Hear from Kimberlee West in our on-demand webinar 'Customer service: How to use AI to anticipate, advise and improve experiences'.
Available here: https://bit.ly/2EFin5u
Consumers are accustomed to being given recommendations and guidance based on their previous behaviour. But what implications does this have for expectations around customer service?
We are living in the advice era, served to us in the form of recommendations, product offers and guidance that influences our choices and helps us take actions we didn’t consider just seconds prior.
Language like “recommended for you” shows up online throughout our day, from news feeds on our favourite social media sites, to popular entertainment channels like Netflix and shopping sites like Amazon.
When the recommendations are useful and relevant, the experience is often enjoyable, personal and intelligent. And thanks to AI 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.
The advice era comes to customer service
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.
As a result, intent-driven engagement is fast replacing channel-centric engagement. What’s the difference? With channel-centric engagement, the focus is on optimising 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 when a central customer engagement platform that can decipher and understand customer intent across channels is a must.
The benefits of good advice
Here are just a few statistics illustrating how consumers today will reward companies that take a more proactive role in guiding their experience.
- 63% of consumers say they’d think more positively of a brand if it gave them content that was more valuable, interesting or relevant.
- 60% of consumers are comfortable with retailers using their shopping interests and behaviour data to deliver relevant offers.
- 61% of consumers are loyal to brands that tailor their experiences to their needs and preferences.
At 7.ai we’ve discovered that with certain clients, ‘good advice’ pays dividends:
- A global banking firm is using AI in the virtual agent channel to deliver specific marketing offers that were tailored to the intent of their customers’ questions entered into a virtual agent. Click-through on the intent-based offers improved by up to 20x and conversion improved by up to 15x, compared to marketing offers that were not tailored to the intent of the customer question.
- A major retailer proactively offers chat support to certain customers who demonstrated likelihood to need or accept the offer to chat. By offering this tailored experience in the form of specific type of channel support, the retailer noticed an increase in average order value (AOV) of 65% on chat-assisted purchases (compared to self-serve AOV) and an increase in overall online revenues by 3.45% (across 60+ product categories supported by chat).
How to 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:
- Profile data such as identity, attributes, and preferences. 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.
- Interaction data such as behaviour, conversations, and transactions. 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.
- Relationship data such as usage, feedback, and social engagement 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 personalised message based on specific relationship data. For example, telecom companies may offer specific plan upgrades to users with high data usage.
Better data = better advice?
Ultimately, it’s all about data. Intent-driven engagement 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.
The more data you have to draw on, and the more reliable that data is, the better and more accurate you can be with your recommendations to customers.
Hear from Kimberlee West in our on-demand webinar 'Customer service: How to use AI to anticipate, advise and improve experiences'. Available here: https://bit.ly/2EFin5u