thank you so much, Srini.

Mike Spencer -- Executive Vice President, Investor Relations

Emma, next question, please.


Your next question comes from the line of Raimo Lenschow with Barclays. Your line is open.

Raimo Lenschow -- Barclays -- Analyst

Thank you. Question for Amy or Brian maybe more. The improvement in profitability or the rate guidance for profitability and cash, is that all timing? Can you talk a little bit about that? Is it just timing, or are there other factors we should consider here? Thank you.

Amy Weaver -- Chief Financial Officer

So, Raimo, why don't I start back and turn it over to Brian for a little bit more color. So, in terms of the great Q1 that we just saw, really pleased to see it's coming in at 27.6%. And, also, really pleased about the 28%, the raise to 28% for the full year. What really drove the 27.6 was two things.

It were the actions that we took that we announced in January with the restructuring, executing on that, as well as having a very disciplined reinvestment strategy. And that led to that. And that's also where we're going to see this going for the rest of the year, driving the expansion to 28%. And then, also, putting us on track for the 30% margin in Q1 of next year.

You know, as I look, though, overall at transformation, I would really divide it into two stages. Benefits that we're getting from that initial transformation. Again, that's what you're seeing in Q1 in this year. And then, the second stage, which is really as we've been going through this comprehensive operating and go-to-market review, we're going -- that review is going to enable the second phase of our transformation.

And that's something that's going to be ongoing and long term over the next few years. You'll see benefits to our margin in elder years beyond FY '24. Brian, anything you would add?

Brian Millham -- President and Chief Operating Officer

Thanks for the question. When we think about longer-term structures, we obviously took the action in Q1. But longer term, we're looking at things like how do we leverage comp plan redesign to drive better efficiencies in our organization going forward? How do we continue to look at self-serve at the low end of the market to drive better efficiencies in our organization? So, resellers is a potential investment that we'll make, and emerging markets is long-term leverage on the efficiency gains. So, lots of things that we're doing that will be in sort of this phase 2 oriented around process improvement and systems improvement, and, again, as I mentioned, top plan design that will drive better efficiencies in the organization.

Mike Spencer -- Executive Vice President, Investor Relations

Thanks, Raimo. Emma, let's go to the next question, please.


 Your next question is from Karl Keirstead with UBS. Your line is open.

Karl Keirstead -- UBS -- Analyst

OK, great. I'll direct this to Amy as well. Amy, congrats on that margin improvement. I've got a two-parter both related to margins.

First, what is the timing of the receipt of that Bain operational review that might ostensibly kick off the second phase of cost cutting? And then secondly, you and Brian talked about this reinvestment in R&D and investing heavily around AI. I'm wondering if those planned investments are greater than you anticipated when you initially set the guidance three months ago such that you need to run a little bit harder on opex management to offset it and keep delivering on your stated margin targets. Thanks so much.

Amy Weaver -- Chief Financial Officer

Great. Thanks, Karl. So, first, on the timing, you know, as I mentioned, we've been doing this end-to-end comprehensive operating go-to-market ratio. The entire company has been involved in that.

It's really no stone unturned. We're getting close to the end of that process, and then we will be moving into the implementation. You'll be hearing more about that in the future quarters. Turning to reinvestment, you know, we are keeping a very close eye on the investment.

Very excited particularly about artificial intelligence, much of what Srini has been talking to you about. I don't view this as a greater investment from what we were looking at earlier. We're really going along with our current plans. We are looking at operating expenses management, and we're looking at it seriously every day.

But that's not something that has changed.

Mike Spencer -- Executive Vice President, Investor Relations

Thanks, Karl. Operator, Emma, we'll move to our last question now, please.


Excellent. Our last question comes from the line of Kash Rangan with Goldman Sachs. Your line is open.

Kash Rangan -- Goldman Sachs -- Analyst

Hi. Thank you very much, team. Congratulations on putting up terrific operational results with good cash flow, good margins, etc. Marc, you talked about a super cycle of buying and technology in the years ahead.

Can you just distill for us, if you don't mind, what is new about generative AI as far as Salesforce's opportunities are concerned and netting out against what Einstein has been able to accomplish for the company. And how does it show up in the product in terms of productivity? What are the scenarios by which customers can experience this amazing productivity? And how can you charge more for delivering that kind of value? Thank you so much.

Marc Benioff -- Chairman and Co-Chief Executive Officer

Well, thanks, Kash, for giving me the opportunity to talk about our AI vision. And I'm also going to ask Srini again to fill in some of the details. But I think it started to occur to me -- I think, a lot of folks know, I have -- my neighbor, Sam Altman, is the CEO of OpenAI. And I went over to his house for dinner, and it was a great conversation as it always is with him.

And he had -- he said, "Oh, just hold on one second, Marc. I want to get my laptop." And he brought his laptop out and gave me some demonstrations of advanced technologies that are not appropriate for the call. But I did notice that there was only one application that he was using on his laptop, and that was Slack. And the powerful part about that was I realized that everything from day one at OpenAI had been in Slack.

And as we kind of brainstormed and talked about, of course, he was paying a Slack user fee and, you know, on and on. And he's a great Slack customer. We've done a video about them, it's on YouTube. But I realized that taking an LLM and embedding it inside Slack, well, maybe Slack will wake up.

I mean, there is so much data in Slack, I wonder if it could tell him, what are the opportunities in OpenAI? What are the conflicts? What are the conversations? What should be his prioritization? What is the big product that got repressed that he never knew about? And I realized in my own version of Slack at Salesforce, I have over 95 million Slack messages, and these are all open messages. I'm not talking about closed messaging or direct messaging or secure messaging between employees. I'm talking about the open framework that's going on inside Salesforce and so many of our customers. And then I realized, wow, I think Slack could wake up, and it could become a tremendous asset with an LLM consuming all that data and driving it.

