Inovo CEO Wynand Smit says detailed analytics, which is only possible with good data, can drive efficiencies throughout the business.
Optimising customer experience (CX) by leveraging emerging and new technology results in a host of positive spin-offs for businesses but it must be approached in a methodical and strategic manner. Every intervention or technology application relies on how a business collects and associates data with its customers.
Ultimately, customer service, and by extension, the customer’s experience, lies in the expectations that are set and how the business is measured against those expectations. There are a number of ways to make this more efficient and proactive, but they rely on getting the basics right, in other words, how the data that already exists in the business is used.
Most businesses have data already. This comes in the form of in-bound phone calls, emails, web forms or even walk-ins to physical shops and using store cards. The first port of call lies in being able to use this data to identify the customer efficiently.
A challenge we often encounter is that a business does have data, but it is primarily focused on information about the customer’s business relationship with the business – products and services.
To improve CX, the associations need to move beyond product and towards preference. You absolutely need to get to a point where you can store a customer’s preference. For example, imagine the system picks up that the customer has spoken to the business before and interacted with a particular call centre agent and rated them 10 out of 10. That is valuable information that stores this customer’s preferences. By building up a repository of this type of information, the business is laying the foundation to start using predictive models driven by artificial intelligence (AI), for example.
However, if a business cannot efficiently identify a customer and associate their preferences, then it is wasting its time thinking about emerging technology. You cannot run a predictive model on random variables with the expectation that it will result in the outcome you are looking for.
Start by using the data you already have, on the channels where you already have it. Once the data is in a usable form, then the business can put in place a structured plan with timelines to broaden the diversity of the data being collected.
Once a business is in a position where it has the right data and can use it, the possibilities are almost endless. One could develop use cases to make the customer experience quicker or more personalised to their tastes or even develop use cases to increase collections and increase sales.
Assuming the basics are in place, a business could run AI models to try and predict the probability of closing sales by directing customers to certain sales agents or even proactively mitigate the fallout from tickets that have not been resolved by proactively contacting customers.
The point is that existing and emerging technology provides us with powerful tools to improve a customer’s personalised journey with a business, but it depends on getting the basics right in how it identifies and associates a customer with preferences and context.
Technology and automation have made measuring and improving all contact channels far more efficient, precisely because it can consolidate a customer’s interaction history and preferences to personalise their journey while providing a single view to the business.