Here is a draft the second part of my article published by Financial Times Press in September, 2007. This article will be posted on FTPress.com on January, 25 2008 (in edited form of course).

Improving the "Stickiness" of Your Website Further:
Part 2: If they like A and B, would they like A+B?

Alex Gofman,

Vice President, Moskowitz Jacobs Inc.

Interactions in consumer research: searching for a needle in the hay

A few years ago, Heinz introduced quite weird Funky Fries – chocolate flavored and blue-colored fries. Heinz bet was on combining some highly popular ideas. Huge army of the consumers loves fries. Even bigger (arguably) crowd is sucker for chocolate. And kids love color.

As you can guess (or already know), the product has failed miserably. The ideas were so divergent that there was no synergy between them in the eyes of the consumers. Quite opposite, by putting the conflicting ideas together they lost appeal of both fries munchers and chocolate connoisseurs producing a negative effect (Bhatnagar, 2003).

In the marketing lexicon, the situation when reaction of consumers (their liking scores, purchase intent, etc.) to the messages (or ideas, elements of a package or a web page, etc.) combined together are not equal to the sum of their individual ratings, is called an interaction. A positive interaction (when customers' liking of the combined offer is higher than the sum of individual items scores) is called synergism. If customers like the combined idea less than the sum of individual liking scores of the components, then it is called a suppression (a negative interaction).

The problem lies in the shear number of possible pairs of elements. For example, if we have six placeholders on a webpage with six possible alternatives for each one, there are 540 possible pairs of elements.

This should explain why until very recently, the effect of interactions either was ignored or considered a middle ground between art and heavy statistics. In latter case, it required an expert guess about possible significant pairs. Such several 'alleged' (guessed) interactions were then tested with the consumers through a sophisticated statistical method of incorporating these pairs into the survey to confirm / reject the hypothesis.

Market researches tried to tackle the issue for many years (e.g., Green, 1973). Yet, many years later, if the expert was right (or lucky?) in foretelling the potential interactions, the results could lead to improved ideas. If not – too bad: some great ideas might have been discarded unnoticed or bad ideas went into production undetected.

Extending RDE to discover all and any interactions

In the previous article Improving the "Stickiness" of Your Website, we discussed Multivariate Landing Page Optimization (MVLPO) approach which helps to identify a winning combination of the elements of a webpage. Rule Developing Experimentation (RDE)
paradigm introduced in the article mixes and matches the elements of the page according to an experimental design and presents synthesized web pages to consumers for evaluation. Collected data then used to estimate individual contribution of every element to the liking of the web pages (conditional probability of people buying from this site, for example). This in turn allowed us to construct the most appealing webpage from the set of elements tested.

In most cases, the results of this approach help you to create optimized web pages. In a number of occasions although, some latent interactions exist between the elements of the page. Using a highly trained expert opinion to guess these interactions is not a very viable option in the fast moving world of web site design not taking into account the price implications. RDE easily overcomes the limitations of the old methods by automatically testing all and every combination of the elements of the page multiple times according to a built-in unique permuted experimental designs. Because the complexity of the statistical foundation are usually incorporated inside the tool, no special knowledge on the user side is needed (if you are still interested, you can find the details in Gofman, 2006; Moskowitz, Gofman, 2004).

Now let's explore how to make sure that the winning individual parts of the pages, when combined, do not fail. Furthermore, let's see how to find a combination of Web page elements that together produces more impact than just the sum of individual impacts. Putting to use the basic math formulas:

We do not want: 1+1 < 2

We want: 1+1 > 2

Golf Gear Case Study: deeper data mining

Note: All the data in this and previous articles are from the actual project, although the visuals and other marketing materials are representative equivalents and not related to any specific website.

In the previous article, we followed the operator of an online golf store who wanted to optimize the landing page to increase the conversion rate and revenue per visit. As it catered to affluent golf players, the general traffic was not very heavy. However, the revenue per customer (RPV) and the customer lifetime value (CLV) were high because the site sold luxury and premium equipment and strived to retain their patrons. The combination of these conditions precluded the operator from experimenting on live website to avoid possible less than optimal experience for their valuable customers.