And then, of course, the idea is that is a new version of Slack. Not only do you have the free version of Slack, not only do you have the per-user version of Slack, but then you have the additional LLM version of Slack. And for each one of our products in every single one of our categories, there's that opportunity to upsell and cross-sell into the next version of generative AI. Not just with Slack, but you can also imagine, for example, even with Salesforce, the ability as we're going to see in June, that many of our trailblazers are amazing, low-code, no-code trailblazers.

But soon, they will have the ability to tap in to our LLMs, like ProGen and CodeGen, that have the ability to code for them automatically. They are not coders. They did not graduate computer science degrees. And if they need to write sophisticated Apex code or other code, it can be a challenge for them, but because, you know, what, is there only 8 or 10 million coders in the whole world.

But now with LLMs, everybody can start to code. That is an amazing productivity and augmentation of everybody's skillset. And that's a great way to look at what could happen, for example, with our core products, but even with Tableau, which has tremendous programmatic engine as well, or even MuleSoft, which is a highly programmatic product that then, coupled with an LLM, can have the ability to go forward. But, of course, those LLMs are highly trained models for those specific types of code.

And then that is something that we would add on, either through partnership or through our own LLM, as Srini described. It's another layer of value that we can provide to our customers. In all cases, customers are going to be more productive, they're going to be more automated, and they're going to be more intelligent. And as we look at some of the examples that we've given, like at the New York World Tour, you saw our marketing cloud do something very cool that it couldn't do even just six months ago.

It segmented the database on its own. It wrote an email on its own. Of course, it required editing. It also built a landing page on its own.

That was amazing. Or as we saw at the Tableau conference, we saw Tableau being able to create its own vizzes or visualizations. That was incredible. And what we saw at our Trailhead DX, we saw Einstein GPT which started to do these amazing next-generation things.

And I think in each of these areas, we can offer more value. But we must do it in the auspices of trust, data integrity, and governance. And that is what we have been working on now for a considerable amount of time. Of course, we've led -- you know, we have always wanted to be the No.

1 AI CRM, and we are. If you look at Einstein's transaction level, I think that's enough evidence right there. But I think this idea of generative AI, this starts to reconceptualize every product. And we will start to build and develop not only extensions to all of our current products, but entirely new products as well.

And we have a lot of exciting ideas of things that we can do to help our customers connect with their customers in a new way using generative AI. Srini, do you want to come in and talk about that?

Srini Tallapragada -- Chief Engineering Officer

Thanks, Marc. So, I think the way I see it is this AI technologies are on a continuum. It is predictive. And they're generative, and the real long-term goal is autonomous.

The initial version of the generative AI will be more in terms of assistance. And like Marc was saying, we are seeing like the most common use case everybody understands implicitly is self-service bots, or in the call center or agent assistant -- assistance, which I think really helps productivity. But the other use cases which we are going to see, and, in fact, I have rolled out our own code LLMs in our engineering org, and we are already seeing minimum 20% productivity. And in those cases --

Marc Benioff -- Chairman and Co-Chief Executive Officer

Well, that's a very key point, isn't it?

Srini Tallapragada -- Chief Engineering Officer


Marc Benioff -- Chairman and Co-Chief Executive Officer

That you're seeing a 30% productivity increase in your own engineering using our own LLMs.

Srini Tallapragada -- Chief Engineering Officer

Twenty percent, we are seeing minimum. In some cases, up to 30%. Now, a lot of our customers are asking the same. We are going to roll Einstein GPT for our developers in the ecosystem, which will not only help not only the local developers to bridge the gaps where there's a talent gap, but also reduce the cost of implementations for a lot of people.

So, there's a lot of value. This assistant model is where we'll see a lot of uptick. And then, I think the fully autonomous cases, for example, in our own internal use cases with our models, we are able to detect 60% of incidents and auto-remediate. That requires a little bit more fine-tuning, and we'll have to work with specific customers to get to that level of model performance.

So, I see this as just at the start of this course. The assistant model is the initial thing to build trust and a human in the loop and validate it. And then, as the models get better and better, we keep taking use cases where we can fully automate it.

Marc Benioff -- Chairman and Co-Chief Executive Officer

And address this one issue that a lot of customers come in like they did yesterday, and they tell us they think they're just going to take all of their data, all their customer data, all of their information, and put it into an LLM and create a corporate knowledgebase, and it's going to be one amalgamated database. Why is that a false prophecy?

Srini Tallapragada -- Chief Engineering Officer

Because, you know, even today, any example you see, even though we have hundreds of Slack channels, there are a lot of specific Slack channels which only you want access to. You don't want that. LLM doesn't know. There is no concept of -- it combines all this information.

So, unless you put the layer, both before, who can access the data, and then when it generates response what it can do, you don't want one wealth manager to generally generate a report -- an account report where you're mixing customers' balances. So, there are a lot of trust issues you have to solve. So, LLMs are good for a lot of very creative, generative use cases. Initially, there is public data where everybody can use it.

Those are use cases. I think there are enough of low-hanging fruit in the initial phases of the assistant model which will solve. The really complex automated cases, the role level, required level sharing, we have a lot of techniques which we are developing which we will do. It's also the search area, too.

That one, I think, we should be tempered with expectations. But there's enough of, like I said -- the developer example I gave, productivity example I gave, there's enough of productivity which we'll get.

Marc Benioff -- Chairman and Co-Chief Executive Officer

Well, we're really excited to show all of this technology at our AI day on June 12th in New York City. And then, also, when we get to Dreamforce GPT, we're going to have an incredible demonstration of this technology. So, with that, we want to thank everyone for joining us today, and we look forward to seeing everyone over the coming weeks. Have a great one.


