Service Contract marketing is one of the more operationally complex areas of the customer life cycle communication strategy. Service Contracts are an insurance-like product, and thus they come with some inherent controls for risk, a set of complex rules for solicitation, and various legal requirements and conditions.

As marketers, our aim is to simplify a potentially complex buying process for the customer and to instill a level of trust between the manufacturer and the customer. Many in this sector focus heavily on the complex details (think marketing materials where 90% of the real estate is taken up by legal language), forget about presentation, and the customer’s perceptions and needs. In this haze of complexity, we simply forget that the end customer probably does not work for an insurance company, probably did not pass the bar exam, and frankly has been conditioned by years of marketing to be suspicious of corporate solicitations.

One funny anecdote perfectly illustrates this problem. We recently re-engineered a program that had become more legalistic than consumer-oriented. Simply increasing the font size increased response, even more so among older consumers, suggesting that the marketing pieces were borderline illegible.

Another trend that many marketers in this area have missed (despite the fact that we’re now 20 years into it!): an increasing number of consumers of all ages are conditioned to buy via digital channels, such as web sites. While complex language, poor presentation, and having to follow multiple steps to complete a purchase will reduce sales through traditional channels, they are fatal on Web or mobile. Customers will simply click away from your site, if you make it hard for them.

This is why the use of the PURL has become a game changer for selling missed point-of -sale service contracts. Used correctly, the PURL allows the marketer to associate details of a specific offer to a specific product. If a customer owns a touring motorcycle, the PURL allows the marketer to speak directly to the customer about their specific motorcycle and why the service contract is a good investment. If the customer is looking to protect their washing machine, the PURL allows us to talk directly about issues other customers have experienced with that washer and how the service contract will cover such items.

The PURL breaks the model of having one generic message for all customers and creates an opportunity to give the customer more personalized and more useful experiences. Using our knowledge of the customer and the product, the PURL allows us to program personal pages to add personal messages, modify images, look and feel, offers, discounts and point out unique terms and conditions for the customer’s product.

All of these new abilities are increasing response and conversion on their own, but most importantly, it also ties every click, visit, and important metric to that customer and product. As an example, we quickly learn if the customer is more risk-adverse or cost-conscious. This information is fed back into the marketing system for future solicitation marketing or the site will begin to offer different communication on subsequent visits.

The use of the PURL and new marketing strategies is just one of the latest innovations that Fulcrum is working on in the Service Contract space. Leave a comment or let me know about other ways you are using new technologies to change marketing. If you would like to talk about your programs and ways to increase revenue and profit, our team is always available for a conversation.

Related Posts:

Service Contract Marketing Part 1: Modernization

Service Contact Marketing Part 2: Customer Continuity

Service Contract Marketing Part 3: Data and Analytics

First, there is an entry by Dieter Brandes, former managing director of ALDI, who makes the following statement:

Don’t leave it to computers. If there is one clear culprit behind today’s lack of competence in assortment management, it is the computer. Too many retailers in the world think that their computers, by analyzing point-of-sale data, can generate all the answers. But with 10,000 or more items to manage, it is impossible even to get reliable answers to specific queries, the huge amounts of data notwithstanding. To comprehend the bigger picture, we need to use our brains first, and talk to our colleagues. Then we can put the computers to work.

A blog entry by Marshall Fisher, a professor at the Wharton School and author of The New Science of Retailing, takes quite a different view:

Assortment-planning processes vary greatly across retailers and product segments but have one thing in common: They rely too much on human judgment and not enough on hard data that might allow a retailer to predict how customers will react to a change in the assortment.

Based on Fulcrum’s experiences working with clients, I tend to side more with Dr. Fisher in this dispute. Assortments present a very complex problem, as the interactions between categories, brands, SKUs, customer preferences, seasonality, pricing, substitution effects, and other factors make it difficult to arrive at intuitive decisions about assortment, at least ones that are likely to have a positive effect on sales. [For the more technically-minded, Fisher includes a link to an academic paper of his that explores a potential methodology for modeling assortment scenarios. It also references other works in the field, such as that of Peter Fader, also of Wharton.]

However, I also see some truth in Mr. Brandes’ views. Analyses can become a substitute for strategic thinking, and sometimes organizations get so immersed in the details, that the larger picture is missed. Yet such failings are hardly caused by the availability of data or analysis. Rather, they would seem to be the result of management decisions that ignore useful information.

