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Using Decision Intelligence to Make Better, More Informed Decisions

Using Decision Intelligence to Make Better, More Informed Decisions

Using Decision Intelligence to Make Better, More Informed Decisions

Enhancing human decision-making through data-driven insights.

It’s fascinating to learn just how long it takes for technology to approach the mainstream. Take artificial intelligence (AI), for example. Just this year two scientists (John Hopfield and Geoffrey Hinton) were awarded the Nobel Prize in Physics for their work on AI. Professor Hinton co-created the Boltzmann machine – in 1985! -- which helped pave the way for modern uses like AI image generation or recommendation algorithms. More recently, yet still some 12 years ago, he built a neural network that could analyze photos and identify common objects, which was acclaimed as a significant milestone in AI development.

Fast-forward to today’s contact center and AI seems all the rage. However, in our market, we too have been talking about automation and AI for quite some time. Things like automating email to resolve incoming inquiries, guiding agents to streamline employee activity and reduce training times, automating resolution of common requests, enabling support staff to dedicate time to high value and complex requests, and even “orchestrating” processes across applications. All in the name of a better customer experience (CX). Are we there yet? AI is improving in our market and beginning to get beyond the basic use cases, and yet we still have promising ideas ahead (such as generative AI).

I like to think of [DI] as the know-how behind what you should do next.

Another technology category that has been around for a while and is now showing its promise in the contact center and CX is decision intelligence (DI). If AI is about autonomous learning and problem-solving, then think of DI as enhancing human decision-making through data-driven insights. DI is “a practical discipline that advances decision making by explicitly understanding and engineering how decisions are made and how outcomes are evaluated, managed and improved via feedback,” according to Gartner. I like to think of it as the know-how behind what you should do next. Today’s DI technology is a blend of discipline, methodology, and technology that provides insights other tech cannot (such as the exact dollar value of every contact center metric you measure).

Most of the DI research to date appears to be targeting the CIO and general ideas for improving decision-making by analyzing various data points (patterns, etc.). The aim seems to be using tech to find new revenue opportunities and reduce costs. That’s great for the general enterprise, but what about the contact center? We have more data in the contact center than perhaps any other department, and yet we don’t always use our data to make the best or most timely decisions, right? Even when data is used, it’s often used to paint an incomplete picture. As one industry veteran of the utilities industry said of their massive interactive voice response (IVR) overhaul project, a different story was used in every leadership team meeting to justify their multi-million-dollar cost savings (rather than a consistent, defensible approach driven by decision intelligence). As another industry expert warned: “Don’t let data be weaponized to solve the wrong problems.”

What if DI could give us the know-how to make better, more informed decisions? There appears to be an enormous opportunity for contact center and CX leaders to learn from all the great work that has been done in DI recently that helps organizations achieve significant value. If we embrace DI in the contact center, we may have a massive opportunity to improve business performance (given all the rich data at our fingertips).

We’ve been hearing for years now that providing a better customer experience results in improved revenue growth. For example, Forrester’s 2024 CX Index revealed that “customer-obsessed organizations reported 41% faster revenue growth, 49% faster profit growth, and 51% better customer retention than those at non-customer-obsessed organizations.”

Powerful research results. And yet, how do these same organizations arrive at the necessary decisions to create this link between CX and revenue (or profit). What “intelligence” is needed to uncover the “economics of CX?” That’s where DI comes in. It can help provide better insights to manage operations in near real-time.

We know that a critical first step in any CX decision-making process is to set clear performance metrics for the contact center that are aligned with the greater business goals. Next, we want to ensure that we are tracking and analyzing these metrics regularly, even in real-time, if possible. But how do we know we have the right goals in the first place?

As is the case with any technology, there are multiple factors that contribute to its success or failure.

The problem for many organizations is that the metrics they rely on every day aren’t tightly mapped to overall business goals (revenue, cost, CX, etc.). For example, do the traditional contact center metrics (FCR, AHT, CSAT, NPS, etc.) relied upon every day also show spending devoted to customer engagement? Decision intelligence can aggregate and analyze patterns that can provide the best outcomes (such as the financial cost of getting CX work done).

Putting decision intelligence into practice for your contact center is a practical method to unearthing the know-how that will deliver exceptional customer experiences that are aligned with business growth. Here are a few steps that contact centers can take to get started:

  • Appoint an executive champion to ensure contact center and CX insights are translated into action across the organization.
  • Create a fast efficient team to review and interpret data, and in turn surface the most impactful insights (especially those tied to your financial success).
  • Match your DI to actual business outcomes. While this may seem like a “no brainer,” realize that business models, offerings, and customer needs shift over time.
  • Revisit what success looks like as far as the specific business outcomes DI must drive.
  • Get serious about data quality. Organizations need to gather, review, and enrich CX information to make the right decisions with data, especially those that link together the economics of CX.

As is the case with any technology, there are multiple factors that contribute to its success or failure. Remember to evaluate all your performance metrics to ensure they align with your business goals. The implementation of DI initiatives, lacking solid financial and data plans, is what can fail. Today’s advanced contact centers need decision intelligence that can do things like explain not only how well customers are served, but also how financially efficient service is delivered.

Ryan Hollenbeck

Ryan Hollenbeck

Ryan Hollenbeck is a startup advisor, go-to-market leader and former contact center technology exec for 25 years. He continues to help companies scale their marketing, sales enablement and CX operations, both early stage and major enterprise software market leaders. You can reach Ryan via email at [email protected].

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