[Operator signoff]

Duration: 0 minutes

Call participants:

Mike Spencer -- Executive Vice President, Investor Relations

Marc Benioff -- Chairman and Co-Chief Executive Officer

Brian Millham -- President and Chief Operating Officer

Amy Weaver -- Chief Financial Officer

Kirk Materne -- Evercore ISI -- Analyst

Elizabeth Porter -- Morgan Stanley -- Analyst

Brad Sills -- Bank of America Merrill Lynch -- Analyst

Brent Thill -- Jefferies -- Analyst

Mark Murphy -- JPMorgan Chase and Company -- Analyst

Brent Bracelin -- Piper Sandler -- Analyst

Srini Tallapragada -- Chief Engineering Officer

Raimo Lenschow -- Barclays -- Analyst

Karl Keirstead -- UBS -- Analyst

Kash Rangan -- Goldman Sachs -- Analyst

More CRM analysis

All earnings call transcripts

Wed, 31 May 2023 13:30:00 -0500 en-US text/html
Killexams : What Is Blue Ocean Strategy — and Where Does It Go Wrong?

HANNAH BATES: Welcome to HBR On Strategy, case studies and conversations with the world’s top business and management experts, hand-selected to help you unlock new ways of doing business. What do Ralph Lauren,, and Cirque du Soleil have in common?  deliver up? They’re all brands that have successfully created “blue oceans” – uncontested, new markets where success is all about differentiation and lower costs. Blue ocean strategy is a landmark business idea, first introduced in 2004 right here at HBR. But its co-creator Renée Mauborgne says it’s not a guaranteed win. Today, we bring you a conversation about what can go wrong when you try to implement blue ocean strategy. You’ll learn about some of the common traps managers fall into – like trying to please existing customers OR focusing on adjacent market niches. AND why knowing who your non-customers are is as valuable as knowing your customers. This episode originally aired on HBR IdeaCast in March 2015. And just a note — we recorded this by phone. While the audio quality isn’t great, the conversation is. I think you’ll enjoy it. Here it is.

SARAH GREEN: Welcome to the HBR IdeaCast from Harvard Business Review. I’m Sarah Green. Today I’m talking with Renee Mauborgne. Professor at INSEAD and co-author with W. Chan Kim of the bestselling and newly updated and reissued Blue Ocean Strategy. They’re also the authors of a new article in HBR called Red Ocean Traps. Renee, thank you so much for talking with us today.


SARAH GREEN: So many of those listening may already know, but some may not know, what is blue ocean strategy?

RENEE MAUBORGNE: So Blue ocean strategy is about how can any company or organization break out of the red ocean of bloody competition or existing market space, which tends to be very crowded and competitive these days, and create uncontested market space where you make your competition irrelevant. So it’s really about how do you create uncontested market space and align your value profit and people proposition to achieve that around differentiation and low cost, and thereby you create a win for the market, a win for the company, and a win for your employees as well.

SARAH GREEN: So in the article especially, you’re identifying several traps that managers fall into when they’re trying to do this, to create and execute a blue ocean strategy. And it seems like one of the common ones is trying to focus on making existing customers happier. So tell me a little bit about why is that a problem? Why does that not help you reach the blue ocean?

RENEE MAUBORGNE: So in the expanded edition of Blue Ocean Strategy, we’ve actually added about two, really almost three new chapters to it. And one of the critical ones is what we call this red ocean traps, which is also the focus of the new Harvard article. And what we found is that many managers increasingly recognize obviously, the need to break out of red oceans of bloody competition. But that despite putting funding behind it, and having an intent to do it, they often find that they’re unsuccessful in that achievement. And what we’ve found is that managers’ existing mental models, the assumptions about what works in competing in existing industry space, they often apply to their efforts to create new markets. And that creates the failure. The issue is, creating new markets isn’t about better satisfying, necessarily your existing customers, it’s about creating all new demand. And to create all new demand, your aim is, of course, talking to noncustomers of the industry. So, if you look at the case of e-readers, Sony came out and they thought, you know this new e-reader technology, let’s unlock the mass of buyers to start using e-readers for practicing books. And they went to the existing early adopters of e-readers, who were the customers of that small industry. And they said, how can we make you happier? And what they said is, the device is a bit clunky and the practicing screen, it doesn’t work very well. Can you make it have a slicker image, a slicker look and be better to handle. And they did that and they actually made existing customers very happy. The problem is, that wasn’t the reason the majority of people didn’t come to the industry. And as we know, Kindle then went with Amazon, looked to noncustomers and they found out the real reason wasn’t the screen of the existing e-readers. It’s just that there were no titles and you couldn’t get them effectively. And so what did Kindle do? Of course they created a lot more titles, today of course, over 2 million titles and they made it very easy to download. And today, Kindle sets the standard of the industry and has exploded the amount of e-reader users for books as we all know. And unfortunately, in the case of Sony, they have exited that industry. So that’s the kind of insights you get by looking to noncustomers.

SARAH GREEN: So do most companies even know what their noncustomers want and how might you get a better handle on what noncustomers want?

RENEE MAUBORGNE: Well, the truth is you’re right. I think most companies really aren’t clear on who their noncustomers are. And when they think of noncustomers, it’s sort of a big blob out there of everyone else. But that’s not true. So in our book, the first critical thing is, how do you define your noncustomers? And in the expanded edition, we have a chapter focused on defining the three tiers of noncustomers out there. And the first tier is people that occasionally use their product. How can I get them to use it much more? Second tier is people who refuse your industry. So they’ve thought about patronizing it, but they choose against it. And the third tier, unexplored. So the book really provides a strong conceptual framework any company can look at or organization to define who are the nontiers. That’s the first thing, who they are. Then the second is that your rightful point is, how do I then know what to do to unlock them. And in our book as well, we have two analytic tools that empower executives in terms of finding those pain points, blind spots, and points of intimidation. And one is called the buyer utility map. And that really allows you to map out and identify those points that keep people away from the industry. But beyond that, we have a supplementary tool called the Six Paths Framework. And that allows us to reframe the question from existing customers to noncustomers and think about, could I take a functional industry and make it emotional, to pull in young people that care about cool, hip things for example. So really to empower executives to act on that concept, the book goes beyond the red ocean traps to the three tiers to help you identify who they are, and then provides two very powerful and practical analytics that you can work with your executives or your team around to start to find these pain points. And I think, usually it creates an exciting conversation and a lot of insights in its application.