I’d be interested to hear other opinions on this topic. Chime in below.

]]> http://www.fulcrum-mktg.com/database-marketing/two-opinions-on-retail-assortment-planning/feed/ 0 http://www.fulcrum-mktg.com/database-marketing/fulcrum-wins-prestigious-marketing-award/ http://www.fulcrum-mktg.com/database-marketing/fulcrum-wins-prestigious-marketing-award/#comments Wed, 14 Dec 2011 13:31:21 +0000 David King http://www.fulcrum-mktg.com/?p=1659 Just a quick note to announce that Fulcrum P2P and Whirlpool Corp. were honored yesterday with the NCDM Excellence Award. More details can be found in today’s press release.

NEW YORK – December 14, 2011- Fulcrum P2P, a leader in marketing services and technology, and Whirlpool Corporation have won the prestigious NCDM Database Excellence award in the technology category, as announced yesterday at the NCDM 2011 Conference in Las Vegas. They have been named a Silver Winner, in honor of outstanding achievement in database marketing.

The NCDM Database Excellence Awards recognize creative examples of analytics, database marketing strategies or technologies that results in measurable benefits to the bottom line and uncovers new solutions to perennial business challenges.

The companies were recognized for the strong results produced by the launch of whirlpoolstores.com, designed so that owners of Whirlpool products can easily buy parts and accessories to enhance and maintain their appliances. Customers receive multi-channel marketing communications that link them seamlessly to buying options tailored to the specific products that they own. Fulcrum provides data management, analytics, creative design, and e-commerce for this program.

“Our goal was to make buying as easy as possible for the end customer,” said Nathan Baldwin, EVP of Fulcrum’s P2P solutions. “This concept of “frictionless” sales and marketing is a core element of everything we do. By using information and analytics effectively, our e-commerce applications provide a real benefit to the customer by simplifying buying, which leads to both to better customer experiences and higher purchase rates.”

About NCDM

NCDM – The Conference for Engaging Customers Using Data & Technology brings suppliers and decision-makers together to find practical approaches to today’s marketing challenges using customer data and technology. NCDM is where over 700 data-driven marketing professionals convene yearly for three days of unprecedented access to sought after experts discussing industry trends and discovering new techniques for uncovering important customer insights from terabytes of online and offline data collected each year. www.ncdmevents.com

About Fulcrum

Leading businesses turn to Fulcrum to increase and accelerate the profitability of their marketing and advertising investments. Fulcrum offers advanced marketing solutions, from building and managing rich, multi-channel databases, to predictive and segmentation modeling, advanced analytical tools, and design and execution of cross-channel, customer-centric marketing programs.

Fulcrum is headquartered in New York City with its major operational center in Fairfield, CT. Visit www.fulcrum-mktg.com for more information.

The Marketing Science Institute is a fifty-year-old organization whose mission is to sponsor academic research on marketing-related topics; its membership is comprised of some of the largest corporations in the world. MSI recently released Consumer Insights: Findings from Behavioral Research that serves as something of a field guide for marketers. Each article is brief: two pages explaining the main insights for each topic, their implications for management, and references to selected research.

Here are a few excerpts from the article titled Perceptions of Price Deals:

These insights are then distilled into recommendations, such as:

The slim volume covers 42 topics, in categories that include information search, pricing, consumer decision making, social consumers, and health and well-being. Each contributor is a subject-matter expert in their area, and the research references are a good starting point for someone wanting to explore more.

As we’ve noted elsewhere, behavioral science is vitally important to understanding how customers view and interact with our brands. Yet, it always strikes me how divergent marketers’ views of behavioral drivers are and how selective we sometimes are in interpreting observed behavior through our own biases. Management decisions, marketing analysis, and marketing tactics need to make sense in the context of fundamental behavioral patterns.

Stumped by what appears to be counter-intuitive reaction to a price promotion your company conducted? Faced by an analyst who cannot adequately explain what she’s seeing in the data? There’s probably a good explanation for such quandaries in the research literature, and this book provides a quick reference for understanding such behaviors and for accessing the wider research on your issue.

To get more information or to order, visit MSI’s web site.

