How Analytics Improve Contact Center Operations and Enable Proactive Customer Engagement

For a long time, the world of customer engagement 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. Now, 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 not only what information should be provided to a customer, but also when, and through what channel. What’s more, businesses can now take advantage of 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

All that said, in today’s environment, there’s no excuse not to take advantage of this customer service technology. 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.

1. Create comprehensive profiles for each customer.

Customers don’t just interact with businesses through a single channel; most interact with a business through a variety of channels, both digital and traditional voice. As a result, it’s crucial to gather data from all channels, ensuring that the details of every interaction remain intact, and to then put 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.

2. Track changes in real time.

Data should be captured as close to real time as possible, and the changes that occur 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.

3. Group similar customers on a granular level.

Identify the attributes needed for proper partitioning and grouping of similar customers, using as many dimensions as possible. This makes it easier to codify actions that agents should take in specific situations with different types of customers. It is, however, important to keep in mind that the relevance of some of these dimensions may not be obvious to a machine learning model.

4. 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 also makes it possible to recompute predictions based on each new change. Without proper, up-to-date context, agents will go into 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.

To learn more about how USAN Services for Amazon Connect can deliver proactive customer service and engagement, visit us here today.