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    Knowing which businesses to target for their conversion potential means knowing whether and to what degree they meet your ICP – ideal customer profile. Some CPGs (consumer packaged goods corporations) may define their ICP loosely, by referring to a handful of characteristics they believe good potential customers need to have. Others may realize that the more specific they can be about businesses relevant to their product categories, the greater the chances they’ll target the right ones and achieve success with them.

    The key questions are:

    1. How do you decide which customer attributes are critical to your ICP? 
    2. How do you go about finding the data that reflects these attributes? 
    3. Once you have the data, how do you use it to generate and prioritize leads?

    These issues present a substantial challenge for CPGs.

    You might have read our previous post about the challenge of accurately identifying all the relevant businesses in your TAM and are familiar with the problem of insufficient data. The same is true for the ICP. Not only is more data needed about target businesses but the particular characteristics that make them good conversion candidates must also be discovered.

    Implementing a scalable ML-based methodology can really take this to the next level. Here are a couple of ideas how to get started:

    Identify attributes critical to your ICP.  The ML capabilities provided by Explorium’s External Data Platform allow you to thoroughly analyze your internal CRM systems to uncover the characteristics typical of your customers, how they differ from non-customers, the type of businesses more likely to be your customer, their purchase categories and frequencies, and do all that on an ongoing basis. 

    Find data to reflect distinguishing customer attributes. Intelligence about your existing customers is used to identify similar businesses – “lookalikes” – using the Explorium platform’s ocean of companies and attributes made available to CPGs. Predictive models are used to construct a realistic ICP, giving CPGs a portrait of the businesses they should pursue.

    Prioritizing the optimal leads. When a list of all the businesses with the relevant ICP is generated, it is fed into the scoring models using Explorium’s machine learning engine. The resulting lead scores intelligently shape future sales efforts in the relevant TAM.

    We encourage you to read more about how CPGs use the platform in our white paper, How You Can Use ML Today to Optimize Your Field Sales. It addresses challenges CPG field sales teams contend with and highlights the path to eliminating much time, effort and expense in pursuing non-relevant potential customers.