Case studies

When a telecommunications company wanted to shift more services online

Deloitte Analytics helped them find a deeper connection with their customers

The client, a leading telecommunication provider in the Asia Pacific region, was undertaking 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.

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

This would allow the team to move beyond a transaction-based view to reach a deep understanding of the behavioral needs of the client’s customers and 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.

Abstract

The client knew that key to driving online adoption was understanding and appealing to the behavioral needs of its customers. The team worked with the client to develop this customer-centric view, working as a collective 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- 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 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? The team 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.

The challenge

The company was traditionally looking at each transaction and trying to migrate that particular type of transaction online. This project 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 all the data was gathered, 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. The data was entered into an environment to model and structure a complete behavioral view of the customer.

Using a clustering technique and artificial intelligence 16 clusters of customer behavior were identified. 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. The team were able to prioritize the high, moderate, and low adopters as an immediate source of focus and momentum for the client.

While dealing with more than eight million customers, the project team were able to pool together all the views around the customer into one environment and with a longer history of data than the client would usually look at.  The client had never seen this done before.

Deloitte Analytics, Corporate Finance and Consulting came together as one single team. Corporate Finance doing the transaction and financial modeling, Deloitte 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. The analysis that was completed was used to show the client the adoption techniques that could be applied to migrate customers online.

How analytics helped

The benefit is the 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, and a proposition to migrating these customers online and what the expected 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.

The solution

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