Sramana Mitra: Let’s say we have a customer support representative who’s answering a call and looking at the knowledge base to look for information, what does that customer support agent find in your system that he would not find through a RightNow system?
Louis Tetu: I’ll give you a practical example. Let’s take GoPro cameras who we work with. In their contact centres, they use Salesforce Service Cloud that has a knowledge base. Presumably, they should deliver the answer. The reality is that what Coveo brings in the GoPro console is aggregation of content from other sources like Confluence.
We will even index a YouTube channel because the agent has to see what the customer sees. We’ll index the entirety of Salesforce.com to find answers and matching cases anywhere. We’ll suggest content based on a number of factors. We look at all the metadata around the case. We look at the text analytics of how the customer describes the case. We’re looking at very advanced suggestions from many sources that we bring to the agents, straight in that console.
Sramana Mitra: If I understood you correctly, the way you’re positioning Coveo is that it’s dealing with a lot more sources than the older technologies that want all the data in one database. It’s working off that database.
Louis Tetu: That is absolutely correct. What happens is, if you look at the reality of enterprises, information is increasingly fragmented. On the one hand, it’s harder than people plan for to retire legacy systems. On the other hand, cloud systems proliferate extremely quickly. As a result, companies deal with communities and social data. They might have Jive or Lithium. They may have stuff going on on Yammer or Twitter. They deal with email and content management systems. They deal with project management and collaboration software. They deal with archiving stuff.
The reality is the best answer for the customer is an aggregate of that. You have to have a radically more agile model to deliver at the relevance levels that customers are expecting. That’s exactly what we’re talking about here. There are actually three steps to that process. The first one is reach. You have to be able to reach data securely and bring it in an index in the cloud. Number two is you have to be able to deliver the insights that matter and cut through the clutter of information. Number three is you have to be able to optimise that process in real time using crowd analytics or machine learning to learn what matters in what context, exactly how Amazon does it for you.
I always give the following example. Amazon is not in the business of recommending the dryer that goes along with the washer that you’re buying. Amazon is much more sophisticated than that. Amazon is in the business of recommending the dryer, that people like you who behave like you and who happen to want to buy the washer you’re interested in. It’s all data-driven. It’s all machine learning-drive. That’s what we’re talking about here. It’s quite powerful.