SARAH GREEN: So when some companies are sort of working through this and trying to identify those blue oceans, it seems like one of the things that they try to do is try to find an adjacent niche. Companies are always talking about adjacent niches. But one of the things that I sort of picked up on in the revised edition of the book and the article is that that’s not actually always a safe bet. So maybe just walk us through some of the dangers there.

RENEE MAUBORGNE: Yeah. I think that first of all, carving out a niche is how you create a unique space in the existing market space. But that’s not the same thing as creating new market space and growing all new demand for an industry. And what you find is that when companies think of new markets, they often do think of niche intuitively. Because that’s how you create small safe havens within an existing industry space. But the issue is, the more that you think about a niche, and the more we look for differences, the more we tend to find them, which leads to smaller and smaller segments of the industry. And the second thing as well as often, especially when higher fixed costs are involved to go after the niche, the niche is too small to support it and you can not only not create a niche, but you can’t justify the cost. So if you look at Delta Airlines, for example, they launched Song Airlines. And it was a concept, and they said, well, no one has looked at high-moving, fast-advancing professional women. What do they want? They fly more. What are they looking for? No one in the industry has focused on that distinct segment of fliers. And they created a product and a service with Kate Spade uniforms and designer cocktails on the flight and they gave exercise bands for women. And while in the low cost segment, that did well, the size of the market wasn’t big enough to support the cost structure. And in the end, I think it was after 36 months, they had to close that airline down. And so the issue is, has the niche become too small to support the cost? And the second thing is, are we really growing it? And what we found, companies that create new markets, what they do is, instead of looking for differences across segment customers, they look for commonalities. And in doing that thinking about not segmentation or niching, but desegmenting and industry. And that is what we found is how you create these broader market segments and grow demand for an industry.

SARAH GREEN: That’s really interesting. And I find that Delta Song example one that’s sort of really puzzling and perplexing. Because on the one hand, I think you’re totally right. It seems like they were focused on this niche that wasn’t necessarily big enough to support what they were doing. On the other hand, when you sort of hear that what their big plan was of the flight attendants were going to be wearing Kate Spade uniforms, I mean as a woman who travels for business, I don’t think I particularly care what my flight attendant is wearing. I mean, so I wonder to what extent is that really an execution problem and to what extent is it really a strategy problem?

RENEE MAUBORGNE: Well, I think that the airline, it was not– of course, Kate Spade is something I’m mentioning now, is much more than just Kate Spade to be fair to the airlines. So they had a number of different elements which I think were interesting. And I think the airline was fairly well-received by people that used that airline. It was just too small a segment for them in and a bit too focused at that price point that they were after.

SARAH GREEN: So I guess one of the questions that I think I’ve heard people ask in regard to this idea is, like wait a minute, when you’re talking about market creation, is that the same thing as differentiation or are they different themselves?

RENEE MAUBORGNE: That’s a great question. That’s a point of confusion sometimes. So market creation is not the same thing as differentiation, just as it’s not the same thing as a niche strategy. If you think about it in academic terms, differentiation is really a position, what economists call the productivity frontier, which is the range of value cost trade offs available to any company. Given the industry structure and best known practices at the time. And basically, differentiation is really about offering premium value on that curve. And when you do, your cost structure tends to go up and so that’s the price point of an industry. Market creation however, market creating strategies, are really about breaking the trade off by reconstructing industry boundaries. So if you think about Yellow Tail, one example in the book, or part of our database, is it the most differentiated wine out there? Is it a differentiated wine? You bet it is. But is it low cost? Yes, it is., is it differentiated in what they did in the software industry? Yes, it is. But is it also low cost? Yes, it is as well. And the issue with differentiation alone, which is a very effective strategy in existing market space, is that it tends to allow you to carve out a premium position in the existing industry. And what you tend to do is focus only on what you can raise and create, which is what lifts your price structure, your cost structure. And you forget about what you can eliminate and reduce simultaneously to drop your cost structure as well and really shift that productivity frontier as opposed to positioning on it. So they are not the same. Market creation is about differentiation and low cost at any price point in the marketplace, market creating strategies, versus being a premium player in the existing market as well.

SARAH GREEN: OK. So I just want to sort of hover over this syllabu for just another moment here because I think it’s interesting to think about the need to be differentiated and cost competitive. I guess I’m wondering is it possible, if there’s someone listening who’s in, say a business that’s a premium product or a high end business, is it possible for that person to be doing blue ocean strategy or are they doing something different according to different rules?

RENEE MAUBORGNE: So you can– the same logic applies, whether you want to create a blue ocean at the high end of the market or low end of the market, so you can think of Cirque de Soleil, right, listed as the price point of circuses multiple times versus a Ringling Brothers Circus. And what they did effectively– it’s one of those Six Paths I was talking about to pull in noncustomers. –is they looked across alternative industries of theater, opera, and ballet versus circus, they priced against it. Are they the most differentiated player? Yes, of course, they’re out there. But to drop their cost structure, in doing that of course, they eliminated animals, which has insurance implications, food implications, travel implications, and star performers as well. So they were high priced, differentiated low cost. Ralph Lauren did the same thing. They created a whole new blue ocean at the high end by taking the best of haute couture, which is a designer name, not the name of a house, using fine materials and they had a higher price point as well, and not a low cost price point. Ralph Lauren, to keep his cost structure reasonable at the time, of course, unlike an haute couture house, which is having seamstresses, he, of course, used more factory manufacturing and the details of course, might have been some done touched by hand in the very higher price points, but most of it was done lower cost via factory. So he had a price point of a Brooks Brothers at the time and of course the name brand, the allure, the image, all of that of a high end and the beautiful fabrics, creating at the high end of the market. So if you look at expanded edition of Blue Ocean Strategy, you’ll find we are talking about companies that create blue oceans at the high end of the market using the logic, at the low price point of the market, and the right smack in the middle price points of an industry.

