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This video demonstrates how to revert to an older version of a Cognos report. MotioCI relieves you from needing to save multiple versions. Cognos users can now revert back to a previous version.
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Demonstration on creating a Cognos schedule, including creating a report view and setting prompt selections.
Completion of COG 101 is required. Cognos Analytics Authoring offers report authoring capabilities including advanced formatting and filtering, prompt generation, and paging controls. This tool allows for a robust experience in data handling and report construction.
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Self-care has many dimensions
Source: Ron Lach/Pexels
It seems we are living in an age of ever-increasing anxiety, stress, depression, and substance use.[1,2, 3] There are many possible reasons for these troubling trends, but researchers are not entirely sure which are most likely and are unable to identify a singular reason. As is the case with many things in life, there are likely many factors at play.
It seems that with the recent trends, a discussion of self-care is timely and needed. More people in the United States are struggling with their mental health, but with increasing costs of mental health services and service providers in greater demand—therefore limiting their availability—more and more people are looking for strategies they can use on their own.
One factor, among many, related to the increase in mental health issues has been the pandemic.  The pandemic is of course a syllabu that has received much media attention. Working with clients during this difficult period, I've noticed them stating that they've had more time in isolation, leaving them alone with their thoughts and struggles without ways to cope or care for themselves. For some, this has created an opportunity for reflection; for others, it has left them to face their own inner challenges without distraction. I argue that the pandemic has highlighted people's need for, or lack of skill or commitment to, self-help.
Yes, social media is saturated with discussions of self-care; it feels like every day, a new article comes out about the benefits of yoga, an exciting new self-help book, or a new self-care product—but the exposure self-care gets leaves much to be desired.
To a degree, self-care getting more of the spotlight is a wonderful thing. Yet the coverage on self-care usually lacks depth and is often commercialized, serving as marketing for a specific product or service. In reality, self-care is not trendy, nor is it new. Human beings have been talking about and practicing self-care since the dawn of time. It has gone by many names and has been a focal point in the teachings and philosophies of many great spiritual masters.
So, take a moment and consider how you practice self-care, how it is integrated into your daily routine and experience, and how you can develop a greater sense of self-care in the future. Below are six dimensions of self-care. Read each one and think of one way you can enhance self-care in that domain of life:
The following Cognos system reports (login required) are recommended by Accounting Services for Research and Sponsored Programs (ASRSP) for administrators to use when monitoring their unit's sponsored projects.
This report displays balances for active projects including totals for direct and indirect expenditures as well as encumbrances by department and/or principal investigator
It can run for negative balances only to monitor deficit spending
Drill through available by project to the GM045 - Sponsored Project Budget Statement
Displays direct and indirect expenditure totals by project against the total budget amount along with project demographic data
Dollars are broken out and shown by Current Accounting Period, Fiscal Year to Date, and Inception to Date
CINCINNATI, Ohio - Everything is coming full circle for the Cincinnati Bengals’ linebacking corps.
Against the Tennessee Titans last year, the Bengals’ linebackers played key roles in limiting Derrick Henry to just over three yards per carry and picking off Ryan Tannehill three times in Cincinnati’s 19-16 win. Linebacker Logan Wilson had probably the best game of his career with a team-high eight tackles and an interception with less than 30 seconds left to help set up Evan McPherson’s game-winning field goal at the horn.
Wilson wasn’t alone, though. On top of fellow inside linebacker Germaine Pratt stepping up, backups Markus Bailey and Clay Johnston each made a stop.
Fast forward to Week 11 of this season, Pratt was the star of the show as the Bengals’ highest graded defender against Pittsburgh. According to Pro Football Focus, he boasted a 91.5 grade for overall defense and 91.2 score for his run defense - the best marks in his career.
Mix in the fact that Wilson has also been trending upward this season, along with reliable snaps from backup Akeem Davis-Gaither, that just might be what the Bengals need as they face off against Henry and Tannehill this weekend.
“We’ve got a great linebacking corps, and really from top to bottom, with our starters down to our last depth guy,” head coach Zac Taylor said this week.
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Wilson and Pratt are the Bengals’ leading tacklers with 40 and 35 tackles, respectively. Each has an interception this season, too. And the Browns’ Nick Chubb (101 yards, Week 8) is the only running back to run for over 100 yards against the Bengals this season.
Wilson said he’s just enjoying the ride with Pratt.
“It’s fun. It’s been a lot of fun being able to grow with him the last three years and the last two as being the starters,” Wilson said this week. “He’s really helped me as a player with his film watching, his scouting, the things he looks for and points out helps me.”
“They play really well together. They have great chemistry. There’s a high football IQ in that room. They see things very fast,” Taylor said of Pratt and Wilson.
