Rosella       Machine Intelligence & Data Mining

Direct Mail Marketing - Catalog sales

In direct mail marketing, it is important to have well-focused targeted marketing strategy. The problem of direct mail marketing is that it borders around illegal spam e-mails (or junk mails) and illegitimate business activities. Modern-day computer and telecommunication technology enables direct mail marketers to send millions of unsolicited mails to unwanted customers very easily. This type of commercial practice is not only undesirable to customers but also harmful to businesses that send such information. First, sending spam and junk mails may not be legal by laws. Second, sending emails may cost very little. However, sending postal mails and SMS messages can cost significantly. Furthermore, sending catalog may cost even more. Third, keep sending mails that are not of interest to customers can turn off customers, losing permanently. For example, customers may include you to spam filters! Therefore, there are strong reasons to keep catalog mailing to a minimum.

How to select mailing lists? - Customer targeting / Customer selection methods

In direct email marketing, sending relevant information only to customers that might be interested will be the right strategy for any reasonable direct marketers. Well-focused customer targeting is need. Data mining techniques can be used for this purpose. There are three data mining techniques that can be used in selecting mailing target customers. Customer selection techniques are as follows;

  • Hierarchical segmentation using Decision tree
    CMSR decision tree can be used to segment customers in a way that increases concentration of positive mail responses.
  • Response scoring using Neural network
    The ultimate method! Build predictive neural network models that can predict customer scores.
  • Profile segmentation for RFM
    Segment customers based on a few to several variables. Not described in this page. For more, read Beyond RFM Marketing.
  • Neural clustering segmentation
    Segment customers into groups having similar demographic and psychographic attributes together. Send mails to groups with higher response rates in previous campaigns.
  • Hottest segment profiling
    Identify profiles of customers that produced most positive responses in previous campaigns. Send mails to customers with the profiles.
  • Rule-based Pinpoint precision marketing
    This is the most sophisticated method which is also the most accurate, but a bit difficult to master. Develop rules and predictive models and combine them to make very powerful customer selection tools with pinpoint accuracy.
  • Cross-sell marketing
    Select customers based on their previous product buying patterns.
What customer information is used in Targeted Mail Marketing?

Targeted direct mail marketing techniques described here are well-suited to companies with a large number of direct customers or data providers with a large number of potential customers. Customer information that can be used in selection of customers may include the followings;

  • Demographic variables describe characteristics of populations and include age, gender, race, education, occupation, income, religion, marital status, family size, children, home ownership, socioeconomic status, and so on.
  • Geographic variables include various classification of geographic areas, for example, zip code, state, country, region, climate, population, and other geographical census data. Note that this information can come from national census data. For more, see geographic segmentation.
  • Psychographic variables describe life style, personality, values, attitudes, and so on.
  • Behavioral variables include product usage rate and end, brand royalty, benefit sought, decision making units, ready-to-buy stage, and so on. This information can be extremely useful for marketing purposes.
  • Past business history includes various business statistics on customers, especially purchasing patterns. This provides essential business indicators and therefore is very important information.

In addition, RFM information can be added to the list;

  • Recency: Customers purchased recently tend to buy again.
  • Frequency: Customers purchased frequently tend to buy again.
  • Monetary Value: Customers purchased most monetary values tend to buy again.

    It is noted that RFM information alone does not provide a great deal of predictive insight. This shortcoming can lead to over-marketing to previously generous customers! Combining with other information may lead to better results. For more, read "Beyond RFM Marketing and Analysis".

Response Analysis and Response Modeling

Different demographic groups tend to respond differently on different product promotions. Analyzing and modeling customer responses can lead efficient marketing campaigns. Customer response behaviors can be captured in terms of predictive and segmentation modeling. Subsequent sections describe how response models are developed and analyzed;

How to collect response modeling data?

To develop response models, data representing customer response behavior is needed. Data may be collected as follows;

  • Extract from past customer purchasing records.
    Past customer purchasing records are essential in developing predictive models. Generally, selecting customers who have shown positive responses in the past are likely to show positive responses in new campaigns. However, there are downsides as well. For example, predictive models inevitably predict past, not future. Customers who bought products recently may no longer interested in buying another in the short term. This may have been embedded in data. Identification and inclusion of such information into models can overcome this limitation.

