Presentation on theme: "Data Analytics: Lessons From Competitive Electric Markets Smart Grid Research Consortium Conference October 20-21, 2011 Orlando, Florida Tom Atkinson Ideal."— Presentation transcript:
Data Analytics: Lessons From Competitive Electric Markets Smart Grid Research Consortium Conference October 20-21, 2011 Orlando, Florida Tom Atkinson Ideal Direct Marketing Execution Consultants, Inc.
The Futures Company 2011 Research on the State of Smart Meters Key Motivators to leading a more environmentally conscious life: ▫I want to preserve the world for future generations ▫I feel it’s the right thing to do ▫I am concerned about my own health and my family’s health ▫It enables me to save money Key Barriers to leading a more environmentally conscious life: ▫It costs too much ▫I don’t believe that products that claim to be “environmentally friendly” are really that helpful for the environment. ▫Even if I do my part, other people will never do theirs ▫I have too many other things to be concerned about
IBM 2011 Global Utility Consumer Survey Provides Insights on Customer Characteristics & Diversity Influences ▫Saving money ▫National economic considerations ▫Environmental Sources of information ▫Bills/Inserts ▫Traditional media/ internet-social media/ friends & family Knowledge ▫Most consumers lack knowledge of electric terms Unit of cost Smart grid/smart meter Time of Use Expectations ▫42% committed to engaging with providers to meet personal goals ▫33% not likely to take added responsibility for these decisions We should invest more in the 42%, but how do we identify them?
Consumer Resistance Consumers are fed up with marketing saturation ▫National Do-Not-Call Registry, DVR’s, TV remotes, etc. Better marketing is not better promotion of a brand Better marketing is a better brand ▫i.e. Customer centricity in both word and deed Marketing must be more relevant and precise
The Smart Grid Customer Data Analytics Challenge Customer program success depends on ▫Extent of information on customers ▫Information on how customer characteristics relate to program objectives ▫Execution of programs utilizing customer information Costs and benefits of customer programs vary with the extent of effective customer focus
The Unfocused Approach We want our customers to allow us to control their thermostat during peak demand Let’s send them all a letter explaining why that is good for them and the cooperative Cost: ▫100,000 customers ▫Cost per letter = $2.00 ▫Total cost = $200,000 ▫Positive response rate = 2% ▫Number of customers accepting offer = 2,000 ▫Cost per customer accepting offer = $100.00
The Focused Approach Let’s mail a small sample and build a model to predict response The model captures 90% of the responses in 60% of the contacts Cost: 60,000 letters @ $2.00 = $120,000.00 1,800 customers $66.66 per customer
Data Quality Coverage ▫Is there a entry for every possible customer? Completeness ▫Are essential facts known? Accuracy ▫How accurate is the information? Timeliness ▫When was the information last verified? Predictive ▫Is the information predictive of behavior? Value ▫Does the benefit justify the cost of the information?
What Customer Data is Available? Billing data Property tax records Census demographics Demographics from compiled databases Credit bureaus Other behavioral/attitudinal data
Billing Data Historical Usage Program Usage ▫Balanced Billing ▫Product History Payment History ▫Disconnect Notices ▫Late Payments Contact History ▫Calls High bill complaints Payment problems Other concerns
Property Tax Records Owner occupied status Square Footage Year built Number of stories Pool Available from County Assessors Office Track ownership changes through County Recorder Consider using a third party source like CoreLogic
Census Demographics ▫Population ▫Housing ▫Employment ▫Education ▫Income Addresses are linked to census geography in an address matching process known as geo-coding Demographics are published by U.S. Census Bureau Also available from multiple commercial providers
Compiled Databases Many providers ▫Acxiom, Epsilon, Experian, InfoUSA Limited facts ▫Name/address/telephone ▫Age of adults ▫Presence of children ▫Income estimates
Aggregated Credit Data Credit data aggregated to a neighborhood level – ZIP+4 Neighborhood attributes within the following categories: All TradesAutomotive BankcardCredit Union Dept. StoreBankruptcy MortgageCollection Personal FinanceSales Finance Available from Epsilon or Equifax
MindBase ® Customer Segmentation Built off the longest running, most in-depth tracking of consumer values and lifestyle – the Yankelovich MONITOR study. MindBase® is an innovative marketing tool that enables businesses to answer the questions “Who will buy?” and “Why?”. MindBase® identifies eight consumer segments that span all four generations: Matures, Boomers, GenX and Millennials. Segment drivers include perspectives on: Level of materialism Orientation to technology Involvement and interest in family Conservatism Social interaction Optimism about the future MindBase® is optimized for direct marketers - By tying researched, fundamental attitudes to specific names and addresses in a client’s database, clients can make more powerful and pinpointed connections with their customers and prospects Source: the futures company MindBase ® is the only marketing tool that links fundamental consumer attitudes and motivations to specific names and addresses in databases.”
