By Laura Shores - July 22, 2020
If you’re an analyst, you know the days of providing weekly revenue updates are long gone. These days, as the company continuously runs experiments and promotions to stay competitive, the business demands real-time analysis to support operational decisions. The good news is that you have more data available than ever before. The bad news is that it’s a struggle to find the answers at the pace the business requires. Whether your conducting complex segmentation analysis to understand changing customer behavior or evolving product preferences, there are simply too many variables to check by hand.
Let’s take a look at how this struggle plays out for an analyst at a large retail company. The company is increasingly competing with online-first brands, and they’re laser-focused on meeting rising customer expectations and delivering a hyper-personalized experience.
Now, when the marketing team runs a promotion, they don’t just want to understand the general performance of the promotion against their KPI, they want to understand “why” the promotion performed well or not, and with whom. And they need answers, fast.
At Sisu we’re focused on getting data analysts to the “why” faster and more comprehensively than ever before, and we’re unlocking new levels of detail for experiment and segmentation analysis. Let’s explore how we’d find the answers for your marketing team in Sisu.
The average transaction-level dataset includes hundreds of columns – everything from acquisition channels, customer demographics, product SKUs, and time spent in-store or on digital properties. Typically, you’d have to simplify or aggregate this data before you could work with it, but with Sisu you can automatically analyze the millions of possible factors in your data for any given KPI and finally leverage all the rich, wide data you’ve been capturing.
For this example, let’s take a look at how the marketing team’s December promo code campaign impacted transaction amount (txn_amount), their primary KPI.
Before we start, we need to identify where the data lives. In this case, it’s an existing table called public.purchases.
Then, we’ll set up a comparison test, looking at the performance of the December promo code (December Promo) against populations that didn’t use a promo code or used other promo codes (No / Other Promo). Setting this up only takes a minute or two, and all that’s left to do is to “Get the facts.”
(Product images use mock data.)
A typical segmentation analysis only compares the performance of two groups. You still don’t know “why” one group outperformed another, which means even when analysts know which group performed best, they’ll still have to spend hours manually digging through hundreds of hypotheses to try and answer this question.
With our new tools for comprehensive segmentation analysis, once you hit Get the facts, Sisu automates that work for you. In seconds it tests every factor in the data and uncovers the subpopulations that are having the biggest impact on the two groups (December Promo and No / Other Promo).
At a glance, we can see the average transaction amount is roughly $46.00 across the two groups, and on the surface it appears that the December Promo didn’t have an effect. A dashboard would show you a relatively flat line, but with Sisu we can see the specific subpopulations where the promo code had a significant impact.
Almost immediately you can see differences in the ratio of the average, which is how the average transaction amount differs between your two groups.
For example, in the fact below we see that for bronze loyalty program members that were shown the week 52 advertisement, the December Promo had a positive impact and lifted the average transaction amount up to $56.20. This is nearly 2 times the transaction amount of the <