Transera Uses Big Data for Customer Engagement Analytics


Transera is an established contact center in the cloud vendor with in-depth interaction routing capabilities. During a recent briefing I learned that it has now supplemented these capabilities by launching a new product that it calls Adaptive Customer Engagement. Although it’s not entirely obvious from the name, the product uses big data analytics to analyze past customer interactions, profile customers, then use these insights to optimize current and future customer-facing activities such as handling a live customer interaction, planning a marketing campaign or focusing agent training and coaching. The objective is to proactively influence these activities so the outcomes are better both from the customer’s and company’s perspectives.

As I have expressed previously, any information-driven activity improves the more data you can include in the analysis. The foundation for adaptive customer management is four types of data:

  • Interactions – data collected about past interactions regardless of which channel they occurred through, including phone calls, email, chat sessions and social media
  • Agent – data about who handled what interactions
  • Customer – data about the customers that initiated the interactions. This can be drawn from both internal data sources such as CRM systems or a customer data warehouse, or external sources such as the Internet or third-party organizations
  • Business – typically financial data extracted from ERP and finance systems.

Using a variety of data integration tools, all these forms of data are deposited into a common data store, or what Transera calls a Customer Interaction Repository. This repository is based on open source big data tools, so information can be stored and accessed easily, and additional data sources can be added with relative ease. Using internally developed algorithms and tools, the software is able to link data from one source to another; for example, using an email address, phone number, account number and Twitter handle, the system is able to identify phone calls, email and tweets made by the same customer, as well as linking each with other profile information such as marketing campaigns, financial status, products purchased and location. Having established links between all this data, the system then uses big data analytics capabilities to essentially “slice and dice” the data in any way users require. During my demonstration I could see how easy it is for business users to run standard reports or analysis, or where required to use drag and drop capabilities to build individually tailored reports and analysis. These reports can be customer-related – what customers have done what; agent-focused – which agents have performed the best; or other business function-related – what marketing campaigns, through what channels, achieved the best outcomes.

Transera has built three application suites to surface information. Its Command Center Application allows users visibility into operation performance, employing dashboards and sending alerts when thresholds are reached. Business Intelligence Console provides users visibility into business-related reports and analysis, including comparisons between actual and target metrics. The final application, Customer Interaction Advisor, allows companies to make a real impact on the customer experience. It includes the capability to extract information in real time from a live interaction, such as from an inbound call. It can then use this information to extract information from the interaction repository to drive real-time decisions on how the interaction is handled. For example, by linking with the Transera (or other vendor) routing software a call can be routed to the employee most likely to deliver the best outcome from the interaction (a new sale, an up-sell to an existing customer, quickest resolution of a query or compliant). The person handling the interaction is also able to see the full customer information, once more enabling the best outcome.

As consumers interact through more channels, my contact center in the cloud researchvr_inin_types_of_interactions_in_contact_center shows overall interaction volumes, and thus volumes of data, are increasing dramatically with inbound calls still leading in 96 percent of organizations. My colleagues Mark Smith and Tony Costentino recently completed benchmark research and have written blog posts about the impact big data is having on analytics and the issues this is creating for companies. These new capabilities from Transera address some of the issues companies have with big customer data. Companies can use the software to improve operational efficiency and the customer experience.

Interaction management is a practice many organizations may not have yet come to terms with, but as companies strive to improve customer-related activities and the customer experience, it is something they should urgently consider, and I believe Adaptive Customer Engagement from Transera is a service companies should also give serious consideration to.

Regards,

Richard J. Snow

VP & Research Director

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