SARAH GREEN: So, Renee, one of the things I’m kind of noticing as we’ve walked through a number of examples and talked about this is that I don’t think any of these examples so far has been technological examples. And I find that remarkable, just because so often when people are talking about strategy today, they’re sort of talking about innovation and technology innovation. And people seem to sort of conflate all these things together. And yet it seems like here we’re not really talking about break through technologies, so why when we’re talking about the need to create new markets, do we so often end up focusing on technological innovation instead of actually just strategy?

RENEE MAUBORGNE: Well, I think you make a great point. So the key to opening up new markets, it can be with or without technology. And technology’s purely a huge trend in the marketplace today that a lot of companies can act on to open up new market space. But what matters systematically is whether we lock it to value. And that’s where the discrepancy often occurs. So if you look at Apple, why do we love their products and services? They have highly technologically advanced products and services. It’s not because of the technology per se, it’s because actually they’ve made those products so stylish, fun, easy to use, reliable, they make us productive, that we love them. In fact, they make the technology almost disappear. is a technology company that created a blue ocean, but again, the software very effectively linked to value. And I think the problem for organizations becomes when they see this technology and they think that’s a trend in the market you can act on to create a blue ocean, but it’s not what unlocks the blue ocean per se. What unlocks it is whether or not a company systematically unlocks and links it to value for the buyer groups that you’re going for in the marketplace. So we always say it’s value innovation, not technology innovation. And in our book, we have the buyer utility map which I mentioned earlier, which really allows you to assess, am I linking that technology to value to what people care about. And in essence, I think most ionization, technology innovations which unlock value they almost make the technology disappear from buyer’s mind. It’s so seamlessly done from a customer’s perspective. Intuit’s Quicken, it has such a wonderful user interface. And it even mimicked the initial checkbook when they came out, making it so intuitive for people to use, people fell in love with it. And so that’s the challenge for organizations.

SARAH GREEN: So I’m wondering, this idea of blue ocean strategy has become one of the classic ideas that HBR has ever published. The book sells ridiculously well. Why update the book? I mean, what was your sort of process like? Why come back to this idea? Why keep fleshing it out? There are many, many, many business books out there that don’t get updated and people continue to refer back to them. So why did you guys decide to sit down and do that and how did you do it?

RENEE MAUBORGNE: Well, thanks for that question. First, we’ve never stopped our research on blue ocean strategy. It’s a very long journey for us. If anyone wants to visit our website, we have videos on there and we have an e-library with all companies around the world that have been applying the ideas. So obviously it’s a passion. We don’t see the idea of blue oceans going away at all. In fact, we’re growing. And really our research has not stopped. So we’ve been talking with companies around the world. And whether they were applying blue ocean or just in this space wanting to, we were documenting what their struggles were in trying to create new markets. What were the areas? Were there questions they had left unanswered? So, we went much more in the new book about, how do you align the organization, the people proposition, to execute on that? So, it’s not just about the analytics of strategy. We had a part about humans and execution in the initial book. But we really wanted to go much more in that to create sustainability. So this value profit in people, we really developed more. And then we saw companies putting the money behind this red ocean trap idea. But they would go in it to apply blue ocean strategy or any idea and they were talking to the same customers. And they were coming back with modified versions or improved of the existing offering of the industry and not breaking out or not going to too small niches. So this whole idea, as you just last questioned, technology innovation, they’re getting so excited about technology, they’re winning awards, but they’re not unlocking markets because it’s not linked to value for buyers. We don’t understand how to use it or there’s no ecosystem. So that was obviously a really critical impetus for us as well in thinking about it. And the third is people are saying to us, well, what if I create a blue ocean? Many them, like JCDecaux, the blue ocean that was created and has expanded since, some of them have lasted 50 years. Now some of them 30, some of them 20. So the companies have all done quite well, even though our unit of analysis strategic move that company.

That said however, people are saying well, what? Because imitation occurs for everybody, right? Every blue ocean eventually becomes red. So they said, can you expand on how do I, as an organization, institutionalize this as a systematic process? And we thought, that’s very valid. So, we talked more about barriers to imitation, how do you build those? But more also at the corporate level, for a multi-business firm and at the individual level, how do you know when to reach for a new blue ocean? What kind of tool and framework can you, to channel and have discussions with your head business leaders on doing it? So, the book really came out to our continuing conversation and our growing database of companies applying the ideas and governments, nonprofits, in action, and our curiosity to understand what were their stumbling blocks and where could we add further value. So it’s just sharing some of the conversations and our passion and our growing research database made us want to do this.

SARAH GREEN: Well, Renee, thanks again for talking with us today.

RENEE MAUBORGNE: Well, thank you very much for having us. And I’m sorry my colleague, Chan Kim, could not be a part it, but it is long career journey we’ve gone on together and we look forward to continuing that with passion. So thank you for the time and thank you for every one that has been interested in Blue Ocean Strategy.

HANNAH BATES: That was Renée Mauborgne, co-author of Blue Ocean Strategy – in conversation with Sarah Green Carmichael on the HBR IdeaCast. If you liked this episode, check out HBR IdeaCast wherever you get your podcasts.  We’ll be back next Wednesday with another hand-picked conversation about business strategy from the Harvard Business Review. If you found this episode helpful, share it with your friends and colleagues, and follow our show on Apple Podcasts, Spotify, or wherever you get your podcasts. While you’re there, be sure to leave us a review. If you’re looking for another weekly dose of hand-curated business and management expertise, check out HBR On Leadership to help you unlock the best in those around you. We’re a production of the Harvard Business Review – if you want more articles, case studies, books, and videos like this, be sure to subscribe to HBR at This episode was created and produced by Anne Saini, Ian Fox, and me, Hannah Bates. Special thanks to Maureen Hoch, Adi Ignatius, Karen Player, Ramsey Khabbaz, Nicole Smith, Anne Bartholomew, and you – our listener. See you next week.