  • Conduct sampling campaigns.
    In this method, a number of customers are selected as a sampling group and catalogs are sent to them. Response information is collected with the sampling group. Response models are developed with this information. Customers who are not participated in the sampling but predicted positively by models will be mailed in new campaigns. Downside of this method is that it takes long to collect information. In addition, sampling that represents customer behavior truly is difficult.

Response and Profit Analysis using Gains charts
Gains chart for response modeling.

In principle, the best response analysis tool is gains charts. The gains chart shown above is specially designed for response analysis and profit modeling. Horizontally, it represents customer populations, 0 ~ 100%. The blue curve shows cumulative ratios of positive responses. A steeply rising curve is a good sign of good customer segmentation. It implies that segments represented earlier in the chart have high concentration of positive response customers. Focusing campaigns on those segments will lead to efficient marketing. The figure also indicates that focusing 30% at the left side segments, most potential profit can be achieved! Therefore saving valuable company resources.

The green curve indicates cumulative capture ratios of order quantities. The red curve shows cumulative profit and loss amounts. When a fixed cost is involved, the curve will start from a loss, i.e., below $0. Then rises as long as segments are profitable. That is, segment mailing cost is less than potential segment sales profits. The peak of the profit & loss curve will be selected as the boundary for mailing lists. All the segments at the left of the boundary are selected in mailing lists.

How to select variables for Response Modeling?

It is important to note that response models should be developed based on variables with predictive implications to positive customer responses. Otherwise, response models will not predict new campaign results properly. Intuition alone does not lead to correct identification of modeling variables. CMSR correlation link analysis and predictive neural network can be used for analyzing variable's significance. For more on modeling variable selection methods, please read Correlation Analysis.

Hierarchical segmentation using Decision tree

Decision tree divides populations into smaller sub segments recursively. At each node, it selects a splitting variable in such a way that resulting segmentation with the variable boosts proportions of either positive responses or negative responses in each resulting sub segment. For response marketing, this can lead to segmentation that maximizes the proportion of either respondents or non-respondents. In another words, segmentation will attempt to congregate potential respondents into certain segments. Such segments will have more concentration of positive respondents. Focusing on such segments will lead to efficient marketing!

What's wrong with classification methods?

Intuitively, classification predictive modeling is a very appealing concept for direct mail marketing. However, there is a serious drawback in applying classification techniques (such as decision tree, SVM, etc) to direct mail marketing. The problem lies with the fact that response rate is extremely low, as some survey suggests about 2%! Developing predictive models with skewed data is very difficult. Decision trees develop predictive models through segmenting populations into smaller groups repeatedly. It uses the dominant value (or most frequent value) of each segment as the predicted value for the segment. Dominant values are the values represented by over 50% segment population. Given the nature of low responses, it is possible that no segments may contain responses in excess over 50%! Even they exist, they may be slightly over 50%! Segments in which 49% customers have responded will be predicted as "not responded", although they are very highly responsive groups! This type of models will have very low accuracy in predicting responsive customers as "responded". To overcome this problem, some may be tempted to use tricks by introducing extra instances. However, such tricks will necessarily distort overall representation of population.

Response scoring using Neural network

Generally, catalog sales is a very low response-rate event. Developing predictive models on skewed data is extremely difficult and therefore models can be inherently un-reliable. Neural network provides a way that can overcome this difficulty and identify positive responses in high accuracy. This method can be implemented as follows;

  1. Build a neural network model that predicts responses (or sales profit amounts) based on a previous campaign result. Note this may be from sampling campaigns. The model will under-predict response values as most customers will have zero response values. None the less, it will score highly for potential respondents with high consistency.
  2. Apply the model to the same previous campaign dataset to predict responses. That is, to compute "customer response scores".
  3. Encode customers of the previous campaign as "responded" and "not-responded". Using CMSR dimensional histogram charts, analyze distribution of predicted default amounts over the encoded dimension. You will see high concentration of responsive customers in higher score intervals. The following figures show an example. Reds are customers who have responded. They are concentrated at high score ranges.

    proportional distribution of response scores. distribution of response scores.

