Pegasystems, the BPM platform vendor, has recently acquired MeshLabs, a social analytics and natural language processing vendor based out in Bangalore, India, for an undisclosed amount.
A very quick glance at the acquisition would suggest a big part of the draw for Pegasystems is that it gets its hands on a pretty nifty advanced text mining engine. With capabilities such as entity extraction, categorisation, faceted search and sentiment analysis, MeshLabs can help organisations uncover insights within unstructured text—particularly those found within online social channels such as Facebook and Twitter. The big prize here for customer-facing organisations, of course, is that they’re able to get better visibility and an understanding of what people are saying about their company, products and brand and use that insight for business advantage.
MeshLabs represents a small but important tuck-in acquisition for Pegasystems. One place where its fits pretty nicely is within the company’s customer engagement offerings; a set of CRM capabilities focused on things such as next-best action marketing and customer service. By weaving social listening and analysis into the mix, Pegasystems can augment its omnichannel architecture with insights into what’s happening within the social interaction process and use it to tailor or personalise interactions accordingly—whether that’s by spotting issues before they escalate, or by adapting the customer service process according to feedback, or by recommending the most relevant offer (perhaps in another channel).
Demand for text analytics is growing in line with the interest, uptake and pervasiveness of social media platforms that now harvest a lot of interesting text-based content about customers or consumers. However the technology and techniques needed to garner intelligence from all of this unstructured information is typically highly specialised and sometimes complex. To succeed in this space Pegasystems, like other vendors, must work to lower the barriers to value for customers—making it far easier for organisations to set up and configure some of the rules and customised dictionaries required to generate business value from the technology.