Executing Effective Cross Sell Campaigns through Association Analysis A Tactic for Improving Marketing ROI

Transcription

1 Executing Effective Cross Sell Campaigns through Association Analysis A Tactic for Improving Marketing ROI Association Analysis is a widely used technique in Marketing Analytics to formulate rules and identify patterns using a data set as the base. This analysis plays a critical role in identifying cross selling opportunities, optimizing marketing campaigns and ensuring the right product placement. Today, the cost of acquiring a new customer is far greater than the cost of cross selling or up selling to an existing customer. Consequently, marketing functions are making concerted efforts to execute effective cross sell and up sell campaigns. This requires an in-depth understanding of how different products in the portfolio are associated with each other and how the presence of a set of products, influences the product purchasing patterns of the customers. The objective of this white paper is to provide insights on how Cluster Analysis and Decision Tree techniques were used to identify product association patterns and associate product purchase probabilities to each product. The results enabled the marketers to convey the product association message to the field sales team and also provide the field sales team with customer wise product selling priority lists. The field deployment resulted in a two fold increase in Average Opportunity and 62 percent increase in Average Opportunity Size. Key Words: Association Analysis, Cluster Analysis, Decision Tree and Marketing Analytics

2 About the Author Arvind Mahishi R PGDM (IIM Lucknow), BE (National Insititute of Engineering, Mysore) Arvind Mahishi is currently working in the area of marketing analytics for a leading Enterprise Software Maker. He holds an MBA in Marketing and Finance and has close to 6 years of experience in Marketing Analytics, Business Development and Project Management. He has worked on several statistical tools and techniques that are used to support marketing campaigns and improve database marketing effectiveness. 1

3 Table of Contents 1. Introduction 3 2 Association Analysis Defined 4 3 Application Background Problem Statement Approach Methodology Solution Results Solution Deployment and Impact 9 4 The Road Ahead 10 Appendix: Key Definitions 11 List of Figures Figure 1: Objective of Exercise 5 Figure 2: Methodology 6 Figure 3: Cluster Results 7 Figure 4: Prdt1 Decision Tree 8 Figure 5: Precision Index Formula 9 Figure 6: Validation Results 9 List of Abbreviations Term B2B B2C CHAID CRM Description Business to Business Business to Consumer Chi-Squared Automatic Interaction Detector Customer Relationship Management 2

4 Introduction Data, Data everywhere, not a byte to use is a common notion among database marketers. With increasing emphasis on precepts such as, it is not what you want, but what your customers want, organizations have begun investing heavily on collating and storing information on every aspect related to the customer. Information on prospects, customer interactions, and customer transactions are all stored in CRM databases. For companies in the High Technology industry, where success or failure is driven by the slogan of speed, it is very critical to analyze the available data and derive meaningful insights from it in the shortest possible time. Marketing analytics is an area, which has proven its ability as an effective tool to analyze this data, comprehend it and produce valuable insights with respect to customer behavior without compromising on time. The recent economic crisis has resulted in companies focusing on reducing marketing budgets and anticipating significant improvements in marketing ROI. In this scenario, marketers must ensure that they are able to prioritize their activities and focus only on those activities that yield better returns. According to estimates, the cost of acquiring a customer is a lot higher than selling to an existing customer, which makes cross selling a high priority activity. Cross selling requires the marketer to answer the following questions: What is the purchasing behavior of my customers? What is the relationship between various products in my portfolio? Association analysis is a technique in marketing analytics that is used to answer these questions and it plays a critical role in identifying cross-selling opportunities, optimizing marketing campaigns and ensuring the right product placement. This white paper provides an overview of Association Analysis. It also provides insights into the business environment in which the client operates, the business challenges faced by the client and the Association Analysis approach followed to address these business challenges. 3

5 Association Analysis Defined Association Analysis (widely used in Marketing Analytics) identifies relationships or affinities between entities and (or) between variables. These relationships are then expressed as a collection of Association Rules. The approach has been particularly successful in mining very large transaction databases and is one of the core 1 classes of techniques in data mining. Retailers are interested in analyzing the transaction databases to learn about the purchasing behavior of their customers. Similarly, this concept finds application in other areas such as, bioinformatics, medical diagnosis, web mining and scientific data analysis. In the analysis of Earth science data, for example, the association patterns may reveal interesting connections among the ocean, land, and atmospheric conditions. Such information may help Earth scientists develop a better understanding of how the different elements 2 of the Earth system interact with each other. 3 Applications of Association Analysis in B2C and B2B domain Cross selling: Offer the associated items when customers buy any item from retail stores. Product placement: Items that are associated can be placed in proximity for enhanced accessibility. If the customers see them, there is a higher probability that they will purchase them together. Affinity promotion: Design the promotional events based on associated products. Fraud detection: Based on credit card usage data, it is possible to detect certain purchase behavior that can be associated with fraud. Customer behavior: Associating purchase with demographic and socio economic data (such as, age, gender and preference) may produce very useful results for marketing. Companies such as, Microsoft, ebay, Dell, Pay Pal, Lenovo and so on have leveraged the power of association analysis as part of their analytics strategy to improve product placements and product cross selling, thereby improving their Return on Marketing Investments Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining,pp

