Frequently getting a list of prospects, ranked according to their probability of being interested by your offer and to potential profit they could bring to your company? Thanks to machine learning techniques, this a reality, no more a fiction.
In a traditional prospect scoring approach (i.e. without machine learning), sets of criteria defining the profile of ideal prospects are first created. Then, these profiles are used to filter “good” prospects from a list of all available prospects.
Using machine learning, this process is reversed, enabling a scoring based on actual data and facts, and not on human-made hypotheses.
We start by collecting historical data from you sales team, for example exporting data from your CRM. We will then use this data to build an algorithm that identifies the best prospects for your business. To do so, the algorithm uses past successes and misses of your sales, the characteristics of the targeted prospects, and in some cases open data that can enrich the modeling of your activity. As a result, we obtain an attractiveness score that is specific to each of your prospects.
This methodology is interesting for several reasons: ability to combine of large set of variables to compute scores, robustness to complex relationships between these variables, obtention of a specific score for each prospect in the market, which allows to rank all prospects in a way that highlights which of them have to be contacted in priority.
Consequently, using this approach, you target only the most promising prospects. You do not spend time and energy chasing low priority leads. Doing so, you enhance the efficiency of your sales team and shorten your sales cycle.
Feel free to contact us for more information: info@weqan.be