The operator chose to use MVLPO in a simulated environment using an RDE tool. She had several options for the banner, feature picture, and different promotions and at the end of the project discovered the best combination of these components (Figure 1). She found out that by choosing 'wrong' elements (the lowest scoring vs. the highest) she would loose half of her potential clients. Or, in reverse, by selecting the best possible elements, she could double the number of happy visitors willing to buy from her site.

In virtually any MVLPO case based on traditional methods, this would be the end of the research stage. RDE on the other hand allows for mining the data even deeper.

Figure 1. Optimized webpage for the golf site without taking into account any possible interactions. The conditional probability of visitors being interested in buying from this site was 48%.

In some cases, there are potential interactions between the elements of the page (both positive and negative). Because of the unique permutation algorithm of experimental design, RDE allows for all and every combination of the elements to appear on the test screens multiple time. This means that it is possible to include them as independent variables into regression model. In our case, we have 90 possible combinations.

If this sounds for some readers a bit like a less than pleasurable lecture in statistics, don't quit reading. The good news – this is all incorporated inside RDE approach and available at a virtually 'point-and-click' level. One does not need to know how bits and bytes are moving inside a processor to use a PC for browsing. The same thing is true about discovering possible interaction using RDE – you do not to be a professor of statistics to find it out – RDE does it for you.

Not every case produces meaningful interactions. In many occasions, interactions are not very strong and could be ignored (considered not significant). If the utility (conditional probability of customers being interesting in buying from this site) of the combination is below the empirical threshold of (+/- 5), it could be discarded. In that case, the results of MVLPO would look like Table 1 in the previous article.

It also should be noted that the effect of the interactions changes the regression model and affects somewhat the rest of the utilities. In a model without interactions, the values of hidden synergies and suppressions are distributed among the individual elements. In a more detailed regression model that includes interactions, the values are extracted and assigned to the cross-terms (pairs of elements).

Comparing Standard and Interactions Models

Table 1 contains the utilities of the individual elements of the web page with several discovered meaningful interactions (right column) compared with the Standard model (middle column) from the previous article. This case does not have very high interactions values (in some cases, an interaction along could add 20 or more points to the liking score) but it does demonstrate the approach.

Table 1. Performance of the elements with interactions. Notice, that the values are somewhat different for the model with interactions compared to the standard model.

			Standard Model	Interactions Model
Base Size				125
Constant			10	9
Banners
A3	Banner 3		0	-1
A1	Banner 1		-1	0
A2	Banner 2		-1	-1
Promo 1
B2	Free shipping		7	7
B3	$5.99 shipping		3	2
B1	Free $50 card		3	3
Visuals
C2	Golfer playing		16	15
C3	High-tech club		8	8
C1	Golf shoes		8	7
Promo 2
D2	Final clearance-up to 65% off		12	13
D1	Save up to $100		8	8
D3	Free personalization		4	4
Promo 3
E1	St. Andrews Sweepstakes		3	3
E2	115% price guarantee		3	3
E3	Golf vacation entry		0	0
INTERACTIONS
A2*C2			N/A	6
D2*E3			N/A	7
C1*E2			N/A	-9

The data suggest that the winning web page from the previous article was not the one that generates the highest interest in customers to buy from the site.

The optimal webpage (from the previous article) based on the standard model was:

(Conditional Probability of visitors buying from the site) =

= Const + A3 + B2 + C2 + D2 + E1 = 48%.

We can get a higher purchase intent score if we use a slightly different set of elements:

(Conditional Probability of visitors buying from the site) =

= Const + A2 + B2 + C2 + D2 + E3 + D2*E3 + A2*C2 =

= 9 + (-1) + 7 + 15 + 13 + 0 + 7 + 6 = 56%,

producing the optimal concept presented on Figure 2.