Wed, 31 May 2023 12:54:00 -0500 text/html
Killexams : RiseUp with ServiceNow expands curriculum to include partner courses No result found, try new keyword!Amy has been at the forefront of skills-based technology education for more than 10 years, most recently leading education, training, and Trailhead Academy at Salesforce ... Professional Certificate ... Tue, 16 May 2023 03:24:00 -0500 Killexams : How can innovative AI-powered tools optimise customer service in financial services?

A Netflix-plus offering is within reach, enabling hyper-personalisation and a democratisation of money management advice, but collaborating with trusted partners is essential, according to experts

Chat matters: AI-powered digital assistance is constantly developing, becoming more personalised and conversational

The introduction of generative AI has turned the head of many business leaders, eliciting mixed reactions. So, while already-high consumer expectations have been further elevated by exact advancements, how could – and should – financial service operators approach AI-powered customer service without turning people off?

Sol Enenmoh, director of digitalisation at HSBC, acknowledges that it’s a tricky balance to strike, but is thrilled by the confluence of technologies – including generative AI, quantum computing, and cloud computing – that promises to evolve digital experiences for customers and also employees. “We are in a position where things are shifting from theory into practice very quickly,” he enthuses. “AI and interconnectivity present incredible opportunities to offer hyper-personalisation preemptively.”

 You can exceed expectations and provide a tailored experience. It is not rocket science

Amazon and Netflix signal the direction of travel for customer service in other industries, posits Sami Helin, managing director EMEA of Coveo, a global software-as-a-service firm with an AI platform that delivers personalisation and enterprise search solutions. “They have figured out that the key to success is to serve each individual personally rather than take a one-size-fits-all approach that will always miss the mark.”

Helin says capabilities similar to Netflix’s “what to watch next” suggestion prompts are now possible in financial services. “You can exceed expectations and provide a tailored experience. It is not rocket science, but to make those recommendations happen at scale in milliseconds is difficult. Here machine learning and AI are extremely necessary.”

Coveo’s “mission”, Helin continues, is to harness AI to “provide relevant, personalised and profitable experiences for people across employee productivity, customer service, or ecommerce.”

Getting sentimental and practicing the digital room

Enenmoh argues that AI is now proficient enough to “take an extra step” on from the Netflix example by going deeper to understand the “mind state” of the user or customer. “AI presents an opportunity to serve you better, having learnt, pivoted and grown with you organically and knowing your current state of mind,” he explains. “That’s the power it has.”

With such competence, Enenmoh says, a financial services operator will know – purely based on the sentiment analysis – whether to offer additional services, if the customer is in a good mood. The AI can read the digital room and act accordingly.

This development area also delights Reena Sukha, chief information officer at Investec, which uses AI on voice calls to understand the emotional state of customers. AI is beginning to “humanise the digital client experience”, and, in tandem, the general public is becoming more comfortable with the ubiquitous technology. “People don’t even know the difference anymore,” she says, nodding to Alan Turing’s famous test for machine intelligence, proposed in 1950.

 People won’t remember what you said or did, they will remember how you made them feel

Originally called the imitation game, the Turing test was designed to gauge the cleverness of a machine compared to humans. Essentially, if a machine displays intelligent behaviour equivalent to, or indistinguishable from, that of a human, it passes the Turing test.

In the context of customer service, all that matters is the quality of experience provided by either man, machine, or a combination of both, states Sukha. She quotes the late American author Maya Angelou. “At the end of the day people won’t remember what you said or did, they will remember how you made them feel.”

That line “really resonates” with the Investec CIO. “I’m excited at how AI is transforming how we interact with our clients,” she says. “The more it is becoming humanised, the more comfortable we – the humans – will be with the technology.”

Co-creating better customer experience using deeper analysis

Alongside the smarter, AI-driven relationships with customers, Investec’s sentiment data also inform and enhance those interactions for everyone. In addition, investment in AI voice technology for customer calls has proved valuable in various ways. Initially, Investec rolled out this solution as it received a high volume of calls from clients, which was costly and the team saw opportunities to Excellerate the customer experience.

A “system analysis” helped the customer service team identify the top 10 reasons for the calls. “We used that information to determine the roadmap for what we were going to do with our platform,” says Sukha. For example, many callers couldn’t find functions online, meaning better signposting was required. Notably, there were activities customers felt “safer” doing with a human rather than online or via a banking app – changing bank account details, for example.

A deeper investigation, through interviewing clients, allowed Investec to test the level of comfort with AI while educating the would-be callers to put them at ease and encourage them to use online functions. “We asked them whether they would feel comfortable with certain things if we were to introduce them,” Sukha continues.

The exercise was a win-win scenario; it engaged Investec’s clients, understood their pain points, and enabled a co-creation of suitable digital tools to solve those challenges. And AI is assisting Investec’s callers in another way: voice biometrics makes authenticating users quicker than before, as well as understanding their emotional state, which has improved efficiencies and seen the length of interactions drop by around 20%.

Chris Waring, head of digital journeys at NatWest, agrees that assessing how and with whom customers wish to communicate, depending on their needs, is a fascinating and rapidly evolving area.

Chatbots learning new languages and ways to communicate

NatWest’s AI-powered digital assistant, Cora, has developed pleasingly since its inception in 2018 when it was more of a “frequently-asked-questions” platform. The chatbot is always available and can support customers with day-to-day banking queries. Last year, there were 10.4 million conversations throughout the year with Cora, with almost half (48%) requiring no human input.

“Being able to surface those service journeys through the channel of choice – whether that’s Cora, or via the website, or through WhatsApp – is what we are trying to drive forward,” says Waring. “While Cora started helping customers with basic queries, it’s increasingly been integrated into various servicing journeys across the bank.” For instance, NatWest has begun using the chatbot to initiate loan deals.

Waring concedes Cora is still not the finished article, but says NatWest is “continually improving” the product and focussing on customer personalisation. However, he stresses that relying on chatbots and conversational AI is not necessarily appropriate for “complex requests that are better served in different channels”.

Geoff Branch, enterprise account executive at Coveo, is equally ambivalent about chatbots, new and old. “When the first chatbots were released, everyone thought they would be the answer to everything, the silver bullet,” he says. “But chatbots – and now in the case of generative AI, like ChatGPT – are a good example where great technology can help you without human intervention for about 80% of searches.”

