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Purchase Funnel Measuring AwarenessWe at TRC conduct a lot of choice-based research, with the goal of aligning our studies with real-world decision-making. Lately, though, I’ve been involved in a number of projects in which the primary objective is not to determine choice, but rather awareness. Awareness is the first – and arguably the most critical - part of the purchase funnel. After all, you can’t very well buy or use something if you don’t know it exists. So getting the word out about your brand, a new product or a product enhancement matters.

Awareness research presents several challenges that aren’t necessarily faced in other types of research. Here’s a list of a few items to keep in mind as you embark on an awareness study:

Don’t tip your hand. If you’re measuring awareness of your brand, your ad campaign or one of your products, do not announce at the start of the survey that your company is the sponsor. Otherwise you’ve influenced the very thing you’re trying to measure. You may be required to reveal your identity (if you’re using customer emails to recruit, for example), but you can let participants know up front that you’ll reveal the sponsor at the conclusion of the survey. And do so.

The more surveys the better. Much of awareness research focuses on measuring what happens before and after a specific event or series of events. The most prevalent use of this technique is in ad campaign research. A critical decision factor is how many surveys you should do in each phase. And the answer is, as many as you can afford. The goal is to minimize the margin of error around the results: if your pre-campaign awareness score is 45% and your post-campaign score is 52%, is that a real difference? You can be reasonably assured that it is if you surveyed 500 in each wave, but not if you only surveyed 100. The more participants you survey, the more secure you’ll be that the results are based on real market shifts.

Match your samples. Regardless of how many surveys you do each wave, it’s important that the samples are matched. By that we mean that the make-up of the participants should be as consistent with each other as possible each time you measure. Once again, we want to make certain that results are “real” and aren’t due to methodological choices. You can do this ahead of time by setting quotas, after the fact through weighting, or both. Of course, you can’t control for every single variable. At the very least, you want the key demographics to align.

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I'm a runner and enjoy participating in races. Last May I ran the Delaware Half Marathon and had my worst race ever. What happened? Poor planning. I failed to put together a training plan to prepare me for my race.

This can sometimes happen in Market Research. Poor planning can lead to disastrous results that provide little insight or fail to answer the objectives of the research. Planning is especially important when advanced analytics are used, for example, conjoint that is often used during product development or pricing research. There are many questions to be asked during the planning phase of conjoint design. How should we frame up the exercise? How many features should be evaluated? How many levels for each feature? How many product choices should be presented to a respondent at a time? How should each feature and level be described? Should any prohibitions be used? Sometimes we can lose sight of the research objective amid all the details. A good conjoint plan will keep all parties focused on the end goal. These are all issues I'm contemplating as I design my conjoint exercise (stay tuned for results in my next blog!). I'm taking the time now to properly plan and design my conjoint.

A well thought out plan ensures quality results just as a well thought out running plan ensures a good race! After my half marathon disaster I planned for my next race the same way I would for a conjoint. I considered a number of questions while designing my training plan. How far in advance should I train? How many times a week should I run? Should I enlist a running buddy for the longer runs? My goal was to run a good race. I'm happy to report the planning paid off as I completed the Marine Corps Marathon (my first marathon!) in the time I was hoping for.

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UFO sighting causation correlation market researchSmallI read a blurb in The Economist about UFO sightings. They charted some 90,000 reports and found that UFO's are, as they put it, "considerate". They tend not to interrupt the work day or sleep. Rather, they tend to be seen far more often in the evening (peaking around 10PM) and more on Friday nights than other nights.
The Economist dubbed the hours of maximum UFO activity to be "drinking hours" and implied that in fact that drinking was the cause of all those sightings.
As researchers, we know that correlation does not mean causation. Of course their analysis is interesting and possibly correct, but it is superficial. One could argue (and I'm sure certain "experts" on the History Channel would) that it is in fact the UFO activity that causes people to want to drink, but by limiting their analysis to two factors (time of day/number of sightings), The Economist ignore other explanations.
For example, the low number of sightings during sleeping hours would make perfect sense (most of us sleep indoors with our eyes closed). The same might be true for the lower number during work hours (many people don't have ready access to a window and those who do are often focused on their computer screen and not the little green men taking soil samples out the window).
As researchers, we need to consider all the possibilities. Questionnaires should be constructed to include questions that help us understand all the factors that drive decision making. Analysis should, where possible, use multivariate techniques so that we can truly measure the impact of one factor over another. Of course, constructing questions that allow respondents to express their thinking is also key...while a long attribute rating battery might seem like it is being "comprehensive" it is more likely mind numbing for the respondent. We of course prefer to use techniques like Max-Diff, Bracket™ or Discrete Choice to figure out what drives behavior.
Hopefully I've given you something to think about tonight when you are sitting on the porch, having a drink and watching the skies.