We have replaced two marginally higher scoring elements in two categories with lower scoring ones: in Banners, we switched from A3 (0) to A2 (-1); and in Promo 3, from E1(+3) to E3(0). Although with these subtle changes we have lost 4% in the individual values, the identified interactions in the case study compensated the shortfall and added additional 8% to the purchase intent (note, that the utilities for the interaction model are slightly different from the standard regression model and the elements in the case study are representative).

Figure 2. The highest scoring webpage created using Interactions Model. Although the differences are very subtle, the page has 8% higher conditional probability of customers buying from it compared to Standard Model optimization (Fig. 1).

Conclusions

This case study does not have the most impressive interactions I've seen in my experience. Sometimes, the synergy between the elements reaches 15-20 points or even more. In some cases, there are no significant interactions at all. Yet in some others, a negative interaction (suppression) is so strong that it negates the high positive contribution of individual elements (if any).

For many years, the researchers knew about the existence of possible interactions and tried to identify them by incorporating several handpicked pairs into surveys, usually by guessing. With the introduction of RDE to MVLPO, the permuted individual designs afforded for testing all and any possible combinations of the elements multiple times allowing for more precise models and more targeted optimized pages.

The bottom line, it is difficult not to agree that improving the conversion rate by 10-20% would make a very big difference for virtually any website operator. It is possible to achieve that by just recombining your existing materials with a tad deeper data-mining available in some tools as a simple push of a button.

References

Bhatnagar, Parija (06/20/2003). Blue food goes down the drain. CNN/Money. Retrieved on 11/21/2007.

Gofman, A. (2006). Emergent Scenarios, Synergies, And Suppressions Uncovered Within Conjoint Analysis. Journal of Sensory Studies, 2006, 21(4): 373-414.

Gofman, A. Improving the 'Stickeness' of Your Website. Financial Times Press (09/21-2007). Retrieved on 11/21/2007.

Green, Paul E. (1973). On the Analysis of Interactions in Marketing Research Data.
Journal of Marketing Research, Vol. 10, No. 4 (Nov., 1973), pp. 410-420

Moskowitz, H.R. and Gofman, A. (2004). A System and Method for Performing Conjoint Analysis. U.S. Provisional Application No. 60/538,787, Patent Pending.

Moskowitz, Howard R. and A. Gofman (2007). Selling Blue Elephants: How to make great products that people want BEFORE they even know they want them. Wharton School Publishing, 2007.

Improving the ‘Stickiness’ of Your Website (Financial Times Press)

Some time ago I have published an article in Financial Times Press (although I am still a bit confused with the editorial 'chain' - the article was submitted to Knowledge @ Wharton). Here is the 'teaser' of the paper:

Financial Times Press, September, 21:
"For a long time, the only solution to make websites appealing and "sticky" was to rely on gurus (web designers who were just supposed to know the "right" answers). But what if the guru made a mistake or did not take into account all the variables and created less-than-optimal pages? Alex Gofman explores ways to involve consumers in the co-creation process in the form of multivariate landing page optimization as a possible solution for the problem of the ever-increasing bounce rate on many websites."

You can read the full paper at:
http://www.ftpress.com/articles/article.aspx?p=1015178&rl=1.

I have just completed a 'sequel' for this paper and hope to post it shortly.

My columns at Daily News and Analysis: How to Defeat Murphy’s Law in the Stock Markets

Daily News and Analysis, October 4, 2007
How to Defeat Murphy’s Law in the Stock Markets
Alex Gofman

Merck & Co recently announced that it has agreed to pay $4.85 billion to settle most of the claims that its painkiller Vioxx caused heart attacks and strokes in thousands of users. Although the settlement amount is almost twice as big as the GDP of Mongolia, it is substantially less than many analysts have expected.