The two are playing their best football just as Henry is playing some of the best football of his career. An MVP-caliber running back less than two years ago, Henry leads the league in rushing yards this season (1,010 yards) and much of the Titans’ offense goes through “King Henry.”
That makes Pratt’s role important.
“You want to set the tone because they’re a run first offense. Not too many teams in the league have a run-first offense,” Pratt said. “We just have to get our feet set, get ready to play and come out of the gate rolling. You know, like last year, just do anything to get the stop.”
That’s exactly what the Bengals did against Tennessee last year. On the very first play from scrimmage, safety Jessie Bates picked off Tannehill to get the game started, setting the tone from there for Cincinnati’s defense, which had a crucial pick in every game of the postseason leading up to the Super Bowl.
Defensive tackle DJ Reader’s return against Pittsburgh last week allows more room for Pratt and Wilson to stop Henry and the Titans’ run game. Wilson said he can appreciate having more help up front in a game where Reader’s presence will be crucial.
“He’s a force to be reckoned with, too. He’s not a guy that’s been given as much credit as he deserves. He’s one of if not the best nose tackles in the game,” Wilson said.
The second half of the Bengals’ regular season won’t get easier from here. They return home next weekend to host Kansas City in a rematch of last year’s AFC title game before welcoming a Deshaun Watson-led Browns team.
If the Bengals’ defense and its linebacking corps can reignite the same run that sparked last year’s divisional title and run to the postseason, then a win Sunday could supply the 6-4 Bengals the vindication they need for December and beyond.
“It’s just two good teams going at it, you know, in late November with all playoff aspirations and all that stuff. We’re looking forward to it,” defensive coordinator Lou Anarumo said this week.
A playoff atmosphere in November just might be what the Bengals need this weekend.
Get the latest Bengals merchandise: Here’s where you can order Cincinnati Bengals gear online, including jerseys, hoodies, T-shirts, hats and much more
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The customer journey involves multiple interactions between the customer and the merchant or service provider.
We call each interaction in the customer journey a touch point.
According to Salesforce.com, it takes, on average, six to eight touches to generate a lead in the B2B space.
The number of touchpoints is even higher for a customer purchase.
Multi-touch attribution is the mechanism to evaluate each touch point’s contribution toward conversion and gives the appropriate credits to every touch point involved in the customer journey.
Conducting a multi-touch attribution analysis can help marketers understand the customer journey and identify opportunities to further optimize the conversion paths.
In this article, you will learn the basics of multi-touch attribution, and the steps of conducting multi-touch attribution analysis with easily accessible tools.
What do you want to achieve from the multi-touch attribution analysis?
Do you want to evaluate the return on investment (ROI) of a particular marketing channel, understand your customer’s journey, or identify critical pages on your website for A/B testing?
Different business objectives may require different attribution analysis approaches.
Defining what you want to achieve from the beginning helps you get the results faster.
Conversion is the desired action you want your customers to take.
For ecommerce sites, it’s usually making a purchase, defined by the order completion event.
For other industries, it may be an account sign-up or a subscription.
Different types of conversion likely have different conversion paths.
If you want to perform multi-touch attribution on multiple desired actions, I would recommend separating them into different analyses to avoid confusion.
Touch point could be any interaction between your brand and your customers.
If this is your first time running a multi-touch attribution analysis, I would recommend defining it as a visit to your website from a particular marketing channel. Channel-based attribution is easy to conduct, and it could supply you an overview of the customer journey.
If you want to understand how your customers interact with your website, I would recommend defining touchpoints based on pageviews on your website.
If you want to include interactions outside of the website, such as mobile app installation, email open, or social engagement, you can incorporate those events in your touch point definition, as long as you have the data.
Regardless of your touch point definition, the attribution mechanism is the same. The more granular the touch points are defined, the more detailed the attribution analysis is.
In this guide, we’ll focus on channel-based and pageview-based attribution.
You’ll learn about how to use Google Analytics and another open-source tool to conduct those attribution analyses.
The ways of crediting touch points for their contributions to conversion are called attribution models.
The simplest attribution model is to supply all the credit to either the first touch point, for bringing in the customer initially, or the last touch point, for driving the conversion.
These two models are called the first-touch attribution model and the last-touch attribution model, respectively.
Obviously, neither the first-touch nor the last-touch attribution model is “fair” to the rest of the touch points.
Then, how about allocating credit evenly across all touch points involved in converting a customer? That sounds reasonable – and this is exactly how the linear attribution model works.
However, allocating credit evenly across all touch points assumes the touch points are equally important, which doesn’t seem “fair”, either.
