Analytics used to speed decision-making and reduce losses at mobile operator

A mobile operator located in West Asia was struggling to make strategic decisions at a high level

Analytics used to speed decision-making and reduce losses at mobile operator

A mobile operator located in West Asia was struggling to make strategic decisions at a high level

Analytics used to speed decision-making and reduce losses at mobile operator
Home > Case Studies > Analytics used to speed decision-making and reduce losses at mobile operator

A mobile operator located in West Asia was struggling to make strategic decisions at a high level. During a period of extremely rapid growth, management had to focus on the challenge of demand for SIM cards far outstripping supply and on network rollout, to ensure coverage kept up with the growing user base. As a result, commercial strategy took a back seat. So when growth started leveling off as competition and market saturation took hold, the Iranian mobile operator shifted its focus, placing a strong emphasis on commercial operations.

The operator launched a new strategy focused on customer experience leadership and defined a set of KPIs to track progress. During execution, however, the Chief Strategy Officer and his team found that many of the KPIs were difficult to measure.

The problem stemmed from the IT systems, which remained separated in corporate silos. The systems that ran and tracked network usage were separated from those that ran and tracked billing, which in turn were separated from systems used to support and track sales and marketing. Moreover, the primary channel through which customers made suggestions and registered complaints, the call center, ran its own systems.

Adding to the complexity of the situation, most of the user data captured in these systems did not have a uniform customer ID. Also, quality control processes for entering and checking data let through too many errors, making it extremely difficult to create a uniform definition and view of target customer segments across all customer touchpoints.

The challenge: too many walls between divisions blocked the view of customer

The net impact prevented the collection and aggregation of data needed for a holistic understanding of customer behavior by segment or at an individual level. As a result, the board and the executive committee spent far too many hours discussing how best to steer the company. Often when the debates were said and done, “gut feeling” rather than concrete data was used to determine when and where to make the next move.

Company leaders knew they were missing things. The organization lacked data-driven decision making. They understood they needed a better view of the customers, one that told them the connection between popular services, when and how often they were used, and what customers liked and disliked about those services. They needed to understand how high-use customer differed from low-use customers, the degree to which different pricing structures impacted usage behavior in aggregate and at the individual customer level, and the effectiveness of sales and marketing campaigns by user segment.

Corporate leaders also knew that frontline workers needed to understand individual customers. Sales teams and call-center representatives must have information that lets them assist or upsell to customers that contact the operator.

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