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Begin with the End—Lessons Learned

Posted by Caitlin Dailey

Fri, Feb 02, 2018

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A former colleague of mine had a post-it note on his wall that read: You have as many hours in the day as Beyoncé. Inspiring words, but for those of us in professional services (rather than entertainment) it can feel like the to-do lists never end.

I recently watched a webinar on productivity given by MIT Sloan School of Management Senior Lecturer Robert Pozen. There was a lot of useful information in the webinar, but one piece of advice really resonated: begin with the end.

“Beginning with the end” means letting your desired outcome drive the planning and execution of your task. If you are cognizant of what your end-goal is, it will make tackling projects of any scope a lot easier—whether that’s writing an email or the final report of a multi-phased segmentation study.

At CMB, we always begin with the end in mind. When kicking off a project, we meet with key client stakeholders to align on business and research objectives. We leverage our proprietary Business Decision tools to identify what the desired business objectives are, and use that information to inform our research objectives and design. This preliminary decision-focused conversation ensures the research solution, story, and results are actionable and will deliver meaningful outcomes with true business impact.

Once the project is kicked off, no time should be wasted—consider building out a narrative and recording tentative conclusions as soon as data starts coming in. It can be tremendously helpful to have a mid-field check of the data to revise those conclusions, and then do a final revision once you have all the data. The story might not change much during this time, but writing and revising your conclusions prior to the close of an initiative can make delivering the final report less stressful.

Particularly in market research, there’s pressure to deliver results faster than ever. When you start with the end in mind, you can be building out the story in an iterative process, rather than scrambling to at the end. Since unearthing a clear and meaningful story is one of the most important pieces of a project, you’re only helping yourself (and your colleagues) by beginning with the end.

Other ways to improve productivity

As I mentioned, there were loads of other useful tips from Pozen’s webinar on how to increase productivity:

  • Write down your daily goals: Rome wasn’t built in a day, so jot down objectives you can realistically accomplish today.
  • Don’t exhaust your schedule: Avoid scheduling every minute of your day. Having a calendar filled with meetings may look productive, but it’s important to include “thinking time” for yourself.
  • Include work and non-work tasks: Your list should include routine essentials like going to the gym or having dinner with your family. This will help maintain a healthy work/life balance and will give you time to “recharge”.
  • Manage your inbox: If you’re in the zone, don’t feel pressured to stop and respond to each email immediately (unless it’s urgent, of course). Instead, set aside time a little later to respond to all emails.
  • Let go of perfectionism: Do you reread an email 5 times before you hit send? Scan through a deck repeatedly? Chances are, it were ready to go after the second review, so save your mental energy for something else and move on.
  • Quit procrastinating: One of the biggest hurdles to getting things done is simply starting them.

I’m now being mindful of how I can incorporate these practices into my life to maximize my productivity, and in turn, hope to tip the scale of my work/life balance in favor of a more stress-free work week. I hope you can too!

Caitlin Dailey is a Senior Project Manager on the Financial Services, Insurance, and Healthcare team at CMB and is looking forward to trying out these tactics to help get her out of the office a little earlier in 2018.

Topics: business decisions, research design, methodology

If you can’t trust your sample sources, you can’t trust your data

Posted by Jared Huizenga

Wed, Apr 19, 2017

people with word bubbles-2.jpgDuring a recent data collection orientation for new CMB employees, someone asked me how we select the online sample providers we work with on a regular basis. Each week, my Field Services team receives multiple requests from sample providers—some we know from conferences, others from what we’ve read in industry publications, and some that are entirely new to us.

When vetting new sample providers, a good place to start is the ESOMAR 28 Questions to Help Buyers of Online Samples. Per the site, these questions “help research buyers think about issues related to online samples.”

An online sample provider should be able to answer the ESOMAR 28 questions; consider red flagging any that won’t. If their answers are too brief and don’t provide much insight into their procedures, it’s okay to ask them for more information, or just move along to the next. 

While all 28 questions are valuable, here are a few that I pay close attention to:

Please describe and explain the type(s) of online sample sources from which you get respondents. Are these databases?  Actively managed research panels?  Direct marketing lists?  Social networks?  Web intercept (also known as river) samples?  

