Big Data and Customer Experience

Big Data is the fuel that powers business intelligence and business intelligence is the fuel that powers productive decision making. The end goal is to be able to streamline operational processes that eventually helps lower company costs and easily/cheaply acquire/attract new clients as well as be able to improve customer experience. Improving customer experience helps the company to retain most of their customers which eventually leads to more sales, revenues and profits.

This is why we at Insense Data Technologies are inspired every day to help our customers bridge that gap between the data they have and their decision-making processes. We know and understand the power of data and through a data-driven decision intelligence system, we are transforming the way people run their businesses and one such field is in making decisions that can help them improve customer experience.

A simple look at research papers and insights will reveal statistics that every business needs to remember:

  1. According to IMRG Report, the probability of selling to an existing customer is 60–70% while the probability of selling to a new prospect is 5–20%.

The statistics on costs associated with losing a customer are also disheartening:

  1. As Allan E. Webber said in Forrester Research of Feb 19, 2008 “B2B Customer Experience Priorities In An Economic Downturn: Key Customer Usability Initiatives In A Soft Economy”, acquiring new customers can cost five times more than satisfying and retaining current customers.

We have concentrated above on the costs of losing a customer as well as the benefits of retaining your customers but just how bad is the effect of poor customer service?

  1. An attitude of indifference towards your customers can cost you 68% of them through defections ( Data source: American Society for Quality)

The bottom line is simple: the driving force to the success of businesses like Amazon and Walmart is customer service. In his book — “The Everything Store: Jeff Bezos and The Age of Amazon” — Brad Stone writes in one of the pages:

Look, you should wake up worried, terrified every morning,” Bezos told his employees. “But don’t be worried about our competitors because they’re never going to send us any money anyway. Let’s be worried about our customers and stay heads-down focused.

It shows us just how much obsessed with great customer service Jeff Bezos has always been and we can see how it has paid off.

How can companies improve customer experience? In the digital age, customer service goes beyond just not being rude to a customer, it goes to understanding the customer as a person and as user of your products/service and offering tailored and personalized experience to the customer. At Insense Data Technologies, for instance, we have developed a set of AI tools that fully rely on the data trail left by the customers to help companies better understand them. We believe, and correctly do so, that the root to offering great customer experience and service is understanding their behavior and there is no better way to do that than by utilizing the power of big data and artificial intelligence.

The root to offering a great customer experience is understanding customer behavior.

The way you treat customers who are at risk of churn, loyal customers, potential loyalists and promising customers — for instance — is different. Therefore, the ability to understand whether the customer is a loyalist or at risk of churn is of paramount importance when making decisions on how to treat the customer. There are customers you reward, there are customers you educate to create brand awareness, there are customers you up-sell, there are customers you offer free trial and there are customers you simply ignore. There is no one-fits-all strategy that will appease all your customers. Even when it comes to the products/services you push to the customers, understanding the customer’s history will help you understand which products to up-sell or cross-sell to them and at what time thereby not only reducing wastages/high costs but also improving on the response rates and returns on investment.

Personalization goes further to being able to predict what the customer might like and when in the future. If you are a mobile app, the ability to understand what the user wants from your app and the various pages they pass through to get there is of paramount importance; the main aim being to use the insights derived to redesign the app such that the user can get the service/product through the shortest path possible. If it is a physical store, for instance, how easily do your customers find the products they need without needing help from the aisle attendants? How fast do your customers take to complete their shopping? These are insights, that through tools like basket analysis offered by Insense Data Technologies, can help physical stores to optimize the layout and display of their products such that customers can easily find what they want and spend the least time possible while doing their shopping. Furthermore, understanding the buying patterns of customers in a product-based business can help the business make effective restocking decisions through data-driven demand planning process — such processes would help ensure that customers always get the products they want at all times thus helping retain them while also lowering the company’s costs of stock keeping by only having what is necessary in the correct quantities.

Differentiated Customer Experience is the ability to apply different strategies of customer engagements and experience to each customer based on their preferences and behaviours with the aim of achieving absolute personalization.

Customer experience goes beyond just communicating to your customers but to telling if you need to communicate to them in the first place. Some customers do not like to be bothered by different types of treatments: be it simple surveys, promotional messages, or rewards; they just want to be left alone otherwise they will become aggressive and switch brands. As part of IDT Lab research on a retailer, 2% of their customers responded negatively (ie were lost) due to unsolicited communications. As our earlier stats showed, that’s an equivalence of 10% increase in company costs. Using data to understand what customers to offer what type of special treatments to not only goes a long way in improving personalization and creating a great customer experience and service but also goes a great way to help the company get higher returns on investment from their customer engagement practices. As we have shown before, Treatment Analysis, one of the tools we have at Insense Data Technologies, can help a company improve their campaign response rates by 48% while increasing their net incremental revenue from the campaigns by a factor of more than five.

From Insights to Actions: with big data, companies can now know if they really need to take an action and if so, what action they should take, how they should take the action, when they should take the action and who the beneficiary of the action should be.

Big Data and Artificial Intelligence does not change what we do to have proper customer engagement and customer service; it helps us to understand how we should do it. It helps us to segment our customers and understand how to treat each of them differently through a process we call differentiated customer engagement to achieve personalization. The end game is to help us improve on our customer retention which eventually saves as costs as well as earns us more money. The merrier the cow, the more the milk.

Get the Medium app
