How Big Data Analytics is powering New Biz Models in Energy Industry ?
How Big Data Analytics is powering New Biz Models in Energy Industry ?
Published on January 26, 2015
Featured in: Big Data, Oil & Energy
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Derick JoseFollowDerick Jose
Cofounder - Flutura Decision Sciences - Energy + Oil n Gas
"If Alexander Graham Bell woke up tomorrow and saw the phone, he'd be astounded ( Telecom grid has massively transformed ) , whereas if Thomas Edison woke up tomorrow and saw the grid, he could not only recognize it, he could probably fix it.( Power grid transformation has been slow) "
This elegantly communicates how the energy industry is following the path taken by telecom industry in transforming itself. The Telecom industry began the transformation journey with deregulation and the Energy market place is following a similar journey of disrupting itself . The first moment of truth was when states like Texas, NJ deregulated its energy markets ( unbundling generation, transmission and retail sales) opening it up to market forces to evolve new business models and unleash innovative energy services. It is estimated that the deregulated energy market has an addressable market size of 100 billion dollars. This massively unlocks a lot of opportunities for various actors to monetize on the once in a lifetime opportunity which presents itself. The energy market is complex & diverse. We at Flutura have been obsessed with simplicity and have mapped the various actors in the new utility ecosystem into an accessible map outlined below - REP ( Retail Energy Providers ), ESP ( Energy Service Providers ), LDC ( Local Distribution Company), Financial service providers and the Industrial/Residential customers
In the intertwined ecosystem, the entity owning the customer experience is the Retail Energy Provider ( REP ) which orchestrates with multifaceted actors in the network to deliver superior value to the residential and industrial customers. So here is the central question
Which are 2 strategic levers available to REP for optimizing revenue streams?
There are 2 strategic levers REPs can use to optimize revenue streams
Strategic Lever-1 : Innovative Pricing Models
Energy prices are driven by the power pool ( which is a financial market ) and depending on contracts being structured with various pricing models
- Fixed Prices
- Block Pricing
- Index Pricing
Optimal calibration of the pricing models can be a source of competitive advantage
Strategic Lever-2 : Value added services
Again like telecom, the energy industry is going thru an unprecedented transition to a market where raw power is seen as a commodity and in order for the industrial customer to perceive value, one has to move into non traditional,value added serviceswhich offer new revenue streams and customer stickiness. Deregulated energy customers now have an opportunity to experience new services which they have not experienced before like
1. Power Quality Services to assure SLAs around quality of power delivered
2. Energy consulting and asset auditing services
3. Financial services like outage insurance, asset refinancing, leasing etc
4. Power reliability services
5. Retail appliances and power quality equipment sales
6. Equipment warranty services
Given this context, How can big data analytics help power new Energy business models ?
1. Pricing Signals
Understanding pricing signals is a crucial enabler which enables REP to shine a light on their pricing blind spots and find answers to questions regarding the optimized rate plan for industrial customers, measure regional / segment specific price sensitivity , the optimal band in which pricing should be done for each market and customer segment
2.EVAS-Energy Value Added signals
Whats the trigger for adopting these value added services ? Analyzing high velocity smart meter data which is streamed typically at 15 min intervals, one can do deep analysis of the load curves from which efficiency signatures can be extracted usingdeep machine learning algorithms . Once discovered, triggers can be placed inline to intercept these signatures as instant parameter streams flow into REP Energy data lakes . This pattern detection would be a strategic enabler for an REP to cross sell VAS ( Value Added Services ) along with ESP ( Energy Service Provider) eco system. The telecom industry has used machine learning techniques like collaborative filtering to recommend value added services to its customers. Similar constructs can be used tomatch energy value adds to customer energy usage profiles.
3. Arresting Churn
Energy consumers in the new business model have choice to switch between REPs. Understanding drivers of churn - pricing, billing error resolution experience, power quality/reliabiltiy experience, touch point efficiency using various machine learning techniques could help tweak their business process and arrest churn which impacts customer life time value
Closing thoughts
Its time to re-imagine the energy market ( Energy 2.0 revolution ) as we know it andEnergy big data analytics will power some of these giant leaps for forward thinking Retail Energy Providers to new business models in the market place. Flutura is already seeing this renaissance in the Texas energy market ( which is acknowledged as having one of the most vibrant retail energy market ) and is happy to be worlds first REP intelligence product powering the revolution using its Cerebra Energy Nano Apps. As John F Kennedy famously said
Change is the law of life and those who look only to the past or present are certain to miss the future – John F. Kennedy
We at Flutura strongly feel that energy deregulation offers an unprecedented opportunity for Data Science Companies to move the needle and propel the Utility industry to embrace the future by reinventing itself !