Two important things I’ve learned from this vantage are:

1. When properly managed, service contract programs are a significant contributor to profitability and customer loyalty, and should be used as an integral component of CRM.
2. Significant risk management opportunities exist in every program, regardless of who carries the risk. It’s often an area that can yield rapid improvement, but that also needs to be monitored continuously.

Nate Baldwin just wrapped up a series on the first of these points. In this article, I’d like to focus on the second point.

Often, when we first become involved with a company’s ESP program, it becomes evident that the manner in which risk is managed has not been reviewed in many years. In fact, it’s not uncommon to see the current structure date from the inception of the program, and many of these programs are several years old.

Modernizing the way risk is structured and measured is a simple way to uncover significant opportunities within a program for enhanced profitability. Such opportunities exist whether a company carries the risk on their own balance sheet or employs an external underwriter, although the details of those opportunities often lie in somewhat different areas.

For companies that carry the risk on their balance sheet, an immediate area to review includes the size of the reserve relative to expected claims for the in-force contracts. In many cases, our analytics reveal that a company has been too conservative in its revenue recognition (if the program claims are front-loaded), which might allow them to either accelerate some earnings or to restructure the program to have a wider appeal. On the other hand, we have also seen a few cases where the reserve amounts are not adequate. In such cases, it is important to take action to either reduce future claims costs by implementing better entitlement and adjudication, supplier management, and other process improvements, or to increase program reserves in a manner that meets requirements and stated guidelines.

Some of the largest opportunities exist in companies that employ third-party underwriters and/or third-party administrators (TPAs). We have many friends in the U/W/TPA business, and they perform valuable services for the industry (including some of our clients). But many of these programs are marked by legacy business models, portfolio-style management, a lack of detailed understanding of the client’s unique historical claims, and sometimes even outdated actuarial quantitative methods.

Here are a few cases to illustrate some of the opportunities we’ve seen:

• An OEM/retailer ESP program (managed by the same underwriter for 6 years) was contractually obligated to a 90% loss ratio (90 cents of each reserve dollar would be spent in claims). The underwriter provided quarterly risk updates showing close to a 90% loss ratio. However, our more detailed analysis demonstrated the ultimate run-off loss ratio was actually 53%. Clearly, the original loss ratio calculations or quantitative methods no longer held; moreover, the client was paying a 15% risk fee as part of the underwriting fees, something that also was no longer warranted given the lower loss ratio and stability of the program.
• A large OEM was carrying their own risk, and we found they were not earning their reserves correctly. In fact, there was over $20 million in excess reserve. We helped them structure an arrangement where they were able to recognize the excess reserves immediately. (We have found this windfall situation for several clients, resulting in immediate income recognition of between $3 and $9 million in each case).
• Conversely, we worked with a large manufacturer carrying their own risk, which was nearly $50 million under-reserved, because their claims were back-loaded and they were earning pro rata. They had no idea because rising sales revenue was masking the issue. We worked with them and their auditors, structured a way to handle the shortfall, and made go-forward changes to ensure future contracts were profitable.
• A major OEM program, in place with the same underwriter for over 10 years, was paying nearly 40% of their premium in risk fees, underwriting profits, and investment income. For their $75-million-dollar annual program they should have been paying approximately 6%. The savings of 34% of $75 million amounted to additional profits of over $25 million per year.

I could keep citing examples, but I think these convey a good idea of how pervasive the issues are and how significant the gains can be. Here are a few tips every program manager should consider implementing:

• Make sure your ongoing analytics are detailed and complete. One endemic problem is that much of the reporting in this area is highly aggregated: quarterly reports at the entire portfolio level. My eyes were opened many years ago, when claims for one of the categories I managed shot up. The top-level report suggested we raise rates across the category, but more detailed analysis revealed almost all of the incremental claims were being driven by a handful of models from a single manufacturer. Instead of raising rates that would have hurt sales, I was able to get a rebate from the manufacturer instead – and that manufacturer in turn was able to get relief from a supplier.
• Get knowledgeable help. The intricacies of extended service plans are difficult to grasp, because there are so many interrelated elements. For example, if you raise rates too high, you may get a good short-term boost in revenue, but the smaller pool of customers that results may have a significantly higher risk of claims. As a result, program profitability and viability could suffer. True expertise is often hard to find. When you do, work hard to retain and grow those resources.
• Find advisors that share your passion for your business. Too often, the folks helping review program performance are in finance or at an underwriter, and your program’s success is not their main job. It’s one of the reasons Fulcrum structures many of it clients’ relationships on a performance basis: we’re fully invested in our clients’ success because that’s how we get paid. No matter whom you work with internally or externally, they should care about helping you improve your business.
• Determine if the fox is guarding the hen house. Most underwriters and administrators in this sector are ethical companies. However, we have sometimes seen situations in which a vendor’s analysis was geared toward steering profits their way, rather than serving their client’s interests.

