Todays Customer Care Metrics Will They Matter Tomorrow

Today, 54 percent of customer service and support leaders say growth is their top priority.

Just 9 percent of them prioritize cost optimization.^[1]

That’s because they see the future of customer care as a revenue source, not just a necessary cost. Consequently, leading brands are rethinking traditional customer care metrics like average handle time and first contact resolution to focus more on conversion, customer lifetime value and wallet share. In doing so, they’re making strategic use of evolving digital tools like artificial intelligence (AI), omnichannel platforms, and predictive analytics and sentiment analysis.

Here’s how:

AHT: Squeezing out value via artificial intelligence

Future-facing brands are reinterpreting average handle time (AHT) to reflect customer care’s shift from mere problem resolution to revenue generation. For instance, a contact center agent for a department store chain might exceed the retail industry’s AHT of 5.4 minutes^[2] by taking extra time to convert a customer complaint into a sale. An increased focus on incremental sales, however, needn’t translate as higher AHT across a contact center. On the contrary, better conversion rates can coexist with overall lower AHT, not to mention higher first call resolution (FCR) rates, as was the case with a fashion brand that trained its agents to improve all three metrics. The result? The brand’s AHT dropped 23 percent, while sales conversion jumped 10 percent and FCR rose 15 percent.

Today’s Customer Care Metrics: Will They Matter Tomorrow?

Although most companies aren’t doing this yet, there’s no reason they can’t, as contact center leaders are proving. The trick is to bring all customer care and engagement channels onto one platform and then integrate them so that your agents have a 360-degree view of your customer in every interaction, regardless of touchpoint. Then, mining omnichannel customer data and applying artificial intelligence to it, you can guide your agents and digital assistants toward the best personalized actions to take with a specific customer in a given situation — and help them do it in the shortest time possible as they resolve customer problems and prevent churn, while also generating incremental sales, growing basket size and increasing customer lifetime value (CLV). These steps, combined with automation and the proactive identification and resolution of customer concerns, are the key transformative actions deployed by today’s leading contact centers.

FCR and NPS: Looking under the hood with cognitive AI and data analytics

Because first call resolution (FCR) metrics and net promoter scores (NPS) are fairly opaque, they’re viewed with caution by brands that have their eye on tomorrow.

Let’s start with FCR. The fact that a customer didn’t follow up his initial contact about an issue doesn’t mean he was happy with the outcome. All that might have been resolved was that a “final sale, no returns” item bought online was indeed unreturnable, even though the customer bought an overly large coat because the website’s size chart was confusing. And so the call, while a negative experience for the customer, shows up as a misleadingly positive FCR score for the brand.

NPS is of equally limited value when you consider that 52 percent of all people who actively discourage others from using a brand also actively recommend it.^[3] Plus, the score itself offers no actionable insights about why the customer would recommend a brand, nor does it reflect demographic factors. Perhaps the same customer would recommend a sporting goods brand to her athletic peers but not to her arthritic parents, or else recommend one of a brand’s products (hand grips) but not recommend another (knee pads).

Such nuanced insights and factors are nonetheless readily available to brands that leverage the right digital tools. Indeed, there’s no dearth of customer data across the various touchpoints customers use to engage with a brand. The key is to integrate and analyze that data, converting it into actionable information that agents and digital assistants can use to deliver and optimize bespoke customer care.

For instance, by using cognitive AI to analyze recorded customer care interactions, brands can not only better understand factors underlying FCR (which might reveal more about the customer than about how the agent handled matters), but also capitalize on unstructured data that agents, who are highly focused on the issue at hand (e.g., a woman wanting to return a floor rug) might not notice or have time to capture, such as incidental household information (the woman mentions the rug wasn’t right for her 10-year-old daughter’s room; a dog barks in the background), which could be useful for future targeted sales promotions (2-for-1 dog-bed sale, 50 percent off kids’ bedroom décor) and subsequent customer care interactions, including cross-selling.

In the spirit of leave no stone unturned, brands should leave no customer interactions unanalyzed. Typically, though, companies analyze a mere 3 percent of interactions, leaving huge quantities of free customer intelligence untapped.

Tapping that intelligence is highly feasible. Future-facing brands successfully doing this have been able to turn their customer care function from a cost center into a profit center. Consider a specialty pet retailer that analyzed unstructured data obtained from customer service interactions and then converted it into insights to drive outbound customer care. Within a year of taking this approach, the retailer saw its customer conversion rate soar to 80 percent (one-third higher than the prior year) and incremental sales jump 25 percent. The sky’s the limit.

CSAT: Getting the whole story with sentiment analysis

Knowing full well that survey-based customer satisfaction scores provide a limited window on what customers truly think, customer care leaders will increasingly look to AI-powered real-time sentiment analysis to gauge customer satisfaction and the underlying drivers.

This will solve a fundamental problem of CSAT surveys, which is that the questions are usually too broad, failing to address customer-care shortcomings or a particular customer’s pain points.

For instance, although a customer might say she was highly unsatisfied with an agent’s ability to resolve her problem, this response provides no insight into whether the shortcoming lay with the agent or with the customer care tools meant to support the agent. Nor is the phrasing of the question likely to generate an accurate answer if one problem discussed with the agent is satisfactorily addressed, while a second problem is not. Plus, few customers complete surveys immediately, when both the consumer and the agent would recall the interaction sufficiently.

Right now, only a few technology companies have the foundational capability not only to break down sentiment analytics in real time, but also to convert the analysis into immediate insights while a live agent is still interacting with the customer. But 10 years down the line? Instead of digesting CSAT survey responses with a should’ve/would’ve/could’ve perspective, companies will turn the ship around while customers are still on board.

Setting sail

Here’s what to keep in mind as you rethink your own future metrics, with an eye on assessing customer care as a profit engine, not a cost center:

Shift to a revenue-generator mindset by focusing less on traditional contact center metrics like AHT and more on value-driven metrics such as basket size and CLV.
Use AI-driven automation to handle standard customer care issues, freeing up live agents for revenue-generation activities such as incremental sales.
Integrate data sources to obtain a 360-degree view of the customer so that you can deliver personalized experiences and recommendations regardless of channel.
Make actionable use of customer data by applying AI such as real-time sentiment analysis and customer retention.
Ensure your agents are well-trained in using digital tools to conduct high-value interactions with customers.
Measure your agents’ satisfaction and its correlation with customer care outcomes that drive better experiences and higher sales.
Peg the cost of customer care to positive business results that outlast the interaction, such as spend per year.
Work with a customer care partner that bills based on business outcomes, not FTE calculations and rate per hour.