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What Does the Fox Say?

Posted by on in Market Research

Nate Silver’s much anticipated (at least by some of us) new venture, launched recently. In his manifesto he describes it as a “data journalism” effort, and for those of us who have followed his work over the last five years – from the use of sabermetrics in baseball analysis through the predictions of presidential politics – there is plenty to look forward to. Apart from the above topics, his website is focusing on other interesting areas such as science, economics and lifestyle, bringing data-driven rigor and simple explanation to the understanding of all these fields. It follows the template of the blog he ran for the New York Times as well his bestselling book, The Signal and the Noise: Why So Many Predictions Fail, But Some Don’t. As a market researcher, I found much to like in the basic framework he has laid out for his effort.

In critiquing traditional journalism, Nate describes a quadrant using two axes – Qualitative versus Quantitative, and Rigorous & Empirical versus Anecdotal & Ad-hoc.

qual quant market researchSource:www.fivethirtyeight.com

He is looking to occupy the mostly open top left quadrant, while arguing that opinion columnists too often occupy the bottom right quadrant and traditional journalism generally occupies the bottom left quadrant. For someone with such a quantitative background he is not dismissing the qualitative side at all. On the contrary, he argues that it is possible to be qualitative and rigorous and empirical, if one is careful about the observations made (and cites examples of journalists such as Ezra Klein, who occupy the top right quadrant).

For those of us in market research the qualitative versus quantitative dimension is, of course, very familiar. Somewhat less so is the second dimension – rigorous and empirical versus anecdotal and ad-hoc. But this second dimension is especially important to consider because it directly affects our ability to appropriately generalize the insights we develop. As practicing researchers, we know that qualitative research is excellent for discovery and quantitative is great for generalizations. But we also know that is not always the way things are done in practice.

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Big news today as Ron Johnson, CEO of J.C. Penney “resigned”. He did so following a series of decisions designed to make the staid Penney’s brand more hip. Sadly, thus far these changes have chased away existing customers without attracting enough new ones to turn around the store chain’s long decline.

Johnson’s decisions, like those of his mentor Steve Jobs, were made from the gut…no need for any market research. Jobs of course had an almost magic touch. Carefully choosing the markets to enter, when to do so and producing products that were seen as cutting edge. Often his decisions were seen as counter intuitive (such as the opening of retail stores in the Internet age), but time and time again he was proven to be right. So, why didn’t it work for Mr. Johnson?

First, Mr. Jobs was producing products for Apple, not J.C. Penney. Apple was known as a producer of fine computers that were easy to use (intuitive is a word often used). Some of the luster came off that reputation when Jobs left the company, but when he returned there was little doubt what Apple stood for and the types of products to expect. From the moment he returned he looked for places where that reputation (intuitive electronics) might find a market. Mr. Johnson, by contrast saw the J.C. Penney reputation as a problem and looked to change it…a far tougher task.

Second, Jobs often had success by leaping into relatively new markets and then using the power or Apple design and engineering to dominate it. He didn’t create the first digital music player, but he created one that was intuitive and he backed it up with a legal way to buy digital music. He could do this without giving up the existing Apple business (computers). Johnson needed to focus resources on shoring up the flailing store chain…perhaps if he’d had the luxury of creating small J.C.Penney Boutiques it would have worked.

Third, I am reminded of something that I once heard the legendary Warren Mitofsky say with regards to flawed sampling, “Results will be right until they aren’t”. Mr. Jobs was not always right. He left Apple the first time a failure (one could argue that others were at least as much to blame), started a new company that was largely a failure and then started his run, first at Pixar and then his triumphant return to Apple. He was clearly brilliant and had incredible vision, but he was not always right. Of course, it goes without saying that not everyone has the same skills (me included).

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