Developing Business Insight Through Data Mining

1. Developing Business Insight Through Data Mining Understanding Customers More Deeply And Reducing Promotion Costs

2. FACT • The more relevant and interpreted customer and potential customer information, the greater is the ability to segment or niche customers and potential customers into distinct/unique pools with similar attributes. • Messaging specifically crafted and targeted to each of these unique pools will have a greater likelihood of stimulating behavior which results in a “sale”. • Media selection for delivery of the message to the various pools should be a mix of low to high cost pathways, i.e. e-mail to broadcast, depending on the purchasing behavior of the target group. • Applying this added sophistication to the definition of customer and potential customer groups, then tailoring the message and media to communicate with these groups will provide a more efficient result. That is, the total cost of promotion will be less, while the “sales” result will be higher.

3. AN EXAMPLE A telephone company wanted to increase its cell telephone revenues in a major geographic-market area. At the same time it wanted to reduce its marketing expenditures during the current fiscal year. The company had approximately 85,000 cell phone users. It was decided to attempt to increase phone usage within its current base versus attempting to sign up new users, believing this tact would be more efficient. Its agency had proposed the idea of running a promotion consisting of giving a telephone battery to users so as to increase usage. The question was which of the cell phone user segments presented the best financial return for the promotion’s cost. A sophisticated data mining process identified the segment with the greatest potential return. Running the promotion campaign to a specifically targeted segment of users resulted in a 15% penetration at less than one-third of the cost of a traditional effort. Here is how this was accomplished. • Three in-house databases (operational, marketing and credit) were analyzed. The operational data files identified usage times and patterns of those who might be most likely to increase phone usage. Marketing data files identified those users who had responded to other promotion programs for other company services in the past. Credit files identified those users who had the ability to easily pay for additional phone usage. These three data files were analyzed and merged to develop the target market. • External census data was acquired and was used to identify commuting habits and locations of the target market. Database information was purchased to obtain purchase behavior and demographics for the target group identified. A number of critical dimensions were identified, such as: occupation, position in career path, time spent in car, wealth. One of the behavior dimensions investigated in this analysis was the person’s comfort referring a product or service to another person. Database information was also purchased to provide credit information on the targets identified. This information was used to cull the list of targets created from the internal information. The target group identified at this point consisted of those who likely needed the extra minutes, could pay for them, and would likely give a referral to another for the service. • Through the use of the data mining techniques, a list of 15,000 targets was identified. The direct marketing campaign was run and the result was a participation rate of 15% of the number of targets identified. This result is dramatically higher than the .5% participation rate typically experienced. Because the target group was smaller than the traditional direct marketing target group, the cost of the promotion campaign was significantly less. 3

4. AN EXAMPLE A large national bank wanted to identify opportunities to obtain a “greater share of their customers’ wallet.” The Marketing Department wanted to capture product and service migration patterns within its customer base. It wanted to anticipate its customers’ needs so as to design, advertise and promote new products proactively, thereby increasing the likelihood of customer retention through their periods of change. The challenge to the bank’s marketing personnel was to identify which customer activities the bank could monitor in order to anticipate the need for a new product. After reviewing and analyzing its internal data, the bank personnel felt stymied. The marketing personnel turned to their advertising agency account executive for ideas. The agency observed the bank’s personnel might be too far into the forest to see the trees, and suggested outside data mining consultants might be of assistance. With the consultants’ assistance, the bank’s personnel discovered that not an insignificant number of their customers, as they became empty nesters, ventured into their own small businesses. As such, these entrepreneurs have a need for higher margin commercial banking products and services, but the commercial side of the bank did not have visibility of the customers’ need. With input from its agency, the bank created several migration promotion pieces to send to customers suspected to need these commercial products. The effort proved to be successful in retaining valued customers and selling new services. To identify the opportunity, the bank needed to go outside the data within its institution. It needed to acquire certain databases and to apply the information acquired against that which it had internally to define its opportunity. Here is how they went about it. • Information from a number of systems within the bank was duplicated and stored in a data warehouse. Transaction data for the last several years from the checking account systems, the mortgage loan systems, the trust account systems, etc. were loaded into the data warehouse for a group of over 100,000 customers. (Not all customers had data in all the systems reviewed.) • Demographic, life-style, psychographic, behavioral, and credit database information was acquired from third parties for the customers in the group being analyzed. This information was put into the data warehouse. • Once all the data was assembled, the complex analysis, that in part consisted of classification and regression decision trees, link analysis, neural networks and cluster detection, profiled the customers’ behavior. • The bank learned, among other things, that if a customer was over 40 years of age, an empty nester, had an average income over the last three years of greater than $75,000 and had drawdowns on his or her equity line account, the likelihood was strong that the customer was starting a small business. The customers were obtaining their business banking needs from somewhere, and in all cases not from the bank. The agency was tasked to do market research to confirm what the data analysis indicated. It confirmed through focus groups that these empty nesters were indeed becoming entrepreneurs with their new found freedom, both personal and financial. With input from the Agency, the bank crafted a business banking package for these types of individuals along with collateral promotion material. The bank now sends this material to customers that fit the “emerging entrepreneur” profile, and wins praise from its satisfied customers.

