DialogTech Launches Dialog Analytics Marketing Platform

Dialog Analytics identifies whether a marketer's call reached a recipient or the call went unanswered, and automatically tags the call accordingly.

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DialogTech, a call attribution provider for data-driven marketers, announced the release of Dialog Analytics, an enhancement powered by the company's Conversation Insights platform that enables enterprise marketers to run analytics on the inbound calls their marketing generates.

Using adaptive machine learning technology and custom modeling, Dialog Analytics allows businesses to categorize data and insights from customer phone calls automatically.

Dialog Analytics identifies whether the caller reached a recipient or the call went unanswered, and automatically tags the call accordingly.

"Ease of use was top of mind for us when we were designing Dialog Analytics. It is ironic that big data is a popular buzzword in today's business lexicon, when in reality nobody really cares about big data," Irv Shapiro, CEO of DialogTech, told eWEEK. "What they care about is gaining insights into their business that allow them to re-evaluate, enhance and optimize their revenue stream and customer satisfaction. These insights are only valuable to companies if they are easy to use."

Shapiro noted most business customers are not interested in complex query languages, three dimensional graphs that are impossible to interpret or complex, hard-to-access solutions that are difficult to install and configure.

"We wanted to make sure that our customers could look directly at this platform and easily understand exactly what they were looking and optimize their marketing efforts from these insights," he explained.

Marketers can use this data to inform customers, internal sales and operations teams that the inbound leads they drive are going unanswered, and to adjust staffing accordingly.

Marketers can choose how they want to group calls based upon what they value. For example, they can categorize calls based on whether an appointment was made, if the call was a quality lead but didn't convert, if the call went unanswered or the call was not a quality lead.

In addition, custom modeling call conversions can be categorized by trends, providing marketers with insight into why customers (and bad leads) are calling. For example, paid search and display advertisers can adjust their bids and campaigns based on what customers are calling for.

The platform contextually categorizes call conversions by combining structured and unstructured data related to Web visits and phone calls to identify customer intent, new trends and the outcome of the call.

"The evolving world of data analytics is no longer satisfied with counting clicks or, in this case, calls," Shapiro said. "Sophisticated analysts want to connect the dots and follow the entire customer journey, from the first search to the sale. Along the way customers may go from a keyword search, to multiple page views on multiple devices, to calling, to an in-person appointment, then more page visits, more calls and finally a sale."

Providing a marketer with access to the most complete customer journey enhances their ability to optimize the customer's experience and increase revenues, he said.