In 2004, the news broke that one of the most powerful painkillers on the market, Vioxx, might be implicated in heart attacks. The following lawsuits, adverse publicity, less than optimal corporate responses by Merck and other drug companies in the pain-killer business had the inevitable impact on the stock prices of Merck and the “Big Pharma” in total. In just a few days Merck’s stock tumbled about 40% bringing down the whole pharmaceutical sector (to a lesser extent) and wiping out tens of billions of dollars in the sector’s market capitalization for shareholders. Investors lost fortunes, although some of the Big Pharma companies fared better than others. If one could predict what would be a reaction of investors in such crisis situation on a company by company basis…

On the other side of the conflict, if a company knows a possible response of investors and general public on some of the messages used by it’s PR in such crisis situation, it could have a tremendous impact on the brand image, finances and the future performance. But do they always know? Even some venerable corporations stumbled under the stress in a crisis because they were not prepared. A classic example of such unapt communications happened shortly after the launch of the Mercedes-Benz A-class in 1997 when one of the cars overturned during a test drive conducted by journalists in Sweden, triggering a major crisis for the car manufacturer. The reputation of Mercedes was at stake as the company was accused of producing unsafe cars. Early ill-equipped PR responses by Mercedes only succeeded in exacerbating the crisis, as they fumbled around with what they were going to say and then said the wrong thing at the wrong time.

Is it possible to be prepared to handle a potential crisis when, according to Murphy’s Law, anything that can go wrong, will? Going a bit further, is it possible to try to capitalize on the stock market during such tumult?

This is what the Rule Developing Experimentation (RDE), introduced in my previous articles (October 4, November 1), augurs to do. I could see some skeptical smiles on the faces of the readers saying, “Nobody could predict the stock market”. RDE does not predict the actual stock market performance. It quantifies the expected emotional reaction of investors to specific news and can even drill down the data on brand specific basis. For example, if the FDA (Food and Drug Administration, particularly empowered to oversee the safety of medications) announced that they discovered some new side effects in a flu vaccine, what would be the attitude of investors toward buying, holding or selling the stock of that company and other players in the sector? An astute and prepared investor could use this knowledge to his advantage with potentially huge profit. The ‘defendant’ would be anxiously sitting on the edge of the chair anticipating the answers on how different would be the attitude of the public if the right set of messages is promptly and confidently communicated. Is it possible for the company to ‘repair’ the damage and ‘engineer’ the public sentiments on the issues? Politicians have manipulated public opinions for ages, so why not?

Chance favors the prepared mind, as Louis Pasteur used to say. To be prepared to answer the questions, we can build a model of the consumers / investors minds using the RDE approach. It is not especially difficult, and a majority of businessmen could easily do that themselves.

Here is an example of the insights one could get from the model that was created at the peak of the Vioxx crisis. We searched the Internet for news and announcements about the case from media, FDA, public, experts and Merck itself. The messages were distilled to concise snippets (called elements), grouped by similarity into silos and put into an RDE tool for an automatic mixing and matching according to an experimental design. RDE created a set of vignettes representing a combination of the messages. A random group of investors was invited to participate in the online project and indicate their proclivity to buy, hold or sell the stock if they see the specific news (the details of the process could be found in Selling Blue Elephants book or at http://www.sellingblueelephants.com/ website).

The resulting regression model was so lucid that some experts called the approach a new behavioral economics sub-discipline. The data suggested that if, for example, investors read that The medication was pulled off the market after the company found the problem, the message would cause about 6% of them to change their attitude from buy to sell. But if the company communicated fast that It is in agreement with the FDA that this medication can be safely used for pain relief. Consumers should not exceed the recommended dose or take the product for longer than directed, this would effectively reverse the impact of the former news as, according to the model, it would increase the conditional probability of investors buying the stock by 6%.

The messages do not have a universal effect, much like fashionable cloth is attractive on models but often ludicrous on the majority of us. The messages are time and brand specific. The same message used by different companies in the same market environment will cause substantially different reaction. A model built in the midst of the Vioxx crisis showed that the message The manufacturer will continue to work with the FDA to sponsor a major clinical study to further assess this medication did not affect investors proclivity to buy the Pfizer’s stock while decreasing it by 10% for Merck. The same message in the same market conditions suggested an increase(!) in intended buying of Bayer and Wyeth shares by 6% and 7% respectively.