Many online sample providers use multiple methods, so these options aren’t always exclusive. I’m a firm believer in knowing where the sample is coming from, but there isn’t necessarily one “right” answer to this question. Depending on the project and the population you are looking for, different methods may need to be used to get the desired results.

Are your sample source(s) used solely for market research? If not, what other purposes are they used for? 

Beware of providers that use sample sources for non-research purposes. If a provider states that they are using their sample for something other than research, at the very least you should probe them for more details so that you feel comfortable in what those other purposes are. Otherwise, pass on the provider.

Do you employ a survey router? 

A survey router is software that directs potential respondents to a questionnaire for which they may qualify. There are pros and cons to survey routers, and they have become such a touchy subject that several of the ESOMAR 28 questions are devoted to the topic of routers. I’m not a big fan of survey routers, since they can be easily abused by dishonest respondents. If a company uses a survey router as part of their standard practice, be sure you have a very clear understanding of how the router is used as well as any restrictions they place on router usage.

You should also be wary of any sample provider who tells you that your quality control (QC) measures are too strict. This happened to me a few years ago and, needless to say, it ended our relationship with the company. This is not to say that QC measures can’t be too restrictive, and in those cases you can actually be throwing out good data.

At CMB, we did a lot of research prior to implementing our QC standards.  We consulted peers and sample providers to get a good understanding of what was fair and reasonable in the market. We investigated speeding criteria, red herring options, and how to look at open-ended responses. We revisit these standards on a regular basis to make sure they are still relevant. 

Since each of our tried and true providers support our QC standards, when a new (to us) sample provider tells us we’re rejecting too many of their panelists due to poor quality, you can understand how that raises a red flag. Legitimate sample providers will appreciate the feedback on “bad” respondents because it helps them to improve the quality of their sample.

There are tons of online sample providers in the marketplace, but not every partner is a good fit for everyone. While I won’t make specific recommendations, I urge you to consider the three questions I referenced above when selecting your partner.

At Chadwick Martin Bailey, we’ve worked hard to establish trusted relationships with a handful of online sample providers. They’re dedicated to delivering high quality sample and have a true “partnership” mentality. 

In my world of data collection, recommending the best sample providers to my internal clients is extremely important. This is key to providing our clients with sound insights and recommendations that support confident, strategic decision-making. 

Jared Huizenga is CMB’s Field Services Director, and has been in market research industry for nineteen years. When he isn’t enjoying the exciting world of data collection, he can be found competing at barbecue contests as the pitmaster of the team Insane Swine BBQ.

 

 

Topics: data collection, methodology

Panels: The Unsung Research Hero

Posted by Will Buxton

Wed, Jan 25, 2017

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Market research has its rock star methodologies—segmentations, conjoint analyses, Bayes Nets —attention-grabbing methods that can garner incredible insights and drive acquisition and growth. You can find a lot of blogs (and white papers and conference presentations) on these methods but this blog isn’t one of them. No, this blog is dedicated to the unsung research methodology: proprietary panels.

Admittedly, a panel doesn’t sound sexy—it's a group of respondents who are regularly tapped to answer business questions relating to anything from product testing to ad testing. Whether it’s a consumer or business-to-business (B2B) panel, panels collect ongoing feedback from a select group of people who adhere to certain criteria.

So why consider a panel for your next research project?

Quality participants: Panels offer on-demand access to a pool of aware, engaged, and knowledgeable participants who are typically well-versed in the client/product offerings.

Speed of production: Panelists provide the opportunity for “quick hit” projects that typically require upfront education, set up, and programming time.

Efficiency: Panels use a standard process for timing, deployment and reporting, all of this saves time—both for the provider and the client.

Cost: Depending on survey length and complexity, a panel can be a more cost-effective way to contact customers/providers because of the preexisting relationship between client and panelist. This can avoid the need for large incentives.

Responsiveness: Panelists are more responsive than Gen Pop sample because of the aforementioned relationship. This allows for a quicker collection of more respondents and a faster project turnaround.

Dedicated resources: Each panel (at least here at CMB) has a dedicated, well-trained team that is privy to how the panel operates, including client restrictions and best practices.

