Is AI-Driven Support Data the End of Manual Customer Surveys?

Moving Beyond Outdated and Ineffectual Survey-Based Metrics

Sometimes it can feel as if “overwhelmed” is the default mode for customer service teams. This is particularly true for growing, service-oriented businesses. These challenges have only been further amplified by the challenging economic environment in recent years, which often seems to necessitate somehow “doing more with less.”

To this end, in a recent piece on growing customer experience (CX) trends, global management consulting firm McKinsey & Company notes how emerging new AI tools, like those offered by TheLoops, are rapidly gaining a foothold and causing a growing number of businesses to fundamentally rethink their CX approach.

McKinsey notes that one of the early impacts of the emergence of new AI tools has been a dramatic shift away from longstanding and widely-used survey-based metrics, such as customer satisfaction (CSAT) scores, customer effort scores (CES), and net promoter scores (NPS)—a subject we also discussed at-length in our recent Q&A with Forrester Analyst and VP Kate Leggett.

The insights are drawn from McKinsey’s recent, somewhat-ironic survey of more than 260 leading CX-based companies in the U.S. Nearly all companies surveyed (93%) reported relying on some form of survey-based metric when gauging their customers’ journeys. But notably, as highlighted below, only 15% of companies surveyed reported being fully satisfied with their CX efforts, while 13% felt their CX data accurately reflected their customers, and just 6% felt their existing CX data enabled strategic and tactical decision-making.

McKinsey singles out several of the weaknesses of survey-based systems most-commonly as noted by companies:

  1. Limited insight due to low-response rates.
  2. Data lags mean they tend to be reactive, rather than forward-looking.
  3. They are ambiguous in their outcomes and difficult to connect to specific business outcomes.

These are among the factors that have motivated increased efforts by CX-oriented companies to find better, more-effective solutions.

“A few leading companies are pioneering a better approach that takes full advantage of the wealth of data now available,” McKinsey writes.

“Those with an eye toward the future are boosting their data and analytics capabilities and harnessing predictive insights to connect more closely with their customers, anticipate behaviors, and identify CX issues and opportunities in real time” through the growing adoption of AI tools.

The Loops Makes AI Easy—From “Nice to Have” to Setup in a Snap

For all the excellent insights one might glean from McKinsey’s perspectives, the simple reality is that most companies need better customer support tools because they are already stretched too thin! As a result, reading about AI-driven customer support can sound like a “pie in the sky” notion to overwhelmed organizations.

Consider, for example, the four steps most companies face when transitioning to an AI platform, according to McKinsey:

1.) First, the company gathers customer, financial, and operational data—both aggregate data and data on individual customers. The company processes these data and stores them in a cloud-based platform.

2.) The company develops analytics—often using several types of machine-learning algorithms—to understand and track what is influencing customer satisfaction and business performance, and to detect specific events in customer journeys.

3.) The algorithms generate predictive scores for each customer based on journey features. These scores allow the company to predict individual customer satisfaction and value outcomes such as revenue, loyalty, and cost to serve.

4.) Information, insights, and suggestions are shared with a broad set of employees (including frontline agents) and tools (such as customer-relationship-management platforms) through an application-programming-interface (API) layer.

While most companies might love such a system, in theory, few can spare the resources, time (and possibly downtime) associated with such a major shift, in reality.

Maximize Your Existing Data with Insights Including Sentiment Analysis

With TheLoops, the pain traditionally associated with cutting-edge customer service capabilities is a thing of the past. TheLoops acts as the AI middle-man and assistant sitting between your business and its customer experience software platforms, making sense of the data it’s receiving.

One oft-repeated buzzword surrounding AI is “sentiment analysis,” which is one component of the insights offered through TheLoops’ platform. The concept seems self-explanatory. However, the capabilities of sentiment analysis have evolved well beyond a simplistic “positive/negative/neutral” framework into what we believe are specific and useful new product insights, including predictive capabilities.

Sentiment analysis has evolved into a thorough framework that is particularly -promising for companies seeking to move beyond vague and increasingly ineffectual survey-based systems and toward a platform that enables specific, impactful business outcomes.

For example, at TheLoops, the product insights component of our algorithm continually scans for and automatically extracts more than 20 different signals from across every platform in your customer support stack. Beyond merely informing you that a customer is upset (which likely doesn’t require advanced AI), TheLoops provides new visibility into the context of that sentiment, allowing you to understand your customer’s intent.

These sentiment triggers feed into numerous broader insights, including upsell opportunities, legal risks or risks to revenue, unexpected behaviors, system-wide access issue and instability within your platform, and much more. In short, you can finally maximize the information gleaned from your existing universe of customer data.

And because our insights easily integrate with your entire existing software stack, there is no ambiguity. You gain dramatically improved insight into operational visibility across your company. This includes an improved ability to analyze the impact of any given CX initiative, allowing you to see what’s working, what’s not, and to more-easily identify new risks and opportunities.

Our platform also makes data lags a thing of the past. With data continually updated in real-time, you can rest assured every member of your team is able to apply these new insights to their particular roles, thereby organically breaking down organizational silos. Perhaps most notably, this includes enabling your support agents to access all of this information in a real-time, user-friendly format.

With TheLoops’ Agent Assist and Monitoring, a convenient widget ensures all of the perspectives gained through TheLoops—including upsell opportunities, potential legal risks or risks to revenue, and much more—are conveniently accessible real-time in your agents’ existing workspace.

As a result, we sincerely believe that AI tools can now offer what was previously only attainable through hiring expensive consultants or developing painstaking custom software solutions.

Spend Less Time on a Cutting-Edge Customer Experience

Once your data is unified through TheLoops, our algorithms continually monitor for notable trends—no custom analytics or algorithms necessary. TheLoops works right out of the box! Simply connect your platforms via our user-friendly, drag-and-drop interface, and benefit from a real-time view into every level of your operation. Once connected, you can easily setup automatic triggers based on these monitored insights to ensure your decision-making is based on the best and most up-to-date information from across your systems.

TheLoops also removes the need for creating specialized custom views or reports for numerous audiences within your organization. No more hunting for the appropriate data point, customer interaction, or record. Our platform provides a simple, but powerful dashboard view that’s easy-to-understand and ensures your entire company is reading from the same up-to-date playbook.

Perhaps best of all, we generally have our customers up-and-running within hours-to-days rather than weeks-to-months. Try out our low-code/no-code/drag-and-drop environment for yourself!

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