In the next installment, I’ll share some additional reasons that we should care about proper risk management, risk structure, loss cost analytics, and program optimization. I’ll also look at when and what to outsource to build a stronger program.

In the meantime, if you have any examples of ways risk has been analyzed or restructured to improve program performance, please leave a comment below.

But technological advances are demonstrating that my pat answer might be less safe than it once was, as researchers figure out ways to combine the best of machine and human computation.

A recent and striking example was the decoding of an enzyme in HIV-related viruses, one that lets them reproduce. It had been a problem that had eluded traditional computing for almost three decades, because proteins can be structured in so many ways that finding the optimum configuration can surpass the combinatorial capabilities of even powerful computing platforms. The solution came in the form of a program called Foldit, developed by researchers at the University of Washington, which creates a game with simple rules that allows users to turn and flip a 3D model of the enzyme. Using the software, a group of gamers were able to find a solution within three weeks. Researchers were so impressed that they even shared credit with some of the gamers in the published research.

Prediction markets have been another area of active development for over decade. Such schemes set up a market to predict particular outcomes, such as the movements of stock indices, performance of motion pictures, or professional sporting scores. Participants buy and sell trade virtual stocks that represent different outcomes, usually with a play currency. The underlying principle has been around for hundreds or thousands of year in the form of wagering on horse races, but today’s technology enables the establishment of markets around many problems. As a result predictive markets have been formed in many areas, and several companies have developed applications to aid in the design and management of virtual markets. Consensus Point is one company that is relatively well known in this field.

[BTW: for an anecdotal example of the power of prediction markets, readers may want to revisit this article from October 13, discussing the relative strength of Herman Cain's candidacy in the polls versus what prediction markets were showing. The author, David Rothschild, attributed the markets' lower rating of Cain to the concern "about what Republican voters will learn" about the relatively unknown Cain. Two weeks later (as I write this), that analysis seems to have been accurate.]

Crowdsourcing is another area in which human knowledge can interact with computing to analyze complex problems. Crowdsourcing has been used in marketing and related areas for a number of years to help with such activities as product design. For example, Threadless.com famously uses customer input and voting to design t-shirts, while television shows, such as American Idol, have made viewer voting a central part of their appeal.

But there are more advanced applications for crowdsourcing, as well. One interesting project is the Aggregative Contingent Estimation (ACE) website, funded by the Intelligence Advanced Research Projects Activity. Launched on July 15, 2011, ForecastingAce examines various predictive problems by soliciting users to make estimates via surveys. Rather than simply counting votes or averaging estimates, ACE will attempt to apply weighting algorithms to arrive at more accurate predictions.

Another start-up in this area is Crowdcast, which styles itself as the “leader in Enterprise Collective Intelligence.” Like ForecastingAce, Crowdcast’s early funding also came from IARPA and it similarly is experimenting with methods that allow information to be extracted from crowds efficiently and with algorithms that then turn the resulting information into intelligence. In the case of Crowdcast, the idea is to harness such knowledge to help solve everyday business problems, such as revenue forecasting and risk assessment.

Both of these business areas have well-established methodologies and a range of mature forecasting technologies, yet are subject to errors in prediction when there are emerging changes in underlying conditions (will there be another recession or not), a lack of information dissemination, or undocumented, but important, conditions (e.g. are a large number of loans in a portfolio potentially fraudulent). The knowledge that a group of people might have or the intuition they can bring to bear have the potential to bridge the gap between formal methods and human hunches.

In marketing, there are any number of useful applications. For example, one of the most difficult items to forecast is the sales of a new product. Again, there are well-established methodologies in this field, but my guess would be that asking a wide cross-section of employees to estimate sales would provide at the very least a useful input and perhaps even a more accurate prediction than traditional methods.