The easy and insightful results - what wins and loses, interactions between brands and messaging - give the stock analyst and the shareholder a sense of what people say they are likely to do. The vox populi, the feelings about each particular stock “in current time” in a specific situation, can then be compared against the suggestions of analysts, to determine where there are opportunities, where the analysts say one thing but the common voice of the crowd suggests something quite different. The same vox populi gives corporations a fair chance to prepare their PR for different crisis situations with suggested measured response.

As universal and resilient as it is, Murphy’s Law can’t be evaded, but its effects can be counteracted, neutralized and even utilized for profit with diligent preparation.

_________________________
Alex Gofman is VP of Moskowitz Jacobs Inc., a NY based company, and a co-author of the book Selling Blue Elephants: How to Make Great Products That People Want Before They Even Know They Want Them (www.SellingBlueElephants.com) written with Dr. Moskowitz and recently republished in India (it is also currently translated in twelve countries). He may be contacted at alexgofman@sellingblueelephants.com.

My columns at Daily News and Analysis: Customer Research and the Curse of the Rear View Mirror

Daily News and Analysis, November 1, 2007
Customer Research and the Curse of the Rear View Mirror
Alex Gofman

Would you trust a driver to bring you to your destination if 95% of the driving time he spends looking at the rear view mirror? Even if it were the best and most sophisticated mirror in the world, with all possible bells and whistles to detect any obstacles and dangerous places AFTER you passed them?

A few days ago, I was a guest lecturer at the Wharton Business School (University of Pennsylvania), which many consider to be the best business school in the world. The class was in marketing research, and approximately half of the students were from Asia (mostly Chinese and Indian). Coincidently, my presentation was built around a hypothetical group of Asian kids successfully creating new-to-the-world products using advanced marketing research tools. Actually, it was not a coincidence. And here is why.

I work in the marketing research field which, in the West, is a huge and well funded industry. Annually, billions of dollars are spent on research that theoretically should help corporations sell more products with more profit to more consumers. On the surface it seems to work just fine – a lion portion of the US economy is based on consumer spending. A dirty little secret of the market research industry is that a huge majority of the money spent on research is wasted or not used.

The ‘staples’ of market research are tracking studies, consumer satisfaction and the like. Tracking studies are what happened in the past. It is quite easy and straightforward (but not necessarily cheap) to conduct them. In many corporations, it is a must (like a white shirt and tie). If you want to succeed in MR and be promoted or moved up one day into a high paying marketing department, you just have to do them! Tracking studies produce very nice looking pie charts in thick reports and give you a chance to shine during a presentation without being challenged. How could one challenge something that happened in the past? Billions of dollars go into this type of research. People get promoted because of it. And only about 5% (!) of the data is ever used!

On the other hand, if someone at a corporation tries to experiment with getting innovative consumer insights and finds better new products or invents a revolutionary service – this is another story. Expect to be annihilated by others who did not get this idea before! Any future forecast is easy to challenge. Corporate America, Europe and Japan are entrenched in the most ‘important’ war, an all-consuming task of … saving their jobs. Forget about the social or even corporate interests! We need to save our jobs! The truth is, nobody was ever fired for playing it safe by the approved rules even if the rules do not, did not and will not produce results. In marketing research, conducting a tracking study or a customer satisfaction survey is a ‘safe’ and ‘prudent’ way to climb the corporate ladder. It looks nice on the shelf and on a resume. If one tries to step out of the box and does something avant-garde that could bring a fortune to the company, this rebel most likely will be humiliated, attacked and even fired for violating the ‘order’.

Do not take me wrong – it’s very important to know what happened in the past. But much more imperative is what we do in the future trying to find new or improved products or services that people need and like. This decision cannot be based entirely on past experience which as we know it, is not a reliable predictor of the future. Of course, looking in the past could help to define the future. But if you spend most of your resources dwelling on the bygone events, you can not move ahead.

