Analytics expertise to assess the price-attrition relationship for improving sales of a retail energy supplier


Identifying and retaining valuable customers:


Energy Sector

Originally, the Federal Energy Regulatory Commission (FERC) provided a single utility company exclusive rights over the supply and delivery of energy over a specified region in the US. As natural gas and electricity were both supplied and delivered by the local utility company, the local utility had a monopoly over the area it serviced.

Post-deregulation in 2008, the generation, transmission and distribution of power were disaggregated from the traditional vertically-integrated structure.
This allowed customers to have an option beyond the utility provider; they could potentially “shop-around” for plans from supply companies that offer products and services that met their specific energy needs.

US Energy Sector Stakeholders before Deregulation

Privatisation allowed customers purchasing electricity to buy from competitive retail suppliers rather than the traditional utility company. For power sold on the same infrastructure to be competitive in the distribution space, private distribution firms also have to up their game. These competitive suppliers often provide an array of service options, such as plans that hedge against price fluctuations or promote energy efficiency. Distributors who compete in this B2C space, need different skill sets in terms of keeping acquisition costs low, using longer term contracts to be able to hedge power supply in the commodity markets. Since these distribution firms compete alongside traditional vertically-integrated utility firms, their competitive advantage needs to stem from better pricing, better service and add-on-services offered.


About the organization


Our client is a third party American retail energy supplier which provides
electricity and natural gas to residential and commercial customers in 12
deregulated states. It has also been the mainstay of Forbes’ & Inc’s list of
“America’s Most Promising Companies”.

Business Problem

The client wanted to understand the attrition behaviour based on price sensitivity, seasonality, tenure, channel of acquisition among other factors. They leveraged their in-house robust database of customers’ rate plans, pricing, and usage history as well as external data such as competitors’ price for various plan levels, and predicted usage based on weather patterns and seasonality as well as global energy futures prices. Our goals were to answer the following questions, by applying robust data mining and advanced analytical techniques, and taking industry specific and exogenous factors into account:

What is the price sensitivity of energy consumers. What is the optimal price increase that can be borne by the market without triggering attrition?
What is the effect of seasonality on attrition?
Does the attrition behaviour vary based on the channels through which they are acquired?
What is the impact of tenure of association on attrition? Do customers with a higher power consumption, show a higher attrition when on variable plan?
What is the price sensitivity of energy consumers. What is the optimal price increase that can be borne by the market without triggering attrition?
What is the effect of seasonality on attrition?
Does the attrition behaviour vary based on the channels through which they are acquired?
What is the impact of tenure of association on attrition? Do customers with a higher power consumption, show a higher attrition when on variable plan?

Our Approach

1. CHURN ANALYSIS TOOL
TEG developed a Churn Analysis tool that took into consideration In-Contract attritions and Post-Contract attritions. The primary objective was to find out at which price point the customers begin to attrite, and to find various factors affecting the attrition.
2. CUSTOMER CHURN SCORECARD TOOL
Using a methodology similar to FICO score, a score related to different levels of attrition was calculated and assigned to each customer cohort. Important characteristic traits of the customers such as tenure in the system, plans opted, usage and bill size were critical in constructing this scorecard. This was an insightful scorecard that quantified the customer’s propensity to attrite.
3. MONTHLY ATTRITION ANALYSIS & NPV TOOL
With the help of a robust database of customers’ rate plans, pricing, and usage history a tool was developed. This was used to generate survival curves and to project Net Present Value of the customers belonging to different cohorts. This allowed the client to find the minimum tenure required to break even on marketing investment for each cohort.
4. SCENARION PLANNING TOOL
TEG developed a Scenario Planning Tool to model attrition projections for different price packages. To evaluate the impact on attrition a simulator was developed for planning and finance teams to model price dependent “What-If” scenarios.
TEG developed a Churn Analysis tool that took into consideration In-Contract attritions and Post-Contract attritions. The primary objective was to find out at which price point the customers begin to attrite, and to find various factors affecting the attrition.
Using a methodology similar to FICO score, a score related to different levels of attrition was calculated and assigned to each customer cohort. Important characteristic traits of the customers such as tenure in the system, plans opted, usage and bill size were critical in constructing this scorecard. This was an insightful scorecard that quantified the customer’s propensity to attrite.
With the help of a robust database of customers’ rate plans, pricing, and usage history a tool was developed. This was used to generate survival curves and to project Net Present Value of the customers belonging to different cohorts. This allowed the client to find the minimum tenure required to break even on marketing investment for each cohort.
TEG developed a Scenario Planning Tool to model attrition projections for different price packages. To evaluate the impact on attrition a simulator was developed for planning and finance teams to model price dependent “What-If” scenarios.

Churn Analysis comparison for 2 channels across different Plans

The TEG impact

Using advanced econometric modelling & regression analysis, TEG devised a customer level scorecard that tracks various metrics that affect customer attrition. Through predictive analytics, this not only helped the client identify “at-risk” customers, but also measured the tipping point of various KPIs that lead to attrition. Thus, moving key decision making from hunches to being data-driven facilitated by a suite of analytics tools developed by TEG analytics, helped answer plethora of questions, using which the client could take tactical decisions that confirm with their strategy.
Using these insights the team was able to take mitigative actions to prevent attrition of the higher risk group by offering specific price to these groups, that helped the client to stay ahead of their competitors. These analysis helped the clients to arrive at a NPV (Net Present Value) which assisted in identifying high valued customer cohorts. This eventually meant lower attrition rates, high brand loyalty and more economic profits.

Insights@Speed of Business


Hide dock Show dock Back to top
Loading