Some argue the touch points near the end of the conversion paths are more important, while others are in favor of the opposite. As a result, we have the position-based attribution model that allows marketers to supply different weights to touchpoints based on their locations in the conversion paths.
All the models mentioned above are under the category of heuristic, or rule-based, attribution models.
In addition to heuristic models, we have another model category called data-driven attribution, which is now the default model used in Google Analytics.
How is data-driven attribution different from the heuristic attribution models?
Here are some highlights of the differences:
A common algorithm used by data-driven attribution is called Markov Chain. At the heart of the Markov Chain algorithm is a concept called the Removal Effect.
The Removal Effect, as the name suggests, is the impact on conversion rate when a touch point is removed from the pathing data.
This article will not go into the mathematical details of the Markov Chain algorithm.
Below is an example illustrating how the algorithm attributes conversion to each touch point.
The Removal Effect
Assuming we have a scenario where there are 100 conversions from 1,000 visitors coming to a website via 3 channels, Channel A, B, & C. In this case, the conversion rate is 10%.
Intuitively, if a certain channel is removed from the conversion paths, those paths involving that particular channel will be “cut off” and end with fewer conversions overall.
If the conversion rate is lowered to 5%, 2%, and 1% when Channels A, B, & C are removed from the data, respectively, we can calculate the Removal Effect as the percentage decrease of the conversion rate when a particular channel is removed using the formula:
Then, the last step is attributing conversions to each channel based on the share of the Removal Effect of each channel. Here is the attribution result:
|Channel||Removal Effect||Share of Removal Effect||Attributed Conversions|
|A||1 – (5% / 10%) = 0.5||0.5 / (0.5 + 0.8 + 0.9) = 0.23||100 * 0.23 = 23|
|B||1 – (2% / 10%) = 0.8||0.8 / (0.5 + 0.8 + 0.9) = 0.36||100 * 0.36 = 36|
|C||1 – (1% / 10%) = 0.9||0.9 / (0.5 + 0.8 + 0.9) = 0.41||100 * 0.41 = 41|
In a nutshell, data-driven attribution does not rely on the number or position of the touch points but on the impact of those touch points on conversion as the basis of attribution.
Enough of theories, let’s look at how we can use the ubiquitous Google Analytics to conduct multi-touch attribution analysis.
As Google will stop supporting Universal Analytics (UA) from July 2023, this tutorial will be based on Google Analytics 4 (GA4) and we’ll use Google’s Merchandise Store demo account as an example.
In GA4, the attribution reports are under Advertising Snapshot as shown below on the left navigation menu.
After landing on the Advertising Snapshot page, the first step is selecting an appropriate conversion event.
GA4, by default, includes all conversion events for its attribution reports.
To avoid confusion, I highly recommend you pick only one conversion event (“purchase” in the below example) for the analysis.
Under the Attribution section on the left navigation bar, you can open the Conversion Paths report.
Scroll down to the conversion path table, which shows all the paths leading to conversion.
At the top of this table, you can find the average number of days and number of touch points that lead to conversions.
In this example, you can see that Google customers take, on average, almost 9 days and 6 visits before making a purchase on its Merchandise Store.
Next, click the All Channels report under the Performance section on the left navigation bar.
In this report, you can find the attributed conversions for each channel of your selected conversion event – “purchase”, in this case.
Now, you know Organic Search, together with Direct and Email, drove most of the purchases on Google’s Merchandise Store.
By default, GA4 uses the data-driven attribution model to determine how many credits each channel receives. However, you can examine how different attribution models assign credits for each channel.
Click Model Comparison under the Attribution section on the left navigation bar.
For example, comparing the data-driven attribution model with the first touch attribution model (aka “first click model” in the below figure), you can see more conversions are attributed to Organic Search under the first click model (735) than the data-driven model (646.80).
On the other hand, Email has more attributed conversions under the data-driven attribution model (727.82) than the first click model (552).
The data tells us that Organic Search plays an important role in bringing potential customers to the store, but it needs help from other channels to convert visitors (i.e., for customers to make actual purchases).
On the other hand, Email, by nature, interacts with visitors who have visited the site before and helps to convert returning visitors who initially came to the site from other channels.
A common question, when it comes to attribution model comparison, is which attribution model is the best. I’d argue this is the wrong question for marketers to ask.
The truth is that no one model is absolutely better than the others as each model illustrates one aspect of the customer journey. Marketers should embrace multiple models as they see fit.
Google Analytics is easy to use, but it works well for channel-based attribution.
If you want to further understand how customers navigate through your website before converting, and what pages influence their decisions, you need to conduct attribution analysis on pageviews.
While Google Analytics doesn’t support pageview-based attribution, there are other tools you can use.