In the 20th century, Americans managed to beat Europe economically because of their risk-taking, ‘could be done’ attitude, inventing and achieving that which had never worked before. Some called them crazy, but the nay-sayers were ignored, and they kept moving ahead. Yet in most cases, this is no longer true. We are not as ‘hungry’ anymore. The initiative has now shifted to Asia, where young and energetic entrepreneurs are eager to get their piece of the world’s riches. They are not afraid to take risks. They are keen to experiment and try the new, ‘risky’ methods and tools available to achieve their goals. There are no ‘approved’ and ‘safe’ approaches (at least, not very many) to bind them to the past.

Many of the innovative methods that help companies create better products and services faster and in a more targeted manner, such as Rule Developing Experimentation (RDE), discussed in my previous column (October 4, 2007), are faster and more enthusiastically embraced in Asia than in the West where they were originally invented. Is it that these methods look forward too much and are thus too risky by Western standards for corporate employees?

And while their American counterparts prepare for self-serving corporate politics, Asian students are looking for everything that they can find to win their place at the world’s table, regardless of how risky from the corporate point of view. They will not be afraid to step out of the box and experiment, once they enter that world. They will continue to press forward, and only use the rear view mirror occasionally, just to avoid possible accidents and accumulate experience. I can see it in them.

__________________________
Alex Gofman is VP of Moskowitz Jacobs Inc., a NY based company, and a co-author of the book Selling Blue Elephants: How to Make Great Products That People Want Before They Even Know They Want Them (www.SellingBlueElephants.com) written with Dr. Howard Moskowitz and recently republished in India (it is currently translated in twelve countries). He may be contacted at alexgofman@sellingblueelephants.com.

My column at Daily News and Analysis: How to Defeat Murphy’s Law in the Stock Markets

After Selling Blue Elephants was republished in India, I got an unexpected invitation from the second largest (and the fastest growing) business newspaper in India, Daily News and Analysis (www.DNAIndia.com), to write a few columns for their Marketing and Management section.
Here are the copies.


++++++++++++++++++++++++++++++
Daily News and Analysis, October 4, 2007
Consumers know what they want. Or do they?
Alex Gofman

My daughter knows exactly what she wants. In a restaurant, she could order without even looking at the menu. And she always like her order. I, on the other hand, regret my choice the moment I see someone else’s dish delivered. THIS is what I want! Why didn’t I order it?!

It is a truism that to succeed in business you need to know what your customers want. In other words, a route to success appears to be simple: just ask your customers about their needs and desires and try to fulfill them. Sounds like a prudent way, but is it?

If you are, for example, in the banking business and want to create a new credit card offer that will send your bottom line off the charts, you could just ask what kind of card people want. Chances are, you will ‘find’ that they want 0% APR for the rest of their life, free airline miles for them and everybody they know and a lot of cash back for just having the card.

Not very insightful results. True, people may want all of that but how actionable is this knowledge? And the ‘insights’ are produced by the same consumers that relatively easily and realistically choose between real life offers and trade-offs. Asking them to explain why they like one or another may not help either. It is like asking a high school boy why he fell in love with the girl from his class. He knows he is deeply and madly in love with her but can he explain what specifically he likes about her? And would other people agree with him?

Asking customers in direct terms what they need and want will not work in most cases. Companies spend fortunes on focus groups and 80% to 90% of new product launches, based on the input from those groups, fail.

As Malcolm Gladwell once said, we cannot always explain what we want deep down (actually, he formulated this idea after interviewing my co-author of Selling Blue Elephants, world-renowned experimental psychologist Dr. Howard Moskowitz, but this is another story).

Does it mean that we eliminate the customers from the process of product creation and contest the famous John Wannamaker’s adage that the customer is always right? No and another categorical no. Customers might not be able to explain what they want and need but they will easily choose the winning offer if they see the options. An astute businessman should experiment with his offering, create multiple prototypes (physical or conceptual) and solicit customer feedback (liking, purchase intent, etc.) to find a potential winner. This is a much easier exercise for the consumers – they get to choose among different products on the shelves, various websites, offers, etc.

