Example reflection – Learning Launch

Challenge

I am member of a commercial team for a Telecom operator. One of our subsidiaries in an Asian market  faced the following challenge about their tariff portfolio: they already had S-, M- and L-versions of a  specific tariff with a good number of customers sitting on the L-version of the product. However, within  customers of that top product there was a significant share using even much more data and voice calls  than what the package included, making them pay more than the original tariff promised.  Facing this  situation, teams were undecided how to proceed, one group wanted to maintain the status quo,  another to introduce an XL-version of the tariff which would offer (much) more value for (moderately)  more money.

Selection

We needed to trial and chose the Learning Launch tool to tackle the issue for various reasons: we felt we  had an idea how to solve the dilemma but there was a lot of money involved and we needed to make  sure we got this right. What’s more, we had sceptics within our own group that needed to be convinced,  they also provided a good shield against confirmation bias, and we knew the Learning Launch Tool  profited from having sceptics in the project group.

Application

Our hypothesis was that the overpaying L-version customers would migrate to an XL-version of the tariff  if we designed it right. Our key assumption was they were looking out for a bigger package anyway, and  that would increase loyalty and tenure with us, which would overcompensate the short-term revenue  dip. We knew this was a “must be true” assumption. We designed a paper version of the XL tariff with  much more data and call allowances for a moderately higher price. We selected app. 100 overpaying L- customers that we picked randomly, conducted interviews, and asked them about preferences and  choices between the L and the XL versions. We made sure to include the sceptics prominently in the  testing process to uncover potential disconfirming facts.

Insight and approach

In general, our key hypothesis was confirmed. The majority of that particular customer group favored  the XL version; some were suggesting this should have been done way back. They would happily pay  more for receiving more value. They also rated their satisfaction with the potential XL version higher  indicating a higher tenure and reduced churn.

We also found some disconfirming facts that helped us to apply the solution in a smarter way for the  rollout. Overpaying customers who still liked the L-version better than the XL-version had a certain social

profile where they preferred flexibility to affordability and we were able so learn that subtle pattern and  were able to exclude those customers from active communication.

As a result, the XL version was launched successfully and a large share of the formerly overpaying L- customers migrated to it plus it attracted also high end customers from the competition that we were  not able to attract before at all.

The beauty of the Learning Launch tool was that it eventually combined the inputs and beliefs of the  proponents and the sceptics into a best-case solution. The main hypothesis of the proponents was  confirmed and created lots of value for us and the customers, and there was also truth in the sceptics  view which was instrumental to come up with a differentiated communication strategy for that product.

