After exploring the benefits of data visualization in retail marketing, the loaded question remains: Where else can binning analysis give us insights? For numeric variables, binning is the process of dividing the continuous range of a variable into adjacent ranges, slices, groups, classes or “bins.”
While it should now be abundantly clear that binning analysis helps marketers visualize insightful patterns (if you missed that discussion read my last blog here), my analysis of the retail data provided us with insights about responsiveness by analyzing the patterns between variables and our response outcome. We noted that retail channel customers were some of the most responsive, so now we’ll dig into the same data set to understand who these customers are and how that information can help marketers improve sales.
You may be thinking: how can you use binning to profile your customers? First, keep in mind that a binning analysis can include several ‘target’ variables. These ‘targets’ don’t necessarily need to come from some unknowable future state, or even be connected to a predictive model. By applying binning analysis to virtually any variable in the dataset, we can learn how other variables connect to that ‘target’.”
To further analyze the set of customers in our retail example, we created a profile variable called “retail shopper,” which has a value of “1” for customers who shopped at any retail store. “0” was assigned for those who only shopped through other, non-retail channels. Recently inactive customers were left out of this analysis by setting their value to missing.
To gain an overall understanding of which variables help identify the profile population of retail shoppers, we bin the data using our “retail shopper” profile as the target and sort it by Information Value (IV).
Retail Shopper Profile Binning - Sorted by Information Value
The display above shows which variables are most related to a retail shopper, and which are not. This allows us to see which variables, such as distance to store or purchase amount, have the strongest relationship to retail shopping versus the two other channels, providing insight into the factors which define a retail shopper.
Profiling Insights from Binning Analysis
Further insight can be gained from examining the patterns within each variable. Variables with high IV represent those that are the most interesting because they better differentiate retail shoppers from non-retail shoppers. Binning details for variables of interest are analyzed below.
Distance to Store
- Distance to store is highly related to retail channel shopping.
- Retail shoppers are more likely to live within close proximity to the store.
- Over 62% of retail shoppers live within 6 miles of the store.
- Over 70% of our non-retail shoppers live over 80 miles away from the store.
Minimum Purchase Amount
- Retail shoppers spend less money per purchase.
- Over 16% of retail shoppers have spent as little as $10.
- Retail shoppers may be motivated by convenience, i.e. picking up a gift or a seasonal item.
Frequency Last 24 Months
- Retail shoppers buy most frequently.
- A small portion of retail shoppers bought 7 or more times in the last 24 months.
Maximum Purchase Amount
- Retail shoppers spend low amounts of money per visit.
- Almost 45% of retail customers have never spent more than $97 in one shopping trip.
Total Dollars spent last 24 months
- Nearly 9% of retail shoppers spent over $635 in the last 24 months, showing that low purchase amounts add up when shopping frequency is high.
- There were higher than average volumes of 0’s (non-retail shoppers) at specific dollar amounts ($50, $90, $150) likely indicating specific “sales items” in the non-retail channel. Further analysis could help identify which items stimulated shopping through the catalog or web channels.
Marketing Strategy from Profiling
Binning allows for insightful customer profiling, which retailers can use to re-engineer their customer onboarding strategy. Not all customers will necessarily become loyal, but the sooner retailers can identify and engage with those that will, the more efficiently and effectively they can allocate their marketing budgets.
So, what can a retailer do with all this insight gained from analyzing the binning results? The retailer can understand how, when, and where customers are likely to buy. With that knowledge, they can develop more effective strategies to build business.
Binning and analyzing the customer data in this blog illustrated that people who live close to the store buy frequently, but tend to make small purchases. It is clear that people who live nearby can potentially become loyal, albeit low spending, customers with some increased awareness. Using this information, a marketing team may increase spending to reach their retail channel customers through local advertisements or postcards, targeted to specific neighborhoods near their stores. Once awareness is spiked with increased advertising spend, the key will be to get customers or browsers to spend more while they are in the store. Perhaps special promotions, discounts, or store displays designed to draw customers into the store may result in more spending.
This is just one example of how binning can be used to profile customers of interest. This same retail data can also be leveraged to target one-time buyers to elicit repeat business, holiday shoppers with special promotions, or high-ticket item buyers for larger marketing spend. More robust data could be used in the creation of “look-alike” models.
If you’re interested in customer profiling and binning, join our discussions in the Analytics Community.