So while traditional MaxDiff or Discrete Choice Model might have more buzzword appeal around the office, don’t underestimate the value a customer/B2B panel can bring to your research project.            [Twitter bird.pngTweet this!]

Will is a Project Manager who is clearly trying to turn CMB into a panel house.

PS – Join Dr. Erica Carranza on 2/1 and learn about our newest methodology, AffinIDSM, that’s grounded in the importance of consumer identity.

Register Now!

 

Topics: methodology, panels, consumer insights

Dear Dr. Jay: HOW can we trust predictive models after the 2016 election?

Posted by Dr. Jay Weiner

Thu, Jan 12, 2017

Dear Dr. Jay,

After the 2016 election, how will I ever be able to trust predictive models again?

Alyssa


Dear Alyssa,

Data Happens!

Whether we’re talking about political polling or market research, to build good models, we need good inputs. Or as the old saying goes: “garbage in, garbage out”.  Let’s look at all the sources of error in the data itself:DRJAY-9-2.png

  • First, we make it too easy for respondents to say “yes” and “no” and they try to help us by guessing what answer we want to hear. For example, we ask for purchase intent to a new product idea. The respondent often overstates the true likelihood of buying the product.
  • Second, we give respondents perfect information. We create 100% awareness when we show the respondent a new product concept.  In reality, we know we will never achieve 100% awareness in the market.  There are some folks who live under a rock and of course, the client will never really spend enough money on advertising to even get close.
  • Third, the sample frame may not be truly representative of the population we hope to project to. This is one of the key issues in political polling because the population is comprised of those who actually voted (not registered voters).  For models to be correct, we need to predict which voters will actually show up to the polls and how they voted.  The good news in market research is that the population is usually not a moving target.

Now, let’s consider the sources of error in building predictive models.  The first step in building a predictive model is to specify the model.  If you’re a purist, you begin with a hypotheses, collect the data, test the hypotheses and draw conclusions.  If we fail to reject the null hypotheses, we should formulate a new hypotheses and collect new data.  What do we actually do?  We mine the data until we get significant results.  Why?  Because data collection is expensive.  One possible outcome from continuing to mine the data looking for a better model is a model that is only good at predicting the data you have and not too accurate in predicting the results using new inputs. 

It is up to the analyst to decide what is statistically meaningful versus what is managerially meaningful.  There are a number of websites where you can find “interesting” relationships in data.  Some examples of spurious correlations include:

  • Divorce rate in Maine and the per capita consumption of margarine
  • Number of people who die by becoming entangled in their bedsheets and the total revenue of US ski resorts
  • Per capita consumption of mozzarella cheese (US) and the number of civil engineering doctorates awarded (US)

In short, you can build a model that’s accurate but still wouldn’t be of any use (or make any sense) to your client. And the fact is, there’s always a certain amount of error in any model we build—we could be wrong, just by chance.  Ultimately, it’s up to the analyst to understand not only the tools and inputs they’re using but the business (or political) context.

Dr. Jay loves designing really big, complex choice models.  With over 20 years of DCM experience, he’s never met a design challenge he couldn’t solve. 

PS – Have you registered for our webinar yet!? Join Dr. Erica Carranza as she explains why to change what consumers think of your brand, you must change their image of the people who use it.

What: The Key to Consumer-Centricity: Your Brand User Image

When: February 1, 2017 @ 1PM EST

Register Now!

 

 

Topics: Dear Dr. Jay, predictive analytics, methodology, data collection

A Year in Review: Our Favorite Blogs from 2016

Posted by Savannah House

Thu, Dec 29, 2016

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What a year 2016 was.

In a year characterized by disruption, one constant is how we approach our blog: each CMBer contributes at least one post per year. And while asking each employee to write may seem cumbersome, it’s our way of ensuring that we provide you with a variety of perspectives, experiences, and insights into the ever-evolving world of market research, analytics, and consulting.

