Using Analytics to Enhance Customer Experience

By Grad Zimunya

Many companies globally and locally are putting customer experience (CX) at the centre of their strategy, with the goal of providing products and services that satisfy customers at every touch point. Great customer experience, that is delivering what the customer wants and experts when they want it can be a source of a great advantage in a crowded multichannel economy.

In the digital economy, defining customer experience as a combination of buyer persona attributes, market segment data and customer purchasing histories is no longer enough. Business now needs a definition that provides a 360-degree view of your customer’s journey covering every interaction with a brand.

To get such a 360 view of CX marketing leaders invest in customer journey mapping taking note of the key touch points. A useful model for understanding the stages of the customer lifecycle was released by Forrester.

The first three stages – Discover, Explore, and Buy – correspond to the sales funnel and explain the element of the customer’s journey that relates to it. Sales and marketing are concentrating their efforts on the front end of the customer journey in order to increase top-line income.

The following three stages – Utilize, Ask, and Engage – explain the customer’s post-sales journey, including how they use products, seek support, and engage with the company’s brand.

With such a model the idea is to immerse marketers in a specific customer experience and direct their attention to each stage of the customer journey. Attention at each of the stages of the customer experience is enhanced when you break each stage into the customer touch points.

Focusing on customer touch points help the marketers manage the customer journey and thereby the customer experience better. Looking at the discover stage in the pre-sale stage, channels like social media and blogs are very important to check the initial customer sentiment when you launch a product. In the post-sale stage feedback from social media, email and other platforms will also help.

Marketing Analytics can therefore help marketers in giving insights on the customer behaviour and feedback on how the customer is relating with your brand.

The first thing that the analyst will need to look at is the data collection. To have sound analytics insights you need to make sure you collect the requisite data to allow for the analysis. Some retail outlets offer loyalty cards which is a way of making sure that transactional data will be tied to customers and this can then assist if you are going to have detailed customer analytics. Others tie transactional data to mobile numbers for the same reason.

In the advent of Big Data where data is being generated online every minute by the interaction between your customers and the digital channels, online platforms like Social Media Analytics tools, web analytics tools also become a very important source of data.

The Donald Trump campaign of 2016 where user data from Facebook was used to target voters with personalised adverts is a very clear example of how data customer data is becoming very important for business.

The collected data can be used for two main purposes.

1. Analysis of the customers experience to see how you are performing. This is more of a hindsight approach which help to see where you need to improve. Sentiment Analysis, Churn analysis are some of the insights that will give a picture of the customer experience.   
2. Customer Experience Analytics which look more into predicting and shaping the future of your customer experience. By applying past data and some statistical algorithms you can predict the future of the customer experience and thereby make adjustments to design a better customer experience.

 Customer experience analytics is very important in designing your customer experience and this will work well with other aspects of Marketing analytics which include:  

• Digital analytics such as optimizing email campaigns, testing content, and analysing digital pathways to optimize website use and experience.
• Customer analytics including lifetime value and loyalty calculations, response and purchase propensity modelling, and micro segmentation.
• Marketing analytics such as demand forecasting, marketing attribution models, market mix modelling, and media budget optimization.
• Sales analytics including pricing elasticity modelling, assortment planning, and sales territory design.
• Consumer analytics including surveys/questionnaires, customer experience research, and customer satisfaction/advocacy modelling.

The idea of hindsight, insight and foresight that is encompassed in analytics can be a very vital tool to enhance customer experience in this data driven environment.

Zimunya is a digital marketing and data analytics trainer , lecturer anf consultant with a strong interest in marketing and customer analytics , digital strategy and digital transformation . He runs a digital agency and lectures at a local university ,a college and is part of the MAZ DIGITAL MARKETING CERTIFICATE FACILITATORS. He holds a Msc in Big Data Analytics . [email protected]/[email protected]