Another potential application would be to have an additional layer over other predictive or forecasting tools, a sort of governor that would tell us when to trust a particular model or not. For example, we know that if we have an incremental response model that was built during a period of “normal” buying behavior, it may become less useful if conditions change. If demand suddenly increases, than the model has less room to identify those customers that need stimulus in order to respond. [Think of a model that predicts which people in a crowd are most likely to head for the exit. What happens to that model's predictions, if someone yells "fire!"?] Similarly, if underlying demand is strengthening, then perhaps I should rely less on the model as a targeting tool. Conversely, if demand is shrinking, the model will still likely perform well, but it may identify so few customers that my marketing program may no longer be viable. By using human-generated forecasts of demands, I could be more proactive, rather than having to wait for actual data to come in and reveal errors.

I’m certain we’ll be seeing more innovative applications as we learn how to tap into “the wisdom of crowds,” as James Surowiecki wrote in his 2004 book of the same name. In the meantime, I will temper my reply to skeptics.

N.B. We’ve certainly had statistical tools that have sought to simulate human thinking. Neural networks in their many forms are perhaps the best known and widely applied, but Bayes’ theorem (and the resulting methodologies that have sprung from it) seeks to account for and use the uncertainty inherent in models, much in the way that humans must deal with uncertainty. What makes the technologies I discuss in this article different is that rather than merely simulate human thought patterns, they enlist humans as part of the system.

You may also replay the presentation with audio by registering at this site:

Note that these links will be active only for ninety days.

Thanks to everyone who came…we’ll do another one soon.

The iPod was a great innovation in product design – an elegant simplification of the user interface for music players that made it enjoyable to use a portable player. But its success was linked to the iTunes Store, which extended the usefulness of the device by providing convenient access to downloadable content. In the Apple ecosphere, the content provided by the store spurred sales of the devices (iPod, iPod Touch, IPhone, and iPad) and was a decent profit center in its own right. The combination of well-designed products and the convenience of instant shopping has allowed Apple to be extraordinarily profitable by avoiding having to discount its products.

Contrast that with Amazon, whose Kindle line of e-readers has continued to evolve, most recently into the Kindle Fire, a 7-inch Android-based multimedia tablet, priced at $199, well below that of similar devices. In the days that followed its release, visitors to Amazon.com were greeted by an announcement that openly declared Amazon’s commitment to pricing its devices aggressively.

Why? It’s clear that Amazon sees these devices as a way to sell more of the digital content (books, music, apps, and video) that comprises a growing and important portion of its revenue. Amazon is pursuing what is sometimes called the razor-razorblade model: sell a product for a low price, and make money from the consumables that the product requires.

While it may be easier to develop convenient consumption for media devices – after all, the consumables are digital products, which are simple to buy and to deliver – I always wonder why other manufacturers don’t do more to try to retain more of customers’ spending over the lifecycle. I recognize that there are hurdles – in product design, communication, and distribution – but surely greater convenience can be built in.

Here are two things I thought about in the kitchen this morning that I would be willing to pay for:

• A refrigerator that would sense when the water filter needs replacing and that would order a new filter
• A coffee machine that would track how many cups I’ve brewed and order a new supply when needed

Now, I know the technology of “smart” appliances has been floated for years, and that the challenges in connecting devices to networks have been difficult to overcome. But millions, probably tens of millions, of U.S. households have wireless networks, on which ordinary consumers are connecting all sorts of gadgets. How difficult would it be to incorporate Wi-Fi Protected Setup into products, thus letting them connect across the Internet? Connected appliances will further grow the manufactures ability to convert consumers to consumable subscription services or extended services. The growth of this coveted long term customer revenue should be considered a part of overall product strategy, the same way Apple and Amazon factor in the value of their add-on services.

This is the thinking behind several initiatives that manufacturers have been talking about. For example, LG Electronics announced LG Thinq at the Consumer Electronics show in January, 2011, but while promising, the technology still delivers fairly trivial functionality (like letting me see whether a load of laundry is done from my smartphone) or still requires me to do a lot of the work (like making me manage refrigerator inventory manually).

Still, it’s a step in the right direction. But it will be important to move such functionality from simply being gee-whiz gadgetry that can be easily copied by a competitor to a service that introduces such a degree of convenience that I’ll be brand loyal and be willing to pay for the convenience.

Feel free to leave a comment on products that you would like to see deliver more service. Or if you think this is a future we’ll never see (or isn’t worth the effort, if we do), leave those comments, too.