Expressive “Carpe Diem” I live life to the fullest and I’m not afraid to express my personality Driven “Nothing Ventured, Nothing Gained” I’m ambitious with a drive to succeed personally & professionally Rock Steady “Do the Right Thing” I think of myself as dependable and I try to lead a positive life Who are the MindBase Segments? Source: the futures company 9% 13% 15% At Capacity “Time is of the Essence” My life is very busy and I’m looking for control and simplification 11%8%20%8% Down to Earth “Ease on Down the Road” I’m cruising down life’s path in my own way, seeking satisfaction where I can Sophisticated “Sense and Sensibility” I am intelligent, upstanding and I have an affinity for the finer things in life Measure Twice “An Ounce of Prevention” I’m a thoughtful planner and I seek both actualization and fulfillment Devoted “Home is Where the Heart Is” I have traditional values and I enjoy the comfort and familiarity of my home
How is Data Used in Developing and Executing Programs? Use data for education ▫Explain why electric costs vary by time of day ▫Show the benefits of peak reduction Savings (for the customer and for the cooperative) ▫Personalized customer usage reports Probability of program participation Personalize message to be more relevant
Peer Usage Communication Email/web report showing customer usage compared to peers Latitude-Longitude/Tax Data/Demographics used to identify closest customers to subject with similar housing and family size characteristics Natural response is to modify behavior to lower usage
Segmentation and Modeling Segmentation and predictive modeling produce the greatest return on investment of any of the direct marketing strategies Knowing what data may correlate to the dependent variable and where to source the data is critical to success Demographics alone are poor predictors of behavior Consider adding behavioral and attitudinal data to your analysis
Customer Program Model Goal: customers switch to a time of use pricing plan Situation: ▫You have developed three creative messages Savings, national economic concerns, the environment ▫You have developed your marketing database to include MindBase segmentation and Neighborhood Credit Attributes Experiment: ▫Which message elicits the greatest response for each MindBase segment? ▫Conduct direct mail experiments 3 messages X 8 MindBase segements = 24 combinations ▫Refine your audience selection using multiple regression to forecast response and further reduce the size of the audience ▫Conduct direct mail campaigns and continue to test additional treatments
Competitive Market Example: Deposit Decision Permissible Purpose Correct initial decision is critical ▫Require deposit from credit worthy In competitive market, likely to keep shopping In regulated market, unhappy customer Utility pays unnecessary interest on deposit ▫Fail to require deposit from credit un-worthy In competitive market, eagerly accepts offer Future bad debt Segmented Credit Policy Retrospective Analysis Pre-approved Vs. “Invitation to Apply”
Segmented Credit Policy 650 No Deposit More likely to buy Deposit Less likely to buy Good Payers Bad Payers 590 85% of bad debt Customers with acceptable risk not acquired 720 85% of bad debt Customers with unacceptable risk acquired
Retrospective Credit Analysis Conduct analysis with multiple credit bureaus Sample of good and bad payment customers ▫Include as much known data as possible for future modeling ▫Include bad debt amount Append multiple model scores from bureaus ▫Bureaus return an analytic file with no Personally Identifiable Information Analysis ▫Kolmogorov-Smirnov Test used to select best models ▫Segment by payment behavior (possibly: renters vs. owners) ▫Rank on model score within segment and determine where you identify the target percent of bad debt you need to protect with a deposit Consider using two bureaus whereby those that narrowly fail at the primary bureau are scored by a second bureau
Pre-screen Vs. Invitation to Apply Pre-screen ▫Advantages Pre-approved may have positive impact on response Low cost per pre-approved prospect ▫Disadvantages Low response rates result in higher cost per acquired customer Onerous FCRA disclosure language may discourage response Must make a firm offer to all that qualify – limits campaign flexibility FCRA opt/outs are eliminated (20-25% of the population) Invitation to Apply ▫Using only company data as predictors, model probability of passing real-time credit ▫Advantages No required FCRA disclosure: possibly resulting in higher response Larger prospect universe because FCRA opt/outs not eliminated No requirement of a firm offer – greater campaign flexibility ▫Disadvantages Payment to bureau is on a per/inquiry basis and is a higher unit cost than pre-screen However, cost per acquired customer may in-fact be lower due to low response rates Cannot claim prospect is pre-approved
