Building Caterpillar Market Share with a Database

This is the story of a very innovative database marketing project through which Caterpillar managed to build their truck engine market share. The project was the brain child of two very experienced database marketers: Alan Weber, Vice President of J. Schmid & Associates in Shawnee Mission, KS and Frank Weyforth, Chairman of the Board at MRA in Overland, KS.

Besides their famous earth moving equipment, Caterpillar builds $2 Billion per year of large truck engines, the kind that are found everywhere in the big eighteen wheelers on the road. Most people don’t know that these trucks are always custom built by the truck manufacturers, who are really assemblers, such as Peterbilt. The customer specifies the make and horsepower of the engine that he wants. For this reason, until they built their database, Caterpillar and the other truck engine manufacturers never knew who was using their engines. The Caterpillar sales force, therefore, didn’t really sell engines, they just tried to influence the choice of engine. The total market fluctuates from 100,000 to 225,000 units per year – each engine selling for about $15,000.

In the beginning, Caterpillar had no database, and no one really saw the need for one. Instead, their executives had a lot of questions: “What truck fleets are we not calling on? What fleets should test our two new engines? How can we get a marketing strategy that can be measured? How to we adjust to the coming downturn in sales?”

To try to answer these questions, Weber and Weyforth got each of the six groups involved in Caterpillar truck marketing to chip in money for a project designed to get the needed answers. They used part of the money to give laptops to the 260 Caterpillar salesmen with this proviso: “you get paid for sales, but only if the customer name and other data is entered into the laptop database.” It worked. When they began, they had data on only 58,000 customers and 11,000 fleets of 10 trucks or more.

The consultants goal was to determine the potential universe for truck engine sales. Once they knew this, they planned to reorganize the sales force to sell the major fleets, target the most profitable customers, and to measure results so that Caterpillar could get accountability. They discovered right away that the sales force was bogged down with administrative problems, and sales promotion which reduced their ability to sell. Only 23% of their time was spent on selling. Many were calling on the wrong people, and many good prospects were not called on at all.

There were four internal databases in Caterpillar which were not compatible with each other. External truck registration files proved to be almost useless, since truck fleets are often registered in another state to save money on taxes. To get the data, the team combined the internal databases, appended data from the National Motor Carriers directory, Duns and TRW data, and trade publication lists. After two years of work, they had a file of 110,000 customers, 8,000 mid range fleets and 34,000 heavy duty fleets: the universe of all heavy trucks in America!

They reorganized the sales force to divide this universe into four groups: National accounts of 300 trucks and up, Regional accounts of 100-300, Dealers of 9 to 100, and small owner operators. They reduced the administrative work, and shifted the sales promotion work to the marketing staff.

Next they did some serious modeling. Using the data they assembled on SIC code, truck owner vocations, engine models, number of trucks and trucking category, they were able to predict using CHAID which non-customers were most likely to buy. They used regressions to predict how many miles would be driven per year for each truck on the road, and the trade cycle (how many years before the engine would be traded in). They grouped their customers and prospects into 83 Heavy Duty Groups and 34 Mid Range Groups.

With the data available, they estimated customer lifetime value. Sales, service, usage and engine model combined determined that value for customers. Prospect value was determined by the group to which each prospect had been assigned. They determined from this analysis, which were the high value customers and prospects that should be targeted. They worked with this serious fact: 85% of all fleets tend to standardize on one engine manufacturer because of the spare parts problem. This fact makes it crucial that you get to the large fleets when they are making a decision that may determine their buying pattern for the next several years.

When they got through all the analysis, they were able to develop a score for each fleet: a real score, based on that they were buying now from Caterpillar, and a potential score based on what they could buy with the type of trucking business that they did. Included in this score was the knowledge that most truck engines are completely overhauled every 500,000 miles. The overhaul is a big sales item for the engine manufacturer.

With these scores, Weber and Weyforth developed a set of different messages that could be sent to each customer and prospect. Each communication was sent from a designated sales person to his targeted customer or prospect. To illustrate the differences in messages, they had to consider these differences: Owner operators tended to look for power in their engines. They like the 600 hp giants. Large truck fleet owners wanted economy: the most economical engine that would do their job. Messages which stressed retention were different from messages that were designed for conquest.

Armed with the customer and prospect groups, scores and messages, they were able to develop a new incentive plan for the sales force. It worked this way:

• you get $X for each engine sold in your assigned territory
• you get $2X for each engine sold to a retention customer on your list
• you get $3X for each engine sold to a conquest customer on your list

That got the sales force attention! For the first time, the sales force focussed on the most profitable targets, and they targeted every profitable target in the whole country.

What was the result of three and a half years work? During the first year with the new database, 1997, they were able to sign up 500 conquest fleets. They sold an average of 50 to 100 engines per fleet at about $15,000 per engine. The total increased sales which could be attributed to the new database system was approximately five hundred million dollars. They successfully launched the two new engines that had been part of the original goal. Caterpillar market share went up by 5% in a period in which all truck engine sales nationwide were down.

This is real database marketing. Few companies today have done what Weber and Weyforth did for Caterpillar. Most companies don’t know how to do it, and don’t have the initiative to put their resources into such a project. To sum up their steps, they are:

• Build a database that includes all customers and prospects for your product, including the data necessary to make intelligent decisions
• Determine potential and real lifetime value of all customers and prospects
• Develop a score for all prospects and customers: know where to put your marketing and sales dollars
• Reorganize the sales force’s incentives to insure that your targeting scores result in profitable action
• Watch the money roll in

 

Arthur Middleton Hughes, vice president of The Database Marketing Institute, has presented 28 seminars on database and email marketing.  Arthur has also authored several books includingStrategic Database Marketing 4^th Edition (McGraw-Hill 2012). He and Andrew Kordek, chief strategist and co-founder of Trendline Interactive, are hosting a two-day Email Strategy Study Group in Fort Lauderdale  March 26-27, 2013, featuring group competition for email marketers responsible for subscriber acquisition, lifetime value, ratings and reviews, boosting their email budget, and doubling their ROI.  To learn how to attend the Study Group,click here.

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