6 Application Background The client is a leading provider of enterprise management or business application software with a strong global footprint. The business software market s primary determinants included corporate capital spending trends and commitments. Corporate capital spending is, in turn, a function of macro economic conditions. In recent times, the weakness in the overall global economy has resulted in reduced expenditures in the business software market particularly related to new software acquisition. In this context, the marketing team of the client steered its focus from initiating new customer acquisition campaigns to campaigns focused on increasing sales to its existing customers. This in turn created the need for the marketing team to understand the buying behavior of the customers and the nature of association between various products in its portfolio. Problem Statement The client marketing team defined the following problem statement. What products shall we market to our existing installed base customers and help them increase business value while improving their loyalty and our revenue? Can we identify the sequence of next-to-buy products for each installed base customer to enhance our marketing and sales success? The following objectives were defined to address the problem statement: Understand product penetration within the installed base Identify product affinities Build a set of Product Association models and predict the sequence of next products likely to be of interest to customers based on those models Understand Product Penetration Identify Product Affinities Effective Cross Selling Build Product Association Models Figure 1: Objective of Exercise 5

7 Approach Considering the spread of the clients business across geographies over time, mature markets were bound to be more familiar with the client products and therefore, more likely to exhibit buying interest and purchase associations. The models developed on data from these markets would then be applied to other growth markets and validated to check if they exhibit the same behavior so as to enable deployment of the results at the field sales level in these growth markets. Premise: Growth Markets follow development pattern similar to Mature markets Methodology Based on the available data, it was decided that a combination of Cluster Analysis and Chi-squared Automatic Interaction Detector (CHAID) would be used to address the objectives. Refer to the Appendix for more details. The methodology followed is illustrated below. Product Centric Better understanding of product positioning Core Products buyers selected from Install Base Focus on customers of core products (50) Customer Centric Better projection of Customer Needs Clusters based on Products and Demographic Information Clustering Analysis to identify Natural Clusters Chi Square Analysis to Identify Product Associations within Clusters CHAID Analysis to obtain likelihood (predicted probability) of Product Purchase Ranking of Product Purchase Likelihood for existing Customers Model to predict Product Purchase Likelihood Groups of Products which are associated Improved messaging & value propositions Improved target selection Product with higher ranking are more likely o be purchased Improved Cross Sell Results Figure 2: Methodology 6

8 Solution Results Product centric solution to analyze product affinities: The cluster analysis indicated that the mature market could be segmented into three different clusters as illustrated in Figure 3. Each of these clusters displayed specific characteristics with respect to Business Demographics like sales, number of employees, and age of the company. Mature Market Customer Base Cluster 1 Sales : $60 Mil Emp : 225 Age : 22 Portfolio : Prdt 1, Prdt 2, Prdt 3, Prdt 4 Cluster 2 Sales : $940 Mil Emp : 2615 Age : 39 Portfolio : Prdt 2, Prdt 3, Prdt 4, Prdt 5 Cluster 3 Sales : $750 Mil Emp : 2000 Age : 32 Portfolio : Prdt 1, Prdt 2, Prdt 3, Prdt 4 Figure 3: Cluster Results Additionally, the clusters exhibited different sets of products with a few products being present in all the clusters. This indicated that product purchase is a factor of Business Demographics and there are some products, which find a place in the customer portfolio irrespective of the size of the firm. Chi Square analysis, which is used as a test of the strength of association, was used to determine the degree of association between products, which were present across all the clusters. The Clusters exhibited that there are a few products that find a place irrespective of the size of the firm The Chi Square analysis indicated that Prdt2 and Prdt4, Prdt2 and Prdt5 displayed significant association with each other. This knowledge would enable the marketing team to improve their messaging while running campaigns related to either of the products. Customer centric solution to analyze cross sell opportunities: Decision Trees using CHAID technique were executed for each product by considering each product as the dependent variable and all others products and business demographic variables as independent variables for the mature market customer base. (A total of 50 products: Prdt1 Prdt50 were considered for the analysis). The productspecific CHAID tree produced product association information with the presence of specific demographic/purchase conditions. The decision tree developed for each product segmented the entire customer base into different nodes with customers belonging to a particular node demonstrating similar characteristics with respect to business demographics. Additionally, the tree considered the presence or absence of various products while creating the segments. The probabilities were assigned to each of those segments and all the customers assigned to a particular segment had the same probability. Using this approach, predicted probabilities for all the products for every customer was obtained. The following graphic illustrates the partial decision tree for Prdt1. 7

