The Future of VoC Actionable Insights: Assistance Engines

Earlier this week I gave a speech called “The Future of CX: Humanistic, Prescriptive, and Responsive.” During that session, I discussed a missing link in today’s VoC technology: Assistance Engines. Here’s a picture of the future that I have in mind.

Architecture For Prescriptive Customer Insights

Before I describe Assistance Engines, I want to go back to 2010 when I labelled VoC technologies as Customer Insight & Action (CIA) Platforms. The naming was important, because it correctly identified that vendors needed to focus more on “insight & action” than on customer feedback.

It turns out that this is still the case. In the future, VoC vendors will be completely judged by results that their clients get from taking actions on the insights that these vendors provide.

Action is the holy grail! All of the efforts around surveying, integrating data, analyzing, etc. are only as valuable as the actions that they lead to. Most of the vendors now understand this key concept, and are working feverishly to improve the actionability of the insights they provide.

Companies still have a long way to go in taking action on their VoC insights. As you can see in our recent infographic, only 24% of large companies think they are good at taking action.

To help refine the insights, most vendors are developing some sort of an Intelligence Engine. This technology combines direct customer feedback with other customer information, and then applies different analytical and machine learning approaches to create predictive insights about large groups of customers.

While this technology is helping companies to better understand their customers, the output does not often translate directly into actionable insights. Why not? Because there’s a wide gap between insights from the Intelligence Engine which are often delivered in charts and dashboards, and the types of information that employees need to make their a day-to-day decisions.

No matter how much smarter these platforms get about customers, they won’t be truly actionable until they also get smarter about employees.

That’s where Assistance Engines come into play. What is an Assistance Engine?

A set of technologies that uses analytics and machine learning to provide increasingly valuable advice to help different employees across an organization make customer-centric decisions.

Or you can think of it more simply as…

Technology that recommends employee actions based on customer insights.

Assistance Engines will provide timely, actionable insights that are embedded within role-based processes, and delivered as answers and recommendations, not as charts and numbers. This technology will also fine-tune its recommendations based on feedback from employees about the types of recommendations that they find valuable.

Think of the Assistance Engine as being like an analyst who works for the employee. A good analyst can comb through data in an Intelligence Engine, understanding her bosses needs, and translate the customer insights into a very relevant set of recommendations. Over time, the analyst gets better at anticipating what her boss needs or wants to see.

Here are some examples of insights that an Assistance Engine might deliver (think about the employee simply asking Alexa a question):

  • When a product manager is defining a new product, the Assistance Engine will recommend a set of features that a product manager should include in its next release.
  • When a contact center supervisor finds that she has 15 minutes free, the Assistance Engine can tell her which agent to spend time with and what to cover during the session.
  • When an executive is looking to improve the companies NPS, the Assistance Engine will identify the regions to focus on and the activities that should be improved in those regions.

The early use cases for Assistance Engines will likely focus on recommendations that are already being made by analysts. But instead of having someone spend a lot of time manually digging through troves of data, the Assistance Engine will simply answer end users’ questions.

Companies still have a long way to go in building out their Intelligence Engines, so we do not expect to see Assistance Engines become mainstream for several years. But the maturing of end-user responsive analytics such as IBM Watson and Amazon Analytics will help accelerate the development.

The bottom line: Actionability requires more focus on employees.

 

Written by 

I am a customer experience transformist, helping large organizations improve business results by changing how they deal with customers. As part of this focus, I examine strategy, culture, interaction design, customer service, branding and leadership practices. I am also a fanatical student of business, so this blog provides an outlet for sharing insights from my ongoing educational journey. Simply put, I am passionate about spotting emerging best practices and helping companies master them. And, as many people know, I love to speak about these topics in almost any forum. My “title” is Managing Partner of the Temkin Group, a customer experience research and consulting firm that helps organizations become more customer-centric. Our goal is simple: accelerate the path to delighting customers. I am also the co-founder and Emeritus Chair of the Customer Experience Professionals Association (CXPA.org), a non-profit organization dedicated to the success of CX professionals.

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