Accurate lead scoring is a holy grail in marketing, with many vendors swearing by their methodology. Sales Cloud Einstein's version can move leads through the conversion process faster, increasing not only accuracy, but also efficiency.
AI predicts which leads are most likely to convert, based on sales history, extracting factors that seem to be reliable indicators -- though it's not clear exactly how. AI identifies the factors and tracks them, lead by lead, so sales can prioritize them.
A customizable workflow also speeds up the engagement process, an equally noteworthy productivity gain. Between the two, Salesforce lead scoring emerges as a meaningful contender in its class. It is now one of the top three vendors in the field, according to Applied AI, and the leader in customer ratings, according to GetApp.
The deciding factor here is machine learning: Salesforce lead scoring, like its IBM cousin, Watson, aspires to get better and better at prediction over time, via its behind-the-scenes machine learning. The vote's not in yet, long term, on how this machine learning is implemented or how well it work, but it's worth watching closely. Forrester, in its Q1 market report, gave the edge to Salesforce, rating it a "leader," while declaring IBM Watson a "strong performer."
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