One of the toughest marketing challenges is determining which products and services to offer customers. Cross-selling to existing customers is an efficient way to boost revenue and profits, but it requires one to be able to identify which product is most relevant at any given time. This challenging problem is compounded when we need to deliver relevant cross-selling messages across many channels.

Interested to see how we are helping businesses turn this challenge into an opportunity? Check out my recent DM News article on this subject; I think you might find some hopeful news!

NEW YORK – October 12, 2011- Fulcrum, a leader in advanced analytics, technology and multichannel marketing solutions for marketing, will present an industry expert from Forrester Research, Inc., in a webinar that will offer marketers keen insights on recognizing and reaching their customers across channels.

Each new channel provides a new opportunity to engage customers and prospects in fresh ways, but at the same time introduces challenges around data integration, customer intelligence, and integrated marketing.

“Unlocking the 5 Key Strategies for Excellence in Cross-Channel Marketing” will feature expert advice and overview from Forrester Senior Analyst Fatemeh Khatibloo, plus client examples from data-driven marketing expert David King, Fulcrum executive vice president. They’ll cover real-world marketing considerations, best practices, and practical solutions for mastering the complexity of cross-channel marketing.

Marketers will learn key strategies for addressing:

1. Data – Learning to control data and build it into a strategic asset for cross-channel marketing by breaking down marketing and customer data silos to effectively reach customers across channels.
2. Customer intelligence – Using analytics to generate powerful insights that drive more effective cross-channel programs
3. Relevance – Turning data and insights into communications that engage customers and drive purchases
4. Investment – Making the right investments in technology and processes that generate the greatest benefits
5. Internal communications – Making a compelling case for investments to C-suite executives and communicating success stories to the enterprise

The webinar will look at these challenges in “the age of the customer,” when marketers now face channel & device fragmentation and consumer demand for relevance, and need to cope by burying the marketing funnel and empowering customer-centricity with customer intelligence.

“As a top customer intelligence expert, Fatemeh has valuable insights to share with all marketers as we deal with this fundamental shift in how we recognize and understand our customers and most effectively communicate with them across channels,” said Fulcrum’s King. “It really is the age of the customer, with a whole new set of challenges and opportunities that can make or break today’s marketers, depending on how well they use customer intelligence to approach this change.”

Join Fulcrum on Tuesday, October 25 from 2:00-3:00 ET for this informative webinar. Click on this link to register for “Unlocking the 5 Key Strategies for Excellence in Cross-Channel Marketing”.

Reliability

Analytics agility "stack"

What we desire from any analysis, whether it is a simple report or a complex statistical model, is that it provide reliable information for making business decisions. This notion of reliability has two sides: on the one hand, we want to make the “right” decisions, ones that provide some benefit to the organization; and it is even more important to avoid bad decisions that cause harm to the business.

Notice that I did not use the word “accuracy” to describe this quality. While a simple report might be said to be accurate when it correctly restates data from a database, with any sort of model, what we get is an approximation – a description – of the underlying data. It’s why we talk about “fitting” a model to the data.

Even a good model will have conditions under which is inaccurate; as long as these conditions are recognized and are reasonably consistent, then we can rely on the model for helping us in making decisions.

The definition of what makes a model reliable merits a whole separate discussion, and quickly can lead to arguments of nearly religious fervor among analysts. I have found it more useful to employ more generalized measures of reliability.  For instance, lets’s say we have a model designed to predict the amount customers will spend. One approach might be to use the point estimates of such a model (e.g., Customer A is expected to spend $101; Customer B, $79, and so on).  Another approach could be to use the relative rank ordering: Customer A will spend approximately 25% more than Customer B.

In many cases, such a model will be more reliable when rank ordering than it is in making point estimates, and will be just as useful for making decisions, which in this case is that we can invest more in Customer A and expect a higher return on the same investment.

Stability

This example leads to a related notion: model stability. In most marketing environments, the conditions underlying a model change over time. Such changes arise from many sources: there are arbitrary changes to the data; population composition evolves over time; and new competitive, regulatory, or macro-economic events arise. What we need is a model that demonstrates resilience to such changes, so that it remains reasonably reliable for some time. Again, all things being equal, the predictive model of future value is likely to be more stable over time when rank ordering than it is in making specific estimates.

