We recently published a book, Prescriptive Analytics for Business Leaders. In order to differentiate prescriptive from other forms of analytics, I started off with a little "day-in-the-life-of" story about a VP Marketing who leverages each form of business analytics: Business Intelligence, Predictive Analytics and Prescriptive Analytics.
Whether you're in Marketing or not, I think the story is relatable and easy to follow. Therefore, I wanted to publish it on our blog so our followers could benefit from it as well. My hope is that, following this read, you're able to better understand how the various forms of analytics can a) help you advance in your role and b) enable you to make more impactful decisions within your company.
Enjoy the read — feedback is always welcome!
Let’s assume we have Barry, a Business Analyst who works within the Marketing function of a consumer packaged goods (CPG) company that manufactures several hundred products. Barry is in charge of all business analytics activities, one of which includes compiling dashboards and grabbing data that answers his boss’s (Vice President Mary) most pressing questions.
Several years ago, VP Mary was struggling with reviewing her budget, so she asked Barry how she could guarantee she adhered to her budget. Barry decided the best option was to create a report for Mary that updates, in real time, what is being spent on promotions, paid advertising, trade shows, and any additional spend categories. He used Tableau to compile a series of dashboards that provided VP Mary ad-hoc insights into her spend and sent her notifications when she was nearing her pre-defined monthly budget limits in each category. Barry’s Tableau dashboard is an example of descriptive analytics — it’s a collection of historical events that are compiled into easy-to-digest dashboards, often reflecting events as they occur.
Because Barry is an exceptional Marketing Analyst, he also grouped Mary’s spend in simple categories that allowed Mary to drill down to specifics, so she could identify exactly where she went over budget from within her dashboards. He also created charts and visuals that correlated Mary’s real-time spend data with historical data and spend targets, allowing Mary to understand how she’s doing compared to her past performance. Enabling these drill-downs and correlations is the diagnostic piece of BI. It involves grouping data appropriately in order to understand why something happened (i.e., identifying deviations from the target or identifying certain outliers).
Thanks to Barry’s dashboards, Mary was finally able to stick to her predefined budgets. She was able to pinpoint which campaigns, lead personas, and channel initiatives had driven the most revenue for her company. However, she quickly realized that having these insights wasn’t enough to streamline her marketing efforts. Sales remained irritated at receiving only “lukewarm” leads, some of her campaigns that she thoughtwould be successful weren’t resonating, and her Client Success Managers were frustrated with seemingly unpredictable customer churn.
When she brought this problem to Barry, he knew exactly what to do. “Simply describing our data isn’t enough anymore — we need a form of analytics that will help us predict the likelihood of all these things like customer churn, deal close, etc. occurring,” he said.
Over the next several months, Barry began compiling relevant marketing and sales data. This included information about deal closes and losses, social media, website engagement, detailed customer behaviors, brand engagement, and campaign information. He then used a variety of statistical modeling approaches to include regression analysis, forecasting, multivariate statistics, pattern matching, predictive modeling, and forecasting to correlate the data and predict the likelihood of outcomes that he and Mary knew had a significant impact on their Marketing performance.
Mary was thrilled with the outcome of Barry’s work. She could now see how likely her leads were to close (this satisfied the Sales team) and was able to segment the data by detailed persons, and lead characteristics, and campaigns to better target different audience segments. She could also now predict the likelihood of customer churn, so her Client Success team could step in before churn occurred. Lastly, she was able to improve her messaging by understanding how likely a message was to resonate with her audience segments across websites, social channels, and emails. Over the next year, the company’s average profit margin per customer and customer lifetime value both doubled — all thanks to Barry’s predictive modeling.
Despite the fact that Mary saw drastic improvements in her metrics since she began leveraging predictive insights, she still noticed gaps in her Marketing Plan (as did her boss — CEO Sara — and many of her higher-level colleagues). All this was impacting her promotion in the company.
Mary continued to struggle with unanswered questions. Not only were they unanswered, but they were the most important questions she had to address in order to drive the most impact organization-wide and get the promotion she wanted. She wondered:
Once again, Mary approached Barry with her problems and, again, Barry found a solution.
“Want to know what all these questions have in common, Mary? They all ask ‘what should I do’? And see, predictive analytics can tell you about likelihoods and probabilities, but it can’t tell you where to allocate your marketing dollars, and it certainly can’t tell you exactly how much to put and where to put it. What you need is prescriptive analytics.”
Barry started working on developing a prescriptive model that represented Mary’s end-to-end marketing business. Of course, he first had to find new software — his BI/predictive tool certainly wouldn’t do the trick.
[While we’ll get into the “how” of prescriptive analytics later on, essentially what Barry did was create a model that describes how their CPG business works — he considered account business rules, business processes, objectives, constraints, preferences, policies, best practices, boundaries, revenue, and costs. He then used that model to provide his prescriptive system (the math piece) with the business intelligence to analyze their data and suggest the optimal way forward].
With a nice User Interface (UI) on top, Mary was able to ask her questions (largely around what-if and scenario analysis) and understand the financial impact of very specific decisions on her predetermined objectives.
Finally, she had a trusted companion that guided her business decision-making process and gave her the best plan forward. She hadactionable insights!
Mary used her prescriptive dashboards for everything:
Prescriptive analytics turned Mary into a strategic business partner, and she finally got that promotion to Chief Marketing Officer she’d always wanted.
Mary saw transformational value across the business, such as:
And, of course, Barry got a huge promotion!
In this article, we defined the three main types of business analytics: BI, predictive analytics, and prescriptive analytics.
Nearly 100% of businesses today use some form of BI. Several of the most common applications are:
The market penetration for predictive analytics is around 20-30%, and this number will continue to grow rapidly over the next several years. Common applications of predictive analytics are:
Currently, at 6-10% market penetration, prescriptive analytics is expected to grow to 36% penetration by 2021.
Here are a few common examples of prescriptive analytics applications:
Although the story was somewhat made-up, the questions answered by each form of analytics and the organizational impact outlines were not. Although not every company or role can benefit from leveraging prescriptive analytics for decision making, it's certainly something to inform yourself on.
Feel free to reach out to me via LinkedIn or email if you have questions, feedback or opinions you'd like to share!
Thanks for reading :-)