When I analyze data and decisions with customers and colleagues, I often hear the question: “why bin the data?” In this blog series, I’ll explore the several benefits of binning, starting with its value for data exploration and insight generation. Examples in this blog are drawn from retail marketing, but apply across business contexts.
To answer the burning question, why bin? I’ll first explain: what is binning. For numeric variables, it’s the process of dividing the continuous range of a variables into adjacent ranges, slices, groups, classes or “bins” (there are so many names). For example, you can group customers by Age, into ranges like 18-25, 26-35, etc. With discrete variables, binning is the process of combining raw data values into similar groups, like binning State codes CA, OR, and WA into “West Coast”.
Binning has some terrific properties for predictive modeling and is often associated with scorecards that thrive on discretized numeric predictors. But even short of formal model construction, binning can help people quickly explore data and unlock the signals and surprises otherwise hiding in datasets. The key elements of binning are the calculations of information value and weight of evidence, which immediately quantify associations and allow for visualization of the relationships between any variable and a business outcome.
In retail marketing, an effective, automated binning algorithm quickly surfaces key insights into customer behaviors. Retail marketers typically measure Recency, Frequency, and Monetary (R,F,M) with a target outcome of response performance. In this blog, we bin and analyze these same variables to demonstrate the insights binning can provide with a real data set. The data are summarized to determine Weight of Evidence (WoE), which in this dataset indicates the relative likelihood of responding, allowing marketers to quickly identify patterns in a customer’s likelihood to buy.
The most immediately appealing aspect of binning is the ability to visualize the relationship between predictors and performance. Using binning to look at Recency (R) allows marketers to see the relationship between time since the customer’s last purchase and how likely they are to respond to an offer.
As shown in the table above, the differences in WoE of binned groups of recency values are visualized in the right most column above, providing quick insight from the data. We see a positive WoE for customers who shopped within the last 10 months, meaning this set of customers is more likely to respond. Customers who have not shopped in 10-18 months have a slightly negative WoE, indicating this group is a little less likely than the average customer to buy again. If a customer has not purchased in more than 18 months, the WoE gets considerably more negative. This bears out the often regarded truth in marketing – a “hot” shopper is worth mailing to, but once it’s been 18 months since you’ve brought someone in the door, you are much less likely to see them again. Since the data was categorized and then analyzed, this adverse relationship between time and response is immediately evident, providing actionable insight for marketers to effectively target outreach.
Looking at Frequency (F) in the table below we see the number of times someone has purchased over their lifetime has a positive relationship with WoE. One of the common plagues for retailers is the dreaded ‘one time buyer,’ and the binned data shows that one time buyers represent 42% of the population; and they are clearly the least likely to respond to offers. In general, this is a difficult insight for retailers to harvest because there is very little behavior upon which to base a marketing campaign. Understanding that the set of customers who have only bought once are not likely to respond to offers, a retailer might choose to minimize their marketing spend on these customers, or seek to take advantage of the little data they do have (channel, type of merchandise, etc.) to better target them. If the spend is high enough, they might choose to invest in “off-us” information to gain more insight.
Grouping customers by Monetary (M), as shown in the table below, reveals a positive pattern of dollar purchase over a lifetime and response to offers. This means that the more a customer has spent in the past, the more they are likely to spend in the future. Binning customer data by their spending habits makes use of readily available information which can help marketers target customers that are likely to spend more based on their past behavior.
Categorical Channel Data
When predictors are already in categorical form automated binning may not be necessary. Still, visualizing this data adds insight to the information we gathered from binning R, F, and M variables. In this case, retailers have categorical data about the channels their customers use to find and purchase their products. In the table below: (--R) stands for retail shopping in store, (C--) stands for catalog shopping ordered through the phone or mail, (-W-) stands for web purchases, and (---) stands for shoppers that have not been active in the last 12 months. If a shopper used more than one channel, they are assigned more than one value. Analyzing this channel data is a simple matter of counting the number of responders and non-responders by channel and calculating the WoE to display results, there is no algorithm needed.
Channel Usage Binning
Color is used to indicate the thickness of data. Red or grey warns that data is getting thin, thus response rates & WoE may be unreliable. Bright blue indicates lots of data.
Analyzing this channel data reveals that shoppers who have been inactive for at least a year are very unlikely to respond to a new mailing. Web customers follow close behind inactive shoppers in non-responsiveness, while retail only shoppers are very responsive and catalog shoppers fall somewhere in between. The best response rates, however, can be seen from cross-channel customers, meaning that it is most effective to target shoppers who purchase through multiple channels.
Binning customer data allows retailers to quickly detect patterns based on a diverse set of variables. Visually representing the WoE calculated for different variables provides insight into customer behavior through several different lenses. This binning summarizes the data, providing intuitive visualizations and making it easy for marketers to effectively tailor campaigns to groups that are most likely to respond based on past behavior. By binning customer data across specific variables, like R, F, and M, marketers can easily see patterns in the relative responsiveness for each group. Binning organizes the data and presents the analyst with information that results in actionable insights.
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In future blogs we’ll address further applications of binning in data discovery, the advantages of binning for dealing with non-linear relationships and missing values, and the use of binning for scorecard development and strategy design.