Retail Energy: 5 Non-Traditional Ways to Acquire Customers

This is a five part series on solving Retail Energy problems using Advanced Data technologies and Analytics

There are over 1000 Retail energy providers in the US, and the number is growing every day. 

Add to this, power is a no-brainer. Consumers don't care what brand of power they are using at home. It has no fendi or Armani tag attached to it.

In a scenario like this, marketing and customer acquisition becomes very competitive. There are only a few finite ways to acquire and retain customers. 

Big retailers have a lot of resources to spare for marketing and advertising. But even though these guys are richer, smaller ones are getting a lot smarter. One of their ways is to pull in all the data from various ecosystems, and milk it to the maximum.

If you are looking for non-traditional solutions to improve customer acquisition, here are 5 ways to do it.

• Using Demographics & Real Estate information to plan better strategies:

Most of the REPs have a specific targeted strategy. Some of them target prepaid customers, some of them target high income customers, some of them focus on small commercial exclusively. But most of this planning is done on excel sheets with some hypothesis, and a limited dataset. Imagine if you could lay out all your prospects on a map, with their publicly available demographic information, how much more effective your strategies would become. Obama's second presidential campaign was so focused on utilizing data to the max as compared to Mitt Romney, the results are here to see.

• Think Customer service quality:

Of all the marketing channels available, the least expensive and most effective strategy is Word of mouth. If service quality is good, people will flock to you. Think xaomi or one plus. The company spends zilch on marketing, and highest on the quality of its phone's components. They get sold out within one day of release.

Understanding and responding to customers' problems proactively is key to increase satisfaction. Textual and Voice analytics empowers customer service reps to be more receptive, and empathetic towards customer problems.

• Stay one step ahead of competition:

Innovative strategies are very important in a saturated market. One of the REPs I was talking to give away free thermostats to people, and control peak demand response by sending out signals to thermostat sensors, without much hassle. The reason why I call this innovative, is because this is a small company doing it, and not a big one. The rise of IOT technologies has broken the monopoly of a few companies in this space. If a fraction of marketing spend is utilized for this, the cascading word-of-mouth will be of immense value, not to forget, the actual savings in customer acquisition cost.

• Utilize online channels to the max:

This is one of the often ignored and sometimes frowned upon way of acquiring customers. Some industry veterans are very openly against competing crazily on online channels. But, here's the truth. Online is here to stay, and it is a great way to acquire customers rapidly without spending thousands of dollars on marketing.  This is a price game, but here's the secret, it’s also a speed game. Whoever is on the first page gets the most customers. To do that, you have to respond in real time. Big data technologies can accumulate data from multiple external online channels and provide REPs real-time access to invaluable competitive information.

• Strengthening your online enrollment pages:

Do you know the biggest reason why people choose one website over another? One app over another? One product over another? Laziness. We don't like to fill lengthy forms, we don't like to navigate complex pages, we don't like pages that lag. Imagine a customer is trying to enroll on your webpage, and suddenly it crashes. Wouldn't you want to be informed about it before he drops off? Wouldn't you want to beef up your infra just to support that one guy? We did this experiment with one of our customers. We dug into customers' browsing and behavior data, analyzed the patterns, and provided inputs to change the website to suit majority's needs. Their conversion improved by 20% in six months.

Most of these problems were solved using advanced data technologies, coupled with analytics. The investment to solve these problems nullify when compared to the returns it provides.

Find more details about our solutions and offerings at utilities.flutura.com

Vertical Data Exchanges (VDE) : Are they key to powering 21st Century Business Models ? ( Part-1)

We live in interesting times ...The last 6 months at Flutura have been very exciting ... We  noticed an inflection point -  Specifically from Houston + Tokyo market places where we operate in the Energy + Engineering verticals. 

