Online Product Design Testing - A New Application for MaxDiff


Online testing of marketing concepts is now an established and proven practice. Numerous methodologies are used to test copy points, features and benefits. One methodology, MaxDiff, is becoming one of the most useful and straight-forward tools available to marketers today. The key characteristic of a MaxDiff design is that it forces respondents to choose between alternatives, which may be very similar and have a very similar appeal. Forcing respondents to choose the most preferred and least preferred item from a set of alternatives, results in a greater ability to analytically discriminate between the alternatives than is possible using traditional monadic rating questions.

Typically, MaxDiff research relies on descriptive elements (such as features and copy points) to define the most motivating marketing messages. Now, marketers are utilizing the MaxDiff approach with product visuals, including product concepts, packaging, logos and more. With MaxDiff, respondents are exposed to a set of images and are asked to select the best (most appealing, most important, etc.) and worst (least appealing, least important, etc.). Respondents typically complete several scenarios where each scenario contains a different subset of images. The scenarios are carefully created using an experimental design that ensures that each item is shown an equal number of times and in different order.

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Avoiding Cannibalization in New Product Development



Business Scenario: You currently offer a premium product in the market place. A competitor recently launched a “knock-off” version of your premium product at a lower price point and it’s eating away at your revenue. To counteract this you want to release a lower cost version of your premium product to compete with your competitor. However, you have one big concern, “Will releasing a lower-cost version of my premium product eat away at the revenue of the premium product – and ultimately reduce overall revenue?”

Good news - this is a common business problem. Even better news – there is a research approach that fits this business problem to a tee. The approach is called Conjoint Analysis. Instead of talking in general terms about how the approach works, we think it would be more valuable to show real-life applications of how this approach is used to address important business problems.

Let’s look at Conjoint Analysis directly through the lens of the business problem – “How do I avoid cannibalization in new product development?”

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Email Deliverabilty and What It Means for Marketers and Researchers



The mail must go through…


I’m reminded of an inscription from the James Farley Post Office in New York City that reads:

“Neither snow nor rain nor heat nor gloom of night stays these couriers from the swift completion of their appointed rounds.”

This quote is a translation from Herodotus, c. 500 B.C.E. That means we’ve been dealing with “getting the message through” for a long time. Add spam to that list and you have the context for getting email delivered to your customers and prospects.

First, the good news: spam is now under 50% of all email traffic, down from a high of just over 70% two years ago. Now for the bad news: without spam filtering, every other email in your inbox would be spam. Making things worse, if you are a business trying to reach your customers, spam filters still trap legitimate email.

Getting legitimate email through to its intended recipient is a challenge. Let’s spend a little time understanding why this is, and then discuss some simple – and not so simple -- things you can do to improve the situation.

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The Problem with Precision


If you had an 82% chance to win a bet, would you place the bet?

Why are we so enamored with precision? Why is it that if we can’t get an exact number, then we don’t want any number at all? There seems to be a misunderstanding among many marketers that we must clear up. It’s much better to be generally right than to be precisely wrong. If you’re looking to project sales volume for a specific customer segment 3 years out and you’re asking for an exact sales volume number, then you’re asking the wrong question. Again, it’s much better to be generally right than to be precisely wrong.


This way of thinking is the norm in other disciplines such as finance. For example, a financial risk analysis involves assessing the probability of a variety of outcomes under a range of input assumptions, and making an educated investment decision. The same strategy can be effective in the marketing world, too. Rather than picking a number, we should evaluate a range of different scenarios and make an educated marketing decision based on the likelihood of various outcomes.

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What TV is Teaching the Whole Advertising Industry



"As an industry we pay so much attention to average ratings and we don't talk about the effectiveness of the media enough. Reach is important, but the opportunity to bring the consumer down the sales funnel is more important." - Howard Shimmel, Chief Research Officer – Turner Broadcasting (source)


In 2010, 50 million tweets were being sent each day. By 2014, that number grew to a staggering 500 million tweets each day. This is a good example of the rapid growth taking place in digital media. To match this growth, spending on digital advertising is capturing more of the budget – primarily from traditional mediums like TV. With the rise of the digital age many people predicted the death of TV, but as we pointed out earlier this year, this assumption is “dead wrong.” On average, Americans spend more time watching television today than they did 10 years ago. Not only are people still consuming television at a high rate but a new study by MarketShare showed that TV remains the most effective medium for driving consumer purchases. The study measured the advertising effectiveness from some of the top advertisers over a 5-year period (2010-2014).

