Weaving Contextual Data into Models

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Many companies are developing opportunity scoring models which essentially assign a predetermined numerical score to specific behaviors or statuses within a database. The purpose of opportunity scoring is help sales people know which opportunities are sales ready and worthy, and therefore take priority. Often variables such as title, company, and industry, serve as the basis for the scoring model. However, behaviors can be used too, such as the completion of a contact form, visiting a particular page on the website, participating or viewing a demo, etc. Contextual data adds another dimension to the model by weaving in predisposition information that reflects content, timing and frequency-for example what products they currently use, the last time they purchased, their complete buying history, the types of keywords they used in their search, etc.

Keep in mind, timing is everything. To be effective, contextual data must be delivered to the right person, at the right time, within an actionable context. For example, the date of a key customer’s contract renewal is posted in your CRM system all year long, but that doesn’t mean you’ll remember or even see it. Think how much more useful that data becomes when your system automatically alerts you to the fact that it’s the customer’s renewal date. Sending email messages about renewals too early just creates noise at best and at worst suggests you don’t know their renewal date. Customers are more likely to respond to call to action when it is in context of their workflow. Communication that is contextual is more personal and as a result feels more authentic, shows value, and leads customers want to act. As a result, you can reduce the cost of customer acquisition and the cost of sales.

The end goal of contextual data is to connect with the buyer when they are most predisposed to buy. As a result, you can use contextual data to help build propensity to purchase models, for prioritizing opportunities to support opportunity scoring, to develop more personalized messages, and select the best mix of channels.

This same concept of contextual data can be used to build propensity to purchase models. By identifying the winning experiences associated with a particular segment, you can use this information to craft more relevant messages to similar targets to increase uptake.

Personalization is a compelling and challenging proposition. It’s a moving target and therefore requires a test and learn approach. By adding contextual data into the process you can make your personalization efforts more effective and more relevant.

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One thought on “Weaving Contextual Data into Models

    koen pauwels said:
    March 26, 2014 at 9:27 AM

    thanks, very insightful as always!

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