Recommended for You!
All across popular culture in the digital world, more and more journeys are being shaped for consumers in real time.
Language like, “Recommended for you,” shows up online throughout our day, from news feeds on our favorite social media sites, to popular entertainment channels like Netflix, to shopping sites like Amazon, and the list goes on and on. Consumers are living in the era of advice, which is served to them in the form of recommendations, product offers, and guidance that influences customers to make choices and take actions that they didn’t think about or know about just seconds prior. When the recommendations are useful and relevant, the experience is enjoyable, personal, and intelligent feeling. And thanks to artificial intelligence and machine learning, the advice that’s being served up for consumers is getting better (meaning more accurately matched to individual needs and preferences) all the time.
Netflix knows this, and wisely tells customers, “The more you use Netflix, the more relevant your suggested content will be.” Most people who use the entertainment service would attest that this is generally true.
As a result, consumers have become happily accustomed to being intelligently guided through their digital experiences more and more. This has big implications when these same consumers enter your digital channels for support.
Not long ago, self-service customer support was all about helping customers move toward the resolution of their task as efficiently as possible. Technology’s primary purpose was to expedite resolutions to problems. When done right, it offered customer satisfaction benefits to the customer along with the cost saving benefits of automation to the company.
Today, the era of advice requires that companies rely on customer engagement technology that is smarter and more capable of offering the right recommendations and advice. Core to this capability is that the engagement platform must be able to understand the intent behind every customer interaction, so it can offer intent-driven engagement from thereon in.
Here’s how intent-driven engagement is different from simple channel-centric engagement:
Channel-centric engagement is the commonplace practice of making a variety of channels available for customer interaction. The focus is on optimizing the performance within the channels, but the channels themselves are fragmented and disconnected, meaning they don’t carry forward the context of a customer’s interactions into other channels.
Channel-centric engagement is more of a passive or reactive approach to customer interaction, and not sufficient for the guided journeys customers want served up for them in this era of advice.
Intent-driven engagement, on the other hand, is all about anticipating a customer’s intent during their journey, and then using data to provide the customer with intelligently-selected next steps that are suited to a particular customer, on their particular journey.
For example, if it is known (i.e. if you have a lot of data) that customers who take a certain action also take a certain second action, you can proactively recommend that second action before the customer has to ask or search for it. This is why your central customer engagement platform needs to be able to decipher and understand customer intent across channels.
In addition, our own experiences with clients using 7.ai technology to provide intelligently-offered guidance in real time to their customers shows that this advice is not only enthusiastically received, but can also lead to purchase activity further along the journey. Here are just two examples:
Customers are giving you the green light to jump into their journeys and advise them on what to do next. So how do you get started?
Delivering tailored recommendations in digital customer support and sales requires the use of first-party data. Unlike third-party data from cookies and tracking pixels, first-party data is directly tied to an individual because it’s the individual who is providing it. Effective first-party data includes three types of information:
This data tends to answer the “who” question to shape the persona or profile of a user or customer. It could include age, gender, location, communication preferences, and frequency, as well as other attributes you may have collected about the consumer.
This information is typically collected from your website or other contact forms, and it often exists in your customer relationship management system.
Data from interactions helps build context because it’s information based on both a person’s transactional history as well as the person’s digital footprint. Interaction data conveys information about what people do, where they go, what they engage with, what they buy, and more.
This data is used to provide richer context and better intent predictions. A company may want to vary who it targets, and how, with a personalized message based on specific relationship data. For example, telecom companies may offer specific plan upgrades to users with high data usage.
That’s a vast reservoir of data that can be drawn upon to augment your company’s first-party customer data, so that you can predict customer intent more accurately to enable smarter, faster outcomes for your customers.
Delivering the kind of personalized and guided experiences that customers want today requires that you have a customer engagement platform that is capable of predicting customer intent, analyzing big data from multiple sources in real time, and leveraging artificial intelligence and machine learning for continued refinement of delivering the right advice to the right customer at the right time. Let us help. Take advantage of these capabilities from 7.ai: