Using Data to Predict Customer Intent

Using Analytics Improve Contact Center Operations and Enable Proactive Customer Engagement

For a long time, the customer engagement world relied on strategic guesswork. Lacking the technology to take advantage of behavioral data, customer service agents had to be content with reacting to customer concerns rather than proactively anticipating customer needs. Thanks to advancements in data collection and machine learning technology, proactive customer service is well within reach.

In modern customer service, proactive customer engagement is possible for virtually any business. Organizations everywhere can now harness the power of automated machine learning models to predict what information should be provided to a customer, when, and through what channel. Moreover, businesses can now use services like Amazon’s Comprehend, which can manage contact handling based on customer sentiment, and Polly, which creates speech applications nearly indistinguishable from humans.

This technology is no longer just for the major corporations, either. In general, tech providers have moved away from the costly model that charged per agent seat and often came with hefty maintenance fees. They moved instead toward a consumption-based model with no minimums or long-term contracts. This has enabled companies of any size to ramp up their data-driven strategies and take the next step into the future of customer engagement.

Using Analytics and Automation for Better Contact Center Operations

There’s no excuse not to take advantage of this customer service technology in today’s environment. Businesses of every size should use it to create a service framework that prioritizes proactive customer engagement. With that in mind, here are a few ways to move into a more intuitive, customer-centric future.

Create comprehensive profiles for each customer

Customers don’t just interact with businesses through a single channel; most interact with a company through various channels, both digital and traditional voice. As a result, gathering data from all channels is crucial, ensuring that the details of every interaction remain intact and then putting them into a single centralized data lake from which machine learning models can be created. This may sound like a tall order, but Amazon Connect tools such as Customer Profiles can provide the necessary framework.

Track changes in real-time

Data should be captured as close to real-time as possible, and the changes in a customer profile should show a time stamp, source, and reason for the change. This ensures that agents aren’t acting on obsolete information. Also, make sure to normalize the data structure across all channels to make tracking changes easier.

Group similar customers on a granular level

Identify the attributes needed for correctly partitioning and grouping similar customers using as many dimensions as possible. This makes it easier to codify actions agents should take in specific situations with different types of customers. However, it is essential to remember that the relevance of some of these dimensions may not be evident to a machine learning model.

Use an actionable context store

Context is a critical part of making data valuable. It allows agents to know the next best actions in advance. An actionable context store makes recomputing predictions possible based on each new change. Without proper, up-to-date context, agents will enter a customer interaction unprepared.

The future of proactive customer engagement is bright for those willing to take advantage of the latest advancements in data and analytics. Now that many analytics services are accessible and affordable to organizations of all sizes, embracing modern contact center operations should be the goal of businesses everywhere.