Behavioral Segmentation

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1 Behavioral Segmentation TM

2 Contents 1. The Importance of Segmentation in Contemporary Marketing Traditional Methods of Segmentation and their Limitations Lack of Homogeneity Determining the Number of Groups Required Segmentation in Two or More Dimensions Logical Groups are Fixed, Subjective and Possibly Arbitrary Segmentation Algorithms from Fuzzy Logix Homogenous within Segments and Heterogeneous Between Segments Cross-Selling Opportunities are Identified Number of Segments is Automatically Determined Segmentation in Multiple Dimensions Useful Metrics Are Captured For Each Segment The Benefits of Self-Organizing Segments Rapid Analytic Discovery Drilling-Down in a Segment Spot Trends and Improve Forecast Accuracy Accelerated Implementation P a g e 1

5 An even more serious challenge is determining how many observations should be in each group. If our goal is to preserve homogeneity in each group, the number of observations in each group will likely be different. While the groups should exhibit dissimilar characteristics, the members within each group should be similar. Accomplishing this using traditional methods will require many hours of experimentation to make sure each group is both the most intra-homogenous (members have similar behavior) and is also most heterogeneous (each groups behavior is dissimilar from all the other groups). 2.3 Segmentation in Two or More Dimensions The limitations of traditional segmentation become even more apparent if we consider two or more dimensions. For example, if we know that retailers are interested in two different metrics for their customers number of visits during a quarter and average amount of purchases per visit, then we can use segmentation to guide us in maximizing both of these attributes at store and customer level. Similarly, a telephone carrier may be interested in segmenting customers using three dimensions how long the customer has been a subscriber, monthly fees paid by the customer and percentage of minutes utilized. Conventional methods cannot handle the complexity of segmenting data using two or more dimensions. For example number of visits per quarter and purchases per quarter. Traditional segmentation is limited in capability to one dimension and doesn t work with multiple dimensions so we will not be able to deal with this problem unless we create a new attribute by combining two dimensions into one. One way of combining could be to assign a certain weight of each dimension and then adding the metrics. For example, Combined Metric = 0.5 * Number of Visits per Quarter * Average per Visit Here we have assigned a weight of 50% of each of the two dimensions. Now, traditional algorithms can segment the underlying data but these assigned weights were completely arbitrary; even if they are a best guess. In addition, the minimum, maximum and averages for each segment will be the result of analyst bias because of the guesswork in weighting. Against this backdrop of the traditional methods not being able to objectively deal with more than one dimension, we probably have to be content with segmenting on something like total purchases in a given quarter by all customers. However this presents another issue since two customers can have the same amount of total purchase in a given period by different means. One of them could be visiting the store quite often and buying low ticket items whereas another could be visiting only a P a g e 4

7 from our clients who are in various industries like retail, media and entertainment, advertising, and others. 3.1 Homogenous within Segments and Heterogeneous Between Segments Our segmentation algorithms have been developed keeping in mind the basic principle that data within a given segment should be as homogenous as possible while making sure the different segments are as heterogeneous as possible. Let us revisit the example of the high-end garment retailer in the Southeastern US. We present the results of segmentation using quartiles and our proprietary algorithm. Segmentation based on quartiles Quartile Number of Customers Minimum Maximum Average Q1 153 \$ 5 \$ 99 \$ 60 Q2 153 \$ 99 \$ 220 \$ 154 Q3 153 \$ 221 \$ 447 \$ 317 Q4 154 \$ 447 \$ 4,038 \$ 979 Segmentation based on Fuzzy Logix s proprietary algorithm Cluster Number of Customers Minimum Maximum Average \$ 5 \$ 172 \$ \$ 175 \$ 375 \$ \$ 376 \$ 676 \$ \$ 683 \$ 1,149 \$ \$ 1,163 \$ 1,993 \$ 1, \$ 2,097 \$ 4,038 \$ 2,874 It is evident that the Q4 segment in the quartile chart has a very wide range of values. When we let the data define the segments, we fine 6 naturally occurring segments. These segments seem much more uniform because our algorithms have derived the optimal mix of homogeneity within a segment and heterogeneity between the segments based on the patterns of behavior in the data. As an example, let us consider segments 4, 5 and 6. In these segments, the average value of customer purchases is higher than the average spend in the other segments, however they are distinct enough to warrant their own category. The algorithms produce near-homogenous behavior within a segment and heterogeneous behavior across segments. P a g e 6

