When a telecommunications company wanted to shift more services online

Telecommunications Case Study
Customer Analytics: Customer Segmentation

Abstract

Our client, a leading telecommunication provider in the Asia Pacific region, approached Deloitte as part of a major transformation within the organization. The company wanted to make better use of its digital channel to connect and serve its customers, but at the same time do this more cost-effectively.

We were asked to analyze the channels of communication and interaction today, distinguishing the type of customer using each channel and the cost impact by channel. We examined the behaviors of each customer by combining disparate data sources on their product holdings, their average revenue, their tenure, and their likely risk of churn. We also examined the nature of the queries, complaints, and feedback interactions these customers have with the organization, their preferred channels for doing so, and the cost impact.

This would allow us to move beyond a transaction-based view to reach a deep understanding of the behavioral needs of our client’s customers. This will allow us to put together a far more granular and measurable adoption plan to help the client migrate these customers online and realize rapid time to value for the organization.

The challenge

Our client knew that key to driving online adoption was understanding and appealing to the behavioral needs of its customers.

We worked with the client to develop this customer-centric view, working as a collective team to bring together for the first time otherwise disparate customer data sources and combine them to create a picture of the customer.

The key challenge was to understand the varying behaviors and needs of these customers. We needed to recognize who was performing what transactions, what channel they were using, and why they were doing it, in order to gain the diversity of data needed to identify customer behavioral patterns.

The areas we needed to concentrate on were: Is the customer calling to query a bill? Query their account? Are they making a complaint? Making an enquiry about a product they already have? Are they enquiring about a product they want to purchase? Or are they just making a general enquiry? We needed to understand how customers were seeking to interact with the company, which of these interactions could be supported online and, from this, work toward an adoption strategy to migrate those customers from more expensive channels to online.

How we helped

The company was traditionally looking at each transaction and trying to migrate the transaction online. We showed them that instead of this, they needed to understand everything about the customer and the transaction. They needed to take into account the customer, how valuable they are, how long they have been with them, what is their typical demographic, what channel they were using for transactions, and how often.

To begin with we gathered all the data, including; who they were – demographic information on age, gender, profession, education, nationality, and lifestage; their customer profile – products held, average revenue, usage, and spend patterns, tenure; how they engaged – phone calls and IVR, online, visits to the retail center, and to third party dealers; and why they engaged – product and general enquiries, billing and account queries, faults and service queries, and complaints. We entered this data into our environment to model and structure a complete behavioral view of the customer.

We used a clustering technique and artificial intelligence to distinguish 16 clusters of customer behavior.
Relevant features and attributes from these clusters were used to inform the client on how heavily customers were using the online channel, what other channels they were using, and the main reasons for the interactions. We were able to prioritize the high, moderate, and low adopters as an immediate source of focus and momentum for our client.

What made this project more impressive was:

• We had a longer history of data than the client would usually look at. We were dealing with more than eight million customers, and brought a richness of data not seen before.
• We pooled together all the views around the customer into one environment. This had never been done before.
• Deloitte Analytics, Corporate Finance and Consulting came together as one single team. Corporate Finance doing the transaction and financial modeling, Data Analytics performing the complex customer clustering and behavioral analysis, and Consulting providing the strategic insight and thinking into an adoption strategy and action plan to migrate customers online.

As this project is ongoing, the analysis we have completed will be used to show the client the adoption techniques that should be applied to migrate customers online.

Solution

The benefit is our ability to give the client a more granular approach which allows them to take a more targeted approach to ‘who’ they seek to adopt online, ‘how’ to best appeal to encourage adoption, and ultimately achieve more rapid ‘time-to-value’ in both cost savings and improved customer retention.

The next stage of the project is to construct a detailed adoption plan – this will include the clusters of customers, the proposition we are putting to the client with regards to migrating these customers online, and what the outcome will be. At this stage our performance will be measured and we will begin to see how effective we have been in helping the company with their adoption process.

We are proposing another piece of work related to customer experience with plan and product design. As we have approximately 60 percent of the data already acquired from the original project it makes good business sense to take this forward.