We recently performed such a pageview-based attribution analysis on AdRoll’s website and I’d be happy to share with you the steps we went through and what we learned.
The first and most challenging step is gathering data on the sequence of pageviews for each visitor on your website.
Most web analytics systems record this data in some form. If your analytics system doesn’t provide a way to extract the data from the user interface, you may need to pull the data from the system’s database.
Similar to the steps we went through on GA4, the first step is defining the conversion. With pageview-based attribution analysis, you also need to identify the pages that are part of the conversion process.
As an example, for an ecommerce site with online purchase as the conversion event, the shopping cart page, the billing page, and the order confirmation page are part of the conversion process, as every conversion goes through those pages.
You should exclude those pages from the pageview data since you don’t need an attribution analysis to tell you those pages are important for converting your customers.
The purpose of this analysis is to understand what pages your potential customers visited prior to the conversion event and how they influenced the customers’ decisions.
Once the data is ready, the next step is to summarize and manipulate your data into the following four-column format. Here is an example.
The Path column shows all the pageview sequences. You can use any unique page identifier, but I’d recommend using the url or page path because it allows you to analyze the result by page types using the url structure. “>” is a separator used in between pages.
The Total_Conversions column shows the total number of conversions a particular pageview path led to.
The Total_Conversion_Value column shows the total monetary value of the conversions from a particular pageview path. This column is optional and is mostly applicable to ecommerce sites.
The Total_Null column shows the total number of times a particular pageview path failed to convert.
To build the attribution models, we leverage the open-source library called ChannelAttribution.
While this library was originally created for use in R and Python programming languages, the authors now provide a free Web app for it, so we can use this library without writing any code.
Upon signing into the Web app, you can upload your data and start building the models.
For first-time users, I’d recommend clicking the Load Demo Data button for a trial run. Be sure to examine the parameter configuration with the demo data.
When you’re ready, click the Run button to create the models.
Once the models are created, you’ll be directed to the Output tab, which displays the attribution results from four different attribution models – first-touch, last-touch, linear, and data-drive (Markov Chain).
Remember to obtain the result data for further analysis.
For your reference, while this tool is called ChannelAttribution, it’s not limited to channel-specific data.
Since the attribution modeling mechanism is agnostic to the type of data given to it, it’d attribute conversions to channels if channel-specific data is provided, and to web pages if pageview data is provided.
Organize Pages Into Page Groups
Depending on the number of pages on your website, it may make more sense to first analyze your attribution data by page groups rather than individual pages.
A page group can contain as few as just one page to as many pages as you want, as long as it makes sense to you.
Taking AdRoll’s website as an example, we have a Homepage group that contains just the homepage and a Blog group that contains all of our blog posts.
For ecommerce sites, you may consider grouping your pages by product categories as well.
Starting with page groups instead of individual pages allows marketers to have an overview of the attribution results across different parts of the website. You can always drill down from the page group to individual pages when needed.
Identify The Entries And Exits Of The Conversion Paths
After all the data preparation and model building, let’s get to the fun part – the analysis.
I’d suggest first identifying the pages that your potential customers enter your website and the pages that direct them to convert by examining the patterns of the first-touch and last-touch attribution models.
Pages with particularly high first-touch and last-touch attribution values are the starting points and endpoints, respectively, of the conversion paths. These are what I call gateway pages.
Make sure these pages are optimized for conversion.
Keep in mind that this type of gateway page may not have very high traffic volume.
For example, as a SaaS platform, AdRoll’s pricing page doesn’t have high traffic volume compared to some other pages on the website but it’s the page many visitors visited before converting.
Find Other Pages With Strong Influence On Customers’ Decisions
After the gateway pages, the next step is to find out what other pages have a high influence on your customers’ decisions.
For this analysis, we look for non-gateway pages with high attribution value under the Markov Chain models.
Taking the group of product feature pages on AdRoll.com as an example, the pattern of their attribution value across the four models (shown below) shows they have the highest attribution value under the Markov Chain model, followed by the linear model.
This is an indication that they are visited in the middle of the conversion paths and played an important role in influencing customers’ decisions.
These types of pages are also prime candidates for conversion rate optimization (CRO).
Making them easier to be discovered by your website visitors and their content more convincing would help lift your conversion rate.
Multi-touch attribution allows a company to understand the contribution of various marketing channels and identify opportunities to further optimize the conversion paths.
Start simply with Google Analytics for channel-based attribution. Then, dig deeper into a customer’s pathway to conversion with pageview-based attribution.
Don’t worry about picking the best attribution model.
Leverage multiple attribution models, as each attribution model shows different aspects of the customer journey.
Featured Image: Black Salmon/Shutterstock