3 Propelling Questions

Customers in our specialized segment had an inkling that data is going to be a key  force to be reckoned with going forward. A mindset traditionally soaked in 'Physics + Engineering' had to accommodate 'Data' as the 3rd dimension... Their conundrum can ultimately be boiled down to 3 propelling questions

1. How can we use data to power new  business models ?

2. How do we go beyond the traditional product/services mindset to create entirely new blue ocean markets ?

3. Are there concrete real life examples of these "Data powered" business models which are truly transformational from a business outcome perspective in Industrial sector?

We must admit, initially it did catch us off guard ...We got inspiration from the gang of 4  in the B2C industry ( Amazon +  Apple +  Facebook + Google ). They paved the way to herald  partnership + platform mindset disrupting business models in B2C marketplace.

As the Zen folks  say when the student is ready, the master emerges ... For us the answer was apparent in a construct called Vertical Data Exchange where ecosystems get created to rapidly experiment and impact business outcomes.

For example , the industrial organisation can hold 15 minute interval data streaming in from millions of commercial, residential and industrial customers in a massive energy data exchange. A narrow and deep, partner created forecasting algorithm can help predict short term peak power needs for an REP and feed those signals into  trading systems for block purchase of energy from the markets 

This is best explained with more real life examples in the Industrial world

5 Real world Vertical Data Exchange Platforms in Industrial economy

1.Vertical Data Exchange in Energy  @ US markets : MDM providers are creating  vertical data exchange  which can stream in data from various smartmeter devices - Landis Gyr, Silver Spring Networks, Echelon etc. Partners are invited to co-create intelligent apps which solve specific energy problems like short term demand forecasting or outage prediction by getting access to rich data pools massive headroom exists to detect patterns previously unseen or solve new problems.

2.Vertical Data Exchange in Auto @ Ford : Ford has a vertical data exchange which gives partner read only access to vehicle state informationlike steering_wheel_angle , engine_speed , transmission_gear_position , gear_lever_position ignition_status , brake_pedal_status , parking_brake_status headlamp_status and accelerator_pedal_position to listen selectively to can bus messages . Partners can use rich historical vehicle state data to develop value added apps to forecast fuel efficiency or part failure using predictive algorithms

3. Vertical Data Exchanges in Oil n Gas @ Norwegian Continental Shelf :

The Norwegian Continental Shelf ( NCS) has one of the largest deposits ofPetroleum and Natural gas in Europe. Exploration and Production Information Management (EPIM)  is a vertical data exchange to share  drilling + production data with an eco system of licensed partners .Operators transmit daily drilling and daily and aggregated monthly production data in XML directly from the platforms on the North Sea using a secure network to EPIM. Analytical apps built by screened partners on top of EPIM allows  new possibilities for  operators to analyze massive data pools across different production fields and licenses.

4. Vertical Data Exchange in Retail Energy Provider ( REP ) @ Texas

As Tony, Sandi & Keerthana from our Houston offices found out, in the deregulated energy provider markets, REPs "dance delicately" on a daily basis between demand and supply pricing . In order to calibrate pricing on a intraday basis REP folks need consumer micro energy consumption habits  to monitor 2 important drivers of demand - competitive energy pricing and ambient weather ( excessive heat  in Texas can trigger A/C usage spiking demand ). Fluturas REP Cloud provides thedynamic contextual signals required for calibrating pricing on a intraday basis.

5.Vertical Data Exchange in Heavy Engineering  @ Tokyo

Okubo San & Krish ( Saki in hand ) had a deep conversation with a leading Tokyoheadquartered Heavy Engineering company with a vision to create the Industrial  Sensor cloud  from high speed train sensors, industrial machinery. They were looking for ways to  monetizing data using partner co created apps

All the above are powerful signals of a subtle force @ work - A new era of verticalized data exchanges to solve business problems. We at Flutura call this the  rise of the  "Data Exchange Economy ( DEE ) " . Data Exchange Economy is the creation of new business models generating new revenue pools and powered entirely by unique access to 'rare to get' data.  So here is the important question

What are 3 Characteristics of DEE - Data Exchange Economy ?

