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Dear Dr. Jay: Predictive Analytics

Posted by Dr. Jay Weiner

Mon, Apr 27, 2015

ddj investigates

Dear Dr. Jay, 

What’s hot in market research?

-Steve W., Chicago

 

Dear Steve, 

We’re two months into my column, and you’ve already asked one of my least favorite questions. But, I will give you some credit—you’re not the only one asking such questions. In a recent discussion on LinkedIn, Ray Poynter asked folks to anticipate the key MR buzzwords for 2015. Top picks included “wearables” and “passive data.” While these are certainly topics worthy of conversation, I was surprised Predictive Analytics (and Big Data), didn’t get more hits from the MR community. My theory: even though the MR community has been modeling data for years, we often don’t have the luxury of getting all the data that might prove useful to the analysis. It’s often clients who are drowning in a sea of information—not researchers.

On another trending LinkedIn post, Edward Appleton asked whether “80% Insights Understanding” is increasingly "good enough.” Here’s another place where Predictive Analytics may provide answers. Simply put, Predictive Analytics lets us predict the future based on a set of known conditions. For example, if we were able to improve our order processing time from 48 hours to 24 hours, Predictive Analytics could tell us the impact that would have on our customer satisfaction ratings and repeat purchases. Another example using non-survey data is predicting concept success using GRP buying data.


What do you need to perform this task? predictive analytics2

  • We need a dependent variable we would like to predict. This could be loyalty, likelihood to recommend, likelihood to redeem an offer, etc.
  • We need a set of variables that we believe influences this measure (independent variables). These might be factors that are controlled by the company, market factors, and other environmental conditions.
  • Next, we need a data set that has all of this information. This could be data you already have in house, secondary data, data we help you collect, or some combination of these sources of data.
  • Once we have an idea of the data we have and the data we need, the challenge becomes aggregating the information into a single database for analysis. One key challenge in integrating information across disparate sources of data is figuring out how to create unique rows of data for use in model building. We may need a database wizard to help merge multiple data sources that we deem useful to modeling.  This is probably the step in the process that requires the most time and effort. For example, we might have 20 years’ worth of concept measures and the GRP buys for each product launched. We can’t assign the GRPs for each concept to each respondent in the concept test. If we did, there wouldn’t be much variation in the data for a model. The observation level becomes a concept. We then aggregate the individual level responses across each concept and then append the GRP data. Now the challenge becomes one of the number of observations in the data set we’re analyzing.
  • Lastly, we need a smart analyst armed with the right statistical tools. Two tools we find useful for predictive analytics are Bayesian networks and TreeNet. Both tools are useful for different types of attributes. More often than not, we find the data sets comprised of scale data, ordinal data, and categorical data. It’s important to choose a tool that is capable of working with this type of information

The truth is, we’re always looking for the best (fastest, most accurate, useful, etc.) way to solve client challenges—whether they’re “new” or not. 

Got a burning research question? You can send your questions to DearDrJay@cmbinfo.com or submit anonymously here.

Dr. Jay Weiner is CMB’s senior methodologist and VP of Advanced Analytics. Jay earned his Ph.D. in Marketing/Research from the University of Texas at Arlington and regularly publishes and presents on topics, including conjoint, choice, and pricing.

Topics: advanced analytics, big data, Dear Dr. Jay, passive data

Tablet Purchase Journey Relies Heavily on Mobile Web

Posted by Chris Neal

Thu, Oct 16, 2014

consumer pulse, tabletsWe all know the consumer purchase journey has changed dramatically since the “mobile web” explosion and continues to evolve rapidly. In order to understand the current state of this evolving journey, CMB surveyed 2,000 recent buyers of tablets in the U.S. We confirmed several things that we expected to see, but we also busted a few myths along the way: 

1. TRUE: “Online media and advertising are now essential to influence consumers.”

  • Reading about tablets online and online advertisements are the top ways in which consumers learn about new brands or products. [Tweet this.]
  • Nearly everyone we surveyed does some type of research and evaluation online before buying—most commonly using online-only shopping sites (e.g., Amazon, eBay, etc.), general web searches, consumer electronics store websites, review websites (e.g., CNET, Engadget, etc.), or tablet manufacturer websites.

2. TRUE: “The mobile web is becoming more important in the consumer purchase journey.”

  • Over half of buyers use the mobile web during the research and evaluation phase, and nearly 40% of buyers do so as a part of the final purchase decision (although very few people actually purchase a tablet using a mobile device). [Tweet this.]

3. FALSE: Mobile applications are becoming very important in the consumer purchase journey.”

  • Although the mobile web is now highly influential, very little purchase journey activity actually happens from within a mobile application per se. This could be because tablet purchasing isn’t something that happens frequently for more individual consumers (high-frequency activities lend themselves better to a dedicated app to expedite and track them). [Tweet this.]

4. FALSE: “Social Media is becoming very important in the consumer purchase journey.”

  • The purchase journey for tablets is indeed very “social” (i.e., word-of-mouth and consumer reviews are hugely influential), but precious little of this socialization actually happens on social media platforms in the case of U.S. tablet buyers. [Tweet this.]

5. FALSE: “The Brick and Mortar Retail Store is Dead.”

  • The rise of all things online does not spell the death of brick and mortar retail in the consumer electronics category. In-store experiences (including speaking with retail sales associated and doing hands-on demos of tablets) were one of the top sources of influence during the research and evaluation phase, regardless of whether they ultimately bought their tablet in a physical store. 
  • Next to ads, in-store experiences were the top source of awareness for new tablet brands and models. 41% of those who learned about new makes/models during the process did so inside of a physical retail store. [Tweet this.]
  • Half of all buyers surveyed actually bought their tablet in a physical retail store. [Tweet this.]

6. TRUE: The line between “online” and “offline” purchase journeys is becoming blurred.

  • Most people use both online and offline sources during their purchase journey, and they typically influence one another. People doing research online may discover that a tablet model they are interested in is on sale at a particular retailer. At the same time, something a retail sales associate recommends to a shopper in a store may spur an online search in order to read other consumer reviews and see where they can get the recommended model the cheapest and fastest. Smartphone-based activities from within a retail store are just as common as interacting with an actual salesperson face-to-face at this point. 

The mobile web is undoubtedly here to stay, and how consumers go about making various different buying decisions will continue to evolve along with future changes in the mobile web. Here at CMB, we will continue to help companies and brands adapt to these shifts.

Download the full report. 

For more on our mobile stitching methodology, please see CMB's Chris Neal's webinar with Research Now: Watch the Webinar

Chris leads CMB’s Tech Practice. He enjoys spending time with his two kids and rock climbing.

Topics: technology research, mobile, path to purchase, advertising, Consumer Pulse, passive data, retail research, customer journey