A win-win situation for everyone involved in the process of branch optimization.
In the past, there was very little one could do when planning to open a new branch. Look at a map, walk around a bit or ask around. At the end of the day, a little more than just a gut feeling; which is hardly a reliable prediction. Sure, most branches were profitable (I surmise), but I believe a lot of them could have been more profitable if they were located elsewhere.
In order to gain useful data for opening a new store location or optimizing their branch chain, some companies went as far as hiring assistants for counting people in the vicinity of their stores. Expensive, labor & time-intensive but not particularly reliable.
None of these practices are necessary, anymore. Managers can rely on real, quantified and reliable data and insights which come from mobile network operators (MNOs). Telecommunication companies have recently discovered the potential of their data and started to, en masse, monetize their anonymized & aggregated data. A win-win situation.
They sit on reserves full of valuable data. Because almost everyone has a phone in their pocket at all times (except when they hold it in their hands, that is) the location information about the users gets recorded in the network. This data is useful for a plethora of internal cases, but it is particularly valuable for external usage and big data monetization. The data they store is of high quality already. Yet, with the rise of 5G technologies, this is only bound to improve (side note: 5G technology utilizes far smaller coverage per antenna due to higher data throughput and higher frequency millimeter waves, which have limited range). All the while the MNOs’ average revenue per user, or ARPU, is steadily decreasing. In terms of external monetization, there is a lot of big data can do. See our blog on smart cities or the use of data for development.
Quality is nothing without privacy and security, we won’t go into this topic now – read our upcoming blog focused on this topic.
Use-case: Branch network optimization
One of the most notable and valuable use-case for MNO big data monetization is to use them for retail branch optimization. Opening a new store or optimizing an already existing chain was never this easy.
Opening new stores and locating blind-spots
Ever since the rise of agrarian capitalism having a store-front was a must for every self-respecting early modern business owner. People who want to buy things need to know where to find them. Think urbanization and the growth of the middle class over the last couple of centuries and you get a decent idea about the importance of having a store-front.
Opening a new store is no less important in the digital age. However, now managers can rely on hard data and choose the best location based on the presence of a specific target group or work location of people who travel frequently. See where people with specific interests gather and open a store there.
This data also allows clients to visualize the location of a target group and its movement over time. It also allows to locate blind-spots and choose the best of several possible locations. It would be pointless, and not to say expensive, to open a new store at a location with very few people during the daytime or have the store close in the evening just when the potential customers flock to the area.
There is, of course, another way. Find a blind-spot with enough people around and not a single competitor in sight. This can be easily done with the right kind of data and the right partner.
What has been done: Case study of a bank chain using Market Locator to optimize branch locations in Slovakia
To have numerous accessible branches is particularly important for banks. People need to withdraw or deposit money, sign contracts and apply for mortgages. Having an extensive chain can, however, be quite expensive. For that very reason, one of our partners approached us to compare their store locations and analyze their individual potential and profitability.
We created a methodology to compare individual locations and to perform so-called branch duels. Our data-science team looked at the performance of all relevant banks in a given city and advised our partner bank on the utilization of different branches. They looked at different indicators, including, for instance:
- Visibility indicator – a total number of “views” / visitors to the area and a number of “unique views”. We classified our findings into subcategories within subsegments such as young people/pensioners, etc.
- Service potential – look at the sleeping and working locations and define the catchment branch. One branch which can potentially provide service for people who normally use a different branch.
Market Locator Platform for retail customers
Many elements of the analysis can be done using the Market Locator platform developed by Instarea. This tested solution provides actionable population and location insights based on a dataset from a MNO or a bank.
- Specific information about the target group in a planned/existing location. This can be over an average day or in real-time on a heat map or on a graph.
- Option to visualize the relevant data, for instance, the competition stores or any proprietary data set with useful information.
- Visualization of a specific demographic or target group on a heat-map in real-time
- An integrated platform connecting all relevant data together with a unique marketing tool in one place
Come to Instarea for a tested and trusted solution
Instarea is quickly growing big data monetization company with substantial experience in monetizing big data of MNOs on 3 continents. It has applied its big data monetization platform for both targeted communication as well as population and location analytics. Our experienced data science team can deliver a custom-made reports and assist telcos with starting a data monetization initiative.
If you are interested in monetizing your big data or using our Platform for a branch optimization you can always reach us on email@example.com.