Before the clock strikes midnight and we bid adieu to this year, let’s take a moment to reflect on some favorite blogs we published over the last twelve months:

    1. When you think of a Porsche driver, who comes to mind? How old is he? What’s she like? Whoever it is, along with that image comes a perceived favored 2016 presidential candidate. Harnessing AffinIDSM and the results of our 2016 Consumer Identity Research, we found a skew towards one of the candidates for nearly every one of the 90 brands we tested.  Read Erica Carranza’s post and check out brands yourself with our interactive dashboard. Interested in learning more? Join Erica for our upcoming webinar: The Key to Consumer-Centricity: Your Brand User Image  
    2. During introspection, it’s easy to focus on our weaknesses. But what if we put all that energy towards our strengths? Blair Bailey discusses the benefits of Strength-Based Leadership—realizing growth potential in developing our strengths rather than focusing on our weaknesses. In 2017, let’s all take a page from Blair’s book and concentrate on what we’re good at instead of what we aren’t.
    3. Did you attend a conference in 2016? Going to any in 2017? CMB’s Business Development Lead, Julie Kurd, maps out a game plan to get the most ROI from attending a conference. Though this post is specific to TMRE, these recommendations could be applied to any industry conference where you’re aiming to garner leads and build relationships. 
    4. In 2016 we released the results of our Social Currency research – a five industry, 90 brand study to identify which consumer behaviors drive equity and Social Currency. Of the industry reports, one of our favorites is the beer edition. So pull up a stool, grab a pint, and learn from Ed Loessi, Director of Product Development and Innovation, how Social Currency helps insights pros and marketers create content and messaging that supports consumer identity.
    5. It’s a mobile world and we’re just living in it. Today we (yes, we) expect to use our smartphones with ease and have little patience for poor design. And as market researchers who depend on a quality pool of human respondents, the trend towards mobile is a reality we can’t ignore. CMB’s Director of Field Services, Jared Huizenga, weighs in on how we can adapt to keep our smart(phone) respondents happy – at least long enough for them to “complete” the study. 
    6. When you think of “innovation,” what comes to mind? The next generation iPhone? A self-driving car? While there are obvious tangible examples of innovation, professional service agencies like CMB are innovating, too. In fact, earlier this year we hired Ed Loessi to spearhead our Product Development and Innovation team. Sr. Research Associate, Lauren Sears, sat down with Ed to learn more about what it means for an agency like CMB to be “innovative.” 
    7. There’s something to be said for “too much of a good thing” – information being one of those things. To help manage the data overload we (and are clients) are often exposed to, Project Manager, Jen Golden, discusses the merits of focusing on one thing at a time (or research objective), keeping a clear space (or questionnaire) and avoiding trending topics (or looking at every single data point in a report). 
    8. According to our 2016 study on millennials and money, women ages 21-30 are driven, idealistic, and feel they budget and plan well enough. However, there’s a disparity when it comes to confidence in investing: nearly twice as many young women don’t feel confident in their investing decisions compared to their male counterparts. Lori Vellucci discusses how financial service providers have a lot of work to do to educate, motivate and inspire millennial women investors. 
    9. Admit it, you can’t get enough of Prince William and Princess Kate. The British Royals are more than a family – they’re a brand that’s embedded itself into the bedrock of American pop culture. So if the Royals can do it, why can’t other British brands infiltrate the coveted American marketplace, too? Before a brand enters a new international market, British native and CMB Project Manager, Josh Fortey, contends, the decision should be based on a solid foundation of research.
    10. We round out our list with a favorite from our “Dear Dr. Jay Series.” When considering a product, we often focus on its functional benefits. But as Dr. Jay, our VP of Advanced Analytics and Chief Methodologist, explains, the emotional attributes (how the brand/product makes us feel) are about as predictive of future behaviors of the functional benefits of the product. So brands, let's spread the love!

We thank you for being a loyal reader throughout 2016. Stay tuned because we’ve got some pretty cool content for 2017 that you won’t want to miss.

From everyone at CMB, we wish you much health and success in 2017 and beyond.

PS - There’s still time to make your New Year’s Resolution! Become a better marketer in 2017 and signup for our upcoming webinar on consumer identity:

Register Now!

 

Savannah House is a Senior Marketing Coordinator at CMB. A lifelong aspiration of hers is to own a pet sloth, but since the Boston rental market isn’t so keen on exotic animals, she’d settle for a visit to the Sloth Sanctuary in Costa Rica.

 

Topics: strategy consulting, advanced analytics, methodology, consumer insights