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."
Dig Deeper on Marketing software and platforms
Related Q&A from Scott Robinson
Sales and marketing terms are like alphabet soup, and the different acronyms can get confusing. Find out the difference between three major platforms... Continue Reading
The customer success manager is an essential CX role that takes a proactive approach to ensuring customer loyalty and retention, balancing out ... Continue Reading
Companies often look at customer relationship metrics to determine how customers feel about a brand. Here are examples of how to obtain and use ... Continue Reading
Have a question for an expert?
Please add a title for your question
Get answers from a TechTarget expert on whatever's puzzling you.