Descriptive analytics was used to identify the root cause of underperformance that stemmed from a tactical misstep in a promotion offer at a major Middle Eastern telecommunications operator.
We all know what they teach in business school: Sales targets are essential parts of any for-profit organization. Product and service innovation are combined with clever marketing to capture new customers and create long-lasting revenue streams and growth.
But it often fails to work that way.
Basically, the law of unintended consequences is alive and well in the business world. Intelligent, experienced, and creative decision makers often take action that ends up backfiring. Fortunately, predictive analytics is making it easier to spot missteps, not just after the fact, but — increasingly — before action is taken.
As an example, consider how descriptive analytics was used to identify the root cause of underperformance that stemmed from a tactical misstep in a promotion offer at a major Persian Gulf-based telecommunications operator.
The mystery: what was causing underperformance?
During the preparation of a quarterly corporate strategy review, the Telecom operator’s Office of Strategy Management (OSM) identified significant underperformance in objectives related to stimulating revenue from the mass-market segment and winning a higher share of wallet from high-value customers. Basically, revenue per user was lower than it should have been.
The first round of root-cause analysis did not identify internal process & capabilities development objectives as the root cause. So the OSM team drew on its robust customer data mart, which merged customer data from several IT systems into an analytical tool. OSM strategy analysts asked OSM data analysts to perform an analysis to understand what aspect of customer behaviour was correlated with the named underperformances.
The data analysts identified a drop in on-network voice revenue as the key reason for the drop in the average revenue per user (ARPU). While the on-network used minutes for the overall customer base had increased dramatically during the quarter, the average on-network price per minute had dropped even more dramatically, causing the drop in the on-network voice revenues. Isolating the customers with a drop in the on-network price per minute showed that the majority of the used minutes growth came from these customers.
The data analysts examined the rest of the customer base. They discovered that a large pool of the customers who did not have a drop in on-network price per minute saw a relative decline in their outgoing minutes of usage, but no drop in their price per minute. Interestingly, they also had a relative increase in their incoming minutes.
The solution that created the problem
To understand the reason behind this behavioural change, the OSM data analysts examined the tariff packages, seasonal promotions, and campaigns adoption of the two types of customers. They found that the customers who experienced a dramatic increase in on-network minutes yet a decrease in price per minute had almost all subscribed to an aggressive promotional offer launched at the beginning of the quarter. The data analysts then discovered that the second pool of customers who had a drop in their outgoing minutes of usage but no drop in their price per minute were not subscribers to this aggressive promotion. Rather, they were receiving incoming calls from the group of customers who had subscribed to the aggressive promotion.
The aggressive promotion was in fact an offer whereby customers were provided unlimited on-network calling in return for a near doubling of their monthly spend. The OSM’s strategy analysts determined that the aggressive promotion, a knee-jerk tactical reaction to a competitor’s offer, was the source of the underperformance. Customers who adopted the offer were in turn increasing their outgoing on-network usage minutes, calling other customers of the operator. The other customers, who did not subscribe to the aggressive promotion, were reducing their outgoing minutes since they were getting calls from the promotion-subscribers.
Making the connection and returning to growth
The overall impact was that the promotion achieved its objective of increasing the spending of the target segments. But it had a side effect: Other subscribers reduced outgoing calls, as they were receiving incoming calls from the promotion-subscribed customers. The aggregate result across both customer segments was an overall drop in revenue.
Reporting this finding in the quarterly review to the management team, the OSM was able to bring to light the unintended consequences of the Commercial Unit’s tactical response to the competitor’s offer. The management team then asked the Commercial Unit to find a solution to fix the problem and so reduce the negative impact.
While it is feasible that manual analysis could have identified the reason for the net drop in revenue, it is unlikely. And even if it did, it would have taken much longer than when using advanced analytics, thereby resulting in greater revenue loss and opportunity costs. Moreover, the telco can use the hard data of this cause-effect relationship to inform predictive models for analysing future special offers.
Certainly, getting analytics in place at the strategic level is challenging and usually requires a systematic framework to get things moving (something I addressed in a prior post. But the example above stands as a significant reminder of the power of analytics to augment the role of the OSM in monitoring the strategy execution. It is a reminder that all firms should heed.
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