Leading Telco Operator in Indonesia (Confidential)
The improvement, optimization, and maintenance of telephone networks represent a challenge for telecommunications companies. In this case, our client wanted to attract more users, mainly from the competition’s need for better coverage. The increase in earnings was just one consequence of all the work that went into it. Learn more about how we worked with this client.
We are seeking support to tackle the opportunity of grabbing a share from MergerCo during its integration stage.
- The overall market has been stagnant since 2018 – barely any growth.
- Competition has now shifted to customer experience, especially video and gaming user experience – as measured by OpenSignal, Telkomsel holds the lead.
- A new consumer segment is emerging – digital native – with implications on the channel, product, and O2O customer journeys.
- OG and Hutch are in the process of merging their entities in Indonesia; in the past, MergerCos have lost market share during the PMI process; this opens opportunities for us.
Tell us the particularities and needs your project needs to be able to implement a plan specially designed for you.
Project development and the final score
For this, Locatium took into account different sets of data, such as network data and coverage data, so we could know where, how and with what characteristics this client was positioned in the market. This is to detect those “confused” users (users who are now part of a merger of 2 telecommunications companies) and are in a kind of limbo and those users of competitors with low or poor signal/coverage.
To win customers through data analytics, Locatium, with its extensive solution history, has relied on data from 5 key areas:
- Footprint optimization and format innovation.
- Geo-spatial analytics.
- Store footprint optimization.
- Localized Marketing.
- Store format innovation.
For this happened we based our machine learning model algorithm on:
- Human mobility
- Telco market share.
- Advanced demographics data.
- Consumer data.
- Real estate and income dataset.
- Future development dataset.
- Satellite imagery.
- Points of interest (POIs).
- Credit card insights.
In this way, personalized marketing campaigns would be carried out for each user according to their specific needs.
To carry all this out and give the best solution and result to our client, we had to take into account the market share, as well as 5G technology, coverage, the capacity of the telephone towers, and the average price of the communication device, of so that the purchasing power of each person could be known, to make an offer.
- Calculated market share of residential population, working population and visiting population in 57,523 micro-markets all over the country.
- Identify the right store locations for 250 stores with maximum commercial potential broken down by segment and match these 250 locations with optimal store format (flagship, conventional, pop-up, experimental, etc.).
- Increased store touch point-related sales from USD 62 m to USD 93 m by opening 62 new stores (39% of the original 158 stores).