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In predicting the weather, for example, a three dimensional grid of temperature, pressure, and humidity values is constructed as a starting point. From here, various computer models attempt to forecast how weather patterns, temperatures, rain, and wind will occur. The resulting predictions (unfortunately still very inaccurate) are challenged by both the inaccuracy of the starting points and the model used to predict an outcome.
Interestingly, an individual model can vary greatly in its ability to predict different outcomes. Temperature may be relatively well predicted, but rain may be no better than guesswork.
In reading the book, I was reminded of marketers' challenges in using lead scoring to predict which leads are good enough to be passed to sales. We have similar elements; the underlying data - in our case digital body language - on the prospect's activities, and the model we use to score leads and determine which is a qualified lead.
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If you see multiple visitors on your website from one company, they are doing deep investigation, and are using compelling search terms when finding you on Google, you know that there has likely been an internal event at that organization that has caused a group to begin investigating your solutions.
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However, if you don't know the key individuals who would be the decision makers on a purchase of your solutions, you can focus your efforts on what you do know - that a buying process is under way at that organization. Knowing that, a role-based discovery service such as Reachforce can easily capture the names you require and get your sales team in touch with them.
Much like predicting the weather, predicting buying behavior can be challenging and imprecise. However, even being able to predict key indicators like the fact that a buying process is underway at a specific organization adds tremendous value to your sales team.
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