Here, too, approaches will vary. For example, it is often necessary to make sure that time series data covers a sufficient amount of time to capture the underlying processes or to guard against such factors as seasonality. Otherwise, a model might be considerably less accurate during some times of the year or at different stages of a customer’s lifecycle.

In some circumstances, particular techniques may be helpful in promoting stability. For example, sometimes useful attributes exhibit high cardinality: one company we helped several years ago managed local advertising in about 1,000 markets and in over 3,000 product and service categories. A typical approach would have been to cluster “similar” markets and categories together in order to reduce cardinality, an approach that might have produced a reasonably reliable model in the short term, but that would have been vulnerable to market- and sector-level changes that would have affected the reliability of the clusters.

Ultimately, we used a Hierarchical Bayesian model that preserved the information, allowed the model to be more resilient to changes in markets and categories, and even permitted us to handle new values (e.g. a new market that the company had entered) by assigning them the Bayesian average for the population. The result was not only a model that made reliable predictions now, but one that proved stable over time.

Agility

From an analytical perspective, agility arises when the decision-making process becomes more efficient, so that a company can react to changes in the market in ways that protect and increase competitive position.

Regrettably, I find that many organizations have developed a reporting culture, rather than an analytical culture. In other words, much decision-making relies more on reports of past and current activity, rather than on analysis that reveals underlying associations and longer-term trends.

Here’s an illustration: let’s say the September sales figures for our company show a 5% decline from August. In some companies, this would lead to immediate panic. Some managers might question the reliability of the report, and ask for the numbers to be reproduced, something that might take several days. If the results were confirmed, management would then embark on a search for the potential causes of the decline, discuss strategies to correct the problem, and then work to design corrective tactics. Implementing changes might take weeks or months longer, by which the situation may have deteriorated even more.

In a more analytical culture, such a decline would have been anticipated. For example, the company might have invested heavily in acquisition in the preceding year, have noted that this cohort of new customers was not repurchasing at the expected rate, and that the unusually large number of acquisitions in September of last year, would lead to a decline in renewals this September. Management would not only have anticipated the decline, but could have worked to mitigate its effects, perhaps by increasing retention efforts among newer customers or by replacing them proactively.

I recognize that this is a somewhat extreme example – only the most obtuse organization would have no inkling of trouble of this magnitude ahead. But on a smaller scale, such reporting tunnel vision is all too common. The expression “paralysis through analysis” should perhaps be changed to “paralysis through reporting,” since a proper approach to analysis has quite the opposite effect. Indeed, achieving marketing agility increasingly requires companies to build an analytical discipline rather than merely a reporting capability.

As company consider what analytics to invest in, I suggest that they focus first on activities that create greater agility on a business level: does forecasting potential price increases let one lock in rates, making one more competitive in the near future? Does better customer research lead to more rapid product design? Does a customer segmentation allow one to create marketing programs more flexibly?

From there, each new analytical initiative and output needs to be vetted for its reliability and stability. Moreover, these factors need to be monitored, so that the analytics continue to be useful to decision makers, and ideally, actually are improved over time.

Having worked with many clients over recent years, we recognized the potential of ESP programs to generate a lot of useful data for companies. Today, Fulcrum uses this data to create accessory programs, maintenance programs, causal parts analysis, and predictive loss cost forecasting to name but a few applications. We’ve also developed a novel methodology to project the insights of ESP behavior to the larger customer base to help with customer segmentation and response models for CRM initiatives.

In this article, I’ll provide a few details on how we found value within the data, how we use it today, and how you may be missing a big part of your business by not using ESP data as an asset.

In the early days of our work on ESP programs, Fulcrum brought its unique analytical skills to solve common business problems ESP’s face. Many of the “asks” were around increasing response, finding the optimal price and increasing the overall value of a product registration while reducing risk.

Over time, we developed an extensive base of statistical methodologies, marketing strategies and execution know-how to solve ESP business problems. What we also saw emerge was a set of customer marketing opportunities largely ignored by traditional CRM folks.

As often happens when our teams work to solve new business problem, they also find unexpected value in the data sets they are provided. When viewed through an analyst’s lens, with marketing and technology people in the room, the ideas and opportunities quickly emerged.

After several different ESP programs had been analyzed, patterns started to develop. The data generated from these programs told similar stories regardless of industry or product. What Fulcrum was finding was that the data itself was an extremely valuable asset, yet the value was not understood by our clients and in many cases was being collected by an outsourced insurance company or servicer contracted to fix or replace broken products. Incredibly in some cases, the vendor contracts did not provide rights for clients to access their own data.

