Thought Leaders in Artificial Intelligence: Rich Green, Chief Product Officer of SugarCRM (Part 3)

Sramana Mitra: Does that mean that you are bringing in their activities on LinkedIn, Facebook, and Twitter where they may be publicly doing something?

Rich Green: Yes. You went through an array of names, but I can’t comment on them. That’s a very good example of the kind of data sources that we aggregate and structure in the system in the context that allows an individual to build a system or portfolio and be able to glean insights.

Sramana Mitra: One of the things that neither Facebook, LinkedIn, nor Twitter allow you to do is to look up people’s handles with email addresses.

Rich Green: There are no issues violating those agreements and covenants. There are a variety of data service providers that provide similar information.

Sramana Mitra: Give us some examples of what you can glean out of this kind of data. You must have ran some experiments. Help me with some examples of the most powerful kind of signals that you got out of applying your algorithm to this problem.

Rich Green: Before I go on, I make sure that the information that we absorb and structure into the system is not just external information. Historically, the deepest vein to mine, in terms of knowledge of individuals and history of transactions, is personal communications like emails or chats. That is a critical part of the data aggregation system necessary to build a profile. It’s not just external, but it’s also internal because, in many cases, there’s an ongoing dialogue that may have occurred previously.

The coefficient that’s interesting in all of this context is one of time. What are the trends and history behind companies and individuals that could lead one to believe that they may be right for contact and purchase. We’ve run some experiments that can calculate when is the best time of day to reach out to an individual in the context of a sales process. What is the status of a deal? Is it 30%?

Typically, people document where they think a deal is in a pipeline versus us being able to derive that based on history, status, and comparisons to equivalent deals. Those are some examples. They are very straightforward examples.

A very large customer of ours is using a version of the system to relate based on comparative analysis of the dossiers that we produce. There are customers that exist in two forms. To date, they have not been able to relate them. They have a corporate agreement with a company and an individual. Then they have a consumer representation of that individual who goes into their stores and purchases online. They have no way to relate them. They have no way to relate that Bob Smith, the corporate customer, is the same consumer customer Bob Smith. That’s extremely important for them to know. With this technology, we can assimilate those two things. This seems very simple.

This segment is part 3 in the series : Thought Leaders in Artificial Intelligence: Rich Green, Chief Product Officer of SugarCRM
1 2 3 4 5 6