When the first chatbots were released, everyone thought they would be the answer to everything

What happens the remainder of the time, though? Branch answers: “We need that augmented service for high-value human interactions.” He adds that generative AI is “going to be right for some journeys, and, as is the case in the regulated environment, not for every journey.”

He observes that AI has changed the way people search for information by enabling more conversational and personalised interactions with search engines and other online resources – instead of simply typing in a keyword or phrase and expecting a list of relevant results, people can ask more natural language questions and receive more targeted and specific answers.

“This shift has led to a change in how people approach information seeking,” Branch surmises. Rather than just looking for a quick answer to a specific question, people are now seeking more in-depth advice and guidance on a variety of topics. For example, instead of searching for ‘how to make spaghetti,’  a person might now ask ‘what are some easy spaghetti recipes for beginners?’”

Interestingly, many predict that “robo-advisors” – part human, part AI – hybrid solutions will soon help customers with money management, including Waring.

Narrowing the financial advice gap with hybrid solutions

Royal London estimates that 39 million UK adults – of roughly 52 million in total – have fallen into the so-called “advice gap”, meaning approximately 75% don’t take any form of professional advice or guidance regarding their finances.

Given that Brits under 25 are more likely to turn to social media for financial advice than a professional adviser, according to Open Money research, there is a huge opportunity to innovate in this space for financial service operators and better engage present and future customers.

There are challenges when moving from person-to-person interactions to a digital setting to ensure we always translate that in a way that is compliant with regulation, even for edge cases, where a particular request is unusual or doesn’t fit the standard or anticipated pattern, says Waring. In some cases, it can be an improvement, as you can often explain options and product features more clearly in a digital setting.

“In commercial banking,” says Waring, “we could use AI and customer data to help structure financing appropriate for a company’s ambitions, together with relationship managers. We’re seeing a blend emerging between customers that want to speak predominantly to a relationship manager at a bank, in a traditional fashion, versus other customers that genuinely don’t want to start the journey that way.

AI can democratise financial advice if the trust element is there

“Giving customers the flexibility and option to bring in some relationship management debt advice, and so on, with digital offering is an interesting prospect. But, as banks, we must evolve to deal with that and bring people in at the right moment.”

Phil Williams, group chief operating officer at The Clear Group, states that “trust is the key thing here,” and financial services operators that update their incentive-based advice model would more likely capture the custom of people conditioned to think financial professionals are either for the wealthy or not worth the cost.

Many of us are going online for advice, with limited success. “We’ve been taught as consumers that high-quality advice costs money, but the person who has the most vested interest in the outcome for me is me,” Williams says. “There is a sense that people will do their own research, go on community forums and social media. AI, though, can democratise financial advice. Providing the trust element is there – and I’m not sure it is at the moment – then high-quality advice could be available to everyone.”

Data-sharing and democratising high-quality money management

Business leaders may also lack trust in AI, not least because purportedly game-changing solutions have deluged the market. “There is a little bit of snake-oil salesmanship, something genius, some hype, and something dubious in what is being offered right now,” says Sandeep Dubbireddi, innovation consulting senior director at Salesforce.

However, shifting the value proposition to make AI products with trust and reliability in mind, including guardrails intended to help guide customers make ethically-informed choices, chimes with him.

“Generative AI use cases are already taking flight across many industries. In wealth management, human advisors beat fintech solutions today, even those narrowly focused on specific asset classes and strategies, because humans are heavily influenced by idiosyncratic hopes, dreams, and fears,” Dubbireddi says.

“LLMs used in Generative AI provide a tidy solution to these problems with a better understanding and thus a better navigation of consumers’ financial decisions. These systems can answer questions, evaluate tradeoffs, and ultimately factor human context into decision making.”

Many people want total personalisation for relevant things, but those same people don’t want to share any details

Another thorny syllabu is personal data, on which AI programmes in financial services, as elsewhere, rely. There is a “dichotomy” here, says Coveo’s Helin. “On one hand, many people want total personalisation for relevant things, but those same people don’t want to share any details,” he suggests. “The question is: where is the sweet spot?” Helin adds that for most customers, unsolicited communications can come across as spammy, if not “creepy”, and urges organisations to tread carefully.

Sukha offers an elegant approach to “take away the creepiness” for financial service operators: be transparent with customers about the data gathered, how they could profit in terms of tailored products and services, and deliver them control, so they can opt in or out of certain things.

Whether granted permission or not, there are other critical advantages of using AI-driven analysis, states Williams. “Having the data allows us to identify vulnerable customers,” he says. “Using sentiment analysis, for example, to understand whether someone is financially distressed, would be an amazing benefit – not to sell them more products but guide them through the journey.”

Education, experimenting and ecosystems – how to Excellerate customer service

There is no doubt AI is improving customer service in the financial services industry, but there is more that operators can do to evolve the offering further. The lack of genuine knowledge at c-suite level is holding back progress, says Enenmoh, and education is crucial so that AI projects – even if pilots – will gain sponsorship.

Further, HSBC’s director of digitalisation stresses the importance of developing and nurturing an ecosystem of trusted expert partners. “The notion that you can be successful by building everything yourself is shortsighted,” he says. “Being in an active and collaborative ecosystem, leveraging existing know-how from future-facing companies, allows you to be more expansive with your horizons.”

Salesforce’s Dubbireddi builds on this theme. “Systems relying on generative AI evolve their behaviour in response to the data they encounter, as well as the history of the interactions they experience. This demands continuous evolution. It’s impossible to keep up with the pace of innovation, so partner with individuals and companies that have a continuing commitment to your company’s success,” he says.

Finally, Sukha warns financial services operators not to be blinded by AI and to never lose sight of the humans the industry serves. “Ultimately, it’s not going to solve all your problems, and it is a tool to help customers,” she says. “So use it for real and meaningful problems.”

