Pitching analytics: recommendations on how to sell your story (part 1)


Ken little leagueI routinely speak with executives who tell me that the ability to “sell” analytical results is just as important as producing them. In this post I will share some of what I have learned in several years of presenting complicated analytical results to audiences, both technical and lay. Some of my tips address the material and some the audience. Why do they care about what you have to say? What is the opportunity cost of inaction? These are just some of the topics that any strong analytics pitch should address. Since we are well into the Major League Baseball season, and the division-leading New York Mets use SAS analytical tools improve their decision making on and off the field, I will frame our analytics pitch with inspiration from America’s pastime. Plus, pitching comes naturally to me, as you can see in the photos I dug out.

The Wind Up: Prepare these topics before the pitch.

1.The Project: How do I support my methodology?

Each analytics project involves some methodological choices, so it is important to articulate these when pitching analytics. Some of my points of emphasis are:

    • Prepare to talk about a model: Whether you are optimizing, estimating or predicting, thinking in terms of a model provides a basis for a well-conceived project. It forms the basis for all data cleansing, transformation and enrichment. There is no way to do analytics without some notion of a model. Writing it down will provide value, saving time and budget.
    • Simplify: Have you made all simplifications possible to solve your objective? Do you know why you chose to simplify? Your solution does not need elegance, unless it materially improves the outcome.

2. The Data: What data am I using and why those data? Some important features of your data might be:

    • Data Generating Process (DGP): What created the observations? Are your data actually telling you about consumer behavior or do your data actually represent poor data collection processes? For instance, is my transactional data system blending retail items that gradually change qualities? This might happen with new products. Will those data ever enlighten me about the product life cycle? Or does B2B data from a warehouse actually tell us anything about downstream consumer behavior? Having an understanding of how the data were created will aid in evaluating their suitability to answer certain questions.
    • Data source: Are these from a transactional database or a third-party? Have you enriched the data? Can you?
    • Perfect data: What would perfect data look like? Understanding the perfect data will help to build a mental model for the project. Concessions can be made later. While being enamored with the academically “best” answer can be time consuming and wasteful, having an idea of the perfect data set will help you prioritize what your efforts around data cleaning and acquisition.

3. The Audience: Who am I presenting to? Understanding the audience is the single best chance you have for adoption when pitching analytics. What can you do to improve your chances? Do a little light research (don’t be stalker!!). LinkedIn and quick internet searches may reveal some useful info such as:

    • Highest Degree Obtained: A recent Ph.D. or Masters in a technical discipline will indicate considerable comfort in concepts of regression and optimization. This may not indicate comfort with certain “jargon.”
    • Field of study: This is the most important item I learn. Why? It tells me exactly how much and what type of math/statistics has been taught and what jargon is used. Only formal statisticians, actuaries, and biostatisticians use the term GLM (generalized linear model). Other disciplines might call a GLM with a Poisson distribution simply a Poisson regression.
    • Publications and presentations: Look on a person’s CV or LinkedIn for publications or presentations. It will indicate where their passion is. Can you motivate your chosen methodology with an example they can identify with?

Do you have any other suggestions on preparing your pitch? In my next blog post I will focus on how you develop materials, deliver a strike, and potential missteps on your path to a successfully pitching analytics. If you do, please leave me a comment below or look for me at the Analytics 2015 conference, where I'll be leading a table talk on selling analytics to management.


About Author

Kenneth Sanford

Sr Research Statistician

Kenneth Sanford, Econometric Evangelist, Advanced Analytics R&D, SAS: Ken joined SAS after working for both a large private consulting firm and holding faculty positions at the University of Cincinnati and Middle Tennessee State University . At SAS, Ken is responsible for helping to integrate the advanced analytical procedures being created by the ETS (Econometric and Time Series) development team into the business solutions offered by SAS. He is also charged with promoting the use of the newest Econometric estimation techniques to our new and existing SAS users. His background is in applied economics with special interest in Industrial Organization and Health Economics. He has articles forthcoming in the Southern Economic Journal and the Journal of Sports Economics. Ken holds a Ph.D. in Economics from the University of Kentucky.

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