    The original score values ranged 0 ~ 4. Notice that the figure shows slightly under-valued than the original.

  4. Apply the neural network model to the current customer dataset. It is recommended that time-variant information is based on the date of mailing. You can select customers having scores beyond a certain threshold.
  5. The scores can be used for response capture and profit analysis.

    The key to success is the neural network scoring. Accurate customer scoring will ensure that most of potential responses will be captured at a minimal cost.

Neural Clustering Segmentation

Clustering groups similar customers together into segments. The rationale behind clustering is that customers with similar attributes often exhibit similar patterns in purchasing behaviors. If customers of a certain group responded positively in previous campaigns, the group is likely to produce similar positive outcome in future marketing campaigns. This approach involves clustering customers into segments of having similarity. If there are segments showing high response rate in previous campaigns, customers of the segments will be selected in new campaigns.

CMSR Neural Clustering is well suited for this. Neural clustering is based on neural network known as Self Organizing Maps (SOM). SOM is how our brains process information and recognize patterns and features using the technique called competitive learning. Neural clustering has magical power as in the way that your eyes recognize similar patterns! As shown in the following figures, neural clustering organizes similar customers into two dimensional grid cells in such a way that customers in the same cells have very similar attributes. In additions, customers at nearby cells have similar attributes. That is, closer the cells, more similar the customers are. The following charts show an example of distribution of customers in segments (i.e., cells). The left shows pie charts for categorical variables. The middle is histograms for numerical variables. Intuitively, the left figure shows that customers of the same values for the variable shown are clustered into the same cells or spread over nearby cells. Note that all cells have single color pie charts, which means cells contain objects of the same categorical value only. Also notice that nearby cells have the same color pie charts. Though less obvious in the middle figure, close examination reveals that nearby cells have similar numerical value distribution.

Customer segmentation cluster analysis for categorical variables. Customer segmentation cluster analysis for numerical variables. Customer segmentation cluster analysis for segments.

For more information, click Neural Clustering.

In neural clustering, the presence of high interest clusters is not always assured. The problem lies with the fact that clustering per se does not include important performance variables. More specifically, you normally cluster customers using demographic & geographic & psychographic information only, without response information. Clustering has no relationship to response information. It will depend on luck to have clusters with high response ratios.

Hottest Segment Profiling

It is common to see that 80% of positive responses are from 20% of customer groups. Even better case can be 90% coming from 10% of customer groups. Instead of unpredictable predictive modeling, profiling those major groups from demographic (and geographic and psychographic) information and mailing to customers who belong to the groups can capture most customers who may respond your marketing campaigns. It can minimize the loss of sales from not sending catalogs, which is a very expensive opportunity cost. In addition, mailing cost will be significantly reduced as well.

For more information, click Hotspot Analysis.

Rule-based Pinpoint Precision Marketing

Direct catalog marketing tends to be most effective for selling special products to special customer segments at special prices. Normally, straightforward predictive modeling and segmentation does not work well or at best provides marginal improvement. There are various reasons. For example, predictive modeling and segmentation is based on past data and therefore predicts past patterns of past campaigns. Future may be very different. Such patterns cannot be captured by predictive modeling alone.

CMSR rule-based modeling environment (RME) is a platform where complex rules can be combined with advanced predictive modeling. Pinpoint precision marketing plans can be developed and executed with the platform. New emerging patterns are captured manually and combined with predictive models which represent past customers' buying patterns. For more about RME, please read Predictive Modeling Software.

Product buying pattern analysis and Cross-selling

Customers tend to buy variety of products from the same businesses. Analyzing such information can reveal valuable business opportunities in various way. The following chart shows customer purchasing behaviors;

Product Purchasing Pattern Analysis.

Cells in red color indicate that there is positive association between two products. That is, when customers buy one product, they tend to buy the other product as well. Cells in blue color indicate the opposite. When customers buy one product, they tend to not buy the other product. Brightness of cells indicates the relative strength of relationships.

Product purchasing patterns can be used for cross-sell marketing. In addition, it can be very useful information in catalog design. Placing products with strong associations together or nearby may improve overall sales!

For information about software used here, please read Data Mining Software. Software download is available from the page.

For information about predictive modeling, please read Predictive Modeling Software Tools.