BOSTON -- Voice of the Customer programs are hot and getting hotter -- thanks largely to the maturation of text analytics technology.
That was the message yesterday from industry experts at the annual Text Analytics Summit being held here this week. The market for text analytics applications, while small, is growing rapidly. Voice of the Customer programs, also called customer experience intelligence or customer feedback management, are a big reason, according to Fran Halper, a partner at Hurwitz & Associates, a Newton, Mass.-based consultancy.
In a survey that Hurwitz & Associates conducted last year of 118 companies with revenue over $200 million, 49 said they were planning to deploy text analytics, and another 30 said they had already deployed the technology in some form. Among those planning to deploy text analytics, 80% were using it for customer care, according to the survey.
"At the end of the day, it's important because you want to get some sort of competitive advantage," Halper said. "It's five times as expensive to acquire new customers as it is to retain them."
Apparently, text analytics is paying off. The majority of firms surveyed that are using the technology have achieved a return on their investment within one year, according to Hurwitz.
Text analytics and financial services
Charles Schwab and Co. Inc., the San Francisco-based financial services firm, is hoping to reap some of those returns. It has launched a Voice of the Customer program analyzing survey responses and is planning to extend the application to analyze text from notes and comment fields in its sales and contact center applications.
Representatives from the company's in-house analytics group spoke about their project at the summit. The analytics group works within the marketing department, conducting statistical analysis and modeling for the entire company, and has been analyzing structured data for years.
"When it comes to the free-form text field data, we ran into a lot of challenges," said Catherine Lee, director of advanced analytics. "For a long time, we had to manually review it all. It's time-consuming, costly and not scalable. We know there are a lot of golden nuggets in text field that remained unmined."
Three years ago, Schwab began exploring text analytics and just recently selected an application from Attensity Corp., of Palo Alto, Calif. Initially, Schwab had Attensity complete a proof of concept to evaluate the system's speed and ease of extracting information from the customer feedback notes, as well as how it combines the text information with other business data.
Six months ago, after successfully completing the proof of concept, Lee brought the application to Guy Bayes, the director of technology.
It's all about the ETL
Attensity's technology works similarly to extract, transform and load (ETL) tools in taking unstructured data from one system, parsing it and putting it into a data model, Bayes noted.
"We've all been here and things look good, but we're never sure if it's going to scale and really work," he said. "Our primary consideration was [extract, transform and load]. It's always the ETL that'll sink ya. We wanted to make sure the exhaustive extraction process was scalable -- hundreds of thousands of lines of text, versus hundreds of lines."
Another of Schwab's key considerations was finding a system with an open architecture to avoid vendor lock-in and scalability. Because financial services are heavily regulated, the company also needed robust group and role security features. While the user interface and reporting capabilities were factors, they were not as important as the open architecture, Bayes said. Finally, he wanted a system that Schwab could maintain itself.
"This was one of my big fears -- we don't want to hire any linguists," he said. "We want to do projects without re-engaging consulting services. We're going for that competency center role."
That's an important point for any company considering text analytics technology, noted Sue Feldman, vice president for content and digital marketplace technologies at Framingham, Mass.-based IDC.
"It's unlikely most companies will have the linguistic expertise to implement and maintain a text analytics application," Feldman said during an expert panel discussion. "That means a long-term consulting engagement or relying on Software as a Service."
Key considerations for a text analytics initiative
There are various questions companies need to ask themselves when evaluating text analytics, according to Halper, because it remains a diverse and complicated market. Firms should consider what kind of analysis they need, she said. Do they need to extract statistical information and marry it with their structured data to run predictive models? Or is just analyzing the text -- such as an insurance firm analyzing claim forms -- enough?
Also, budgets are a significant factor. Some of the syndicated services can provide analysis for $5,000 to $10,000 a month. Software as a Service applications will typically cost between $5,000 and $20,000 per month, and licensing the software can reach into the six figures, Halper said.
They should also ask whether other departments within the organization are looking at text analytics, or could potentially make use of it, and piggyback on those projects. Support for the languages the software needs to support is a key decision as well.
Early adopters of text analytics have seen challenges with getting data from source systems to analysis systems, developing taxonomies and establishing rules built around the built-in functionality, Halper added.
"If you want to buy enterprise software, ask [whether you] need a taxonomy. Some vendors say you don't," she said. "Where am I storing all that data if I don't have a good data warehouse in place?"
Schwab has piloted the project with the transcriptions from recorded feedback that customers leave on its interactive voice response system. The next step is to unleash text analytics on the millions of records in its CRM system and the sales and customer interaction notes. Ultimately, the firm wants to use text analytics to help establish a client promoter score, similar to the net promoter score -- the likelihood that a customer will recommend a company's product or service to someone else.
"As an advanced analytic group, we're most interested in the ability to combine structured information with unstructured information for input into new modeling," Lee said. "We're expecting that to improve our predictive modeling strengths by adding new data sources."
For now, Schwab is analyzing only internal data, but Lee said they're intrigued by leveraging external sources as well. In fact, the ever-increasing amount of text-based information promises good things for the industry, according to IDC's Feldman.
"If you think of Web 2.0 and interaction, the future of text analytics is pretty bright because in order to manage the exchange of information as we move to conversational systems, you really need text analytics in the next generation of these applications," Feldman said. "Those are on people's minds and drawing boards right now."