The client’s question

Our client, Frank, is Director of Consumer Insights at a large telecom provider, which offers advanced video, voice and internet services to many households. The market is developing fast, with rapid changes in technology and the rise of new players to the field. As an incumbent operator, Frank’s company needed to keep up and try to outrun competitors by offering new services and appealing propositions to their customers.

To understand how to optimize his company’s  portfolio, Frank, turned to us to conduct consumer research. The research needed to tell the operator:

  1. What is the optimal configuration of the portfolio?
  2. What is the added value of various additional services?
  3. How many propositions should the portfolio contain?
  4. What is the optimal pricing of each proposition?

Our approach

To answer these questions, we needed to understand consumer preferences and, therefore, we designed an online survey with a Choice-Based Conjoint (CBC) exercise. The propositions shown in the CBC choice tasks were described by means of predefined attributes and levels that together formed holistic propositions.  In the CBC exercise, we made sure to confront consumers with scenarios that were as realistic as possible; both in terms of the actual propositions consumers could choose from as well as the look and feel of the CBC choice task, in order to replicate the actual choice consumers have to make in real life. We added a dual-response none to the choice task to accurately measure the percentage of consumers that would actually switch to a new subscription.

The results

At the end of the study we provided Frank with a full recommendation on how to optimize the portfolio, by digging deep into consumer preferences. We ran a needs-based segmentation that allowed us to understand how to build a portfolio that would address the needs of various market segments. By using an optimization algorithm, we ran thousands and thousands of scenarios in which the number of propositions and its specifications varied each time, looking for the optimal scenario in which the telecom operator would increase shares, revenues, and profits.

Besides the specifications of the propositions, we also looked at price, leading to a full recommendation on how many propositions should be offered, how they should be defined, and how they should be priced.

During a workshop at the operator’s offices, we further interpreted the results and brought Frank and his team up to speed on how to use our Excel-based market simulation tool, which allowed the team to run many more scenarios.

Frank and his team loved the recommendations and the simulation tool. After the study, the telecom operator redesigned their portfolio, which led to a rise in new subscribers and increasing revenues per user.