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Youme Yai

Youme Yai is a Project Manager and believes a summer evening at a ballpark is second to none.

Recent Posts

The Essential Ingredients of a Successful Market Research Project

Posted by Youme Yai

Fri, Jan 11, 2019

baking ingredients-1.11.19

I’m a big fan of The Great British Baking Show—a tv series following the trials and tribulations of amateur bakers vying to be named the UK’s best baker. Each episode, the bakers tackle a different skill with increasing difficulty as the competition unfolds.

For those unfamiliar with the delightful show, the second portion of the competition, called the Technical Challenge, requires bakers to make an unfamiliar recipe with scant instructions. They must leverage their baking prowess and creativity to successfully make the recipe and impress the judges, Paul and Prue (or Mary, depending on which season you’re watching). Meanwhile, the competition is timed—which can be really tricky when baking time is unspecified.

As a custom market research project manager, I empathize with these contestants. Here are three ways the Technical Challenge is like managing a market research project:

  1. It's possible contestants have come across the Technical Challenge bake subject or recipe before. But even if they’re somewhat familiar, the recipes always have some unique element—making them feel new. Similarly, as a project manager you’ve probably done your fair share of customer journey, segmentation, or optimization. And while you may be an expert in each topic and approach, every project is 100% customized and will almost always include “new” elements. Maybe the sample is unique, or your client has a very specific business objective. Whatever it is, you’ll need to approach each project armed with your industry experience and thinking cap. Even with brand trackers which are usually repeated, each wave is unique and poses different circumstances. There are no exact prescribed instructions in market research—you must be innovative and open to new challenges.
  2. Ingredients are provided during the Technical Challenge, but not always with an explanation of how to properly integrate them into the bake. In a new research project, research objectives, desired business outcome, and potentially a sample list, may be the “ingredients” provided, but you as the project manager must successfully incorporate these components to uncover actionable insights that meet clients’ (or judges’) needs and expectations. While a crude “recipe” exists for research (e.g., Step 1: Questionnaire Development, Step 2: Fielding, Step 3: Analysis, Step 4: Reporting, Step 5: Delivery and consultation to client) it’s up to the research team to use their industry knowledge and experience to successfully account for all "ingredients."
  3. A fielding period, like the bake time in a Technical Challenge, may not be explicitly specified at the onset of a project. Therefore, you must use your best judgment and expertise to determine the length of fielding. Much like the bakers watching the oven, you must carefully monitor the metaphorical research oven—response rates, panel entries, etc.—until you’ve achieved desired results.

Unlike the British cake bakers and pastry makers, research project managers are backed by a dedicated team that is integral to the success of each project. From the Advanced Analytics team and Senior Consultants with robust industry expertise, successful custom market research projects are a team effort.

Of course, there are a few more differences (no worries about soggy bottoms or overbaked Genoese sponge for example).

At the end of the Great British Baking Show, the bake is eaten, contestants are judged, a winner is announced, and that’s that. But as market researchers, we don’t just deliver a final report and the show ends. A successful initiative means socializing the findings, conducting follow-up discussions, and more--being true, consultative strategic partners to our clients. 

Still, when our clients tell us how our insights and recommendations have made a concrete difference in their business—well that’s as good as a Paul Hollywood handshake.

Youme Yai is a Project Manager at CMB who is on a search for the perfect chocolate chip cookies recipe (suggestions welcome!)

Topics: research design, project management

Does your metric have a home(plate)?

Posted by Youme Yai

Thu, Sep 28, 2017


Last month I attended a Red Sox/Yankees matchup at Fenway Park. By the seventh inning, the Sox had already cycled through seven pitchers. Fans were starting to lose patience and one guy even jumped on the field for entertainment. While others were losing interest, I stayed engaged in the game—not because of the action that was (not) unfolding, but because of the game statistics.

Statistics have been at the heart of baseball for as long as the sport’s been around. Few other sports track individual and team stats with such precision and detail (I suggest reading Michael Lewis’ Moneyball if you haven’t already). As a spectator, you know exactly what’s happening at all times, and this is one of my favorite things about baseball. As much as I enjoy watching the hits, runs, steals, strikes, etc., unfold on the field, it’s equally fun to watch those plays translate into statistics—witnessing the rise and fall of individual players and teams.

Traditionally batting average (# of hits divided by number of at bats) and earned run average (# of earned runs allowed by a pitcher per nine innings) have dominated the statistical world of baseball, but there are SO many others recorded. There’s RBI (runs batted in), OPS (on-base plus slugging), ISO (isolated power: raw power of a hitter by counting only extra-base hits and type of hit), FIP (fielding independent pitching: similar to ERA but focuses solely on pitching, and removes results on balls hit into field of play), and even xFIP (expected fielding independent pitching; or in layman’s term: how a pitcher performs independent of how his teammates perform once the ball is in play, but also accounting for home runs given up vs. home run average in league). And that's just the tip of the iceberg. 

With all this data, sabermetrics can yield some unwieldy metrics that have little applicability or predictive power. And sometimes we see this happen in market research. There are times when we are asked to collect hard-to-justify variables in our studies. While it seems sensible to gather as much information as possible, there’s such a thing as “too much” where it starts to dilute the goal and clarity of the project.  

So, I’ll take off my baseball cap and put on my researcher’s hat for this: as you develop your questionnaire, evaluate whether a metric is a “nice to have” or a “need to have.” Here are some things to keep in mind as you evaluate your metrics:

  1. Determine the overall business objective: What is the business question I am looking to answer based on this research? Keep reminding yourself of this objective.
  2. Identify the hypothesis (or hypotheses) that make up the objective: What are the preconceived notions that will lead to an informed business decision?
  3. Establish the pieces of information to prove or disprove the hypothesis: What data do I need to verify the assumption, or invalidate it?
  4. Assess if your metrics align to the information necessary to prove or disprove one or more of your identified hypotheses.

If your metric doesn’t have a home (plate) in one of the hypotheses, then discard it or turn it into one that does. Following this list can make the difference in accumulating a lot of data that produces no actionable results, or one that meets your initial business goal.

Combing through unnecessary data points is cumbersome and costly, so be judicious with your red pen in striking out useless questions. Don’t get bogged down with information if it isn’t directly helping achieve your business goal. Here at CMB, we partner with clients to minimize this effect and help meet study objectives starting well before the data collection stage.

Youme Yai is a Project Manager at CMB who believes a summer evening at the ballpark is second to none.


Topics: advanced analytics, data collection, predictive analytics