Businesses (some of them intuitively) understood this long ago. Companies like Seiko go through thousands of designs, tested in real stores (like in the Akihabara district of Tokyo), before shipping them around the world.

What is missing in many cases, is a disciplined approach to the experimentation afforded by the new paradigm Rule Developing Experimentation (RDE) co-developed with the Wharton Business School of the University of Pennsylvania (the best business school in the US and arguably in the world) and introduced in the book Selling Blue Elephants: How to Make Great Products That People Want Before They Even Know They Want Them.

RDE is a systematized, solution-oriented business process of experimentation that designs, tests, and modifies alternative ideas, packages, products, or services in a disciplined way so that the developer and marketer discover what appeals to the customer, even if the customer can’t articulate the need, much less the solution!

Scientific details of RDE, which is based on a unique application of experimental designs (conjoint analysis), might be daunting for a leisurely reader and well beyond the scope of this column. Until some time ago, it was an exclusive domain of statisticians and university professors. Fortunately, recent advances in software development and proliferation of the Internet allowed the algorithms to be incorporated in simple-to-use WEB based tools that can be deployed by anybody, anywhere around the world, without virtually any knowledge of statistics. The task is quite simple. First, you need to split your potential proposition into parts (buckets of ideas). In the case of the credit card offer, it could be different APRs, Security Guarantees, Rewards Options, Prestige Messages, etc. Second, you enter several options for each of the ‘buckets’ such as 2.5% cash back for gas purchases; One airline mile for every 100 Rupees spent, etc.

An offer may have 3, 4, 5 or more such ‘buckets’ with several options in each. An RDE tool will automatically mix and match the ideas according to an experimental design, present them to customers via WEB interviews asking them to rate how likely they would be to apply for this card - screen by screen (usually, between 20 and 50). This task is very simple for the majority of consumers. The tool accumulates the responses and at the end of the interview automatically calculates how much each idea individually adds or detracts from the purchase intent (regression model).

RDE helps businessmen to create better products, marketers - to optimize advertising, web designers - to find the most impactful landing pages, political candidates - to fine-tune their platform and messages, package designers - to synthesize packages that ‘fly’ off the shelves, investors – to know the reaction of the stock market on potential news, etc.

Many Fortune 500 companies like HP, Citibank, Unilever, Microsoft, Pepsi-Cola, etc. have benefited from using RDE. Their RDE experience could be summarized as the following: if you want to succeed by knowing what your customers want and need, do not ask them directly – show them experimentally designed prototypes according to the RDE rules and let them rate the prototypes (whether it’s a new product, an advertisement, a promotion idea, a mixture of ingredients in a soft drink, etc.). The result - the algebra of the consumer mind with precise knowledge about what works, what does not and for whom.

The very first use of RDE for credit cards (similar to our example exercise in the beginning of this column) by the HSBC bank in Hong Kong helped the issuer to achieve annual goals of new customer acquisition in the first two months. Six banks tried to issue affinity cards linked to the world football cup at the same time. HSBC’s use of RDE helped it to win while all other launches failed. Currently, MasterCard and Discover license this technology worldwide.

In another example, a wide cross-divisional use of RDE by Hewlett Packard helped the computer giant to create what they called “an always-on intelligence system”. The technology company has brought the consumer to the table in every design initiative or marketing decision in a way and scale that was unprecedented for HP. RDE fit in perfectly with HP’s new goals becoming one of the “evidence-promoting” components of their business and, in HP’s own words, with some spectacular results.

It is easy for the businesses to work with the customers like my daughter – just asking what they want will do. For the rest huge majority of us, one has to use more sophisticated approaches like RDE. For that and for many other applications of RDE – read the book. It’s all there.

__________________________
Alex Gofman is VP of Moskowitz Jacobs Inc., a NY based company, and a co-author of the book Selling Blue Elephants: How to Make Great Products That People Want Before They Even Know They Want Them (www.SellingBlueElephants.com) written with Dr. Howard Moskowitz and recently republished in India (it is currently translated in twelve countries). He may be contacted at alexgofman@sellingblueelephants.com