June 23, 2014

Fluturas 5 Analytical Constructs for Modelling Energy Prices

As IOT Data proliferates one of the game changing use cases which it enables is dynamic pricing. As assets get instrumented one can have usage based pricing of assets on lease. We are already seeing disruptions in pricing model in the automotive industry where sensor data which is a proxy for driving habits is fuelling usage based insurance premiums.

Flutura has been working with Utility companies which is one of the industries in Industrial Internet/IOT category undergoing fundamental shifts because of deregulation and increased instrumentation of the grid. Pricing in the utility industry is a very crucial lever and there is a lot of headroom for Utility companies to innovate on their pricing levers using data science. In this blog Flutura outlines 9 components in the pricing framework  out of which 5 analytical constructs can be used to dig deeper into pricing models. Dig deeper into the science of pricing using big data analytics can impacts multiple Utility outcomes like Profitability, Customer churn (now that Utility is getting deregulated) and Peak grid stress (influencing customers behaviour at peak time using pricing).


Based on the maturity of the market, the intended business outcome and the amount of deregulation in the distribution of energy a number of pricing models can be adopted. One could adopt TOU (Time of use) pricing which charges a premium for energy consumed at peak time. One could adopt UBP (Usage based pricing) model where based on the deviance from the median usage a customer could be charged a premium for the differential energy consumed. The construct to operationalize will have to be carefully chosen based on the factors outlined above. Some of the specific signals governing the choice of model can be the sensitivity of the neighbourhood to price changes, the sentiment of the end consumer to marginal price changes, the kind of segment the the customer belongs to and past knowledge bank of what stimuli worked for the consumer segment. Each of these are elaborated below.


Price elasticity process essentially answers a simple question – Is the neighbourhood responsive to price changes? Is Houston more sensitive to the$ 0.25 cent increase in unit energy pricing than Dallas? What is the % change in peak energy consumed in Palo Alto and its neighbourhood when there is a change in peak price? Do households in Austin change their energy consumption pattern in response to time of use pricing (TOU) or Usage based pricing or Location based pricing framework? Depending on the observed behaviour we can be tag that Austin as price sensitive neighbourhood using markers. Also if 2 neighbourhoods have similar characteristics, one can do what if scenario analysis to understand the potential reduction in energy consumed in that neighbourhood as a function of changed pricing

Analytical Construct-3: PRICING MODEL A/B TESTING

Let’s assume that there are 2 variants of energy pricing model and utility wants to test which of the pricing models has an impact on the intended energy consumption habit of the user. One is Time of use pricing where energy consumed at peak time , say 10-3 and the other is usage based pricing where we have differential charging for consumers who cross a certain threshold of energy irrespective of the time at which they consume. The utility wants to figure out version of pricing model is better. In order to do that the Utility subject both versions to experimentation simultaneously. In the end, they measure which pricing version was more successful and select that version for real-world use. A/B test is a perfect construct to quantify the impact pricing has on energy behaviour


A consumer can be segmented based on his/her behavioural profile using clustering algorithms like K means / neighbourhood. Depending upon the segments which emerge we can map a pricing model for each emergent behavioural segment and measure their response to that pricing stimuli


It’s very important to factor in consumer sentiments as a signal into the pricing model so that one is able to find the graceful balance between intended consumer behaviour at peak time and churn ( specifically as markets get deregulated and consumers have a choice ) . Two Key places where one can sense customers pulse for a pricing change are twitter feeds for real time expressions and call centre channels where representatives can dig deeper into their response

Flutura strongly feels that as the world around us gets digitized increasingly using sensors, IOT industries like Utility, Auto and Asset engineering firms will use real time data combined with advanced math as a strategic weapon to compete on pricing.  As Warren Buffet rightly said "Price is what you pay. Value is what you get."