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Why Every Business Needs Conjoint Analysis


Do you know what Conjoint Analysis is? Well, you should. Every business should be doing Conjoint Analysis on a regular basis. Why am I so passionate about this? It’s because I’ve seen first-hand how powerful this methodology is. Granted, Conjoint Analysis has been around for a while, but it’s still vastly underused. I spoke with a brand manager for a nationally recognizable product last week and he described to me all of the business problems on his plate. About half of what he’s working on fits Conjoint Analysis to a tee so I asked him, “Are you familiar with Conjoint Analysis?” He looked at me like I asked him what the square root of 1,936 is. I couldn’t believe he had never heard of it. I felt like I had the secret answer to all his problems and I had to share it with him. Ok, maybe that’s a slight exaggeration, but I think everyone should be aware of how powerful Conjoint Analysis is. Don’t believe me? Let me show you…
First off, you may be wondering, “Who are we to be talking about this?” We don’t like to tip our own hat often on the blog but we know Conjoint just as well as anyone. We’ve conducted over 500 Conjoint studies in a variety of industries.
Here are a few things you should know about Conjoint Analysis…
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Practical Predictive Analytics Trends


I've been working in the predictive analytics ("data mining", "big data") field for several years and have noticed a few trends:
  • Clients have better (more, higher quality) data and are able to organize and deliver it more quickly
  • Access to "external" (e.g. weather, economic, etc...) data has improved dramatically
  • ETL tools have improved -- and don't have to cost as much as a new mini-van
  • Off-the-shelf statistics packages have added non-parametric predictive analytics tools
  • Purpose-specific modelling tools have improved in both function and form
  • Credible open source tools are available -- some have established themselves as "must haves" in the toolbox (R comes to mind)
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Historical vs Forward-Looking Research


What are you trying to solve for? What is your business objective? These are questions we regularly ask in order to make sure the research we’re doing will provide actionable results for our clients. Sometimes a client will come to us and tell us they’re working on developing messaging for a new product and want to do some historical data analysis to determine what messages will increase likelihood of purchase. Well, unfortunately, the historical research strategy may not be the best way to answer their question. One question we face for many projects is, “should the research look backwards (historical data) OR be forwarding-thinking (predictive and prescriptive)?”
The best way to frame this discussion is to think about the disclaimer mentioned on every investment product… “Past performance is not a guarantee of future results.” In other words, diving into what happened in the past may not be the best predictor of the current or future market.
Both historical and forward-looking strategies have their place. Let’s quickly look at when to look backwards and when to be forward-thinking.
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Why Ideas Are Crucial to Your Success


We often tell people we’re in the idea business. The ideas we work with primarily help shape two things: 1) New product development and 2) Messaging/communication. The idea generation and optimization process happens early on but, sadly, early-stage “fuzzy front end” research is rare and seems undervalued. It’s tempting to just go with gut feelings when addressing common business problems like, “What should we say?” or “What features should our new product have?”
Additionally, people often justify not investing in early-stage research because they can’t afford it. In reality, you can’t afford NOT to do it. According to Cincinnati research agency AcuPoll, a whopping 95% of new products introduced each year fail. And a study by Montoya-Weiss and O’Driscoll showed that even the slightest improvements in an organization’s new product development process can yield significant savings.
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Characteristics of Successful Tracking Studies



Formula for a Successful Tracking Study

According to the last few GRIT reports, tracking studies are in decline. Although this trend has been reported for years, we’re still seeing extremely well-done tracking studies on a regular basis. So this begs the question, “why is there a decline in a proven, viable methodology?”… and more importantly, “what are the characteristics of a “successful” tracking study?” Today we’ll focus more on addressing the latter because I think it will help give us insight into the first question as well.
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