It essentially involves 

1. A Partnership mindset

2. Moving from data streams to new revenue streams powered by dataproducts /algorithms

3. A non linear business model which goes beyond product license.

So how can one differentiate between an organisation which "thinks Platform" from a company which "thinks Product" ? It can  be visualized & contrasted on 4 significant dimensions outlined below

The world has changed now. Industrial organisations are moving away from product mindset to a platform mindset .

While there is a lot of ground to be covered,  our intent was to heighten the awareness of this new emergent trend. In Part-2 of the blog we would dig deep and look at 3 aspects of the emergent Data Exchange Economy in Industrial side

• Business  Models : What is the economics underpinning vertical data exchanges ? 3 Examples of new revenue pools created in Engineering + Energy sector using data

• Architectural Models  :  What are the key technological building blocks under the hood of a  vertical data exchange ?
• Mental Models : What kind of 'fertile ground' needs to be created  from an organisational mindset perspective for data powered business models to 'bloom' ?

 In a nutshell

 The Data Exchange Economy ( DEE) is here to stay in the Industrial world and is irreversible. as value perceived by end customer is shifting from engineering to intelligence (atoms to bytes)

• Creation of Verticalized Data Exchanges is  the first step to unlocking value

• It requires a new thinking on 3 dimensions - Business Models + MentalModels + Architectural Models

• The industrial B2B sector is following the  Gang of four ( Google+FB+Amazon+Apple )  B2C platform world with a lag

As always  curious to hear  how you see the world ...

How we survived a near death experience in a Big Data Project ? A Story from the trenches

One month back Flutura was in  a meeting with a customer in Houston where we were involved in an IOT big data project . It was a very tense meeting ... Budgets were being slashed, heads were rolling because of the steep drop in Oil prices. The CEOs office was forensically examining all projects thru a massive magnifying glass. The tension in the room was palpable. The cross functional team had spent a lot of time building a platform and the CEO wanted to see tangible business outcomes and how it made money for them. Given the intense market pressure, if the team did not articulate an ultra specific path to monetization, the project was definitely going to be axed. After about 3 hours of intense conversation the cross functional team of Engineering and IT folks were able to hammer out a 1 slider to make the case to the CEOs office .The meeting happened a week later.The project survived. Everyone breathed a sigh of relief ....

So what did we learn from the near death experience ?

The beauty about this near death experience in executing the IOT big data project ( We must admit ...It was a quite nerve wracking experience for us at Flutura as we went thru the turbulence ) was that it forced us to focus on the core . We have synthesized our learnings and we would be the first ones to admit that many of them are obvious but we felt the strong need to reinforce and internalise them.

Lesson-1 :  Think Outcomes ( for example : The project would reduce fuel consumed by 3 % resulting in $ 34,000,000 savings per year in first 2 years )

Lesson-2 : Are we  solving a top 5 problem which keeps business awake at night ( for example one should not be solving problem no 18  in the problem list from a business perspective ... forcibly ranking problems helps )

Lesson-3 : Is Status Quo an option ? ( One would be surprised as to how status quo is an option for many industries, businesses and processes. You may be excited about the possibilities but )

Lesson-4 : Don't use the following words while speaking to CXO team

• "Big data"
• "Analytics", "Data Science", "Algorithms", "Statistics" etc etc
• "Hadoop", "Spark" , "Data Lake", "Lamba Architecture" etc etc

These are sure ways to short circuit a project as it activates the wrong  neural pathways for attention starved executives

Lesson-5 : Spend 70 % of conversation in first 4-6 weeks of a big data project ontangible Business case and not solution or architecture.

To conclude

These are tough times ...Markets are undergoing tectonic transition ... Industries which were immune to recession are tightening the belt.Big data and analytics has a huge role to play in driving efficiencies in many industries. The simple but obvious things evade us. We are reminded of a quote by T S Eliot

What we call the beginning is often the end. And to make an end is to make a beginning. The end is where we start from.

We hope the above learnings reinforce your experience and happy to hear your stories :)