9 Figure 4: Prdt1 Decision Tree All the product probabilities were ranked for each customer, which resulted in sequencing of the candidate products recommended for the next Marketing and Sales engagement. Note: Products already purchased by a customer were suppressed from ranking. With the premise that growth markets follow the same development pattern as mature markets, the rules generated by the model were applied on the customer base from the growth market. The probabilities based on products obtained were ranked so that there would be ranks corresponding to all the products that were yet to be a part of the customer portfolio. Validation of data To assess the overall model effectiveness, a precision index was created for each customer. The index had a value range from 1 to 50, where 1 indicated complete alignment of projected versus actual products purchased. In other words, lower the value, higher is the alignment. 8

10 Figure 5: Precision Index Formula For example, if a customer acquired product Prdt1, Prdt2 and Prdt3 while the projected ranks for these three products are 1, 2 and 3, respectively, the index calculation would result in a value of 1. If the model placed ranks of 47, 48 and 50 on the three products, the index value would be In the extreme case where only one product was purchased and the projected rank is 50, the index value is at the highest, 50, indicating completely incorrect ranking. Figure 6: Validation Results Solution Deployment and Impact The individual rank and associated probability for a product have to be considered in tandem while prioritizing products to be sold because there could be scenarios where a product associated with a high rank would have a very low probability. The information on the individual product rankings and probabilities for customers in the growth market was handed over to the client marketing team for field deployment. The information on product association and product ranking/probabilities was used by the client marketing team for improving their messaging while running product centric campaigns and improving cross sell targeting capabilities. The field deployment resulted in a two fold increase in Average Opportunity and 62 percent increase in Average Opportunity Size. 9

11 The Road Ahead The existing models built did not incorporate the information associated with the time periods when the product purchases were made into the analysis. Models based on sequence analysis (another technique in Marketing Analytics), which uses the purchase time period information to determine patterns in product purchases made by customers can be developed. It also predicts the next sequence of products that will be purchased and can be used to enhance messaging and significantly improve cross selling capabilities. Recent trends show technology companies exhibiting increased focus on enabling tighter integration between Business Intelligence/Predictive Analytics Tools and Databases. This coupled with the advancements in tools supporting Business Intelligence and Predictive Analytics will enable Association Analysis to evolve from being a concept that focuses on analyzing past data and providing recommendations to a more real time data analysis and recommendation provision mechanism. This will enable businesses to improve their operational and tactical strategies and deliver more value to their customers. 10

12 Appendix: Key Definitions CHAID Analysis CHAID Analysis is a tree segmentation method used to classify objects into tree nodes under known criteria. In sales association modeling, there i s essentially one CHAID model built for eac h product, where a given customer woul d be classified into one of the ending nodes. Percentage of presence of the said p roduct within the node becomes the p redicted prod uct buying probability for all the customers in the nodes. Chi-Square Test Statistical method to test whether two (or more) variables are: (1) independent or (2) homogeneous. The c hi-square test for i ndependence examines whether knowing the value of one variable helps to estimate the value of another variable. The chi-square test for homogeneity examines whether two populations have the same proportion of observations with a common characteristic. Cluster Analysis Clustering Analysis is a statistical technique used to identify natural group ings and classifies objec ts on the basis of the similarity of the characteristics they possess. In affinity study, customers who purchase similar products would be group ed together to form natural clusters. In the process, each record is tested as a group and non-group member against each cluster before final membership is assigned. Cross Sell Cross selling is defined by the Oxford English Dictionary as the action or practice of selling among or between established clients, markets, traders, and so on or that of selling an additional product or service to an existing customer. In practice, businesses define cross selling in many ways. Elements that might influence the definition might include: the size of the business, the industry sector it operates within and the financial motivations of those required to define the term. The objectives of cross selling can be either to increase the income derived from the clients or to protect the relationship with the clients. The approach to the process of cross selling can be varied. Unlike the acquiring of new business, cross selling involves an element of risk that existing relationships with the client could be disrupted. Thus, it is important to ensure that the additional product or service being sold to the clients enhances the value proposition to the clients. 11

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