The Science of Marketing

In a recent post, Ardath Albee describes how to score leads based on activity (e.g., download a white paper, web activity). This is an important part of database marketing and gives your sales and marketing team actionable intelligence on what products and services are of most interest to potential customers. In the world of high tech, where you often have a new technology, a large target list, and little activity intel, how to you prioritize your target list to make efficient use of a finite sales resource? Enter the Opportunity Index - a methodology to effectively segment a market with measurable attributes that best match prospects’ needs with a company’s solution.

Many high tech companies have embraced database marketing as a means of increasing the efficiency of their sales efforts. Most of these efforts start with a goal of using relevant weighted metrics, or attributes, to prioritize target prospects and increase odds of success. In other words, solid segmentation begins with an Opportunity Index.

There are three steps (and a lot of thinking) involved in creating an Opportunity Index.

1. Create, Index and Weight Attributes

The first step in creating an opportunity index is to create the attributes - characteristics of targets that have relevance to your solution. Indexing is the segmentation of an attribute into ranges that are assigned a number, say 1-5, the higher the number the better fit to your solution. Weighting is a way to assign relative importance of each attribute to your company’s solution. Weights are usually assigned in the range of 0-1 in 0.1 increments, where 1 represents the highest weighting. The goal of creating, indexing and weighting attributes is to set a foundation for objectively analyzing raw data.

To see how this works, consider the fictitious Smart Check RFID company, OG Corporation, that we used in a blog post earlier this year. The OG Corporation sells gaming chips with embedded RFID technology to casinos. These systems help casinos reduce fraud loss and broaden their base of loyalty programs by tracking the betting tendencies of customers. The OG Corporation has determined that there are three critical success metrics in the sale of their system to casinos.

• Fraud Loss - the more casinos lose from fraud, the more likely they are to be interested in Smart Check.

• Dominant Clientele - OG has segmented casino patrons into three categories: whales (high rollers who are already comped and have personal attention), casual high rollers ($100 a hand players who are off casinos’ radars), and low rollers (people who play penny slots). OG has determined that casinos who predominantly cater to casual high rollers will be most interested in Smart Check as a way of cultivating customer loyalty in this group.

• Rerack Timeframe - casinos rerack (replace their chips) periodically and after compromising events. A near-term reracking is a key opportunity for OG’s Smart Check system.

In addition, OG has determined that Rerack Timeframe is the most important attribute and has assigned it a weighting of 1.0. Fraud loss and Dominant Clientele are less important and assigned weightings of 0.8 and 0.5, respectively.

The attribute indexing and weighting would look like this.

Note that if you can’t obtain the raw data needed to index, don’t define an attribute for it.

2. Gather the Raw Data

The raw data is the actual value of the attributes for each targets. The only challenge is collecting the data (see the note above).

The raw attribute data for three target casinos (fictitiously named O’Patrick’s, Belugio and Fantasy Island) looks like this.

Of course, if there were only three targets, the OG Corporation wouldn’t need an opportunity index; it would just assign all three casinos to one or two sales executives (and probably also go out of business). An Opportunity Index is really useful for a large number of targets as a way to focus the sales team.

3. Convert Data to Target Rankings

The last step in creating an Opportunity Index requires simple math (and a spreadsheet makes it easy). Each of the raw data is converted to its corresponding index value and multiplied by the attribute rating, as defined in step 1. The Opportunity Index is then calculated by summing the weighted index values for each casino. This has been done below for the raw data in step 2 and the target casinos have been ranked from the most to the least promising prospect. Now marketing can direct the sales team to spend resources hunting O’Patrick’s and Fantasy Island while leaving Belugio for later.

One of the keys to useful Opportunity Indices is maintenance. Are the attributes indexed correctly? Are the weightings reflective of the sales team’s experience? Is there an important attribute that has been overlooked? But, in the end, if you have confidence in your assumptions and believe the data, Opportunity Indexing can inject a little science into your sales process and set you on the path to world-class database marketing.

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