How Location Analytics Can Empower Better Decision Making For Telcos - Amit Jain

This guest post from Amit Jain, SM-Product Management and Marketing, Flytxt talks about how location analytics can support better decision making for Telcos

mobile location analytics

Editor’s note: This is a guest post from Amit Jain, Senior Manager, Product Management and Marketing, Flytxt - a software product company that specializes in Big Data Analytics enabled mobile marketing and advertising for the Telecom industry.

Location analytics is fast becoming a buzzword in the mobility industry. First, let us try to understand what it is. Location analytics integrates software and data to capture, manage, analyze and display all forms of geographically referenced information. Location analytics allows one to view, understand, analyze, interpret, and visualize data in ways that reveal relationships, patterns, and trends in the form of maps, reports, and charts.

Location analytics could lead to new business opportunities and innovations such as new pricing models, new ways to engage with customers and partners, improvement in operational efficiencies, new means of monitoring compliance and risk, and so on. In fact, a research report suggests that 80% of all data maintained by organizations around the world has location component. As per a latest report published by Berg Insights LBS research Series, “Mobile Location based analytics is going to become a € 10 billion market in next 5 years”.

Just imagine, an operator with a global presence experiencing a downward trend in revenue. They decide to down-size their network by shutting down few of their sites. Too often, taking an immediate decision on such issues is not an easy task, especially when the decision makers are not backed with right and enough information. They can integrate location data acquired through a Geographic Information System (GIS) application to their current analytical models.

This could help in getting a spatial representation of their best and the worst performing sites in each region, helping them to swiftly shut down and even re-locate few loss-making and under-utilized sites. The geographic data with location attributes, if used effectively in operational and analytical applications, workflows, and decision-making, can potentially lead to revenue enhancement, cost reduction, productivity improvement and overall customer satisfaction.

Amit Jain FlytxtFor CMOs, location analytics would help them come up with new innovative customer engagement plans, thus allowing them to communicate right offers, to the right customers at the right time. For example, if the Communication Service Provider (CSP) already has the transaction history of the subscriber and has used analytics to identify their price affinity and channel affinity, this insight can be combined with the subscriber’s location coordinates to send highly personalized offer whenever they are close to the CSPs or any other brands retail stores.

For CTOs/CIOs, location analytics would help them to identify the best location for their new retail stores, new cell sites or even service sites.  While this technology is still under-utilized, fusing location analytics with current predictive, prescriptive and exploratory analytical models can help CSPs to answer questions which continuously daunt them, such as:

  • What should be my subscriber engagement strategy based on their location? For example, should a marketer run any retention campaign for an inactive subscriber who is in international roaming?
  • Are there location based patterns related to subscribers’ purchase decisions? For example, high incoming international call in tier-1 cities vs. rural locations can mean different things.
  • Where can I find potential subscribers similar to existing subscribers?
  • Where have been our marketing efforts the most or least successful and why?
  • Do we have subscribers at the risk of churning, due to poor services, poor network connectivity, no presence of retailers and distributors, and what impact might that have on the business?

There are a lot of un-answered questions which can be easily solved if the right combination of location analytics and technology is used. The answers to these questions lies within the data available with the CSPs and combining business with location analytics can help CSPs to unlock lot of hidden insights. How exactly does the combination of location analytics with multi-dimensional analytics benefit CSPs business? Here are few examples of the powerful synergies between the two technologies:

Sales and Distribution Management: - The CMO of a large CSP wanted to improve its sales and distribution channel for more acquisition and effective marketing. When the CMO was shown the tables and charts with each distributor and retailer level spatial or geographic spread, he immediately could identify potential market area. The CMO’s organization now uses this enriched data for better planning, actual retailer and distribution channel implementation and even to evaluate the effectiveness of various local retailer and distributor level marketing efforts with better segmentation and marketing programs. The fusion of location analytics with available business data allowed the CMO to take better decisions as well as the time-to-action reduced drastically.

Network Planning: - Location is everything for the CIOs/CTOs of CSPs, especially when they are planning new cell site implementation or acquisition. Every major decision, which is the right location, total population coverage and geographic distribution is centered on location/geographic factors.  CSPs are increasingly combining location analytics with their business data for better decisioning and planning. They are now able to get answers to various issues such as where are the operational costs very high, which is the most profitable site and why etc.

Combining location analytics with available business data allows the decision makers to visualize the influence of location on various parameters such as marketing activities, sales and distribution management, network planning etc. the result is improved and effective data-driven decision making with better insights.

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