Could analytics improve electric vehicle adoption?

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In August 2017, Britta Gross spoke about General Motors’ perspective on bringing electric vehicles (and their derivatives) to market. Her point of view reaffirmed GM's research on consumer awareness of electric vehicles (only 60 percent) and consumer adoption concerns with this emerging technology. She also revealed the portfolio of cars and trucks GM plans to offer the global market and its electrification strategy.

Power plug of electric car -- close up man's hand holding 3 pin power plug of a blue electric car

Of interest, Ms. Gross also mentioned the partnership between GM and Electric Power Research Institute (EPRI) which was billed as the “largest existing auto-utility collaborative effort.” I found this partnership to be wise given the symbiotic relationship that automotive manufacturers have with utilities and grid infrastructure parties.

Think about it: if I, as a vehicle buyer, cannot feel good about re-charging, at my desire, my Chevrolet Bolt, BMW i3 or Nissan LEAF, am I going to even consider buying one?

GM realizes there needs to be investment amongst cities, state and the federal government, plus with utilities, to enable electric vehicle owners to avoid the dreaded “range anxiety” concerns. Add to this city governments who want fleets of electric buses yet need scalable support to keep them “amped up” for mass transit use. Another concern is that residential owners of electric or hybrid vehicles may have to pay extra to have fast-charging stations near their residence, which adds to the base vehicle purchase.

Beyond cost concerns for buyers, utilities also have growing dilemmas. How do they manage variable demand on the grid? What location analytics should they monitor to avoid a power shortfall when a growing fleet of electric vehicles come online? What is their role in providing new charging stations to cities and residential owners (and at what price)?

As I write this, it almost makes me consider a new, literal definition of the term, "connected car." What pieces and partners do we still need to connect to bring these cars to market and increase adoption? It's literally a new connection that hasn't been figured out yet by the power providers or auto manufacturers.

Practical Applications

Analytics could be that connection point that helps bring car manufacturers, utilities, cities and consumers together to adopt electric vehicles. Let's walk through a couple of joint marketing scenarios using power usage, vehicle ownership and customer data:

  • Discounts & Offers: If I'm Nissan North America (headquartered in Nashville, TN), I might want to entice people to test drive a Nissan LEAF (which is a EV) by way of a joint awareness campaign with a local utility provider. Taking our middle Tennessee example a step further, if NES (Nashville Electric Service) agreed to share their customer database with Nissan, Nissan could identify "look-alike" customers on the NES database (via predictive modeling approaches) which score high for being a candidate for a LEAF based on owner characteristics possessed by Nissan. Now Nissan and NES could provide enticements to a targeted subset of the NES customer base. (By the way in this example, we don't have to think of only traditional residential customers -- NES services commercial businesses and the like so there's a commercial / fleet sale benefit to Nissan here!). Customers could see new inserts inside their monthly billing statements; they could see online banner ads promoting their local dealer and the LEAF when individuals login to their NES account; dealers in the area could be given manuscripts of NES customers so they could invite them for a test drive. Furthermore, NES may hedge that some of their high electricity customers will also invest in a home re-charging station. Such customer profile data can inform another part of the campaign so Nissan can pinpoint re-charging station prospects. This upper-funnel sales effort is a classic example of target marketing between two parties benefiting from having more EVs on the road.
  • Credits Stimulating Commerce & Tax Revenue: As power usage accrues among a utility's customer base, utilities have a chance to partner with local cities in order to provide benefits such as redeemable credits. These credits could be applied to, as examples, parking or free minutes for EV re-charging stations. Yet determining which services matter to which utility customers is a huge part of the challenge here. Analytics in the form of a trade-off (i.e. conjoint) analysis can help utilities see where the highest value for special "perks" may reside within their customer base. Inviting utility customers to take a survey to answer preference questions is invaluable. Do I offer 30 free minutes / month for EV re-charging sessions or would $20 discounts in parking near the swankiest shops / restaurants be a better choice? Should we offer VIP experiences to grand openings during the annual summer art fair (with free parking) or would a three-month "mug club" membership to a new downtown microbrewery be better? Once the optimal combination of city perks are identified, the utility and local city planners can draw up enticing programs to build motivation for citizens to spend their credits, thus helping local businesses from increased commerce (and eventually sustained or boosted tax revenue for the city).

I trust you understand how analytics helps multiple parties given the above examples. You’ll also be delighted to read brand new research that SAS participated in with Intel and Navigant Research which looks at the data analytics infrastructure needed to support the electrification of transportation. It discusses various utility business models, regional trends, competition, challenges/opportunities, and recommendations. Take a peek – our teams raise some provoking points.

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About Author

Lonnie Miller

Principal Industry Consultant

Lonnie Miller is the Principal Industry Consultant for the automotive sector at SAS. He focuses on market opportunities and risks with emerging technology and competitive market dynamics in the auto industry while advising how to leverage analytical approaches to improve connected vehicle, smart mobility and customer experience strategies. Prior to joining SAS, Lonnie held a variety of senior leadership roles with R. L. Polk & Co. (now IHS Markit). This included leading the company’s Loyalty Management Practice, their Marketing and Industry Analysis unit and the company’s Analytical Solutions team. Lonnie holds a B.B.A. in Marketing from The University of Michigan-Flint and a M.A. in Advertising from Michigan State University. You can find him on LinkedIn at https://www.linkedin.com/in/lonniemiller.

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