Thu, 25 May 2023 23:15:00 -0500 en text/html
Killexams : Fiscal Q1 Earnings Snapshot

SAN FRANCISCO (AP) — SAN FRANCISCO (AP) — Inc. (CRM) on Wednesday reported fiscal first-quarter profit of $199 million.

On a per-share basis, the San Francisco-based company said it had profit of 20 cents. Earnings, adjusted for one-time gains and costs, were $1.69 per share.

The results beat Wall Street expectations. The average estimate of 17 analysts surveyed by Zacks Investment Research was for earnings of $1.61 per share.

The customer-management software developer posted revenue of $8.25 billion in the period, which also beat Street forecasts. Fourteen analysts surveyed by Zacks expected $8.17 billion.

For the current quarter ending in July, expects its per-share earnings to range from $1.89 to $1.90. Analysts surveyed by Zacks had forecast adjusted earnings per share of $1.27.

The company said it expects revenue in the range of $8.51 billion to $8.53 billion for the fiscal second quarter. Analysts surveyed by Zacks had expected revenue of $8.06 billion. expects full-year earnings in the range of $7.41 to $7.43 per share, with revenue ranging from $34.5 billion to $34.7 billion.


This story was generated by Automated Insights ( using data from Zacks Investment Research. Access a Zacks stock report on CRM at

Wed, 31 May 2023 08:20:00 -0500 en text/html
Killexams : Dell and Nvidia launch Project Helix, generative AI for businesses

In context: The exact influx of announcements showcases a global trend of new offerings aimed at introducing generative AI capabilities to businesses. From tech giants like IBM, Google, Salesforce, Microsoft, Amazon, to Meta, it appears that every tech company is capitalizing on the excitement surrounding this transformative new technology.

It has become increasingly clear that most organizations are eager to embrace AI. Businesses are rapidly identifying potential productivity enhancements, efficiencies, and other benefits that AI can provide. However, a significant problem arises when these companies aren't entirely sure how they can start leveraging generative AI. Experts with deep knowledge of how the technology works and how it can be implemented are scarce and, not to mention, very expensive.

Recognizing this disconnect, Dell Technologies and Nvidia have put together an offering called Project Helix, specifically designed to simplify the process of getting started with generative AI. Project Helix focuses on creating full-stack, on-premises generative AI solutions that allow companies to either build new or customize existing generative AI foundation models using their own data.

One problem that has emerged in businesses beginning to use generative AI services is the risk of internal IP leakage. In fact, several companies, including Samsung and Apple, have implemented policies preventing their employees from using tools like ChatGPT for work purposes due to concerns related to this issue.

Part of the reason for this concern is that virtually all the early iterations of generative AI could only run in massive cloud-based data centers, many of which collected the data entered into their prompt inputs. However, in the incredibly rapid evolution of the foundation models that underpin generative AI applications, a number of these concerns have been addressed. Notably, there is now a wide range of open-source models available from marketplaces like Hugging Face. Many of these open-source models can run very efficiently with more reasonable computing requirements, such as those in an appropriately equipped on-premises data center. Additionally, some of the big tech companies have started to shift the rules about where their models can be run and are creating smaller versions of their models optimized for on-site use.

Moreover, we've seen several companies, including Nvidia, begin to offer models specifically designed for enterprise applications. Nvidia's development is interesting on multiple levels. The company is strongly associated with generative AI primarily because of its hardware. Nvidia's GPU chips power a large majority of current generative AI applications and services in the cloud. At the company's last GTC conference in March, they surprised many by unveiling an entire range of generative AI-related software, including industry-specific software foundation models and enterprise-focused development tools, notably its NeMo large language model (LLM) frameworks and NeMo Guardrails for filtering out unwanted topics. It was no surprise that these models were optimized to run on Nvidia hardware.

Project Helix represents a collaborative effort by Dell and Nvidia to assemble a range of Dell PowerEdge server systems. These include Nvidia H100 GPUs and Nvidia's line of Bluefield DPUs (Data Processing Units, used for the high-speed interconnects between servers that AI workloads require) and are bundled with Nvidia's Enterprise AI software.

Additionally, Dell provides several different storage options from its PowerScale and ECS Enterprise Object Storage lines, optimized for AI workloads. The result is a comprehensive solution that enables companies to begin building or customizing generative AI models. Potential customers can either use one of Nvidia's foundation model options or, if they prefer, select an open-source model from Hugging Face (or a solution from another tech provider) and start the process.

The bundled Nvidia software allows importing an organization's existing corpus of data – ranging from documents, customer service chats, social media posts, and much more – and then using that to either train a new model or customize an existing one. Once the training process is complete, the tools necessary to run inferences and create new applications leveraging the newly trained model are included as well. Dell's bundle also provides a blueprint for helping companies navigate the process of creating/customizing these models and building these tools, along with a range of technical support services.

Most importantly, because this work is done internally, Project Helix can help mitigate the IP leakage issues that concern many companies – even those that have started working with generative AI tools.

Another significant benefit of Project Helix is that it allows companies to leverage generative AI in a more unique and personalized way. While the general-purpose tools currently available can undoubtedly help with certain types of applications and environments, most companies recognize that the real competitive advantage of generative AI lies in customization. There's considerable interest in incorporating a company's own data into these tools, but there's also much confusion about how exactly to do that.

Putting together an "easy kit" for generative AI doesn't mean many organizations won't face challenges in leveraging their data and technology to create the solutions they need. It's crucial to remember that the concepts behind generative AI are still very new, and it's an extremely complex technology. Nevertheless, by bundling the necessary hardware and software that's been pretested to work together, along with information on how to navigate the process, Project Helix appears to be an attractive option for organizations that are eager – or feel competitively compelled – to dive into this exciting new realm.

Bob O'Donnell is the founder and chief analyst of TECHnalysis Research, LLC a technology consulting firm that provides strategic consulting and market research services to the technology industry and professional financial community. You can follow him on Twitter

Tue, 23 May 2023 05:38:00 -0500 en-US text/html

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