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Applying Data Analytics to Customer Interactions
A three-part framework for making data work for your business.
Posted Nov 9, 2012
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Identifying the available types of data is one thing. Applying it in real time to drive outcomes is another. Companies must apply a simple yet powerful, three-step framework to describe the philosophy behind this application of data analytics to customer interactions.

Anticipate. Determine who you are talking to, what they are trying to get done, and when they require help. Consumers reveal their intent through their behavior on a channel. For example, a journey on an e-retailer's Web page reveals information about the products that the visitor is interested in purchasing. Blending current journey data with the identity of the individual and his/her context from recent interaction history across channels can substantially drive up the intent prediction accuracy. Factoring location data into this can further boost the prediction. We can compare behavior in a single interaction with the behavior of thousands of similar customers using various statistical analysis and machine-learning techniques. We can also identify the exact points in the journey when the customer is most likely to take action or require an intervention. All this can happen in real time during the regular course of the customer's journey without requiring the customer to provide any explicit input. This is in clear contrast to other common types of interaction software, such as virtual agents, search, or button chat, which require explicit user input.

Simplify. Once you have determined a customer's identity and predicted her intent, use that data to help decide the best interaction type to make the experience simple and fruitful for your customer. This includes decisions on the right channel (or channel combinations) in which to engage her. One of the powerful tools that modern technology gives us is the ability to conduct multiple experiments with samples of our customer base to test responses. These tests, which follow a design-of-experiments framework, are known as multivariate tests. They allow us to identify the best presentation methods and channel combinations to engage with consumers and resolve their problems. For example, today in the Web world it is very easy to segment visitors and treat each segment differently. Different Web page designs can be shown to different people to quickly understand customer responses to different designs. We can leverage such techniques to determine the experiences that drive the most effective customer experiences, leading to reduced customer effort and successful resolutions.

Learn. The most critical step in this framework is applying the results from the previous steps to make subsequent interactions better. The critical fuel for this is more of the data itself. Smart customer service applications can use the data that they generate to self-correct, automatically learning from each interaction to improve customer targeting, prediction accuracy, and outcomes.

Applying the Framework

Anticipate-Simplify-Learn (A-S-L) is a powerful and effective framework for organizing and orchestrating the range of tools and processes at our disposal and driving relevant outcomes. Unifying platforms need to bring together advanced prediction and simplified experiences to serve customers through decisioning and experience engines. They must mine both unstructured and structured data at a massive scale to deliver accurate predictions.

Effective unifying platforms must do two things:

Accurately predict consumer intent. Current predictive models used in customer service are rudimentary. The most sophisticated typically consist of basic business rules that segment customers using broad brushes. For example, people who spend more than 30 seconds on a particular page may be exposed to a particular offer. In contrast, our machine-learning models are trained on interaction-level data that spans customer touchpoints from Web click-stream data to detailed IVR interaction logs to transcripts of chat and voice interactions with customer service agents. This is in addition to the transactional and CRM data. These models are evaluated dynamically, in real time, based on the customer's historical activity as well as his current interaction with his mobile device, to provide predictions about current intent and provide context to the experience engine. As a result, these models are completely customized to that particular customer's situation. With such an approach, we can better understand the preferences, interests, and needs of consumers to predict the reasons for their interactions with a company.

Improve assisted interactions through data models. The second dimension is the ability to leverage big data to analyze interactions with customer service representatives. This data is in addition to that obtained from self-service interactions (Web, mobile, or phone). Due to the difficulty in processing unstructured data, the standard industry practice is to analyze customer surveys as a proxy for interaction data. In contrast, unifying platforms must leverage a big data infrastructure that mines 100 percent of customer interactions, including customer chats and phone calls. This allows for learning at scale from all customer interactions, providing richer data for the predictive models and, in turn, increasing their accuracy. It also allows for analysis of agent behavior in great detail and scores every single interaction on multiple dimensions such as agent soft skills, agent performance, and other drivers of successful outcomes. This provides powerful insights for contact centers and can be used to drive decisions on training, staffing, and agent management. The fusion of unstructured data from interactions with customer service representatives with the other elements of the customer journey also captures any intent prediction errors, thus creating a self-correcting feedback loop. An actual conversation of a customer with an agent is probably the best data to identify true intent. The insights gained from mining all service representative interactions are also used to improve the resolution experience of self-service applications.

The Integrated Platform

As enterprises prepare for the deluge of customer data, nobody can deny that this is, in a sense, a virtual arms race, where there is a lot at stake for even the smallest competitive advantages.

Today, enterprise tools and techniques are fragmented, as are the repositories of channel-specific and department-specific customer data. As customer care embarks on the biggest transformation in its history, there is a need for a customer-centric care model that is able to leverage this data intelligently across channels to reduce customer effort. Enterprises need to reorganize around the relevant sets of technologies and processes that lead to higher customer centricity and superior, differentiated customer experience delivery.

At the same time, the required velocity of change, combined with the relatively slow response times of typical IT cycles, demand alternative delivery mechanisms that can accelerate your transition without requiring significant capital investment. How prepared are you to take advantage of the big data explosion and its transformative potential for customer care?

The technical ability and the processes are available today, in a globally integrated, cloud-based platform, for multichannel customer interactions. Enterprises leveraging this type of platform are able to put their enterprise and customer data to use intelligently; anticipate customer intent, simplify customer experiences, and learn from every interaction, transforming customer care and driving superior outcomes; accelerate the transformation of customer care using big data, without IT dependencies; and deliver differentiated customer care that is nimble and responsive to customers and competition.


Ravi Vijayaraghavan leads the analytics and data sciences organization at [24]7, where his team builds data-driven solutions and predictive systems that enable superior customer acquisition and customer service. Previously, he  worked at Ford Motor Company in diverse leadership roles from scientific research to IT and strategy.

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