8 The uniformity within segments and the differences between any two segments in this example provides a much better opportunity to understand customers behavior and thereby guide targeted marketing. 3.2 Cross-Selling Opportunities are Identified One of the uses of segmentation is to be able to identify those customers who could migrate from one segment to another. Those who could migrate to a higher value segment present upselling opportunities whereas those who have a strong likelihood of migrating to a lower segment present threats that need to be mitigated. Our algorithms calculate the probability of segment migration and can be used for further targeting and up-selling. In our retail example, segment # 3 has a range of average purchases from \$376 to \$676. A customer whose average purchase is close to the upper range in this segment may be a potential up-selling opportunity, and over time, move to the next higher segment. In the following exhibit, the probabilities of staying in segment 3 or of migrating to segment 4 have been presented for five customers who are at the upper end of segment 3. These probabilities are calculated by our algorithms and can be used very effectively for managing customer migration and upselling. As an example, we can target all those customers who have a more than 20% probability of migration to segment 4 for a targeted campaign. A somewhat similar strategy can be adopted to prevent customers from migrating to lower segments. Therefore, we can improve customer profitability by moving customers to higher spending segments and preventing others from moving to lower value segments. Segmentation statistics and crossover customers Cluster Number of Customers Minimum Maximum Average Average Segment# 3 Segment# \$ 5 \$ 172 \$ 87 \$ % 21.97% \$ 175 \$ 375 \$ 261 \$ % 26.10% 3 99 \$ 376 \$ 676 \$ 489 \$ % 28.68% 4 49 \$ 683 \$ 1,149 \$ 854 \$ % 39.19% 5 28 \$ 1,163 \$ 1,993 \$ 1,515 \$ % 40.59% 6 11 \$ 2,097 \$ 4,038 \$ 2,874 Probability 3.3 Number of Segments is Automatically Determined Our segmentation algorithms can automatically determine the number of segments that are required to best describe the data. This is a key distinction because the behavior of the data determines the number of segments as opposed using assumptions to try and remove the bias of P a g e 7

9 the model builder. With our algorithms, users do not have to decide the number of segments that he or she wants the data to be grouped by. Since the model does the grouping, the user does not have to manually iterate through a number of options before determining the appropriate number of segments. The algorithms that we employ are equipped with artificial intelligence to be able to make this determination on its own. We have tested these algorithms with large amounts of data to ensure that the number of segments generated by these algorithms is indeed correct and coherent and we have worked with industry experts to incorporate their input into the design of our algorithms. In the examples below, we present a view of using traditional segmentation vs. true behavioral segmentation and the ability of our algorithms to automatically determine the number of segments. In the first example, using traditional segmentation, the scatter-plot shows that there are four distinct groups, as illustrated by the dotted blue circles. Notice that segments 3 and 4 in the upper right corner have overlap in membership and are not significantly distinct. When interpreting the chart, also notice that the center of the circle is the average value for that segment. Example 1 Average Cluster Dim 1 Dim 2 Obs In the second example, we use our algorithms to determine the true number of segments based on customer behavior and the optimal mix of inter-segment homogeneity and betweensegment heterogeneity. On visual examination, we see that there are three distinct clusters. Artificial intelligence enables the algorithms to automatically determine the number of segments required. P a g e 8

10 By letting the model derive the segments our methods will produce segments that identify the true behavior of customers, therefore your business decisions will yield results that will be more cost effective and produce higher yields. Example Simulated Data 250 Dimension Average Cluster Dim 1 Dim 2 Obs Dimension Segmentation in Multiple Dimensions An example of two-dimensional segmentation for a high-end garment retailer is presented below. The two dimensions are number of visits by customers in a given quarter and average sales per visit. In the following exhibit, the sales from all customers in each of the segments has been calculated and displayed for current quarter as well the same quarter the previous year. The size of the bubbles in the bubble chart is proportional to the value of sales. The information in charts like this helps the end-users make comparisons and draw conclusions. One classic example is segmentation of retail customers by number of visits per quarter and average purchase per visit. Our algorithms can easily handle a case like this and produce actionable results. As shown in segmentation statistics, there is enough variability in both these dimensions to warrant creating four different segments. One would argue that the number of visits in segment 3 and 4 are the same and therefore, it does not add additional value to use two segments for this data instead of one, however not the dramatic difference in sales amounts per visit in these groups. In both groups, the number of visits is the same; 1.9 per quarter, but the average amount spent in segment P a g e 9

