Building context-aware customer service that works

For decades, the goal for brands has been to find ways to understand and predict what their customers might want next. A wide range of techniques have emerged with the intention of getting closer to consumer attitudes, wants and needs. Greater digitalization has given rise to the attitude that if brands just have enough data on their customers and their preferences, the answer will become clear.

Those in CX are already familiar with the problems that emerge in putting this data to work and – utilizing it at the right time. Businesses have more customer data than ever before, but it means channel disconnects are increasingly difficult to justify. 

However, the problem persists. Chatbots provide one example. Primarily introduced to meet the customer's need for efficiency and speed in service, many chatbots were optimized for ease of use rather than memory; prioritizing efficient responses over maintaining the context that makes interactions feel personal and seamless. Instead of feeling recognized – and their time respected – these experiences have the downside of making customers feel like they're starting from scratch each time.

It's one of the most frustrating aspects of interacting with brands; creating unnecessary friction and making customers feel like they're being given the run around rather than the quick, simple answers they're seeking. With more and more experiences now starting with a conversational interface, customers are more easily frustrated by bad experiences. 

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Context-aware automation, customer trust and excellent service  

This represents a major failure in a world where customers expect to pick up where they left off, every time, across whatever communication channel they're using. A chatbot should be able to use context-aware routing, to connect customers to the right person to help, or the right self-service tool, depending on the individual and their query.

Advances in the technology powering these chatbots are making this increasingly possible. And there are many brands today using context-aware bots to assist journeys outside of service, such as sales and product discovery.

When organizations demonstrate that they remember previous interactions and understand nuance, every interaction feels more attentive, trustworthy and competent. In an attention and information-saturated world – where according to Twilio research, the average Brit is sitting on more than 1,000 unread emails – competitive advantage will come from being the most considered, not the loudest. 

Consumers want to be heard and understood, and in service, get their problems solved quickly. 

How can organizations use customer data for automation that builds trust? 

In combination with harnessing AI's ability to memorize, brands can do this by using automation to solve issues proactively. Tracking customer behavior creates opportunities to trigger helpful notifications, reminders, or status updates, keeping customers in the loop, reducing deflection rates, and preventing support bottlenecks from starting in the first place.

Here are some examples from across industries: 

• If you can send a follow-up email with a 'how-to' guide right after a customer buys a complex product, for example, you instantly reduce frustration.
• Proactive messages about service or delivery status, before a customer even thinks to check, builds immediate trust.
• A travel provider could alert passengers to disruption before they leave for the airport.
• A bank could proactively notify customers of unusual account activity before they spot it themselves. 

These small, timely interventions save customers time while reinforcing confidence that the brand is looking out for them, rather than simply reacting when something goes wrong.

How can my brand utilize data to improve experiences over time?

The next step is then using that data to improve experiences over time. By monitoring how customers navigate touchpoints and interact, brands can see response rates, examine the drop-off points where people get stuck or lose interest, or identify if there are any patterns to queries. Refining the messaging and timings in response lets brands get themselves even closer to their customers. 

Beyond improving individual interactions, organizations can also use customer insights to identify broader trends. These could include: 

• Are customers repeatedly asking the same questions?
• Are they abandoning a journey at the same stage?
• Do certain issues consistently require escalation to a human agent?
• Understanding these patterns lets them simplify journeys, improve self-service experiences and remove friction before it affects more customers.

It's equally important to measure whether personalization efforts are delivering meaningful results. Metrics such as customer effort score, first-contact resolution, repeat contact rates and customer satisfaction can help organizations understand whether customers are finding it easier to achieve their goals. These metrics also provide a clearer picture of where AI and automation are adding value, and where a more human touch may still be needed.

Ultimately, personalization is not about using more data for its own sake, but using that data to make every interaction feel simpler, faster and more relevant.

In a saturated world, customers want to cut to the chase. Many brands have the data – it's now time to put it to work.  

Quick links

• Three ways to build a profitable CX value chain
• How it started, how it's going: 5 Ways CX spending has changed this decade
• The 10 major changes on the horizon for CX