January 25, 2015

How Big Data Analytics is powering New Business Models in Energy Industry ?

"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 services which 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 to match 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 and Energy 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 itsCerebra 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 !

January 15, 2015

Energy Industry + Big Data = Unleashing Blue Ocean Opportunities

We came across a comprehensive big data report which rank ordered Industry verticals on 2 dimensions
1. Big data spend
2. Business ROI realized
How did the the Utility industry fare ?
On the dollars spent in Big data, the Utility industry was ranked 4 th largest spender after Telecom, Healthcare, Retail . Whereas on the ROI realized it was ranked No 1.
What this implies is that even though they were spending relatively less on big data, the ROI realized is massive. There is a lot more headroom for growth in monetizing Utility data.
Over the last 2.5 years Flutura has deeply listened to the needs of the Energy markets globally while executing big data projects to solve business problems. We have synthesized 3 fast converging trends + 10 real problem statements at various stages of maturity.

So what are the key trends influencing the energy market place ?

The 3 D s - Irreversible forces which are redefining the energy markets are
1. DEREGULATION : Emergence of Retail Energy Providers in markets like Texas, NJ etc
2. DECENTRALIZATION: Emergence of distributed power from Wind / Solar where a Consumer transitions to being a Prosumer
3. DIGITIZATION : Increased instrumentation of the grid with sensors, SCADA and smart meters which emit unprecedented data with monetizable patterns buried within them

What opportunities are unlocked because of these 3 Ds ?

Fluturas specialist team of Energy Data Scientists then segmented the use cases into 2 categories
1. Energy big data use cases which HAVE a Business Case
2. Energy big data use cases which DO NOT HAVE a Business Case.
We are happy to share our experiential list of Energy big data use case which have a business case and warrant an investment in big data applications.
1. How to Price energy in deregulated markets ?
2. How do we stabilize micro-grids power quality by deploying assets ( capacitor banks for example ) ?
3. How do we reduce last mile revenue leakage in distribution by detecting payment and power leakage in real time ?
4. In deregulated markets, how do we segment energy usage for commercial/industrial customers to detect signals for selling value added serviceslike outage insurance, energy audits and asset refinance ?
5. How do we minimize outages by doing detailed forensics by triangulating power , asset and ambient data ?
6. How do we arrest customer churn in deregulated energy markets by harvesting signals from power quality data pool, outage systems, billing resolution signals , customer experience signals,customer life time value etc ?
7. How do we minimize spot energy purchase ( which is quite expensive ) by aggregating bottom up load curves into the energy forecasting process ?
8. How do we gather granular asset intelligence to drive budget allocations to replace/refurbish grid assets ?
9. How to reduce peak power stress on grid and reduce need for expensive peaking plants by influencing customer actions driven thru bill and promotions (A/B tests to figure out action resonance )
10. How to enhance customer experience( brown outs /black outs /billing errors) by having a holistic 360 degree view of their energy usage profiles, outage experience metrics, billing patterns, call center recency etc ?
Depending upon the market and maturity, the rank order of the problem statements keep changing. If you are at Distributech in San Diego please stop by. Tony who is from our Houston office would be happy to show live demos of how each of these problems are being solved using Cerebra Nano Apps.
And please remember Data is a liability before it becomes an asset !