5. AN EXAMPLE A large retail store and catalog house sent out well over 150 million catalogs each year. In one year the company set out to reduce the cost it expended in its catalog business, while it increased the gross margin dollars generated from its catalog sales. To determine how to achieve this seemingly contradictory goal, the personnel in the Marketing Department summoned the advertising agency account executive, the catalog distribution company account representative and the strategy consulting firm’s representative. After receiving all the learned inputs, the Marketing Department personnel decided that collectively they did not know enough about their customers purchase behavior to develop a solution. It was decided to undertake a data mining exercise to determine if the company could garner the “right” insights to their customers so as to meet its dual goal. The data mining effort revealed two important observations, 1) looking at store sales data it was found that certain stock items were typically purchased together, resulting in a higher revenue/margin ticket; 2) stock items purchased from catalogs varied regionally; and 3) there was a cluster of people that received the catalog, but never purchased from it. Armed with this information the catalog planners reformatted the catalog in two ways. First, the catalog contents were varied by a number of defined regions with the United States with the net result being eight different (70% of the stock was the same) catalogs with fewer pages in each. Second, page layouts were changed to position stock items, which were typically purchased in the store at the same time, within close proximity on the catalog pages. Additionally, the mailing list was culled for a number of no purchase names. Here is how this was accomplished. 1.     Sales tickets from the catalog sales for the last three years were analyzed. This analysis looked at individual customer purchases individually and over time. This purchase behavior was contrasted with purchase behavior revealed in an analysis of store tickets from the retail operations. The same purchase linkage between stock items was not seen in the catalog sales data. (Purchase linked stock items are placed next to each other in the retail stores.) 2.      The analysis of the sales tickets was also completed on a geographic basis using mapping technology. Once the analysis was complete it was clear that certain stock items were moving in different parts of the country. But the most interesting aspect of the findings was that this was not solely due to expected regional preferences, i.e. shorts in the South and mittens in the North. It seems the customer demographics were different. This characteristic was identified by applying socio-economic factors to the customer data. This discovery provided useful input for the advertising agency so as to adjust the messaging in different sectors of the country. 3.  The most difficult decision to make was that of deleting names from the catalog mailing list. Again, the mailing lists were analyzed against the socio-economic data collected and the purchase mapping data. The mailing list was culled of approximately 10% of the names. The net result of these efforts was that the company was able to achieve its seemingly contradictory goal. Catalog costs were decreased and gross margin dollars generated were increased.

6. HOW WE DO IT • Our data warehousing and data mining applications can be added to the agency’s tool box, specifically to: • Aggregate and expand the information of current and potential customers • More finely segment customer and target information • Manage and maintain larger more sophisticated customer and targeted databases over time

7. WE CAN ASSIST • Our tools acquire, transfer, warehouse and analyze data which is both within the organization and external to it • Our consultative approach assists the organization to mine the data and convert it to valuable customer information, which can be applied within the sales and marketing function to: • improve the efficiency, accuracy and impact of promotion programs • increase customer acquisition and retention • increase customer loyalty, satisfaction and lifetime value • increase cross-sell opportunities among customers • Both our tools and our approach to the conversion of data to useful information are transferable to our clients for ongoing use

8. SERVICES Market Research And Data Mining