Perhaps you’ve heard him in meetings — he is the one questioning your results. Perhaps you’ve seen him at his desk surrounded by tombs and tables in an effort to lower incremental sales calculations — he calls it reducing bias. Perhaps you’ve hoped he will not be assigned to your project — he delivers lower lift estimates than his peers. He is the measurement curmudgeon.
How do you detect if a measurement curmudgeon resides in your office? Listen for the following clues/questions:
Is that control group really comparable to the experimental group? Isn’t it biased toward less engaged customers and inflating your measured lift?
Wasn’t that concurrent with our fall promotion? Isn’t that event likely accounting for most of your positive results?
Haven’t sales been trending up? Did you incorporate that trend into your analysis?
Many have commented on the 14 product enhancements announced at last week’s Google Analytics Summit (GAS), but I attended to learn about their new Data Driven Attribution (DDA) tool. Why travel to Mountain View, CA “just” to focus on a new advanced attribution tool?
Digital pathways are rarely last click. Imagine a consumer who clicks on a banner ad sending them to your YouTube video. They watch the first 45 seconds and then enter your website through natural search. Do you really want to give zero credit to the YouTube ad? Assuming this pathway is common, should you increase or decrease your banner ad budget? Now with Google’s DDA (or competing tools) you can get an accurate answer for each touch point.
Pathways are rich with insights—in theory. Now imagine your team is struggling to optimize YouTube across a set of products. Also imagine you could measure how the influence of YouTube varied across journeys based on what was purchased, lifecycle stage and persona. Armed with those insights your team could develop a content creation schedule or define the role of YouTube in new product launches. Unfortunately Google’s DDA is limited in pathway comparisons, but I predict expansion of that functionality in the next 12 months. I applaud Google for its simple interface, but marketers need more options than the limited demographics and attitudes available today.
Last week I had the privilege of participating on the Advisory Board for the Retail Marketing Analytics Program (ReMAP) at the University of Minnesota, Duluth (UMD). Perhaps the best part of these sessions is the opportunity to meet with the students, many of which will be tomorrow’s marketing scientists.
During a few conversations on this visit, I was asked how to secure an entry-level position that would involve lots of cool predictive analytics. I want to focus on one of the answers I shared — don’t tell anyone you’re doing predictive analytics. What do I mean? Imagine you’re a freshly minted analyst in the following situation:
Your manager asks you to quickly evaluate who responded to a promotion.
You have many factors to investigate (because you have lots of data).
You have very limited time to find a great answer and build a deliverable.
The required deliverable needs to be simple and free of analytic jargon.
You don’t need to be a fine woodworker to sit in a chair. An inability to precisely construct an angled mortise and tenon joint does not preclude you from resting your feet. Similarly the time is rapidly approaching where you won’t need to be a marketing scientist to deploy analytics. Ignorance of neural networks will no longer impede your ability to use them to improve a campaign. The democratization of predictive modeling or other trends involving the intersection of customer analytics and marketing technology is much of what I will cover for Forrester Research.
In my new role as a senior analyst I look forward to helping Customer Insight professionals increase marketing and business returns through becoming more intelligent enterprises. This might involve guiding clients on technology decisions, organizational strategy, or benchmarking to their peers. What topics would you like to see me cover?