Customer experience (CX) may mean different things to different companies but it means only one thing to customers: whether they liked what a company offered or not in a given context or setting. And whether they are going to have that offering again—and, yes, whether they would grab anything else the company wants to sell them. Perhaps they would also spread the good word on the product or service used.
Alas, in most cases, it is the bad word that gets thrown around—often wildly and out of the company’s control into the ruthless arenas of social media.
For the past several years, most organizations have responded by throwing back more and more technology to fix their CX efforts. According to research firm Gartner, global spending on customer experience and relationship management (CRM) software reached $48.2 billion in 2018, a growth of 15.6% over the previous year.
But, despite the rising expenditure on tech and the best intentions of companies, the struggle to get a handle on customers and delight them with exceptional support and service continues. So, what is going on here? What challenges are companies facing in putting together a complete picture of their customers and serving them better? Whatever happened to the promise that up and coming technologies such as chatbots and artificial intelligence (AI) were supposed to hold in equipping organizations with the wherewithals to delight their customers (and do so at lower costs)?
We spoke to a few industry analysts and experts to dig deeper and see what gives.
One of the fundamental problems, they say, has to do with the ability to use the right data to get a comprehensive view of the customer. “After all these years, having a 360-degree view of the customer is still on the agenda for companies. One of the issues here is, what do you understand by a 360-degree view? Is it an electronic Rolodex? Is it an extended set of data about the customer, something that different vendor tools are now increasingly exchanging? Or is it something else?” says Brian Manusama, a senior director analyst at Gartner.
On his part, he offers a simple, functional definition. “If you ask me, it is the right data in order to serve your customers well. It can have just two or three components or even hundreds of components, including different metrics such as customer sentiment or behavior,” he avers.
There are other aspects to this challenge. According to Ray Wang, principal analyst, founder, and chairman of Constellation Research, “On the one hand, most companies don’t have access to all their internal data. This lives in siloed departmental systems that rarely talk to each other. On the other hand, most companies now rely on more external data which is often seen as not secure, not as safe, and in different data formats. The last part is that data often does not tie back to business processes or journeys—which means it’s hard to determine a recommendation or next best action.”
The ‘recommendation or next best action’ typically refers to suggestive responses provided by the AI engine that is increasingly getting embedded in chatbots, CRM, and other business software. Such recommendations are based on a knowledge repository comprising standard answers mapped to frequently asked questions, previous customer interactions, etc. It is now common industry wisdom that for better recommendations, it is necessary to have a rich data repository and a finely tuned machine learning model.
Wang points to a basic flaw in how most organizations have traditionally dealt with customer experience. “Most [customer] journeys have been designed for internal efficiency, not external efficiency. Customers don’t care what department you are in and this means the design point must revolve around the customer,” he says. To correct this anomaly, a lot of organizations are now “retooling” to support this from an internal process and technology point of view.
Another big headache for companies is to make their disparate CX systems talk to each other and work as an integrated solution. Today, there is a dearth of holistic solutions that can manage the entire customer lifecycle—from acquisition to retention to life-time value (LTV) management. “There are different piecemeal offerings from different solution providers. For example, there are a lot of sales analytics companies out there who help sales teams optimize their processes; likewise, there are a lot of marketing attribution and automation software that have AI capabilities to help marketers spend their budgets more optimally and so on. Similarly, on the customer success side, there are tools for churn prediction and other areas, but the overall customer journey stack is broken,” says Swaminathan Padmanabhan, director of data science at Freshworks.
According to him, it will be of fundamental value to customers “if we can tie all these capabilities together.”
A multi-experience world
Customers are now interacting with brands through a complex mesh of interfaces and touchpoints—physical as well as digital. “Do you know how many different ways one can order pizza from Domino’s? Twenty four!” says Manusama by way of an example. Such ordering ease includes the use of phone, text, social media, and voice assistants, besides showing up at physical stores and giving the order over the counter.
“We are moving toward a multi-experience world with three different modalities of customer experience across multiple digital touchpoints—gesture, text, and voice,” he says. At Gartner, analysts now call upon tech leaders to get ready to serve ‘the everything customer’—one who requires conflicting things at the same time: to be treated like everybody else but served on their own unique terms, to be connected yet sometimes left alone.
When it comes to customer experience, companies are compelled to move from a reactive way of working to a more proactive way. And while this complexity is generally good for customers, as it gives them more choice and hands them greater control over how they want to be served, it leaves companies in a constant state of flux.
The growing role of AI
Analytics and AI are playing a more important role than ever in improving customer experience, according to Wang. “We are moving from gut-driven to data-driven decisions and this requires a ton of analytics to quantify and anticipate customer needs and requirements,” he says. The rising capabilities of AI offer hope to organizations. “Over time, machine learning will support precision decisions, which means better personalization, fraud detection, and customer experience,” says Wang. He doesn’t hesitate to call AI “the biggest shift” in CX.
Padmanabhan refers to a six-layer maturity model of AI to lay out the path ahead for customer engagement. In increasing order of sophistication and capabilities, these layers are Data Representation Layer, Knowledge Layer, Ranking and Relevance Layer, Forecasting Layer, Recommendation Layer, and Autopilot Layer. In his opinion, most companies and systems today are operating at the Ranking and Relevance Layer.
“For example, when a customer query comes up, the bot ranks the different solution artefacts and suggests the best solution artefact. Similarly, when you have a bunch of sales leads, the lead scoring system ranks them according to their probability of conversion,” he explains.
As the AI system matures, one can expect AI-based recommendations such as “increase the ad budget by 15% to 20% for a 10% increase in customer acquisitions” or “use this workflow to optimize customer experience” and other actionable insights like these.
The pinnacle of AI capability, according to Padmanabhan, would be realized in the Autopilot Layer. As the name suggests, at this level, AI can replace some common functions performed by service agents or other team members. Rather than recommend something to be done, an AI can execute it as well.
Not that AI will possibly replace humans fully—nor is that the direction taken by companies or recommended by experts. “Today, we don’t say that we are going to completely replace human labour but say there are a lot of repetitive tasks that are involved in the support workflow or the sales workflow or the customer success workflow which can be automated. So the agents’ time can be better spent by using AI,” says Padmanabhan.
Keeping the human element in customer engagement while still using AI is “actually a question of service design,” says Manusama. What customers want are four things in how they are served: effortless, quick, convenient, and seamless across different channels. “Many companies are discovering that they can do this through self-service. However, for more complex situations, having the human touch will often be more relevant or appropriate. Basically, companies need to answer this question: Where is the business value getting generated for my customers?” he adds.
Another trend he sees is customer service vendors consolidating their solutions into engagement clouds. “Silos that existed previously are getting broken down,” he observes.
Wang’s bet is on a future built around “ambient experiences”. What we have to ask ourselves, he says, is this: When do we automate, when do we augment with humans, and when is something a pure human interaction.
The role of engagement clouds or customer engagement platforms assumes greater significance in this context. “We need common data models, great integration, and very good journey orchestration. You can do it in platforms or you can do it with really good tooling. I’m betting that the platforms will do 80% of the work and the tooling will carry the other 20%,” says Wang.
Whichever way organizations tilt, AI is likely to play a greater role in a multi-touch, multi-experience world. Now, depending on how they are able to lend a helping hand—through automation with a smile or by being pesky or ‘unintelligent’—customers will choose to give them a thumbs up or thumbs down.