3 ways intelligent search and personalization future-proofs customer service
As we transition into 2023, a decade after Forrester published a report announcing that the Age of the Customer had begun, users have become so accustomed to effortless, personalized experiences that companies that have not adapted are materially punished in the marketplace.
When done right, enterprise search can play a pivotal role in enabling companies to create superior, personalized experiences for their customers and thrive in today’s customer-centric economy.
The three prevailing customer service trends in response to this market reality, as specified in Forrester’s 2022 report on customer service megatrends, are (1) future-proofing customer service stacks, (2) surfacing unstructured “dark” data to improve the customer experience, and (3) driving humanization into the contact center.
In this article I show how a unified search and relevancy transformation solution can help companies deliver on all three imperatives.
1. Future-proof customer service technology stacks
A plethora of research confirms that consumers prefer to self-support. In fact, in many cases, when a customer chooses to engage with a live agent, it’s only because they weren’t able to find what they needed online.
Today’s support solutions need to excel at delivering relevant information to users where they want, when they want it, with minimal effort. And this requires a robust artificial intelligence and machine learning (AI/ML) solution that is able to dynamically adjust to the preferences of individuals and users like them.
In the past, future-proof meant scalable. Now, to be future-proof a solution must not only be scalable, but intelligent: it should learn from transactional history (via AI and ML) and deliver increasingly better content to users based on what they are most likely to find valuable.
Customer expectations have been raised through their daily interactions with service providers like Amazon and Netflix to the point that they expect this level of personalization from everyone – and are disappointed when they don’t get it.
Companies must leverage data to “know” their customers well enough to not only meet their needs, but to anticipate them so well that users can access valuable content without having to search for it.
Surfacing the most relevant content to the user at the most opportune time is accomplished through machine learning and an AI-enabled recommendations engine. This intelligent recommendations engine must be able to evolve dynamically with users’ changing needs and preferences throughout the customer life cycle.
2. Harness unstructured data
In 2020, zero-click Google searches rose to almost 65%. That means that more than half the time, when someone enters a search query on Google, the answer to their question appears at the top of the results page and the person does not need to click on any links to see the information they seek.
With this now the level at which the user expectations bar is set, companies need to get better at delivering unstructured content in response to customer queries. Between 80% and 90% of the world’s data is in an unstructured format, according to most analyst estimates. This means it does not adhere to conventional data models and therefore cannot be stored in a relational database management system (RDBMS) or easily surfaced in search results.
If the solution your customer is looking for is buried in an unstructured format (pdf, slide presentation, or media files, to name a few), finding that information is like looking for a needle in a haystack.
The solution to this challenge is to deploy an enterprise search solution that not only indexes the high-level metadata of the unstructured content, but also is able to analyze the granular details within the files themselves so the most relevant information is surfaced immediately and consumed effortlessly.
3. Humanize the contact center
Humanizing the contact center can be looked at from two perspectives: the customer perspective and the contact center agent’s perspective. From the customer perspective, a high level of personalization must be delivered through your service delivery platforms.
When AI and ML are applied effectively, the recommendations engine that surfaces content to your users can get so good at understanding their needs and preferences that it can serve up the most relevant information immediately and effortlessly, as well as propose additional information that is helpful, even if the user didn’t search for it.
This functionality has deflected upwards of half of our clients’ tech support tickets and saved more than $1.5 million per month in support costs, in some cases.
From the support agent’s perspective, an intelligent search solution can make your technical assistance engineers much more effective and productive.
For example, an intelligent recommendations engine and unified search capability can be deployed on technical assistance consoles so relevant content is dynamically suggested as support agents type their case notes into Salesforce or other CRM, increasing the speed at which issues are resolved.
This solution can also be added to Slack, Teams or other collaboration platform so solutions are immediately surfaced when technical assistance agents create a new discussion to “swarm” or collaborate on a support case.
Similarly, your company’s CRM can be enhanced so that if the perfect answer to the question at hand does not currently exist in the knowledge base, a functionality can be created so that a new knowledge asset can be created with one click.