11 four is more than double the amount spent in segment three. Using this information the retailer devised specific marketing campaigns for each segment and achieved a higher response rate that from previous campaigns driven by recognizing the similarity of visits, but the difference in spending. Customer segmentation in two dimensions It has been illustrated in the previous example that our algorithms are capable of segmenting data in two dimensions. In reality, these algorithms are powerful enough to deal with any number of dimensions. From our experience, we have found that in many instances, segmentation in only two dimensions is valuable, however, there are a times when more dimensions are required. example in cell phone carriers are interested in grouping their customers by length of subscription, monthly fees and percentage utilization of their assigned quota. 3.5 Useful Metrics Are Captured For Each Segment One of the added values from segmentation is the ability to analyze various metrics for each segment. Once the models run, you manage the members of each segment based on their behavior. It is enlightening to see the differences in values for each segment. In our example you can see that while customers have similar numbers of For Multiple metrics captured for each segment help us perform comprehensive analysis and identify critical differences in customer segments. P a g e 10

12 visits, the amount they spend can vary widely. If we were to add another dimension, for example average discount, we would gain insight into the customers who are motived by discounts and those that normally pay full price. 3.6 The Benefits of Self-Organizing Segments Things change. People change their behavior, inventory items that once flew off the shelf now move slowly, a marketing mix that was effective one week isn t the next. Using traditional segmentation could lead you to miss the changes and continue to market to individuals and segments based on old behavioral patterns. By using our models, you can capture the change as it happens. For example, when an entire population changes their behavior, such as the move from voice to data usage with cell phones, you need models that can self-adjust not only the segment, but also the members of the segments. One of the benefits of running our behavioral segmentation models are that each time you run the model, the segments are recreated based on the data, so as the behavior changes, the results reflect that change. 3.7 Rapid Analytic Discovery Analytic discovery can take multiple iterations. To understand customers better, you may want to view their behavior using different dimensions. For example, a retailer may want to view customers using dimensions such as amount spent per visit, number of visits per quarter and number of products purchased, repeat customer, new customer, etc. They might also want to include things such as social media metrics (number of post, number of comments, number of like, time on site, number of site visits) and then add some demographic factors (income, age, sex, marital status, etc.). Using different combinations of these dimensions can help create a complete picture of customer behavior, with certain questions answered by one group of dimensions and others by a different group of dimensions. Because our models are easy to use, can be run from existing reporting tools and also because of the speed and scalability available, you can try many different combinations of dimensions very quickly and without waiting for the answer to be pulled from specialized software that only a few can use. By allowing business users to quickly work through the analytic discovery process, decision making will be accelerated and insight increased. P a g e 11

13 3.6 Drilling-Down in a Segment Once the segmentation is complete, it will be important to drill-down each segment to see the members of the segment and the related information. A good model will generate easy to understand output. The example below shows how it s possible to use a reporting tool to easily drill-down into each segment. Here you can see the data related to retail customers by all, repeat and new customers and sales by category, brand and salesperson. In addition, the results can be sorted using any of the columns by clicking the header. The ability to use reporting tools to run segmentation and drill-down into the results means that you can see details to help you understand the characteristics of those in the segment take action. Drill-down capability for each segment 3.7 Spot Trends and Improve Forecast Accuracy Another useful benefit of segmentation is in improving forecasting accuracy. Since the groups are more heterogeneous and the members of the group are more homogenous than the general population, forecast accuracy will be improved. The reason is that forecast models try to account for variability in an attempt to not over or underestimate the future values. Segmentation reduces variability because it groups items with similar behavior so the forecasting model will produce more A forecast that is obtained by taking into account the attributes of each segment will be more accurate than an aggregate forecast for the whole enterprise. P a g e 12

14 accurate results than if the same model is attempted on an entire population whose members may have very different behavior. 4. Accelerated Implementation Even though the math in our algorithms can be quite complex and cover a wide array of functionalities, the implementation of these algorithms is straightforward. We are able to achieve rapid software deployment by virtue of the following: Our models typically install in less than 30 minutes. Over the years we have performed extensive research and development in various data mining techniques and have tested and fine-tuned the models. We do not start from scratch in a new enterprise, but rather leverage our previous work to speed the implementation. This methodology leverages our existing software infrastructure and algorithms and ensures rapid deployment with minimal re-work. We offer proof-of-concepts to demonstrate the effectiveness of our algorithms. The period is typically two to four weeks. Our solution can work with almost all databases. The underlying code used in our product suite is highly optimized to ensure speed and throughput with your existing hardware. P a g e 13

15 The Fuzzy Logix white paper series Fuzzy Logix, LLC David Taylor Dr. Suite 130 Charlotte, NC USA Contact: P a g e 14

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