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Do You Even Data

A data-driven marketing blog

Want to learn how you can translate incredible data list information into killer marketing campaigns? Want to better understand how data research and models can enhance the data you already have?

Learning for Electric Utilities and Non-Regulated Products: Transforming Purchase Intent to Purchase Forecasts

Until (relatively) recently, electric utilities pretty much had a single mission: the generation, transmission, and monetization of electrons.  It was a simple business model.  The customer flips a switch, or claps their hands, or yells across the room to Alexa to turn on the lights, and the fulfillment process happens, literally at the speed of light. For many years, that was enough.  But what happened before is not what happens today.  Instead, we live in an economic marketplace where cable service providers provide home security, phone companies provide video entertainment options, drivers with some spare time provide rides from the airport, and Amazon provides, well, everything.  Utility companies learned that within a fairly narrow space, they too can compete for share of consumers’ share of wallets for products that most consumers would have traditionally sought elsewhere.  The “narrow space,” at least today, tends to center on either 1) products and services directly related to the home, such as things like home warranties, appliance repair and maintenance, home management and efficiency systems, and yes, home security; or 2) optional, voluntary programs like community solar. Configuring and ultimately pricing these types of products is a well-established, tried and true exercise in product development research.  Got ballpark pricing questions? Use Van Westendorp Price Elasticity research.  Want to see what consumers will trade off for lower prices?  Use conjoint analysis.  Want to offer complex pricing models based on tons of optional add-ons and choices?  Use adaptive conjoint.  Want to pick a winner among some product finalists?  Use a monadic experimental design. The limitation with all these methods is that they generally yield relative consumer adoption estimates, not absolute ones.  Relative metrics reliably show which product will outperform the others but doesn’t help much in populating that top line in the product’s pro forma.  And obviously, it helps to know how many people will sign up for a community solar initiative—and at what price—before a utility can reasonably estimate their investments in hardware, marketing, insurance, rolling trucks, regulatory compliance, and, importantly, impact on their brand. In research for consumer package goods, the transformation from relative adoption estimates to hard forecasts of adoption and dollars has relied on mountains of historical data that model specific products in specific categories (e.g. a new flavor of potato chips in the salty snacks category).  For new concepts like community solar initiatives, however, this history doesn’t broadly exist. Fortunately, many utilities do have historical adoption rates for many analogous, if antiquated, products.  In the final, monadic stage of product development, a key question is about purchase intent.  Generally speaking, the concept with the highest PI wins.  Producing a forecast from these outputs, while far from an elementary exercise, is founded on best-practice modeling and marketing science.  Adoption of some original products can be treated as dependent variables is a logistic model, with things like household demographics, marketing outreach, seasonal factors, and other characteristics as predictors to that outcome.  A similar process can then be applied to the “new product,” using an outcome of “definitely would purchase” as the binomial target result and the current values (or, in the case of marketing effort, estimated values) along with their previous coefficients as the predictors.  The final data science application involves scaling the model’s results to the adoption levels of previous products.  In a nutshell, the modeling application for these utility applications is similar to the large-scale efforts applied in CPG and other sectors, except that the process tends to be unique to each utility, based on their history, customers, and many other factors.  This fact doesn’t allow for an easy, out-of-the-box solution, but it does allow for a scientific, empirical, and defensible approach to forecasting new product sales.

  • 4 min read
  • Dec 5, 2018 12:12:46 PM

Stop Overspending on Data Collection Use Multimode Fieldwork, Telephone, and Online

For the past twenty years, DDG has been collecting data for a massive, multi-utility customer satisfaction study. Because many of these utilities service residential customers in geographies that are heavily rural, where Internet access is not a guarantee, this study has heretofore been conducted using a telephone data collection methodology. However, as TCPA restrictions on the use of predictive dialers, decreasing penetration of land lines, and flagging telephone response rates have made meeting monthly targets more and more challenging, DDG undertook a significant redesign of the study to allow for multimode data collection. For the first time, online interviews would supplant some of the telephone interviews.

Data Fusion for Electric Utilities

This will be the first in an occasional series about linking consumer databases to other, non-traditional data sources for deeper and innovative insights.  Today we pick on electric utilities.

  • 5 min read
  • Jun 16, 2017 4:29:09 PM

EMACS 2015 Utility Customer Experience Conference Recap

EMACS 2015 Highlights Another excellent EMACS conference has wrapped up in San Diego. Here's a brief recap of the conference along with a few useful links for more information. Some of the key highlights that attendees liked most include: How TXU is using advanced technology (natural language) to drive its IVR solution How Ameren Missouri is focusing on the “return on customer satisfaction and engagement” How National Grid is pioneering the use of social media strategies in the utility industry

NJNG Earns Best Practice with FGI's Online Customer Panel Research

FGI Customer Spotlight: NJNG New Jersey Natural Gas (NJNG) was recently recognized by Chartwell as a best practices leader in using online customer panels for advanced marketing research in the utility industry. Using FGI's online panel solution, NJNG is generating customer insights faster and better than ever before. 

Recap of the 2013 Chartwell Utility Market Research Summit

We just get back from the 2013 Chartwell Utility Market Research Summit in sunny Phoenix, AZ.

New Case Study: Build Green Energy Programs with Customer Participation

In the last 5 years, green energy programs and products have become a popular option for utility companies as they monitor expanding grid use and make the switch from fossil fuels to more sustainable (and often money-saving) practices.

What I Learned at Chartwell’s EMACS Customer Experience Conference

This was my first EMACS conference and I came away impressed with all the initiatives to help energy customers be more engaged and save money, and the drive to make customer experience the number one focus for utility companies.

Online Panels Pose Benefits, Risks for Electric Utility Market Researchers

Custom online panels are very popular with market researchers within electric utilities. Designed and used properly, they are powerful assets in the research department and throughout the entire electric utility organization.

What’s the Secret to Better JD Power Scores for Electric Utilities?

For most electric utilities, their JD Power customer satisfaction ranking is the critical measure of their success. There are many reasons why these scores and rankings matter.