Last week I wrote about how to organize your data to allow actionable insights to emerge. This article will discuss some ways of organizing that data once it is collected. The goal is to demonstrate how to measure effectiveness of marketing. Next week we’ll close out this mini-series with set of case studies on how this approach has been applied by other brands and marketing agencies.
Your goal, once you have collected your “delivery” and “results” data, is to organize that data in such a way as to make it actionable. (Note, this method also works well if you’re working with data that was originally collected for another purpose, but can be re-purposed to address a different need.)
Organize the variables into two groupings: “Delivery” and “Results.”
Nine times out of ten, your delivery data will be something over which you have control. By “control,” I mean those aspects of your business that you can change pretty much at will. When thinking about how to measure marketing results, you need to keep in mind the things that define performance. Last week we gave four examples of delivery data:
As a business manager, you or someone in your company, have control over these four items (and the hundreds more that define your business operations). For example, you can choose how heavily to staff the check-out counter. You decide the volume packaging segments. You know what type of TV buys or PPC online advertising to buy, or when a discount is offered.
It would be fair to expect that each of these items will have some impact on your measured results. For example, you would expect staffing cost to impact sales volume.
Here’s the Market Research Consultants’ Trick
The trick is to test these relationships (using the proper statistical methods) to see what levels in a delivery variable are associated with a meaningful change in a results variable.
Let’s say you measure a 10% increase in sales volume after you dropped the price by 10% in January. In fact, you see roughly the same change in sales every time you drop the price by 10%. Moving forward, you could, therefore, increase sales by 10% anytime you wanted to. Just drop the price by 10%. This finding is therefore “actionable.”
It’s common for sales volume to go up when price goes down. For the most part, people don’t tend to consider the obvious as “insightful.” However, there are many example of this in your business that are not so obvious. These might include things such as:
- The relationship between packaging and seasonal sales fluctuation.
- The color of your “add to cart” button and how many people opt-out of the shopping cart.
- The placement of your 800# on a flier and the related call volume to your call center.
The goal is to scrutinize your data to find these relationships. Then deliver your findings to the people who need to know, in a way that they can then make positive business decisions based on these newly discovered relationships. This is the type of work we do at Portland Marketing Analytics every day.
Next week we’ll provide some case studies describing how other agencies and brands have used this process to make insights actionable.