Competitive Market Application: Acquisition Model Three characteristics of a “best” electric customer ▫The prospect is “in the market” and more likely to accept your offer ▫The prospect’s electric usage is above average ▫The prospect is not likely to exhibit bad payment behavior A past campaign forms the basis of the analysis ▫Ideally, a random population with no selection bias ▫Response is the dependent variable Is the prospect at the anniversary of a term agreement? ▫Home purchase date from tax records ▫Former customer defection date ▫Other possible sources of contract end dates Is the small savings enough to motivate a switch? ▫Prospects that struggle with cash flow are more likely to switch ▫Epsilon Target Neighborhood Credit Attributes – Geographic aggregation of credit variables Consumer attitudes ▫Promotion History - Frequent past promotion has negative correlation to response ▫the futures company Mindbase segmentation system
Competitive Market Application: Usage Model Normalize historical usage using historical weather data Key predictors ▫Property tax records Year built Square footage Market value ▫Census demographics Fuel used for heating Age/Income/Family size ▫Normal Weather Heating degree days Cooling degree days ▫Household demographics Age/Income/Family size Develop/validate models Expectations ▫R 2 in the range of 0.60-0.70 ▫Compare forecast usage decile to actual decile
Access and Campaign Execution Marketing should be equipped with Campaign Management software to easily plan and execute multi- channel campaigns ▫Companies that avoid these tools often find themselves slow to market and overspending on highly technical staff Among the leaders identified by Forrester are SAS, Unica, Alterian, Aprimo and Siebel
Use analytics to Test – Test - Test Offer Incentive Creative Message Design of Experiments ▫Two alternative design Simple null hypothesis: treatment A Vs. treatment B ▫Factorial Design Tests all possible combinations Offer, Incentive, Creative, Message, Population Segment
Application to Electric Coop and Public Utility Markets Optimize the ROI of customer communications using data and analytics to target those most likely to respond to your offers for: Cost containment program development ▫Direct load control ▫Pricing ▫Programmable communicating thermostats Competitive considerations ▫Provision of services offered by others Security, appliance purchase and maintenance, etc. ▫Future service offerings by others Solar installations MicroCHP (Honda 1kW unit)
Conclusions: Build a marketing database Focus on data quality Use Analytics to maximize Return on Investment Use data to educate customers Test, Test and Test some more It won’t be easy, but data and analytics will get us where we need to go.
About Us Tom Atkinson Tom is a direct marketing operations professional. Tom worked at Reliant Energy in Houston Texas for eleven years, most recently as Director of Database Marketing Development. He selected the providers of marketing information and managed those relationships for Reliant as well as providing subject matter expertise in the development of their marketing database. Tom organized a retrospective analysis to improve the credit policy for residential and small business. He selected sources of specialized data by demonstrating, along with the statistical analysts, how the data could improve the power of response models. Tom demonstrated the power timing of solicitations to the likely “last decision” anniversary has to lift response. Earlier in his career at Reliant he managed the campaign list development team. Prior to joining Reliant, Tom was the Director of Data Acquisition for Donnelley Marketing, now part of the InfoUSA group of companies. In addition to negotiating significant agreements with data partners, he provided thought leadership and business rules for how data was to be incorporated in the Donnelley consumer database. Early in his twenty-two year career at Donnelley, he directed the team of software developers responsible for maintenance of the Donnelley consumer database. He earned a Master of Arts degree in mathematics from the University of South Dakota with an emphasis on statistics, operations research and computer science.