As one example of the data’s richness, Fulcrum found the purchase of accessories or the use of OEM consumables to be one of the most predictive values of ESP adoption. As it turns out, the inverse is also true: ESP adoption also is a great predictor of accessory and consumable purchase.

Other insights included the ability to predict repurchase timing, the likelihood to participate in a recall, register other products, or become an advocate in the social space.

Once these predictive values were understood, Fulcrum began to fold them into various CRM applications. In many cases, the data itself created more value than the original ESP program being analyzed or the steps made to optimize it.

From a purely analytical perspective, two main products were born out of the wider work. Predictive Loss Cost, the statistical methodology to accurately predict loss run-off at product levels and Causal Part, a statistical methodology to find the reasons for failure and the predicted breakage on units already sold. The former is critical to finance departments that need to manage risk and that need to comply with accounting standards. The second is highly useful to engineering, manufacturing, and sourcing groups, since we can isolate problems with specific assemblies and parts.

Each of these studies provides incredible new insight to our clients, but it also changed the way they look at their business. From a risk and reward perspective, clients now have the ability to adjust price or limit risk at the product level, not just the average of all the products together (which is the typical insurance model). And for engineering, they now have the ability to proactively make changes before the issue grows and provide maintenance programs that can reduce the loss of money and customer perception. In the end, both of these insights help both the company grow and provide better customer offerings and service.

There are many other valuable insights from ESP data. The data created and collected from these programs should be protected and used as a company asset. Leave a comment or give us a call if you have any other suggestions for using ESP data. Our team is always available for a conversation.

One of the biggest challenges CRM marketers face trying to creating customer continuity is the lack of legitimate and tangible value propositions available between purchases. Marketers have used their seemingly boundless creativity to develop benefits to create this perception. As an example, many companies tackle the problem by designing some form of loyalty currency (miles, points, credits, stamps on a card, etc.) to extend and hold this valuable relationship.

One tool that is rarely discussed as a part of an overall strategy is the ESP program. Putting aside that ESP programs are generally run by finance or service groups (or are outsourced) for a moment, the real barrier to using ESP as a customer continuity tool is the low conversion rate that arises when ESP programs are geared solely toward short-term goals of operational efficiency and immediate profitability.

I suggest that the potential long-term gains in customer value from ESPs is far greater than the current strategy designs for short term efficiency.

What if you took a different approach?

What if you took long term value into consideration by using the inherent extended customer bond that is created by ESP’s?

Imagine if you significantly increased adoption and contract term lengths by lowering the ESP price in return for only allowing covered and non-covered repairs to be made with genuine manufacturer parts. Could you off-set the increase in claims with significant increase in genuine parts sold to servicers and dealers? Surveys show that a significant proportion of customer dissatisfaction around repairs is the need for re-repair, something often traced to the use of non-OEM parts. Would a revised service program like this not only provide a good revenue stream, but improvements in customer experience, as well?

Or…what if in return for a lower price and a longer contract term, we request the customer to schedule an annual, low-cost maintenance appointment? Would the additional margin from additional services and accessories sold cover the additional risk of extending service and the cost of the appointments? This would give an opportunity to provide a positive service experience at solicit for add on accessories and services once per year for a high percentage of your customer base.

Too risky? Wait? You know what the risk is. You’ve been keeping a watch on your reserve for years – you need to project out to the higher adoption numbers of your customer base. Is that a number you can cover with other services and programs, if you have guaranteed access to that customer base? Is that a number you can beat?

Naturally, the answers to the preceding questions will be influenced by the specifics of your products and customer base. Fulcrum has designed many innovative approaches to increasing profit between purchases, while at the same time providing higher levels of customer continuity and loyalty. We’ve found that it’s important to weigh short-term opportunities against longer-term, and potentially more valuable, ones,

Try including ESP programs as part of the discussion for increased growth and loyalty in your next planning session. A well executed ESP program can serve as an important platform to create a relationship with your customer base on your own terms – this is the true value of ESP.

In the next installment I’ll discuss how the data collected from these efforts can offer new insights into customer behavior and product engineering, as well as create new profit centers and predict when a customer is in a product replacement cycle.