We are going to start supporting the Business intelligence team of the
marketing department of a Mobile operator trying to analyze the
customer behaviour impacting on a revenue drop because of the MOU's
decrease.
As most of the emerging operators in which we are working, there is no
Customer centric culture and therefore, no customer value management
is done. Being asked by our clients on the objectives and methodology
to be applied in the next assignment, we have defined an end-to-end
approach to enable a decision-making tool based on the customer
economics.
This approach will handle the following analysis:
1. Top-down analysis: based on the detailed assessment of monthly
performance, the main factors contributing to top-line deterioration
(e.g., market evolution, competitive dynamics, regulatory compliance,
operator performance, etc.) will be identified and their relative
quantitative impact will be allocated
2. Bottom-up analysis: by looking at the evolution of detailed
operational KPIs, we will be able to draw multi-faceted conclusions to
better pinpoint those areas for improvement. This will allow to
further break down the extent of the operational drivers of revenue
shortfall. Different dimensions need to be taken into account and
crossed:a. Traffic volumes and patterns evolution, by typology (on-net
/ off-net, peak / off-peak, billed / not billed, outgoing / incoming,
etc.); b.Shift of minutes and/or subs to the competitors by analyzing
incoming traffic patterns; c. Revenue loss analysis by customer
segments; d. Regional subs & traffic distribution, by sales channels;
e. Recharges volumes and patterns evolution
For this work, we will start designing the customer behaviour
datamart, based on current datawarehouse and complementing it with
additional sources of information, considering the different needs for
the data (end-user can be several departments, different "historical
data" needs, different product structures…) and that a Datamart is a
"living tool": it should evolve as needs evolve (customer segments,
products, analytical requirements, etc.)
In the particular case of time-series data, it is key to consider not
only what happened in the past but also on what is needed to be
tracked on time, for how long, and with what level of granularity.
This is the first step for defining a customer datamart and to obtain
customer value analysis. The faster we get to the industrialization of
the analysis, the better our decision-making processes will be.
Will keep you posted on how this project evolves. Best regards. CVA
