MarketTools Blog

In a recent post on the GreenBook Blog, Tony Cosentino highlights the “quote of the conference” from last week’s TMRTE (The Market Research Technology Event), made by one of the presenters during Google’s demo of its new market research software offering. Cosentino related, “after a few minutes [the presenter] brought up the prompt to write the actual question. He said with all seriousness (and I’m sure without intention), ‘This is where all of your expertise is put to the test. This is where you write the question.’ I couldn’t help but chuckle and think that is how the technology industry perceives the market research industry – a bunch of question writers.”

This perception is probably all too prevalent — yet it’s missing a very important aspect of our expertise: analysis.  We can take it a step further, and make the case that market researchers were the original data scientists, practitioners of that discipline blending statistics, applied mathematics, and computer science to solve complex business problems.

As we move into a truly global marketplace, major CPG companies are looking to expand their brands’ presence in emerging markets.  The economic growth in countries such as Brazil, Russia, India and China continues to outpace growth in developed nations, driving increased demand for global brands. The push to get products on the shelf for the enthusiastic new consumers in these markets is happening at a frenetic pace.

With this push comes the realization that the “infrastructure” for CPG product movement is quite different in emerging markets. Supply chains are different, alternate channels such as kiosks and open-air markets are more important, marketing spend is far less impactful, and tastes differ regionally. Each of these challenges can give local brands a huge edge over global brands.

Today, we announced the expansion of MarketTools’ Shopper Research Solutions to include new technologies that increase respondent engagement and provide deeper insights into the shopper’s path to purchase.  The announcement also cited a recent MarketTools study of grocery shoppers revealing that 63 percent of respondents say saving money is somewhat or much more important now versus last year – and that 80 percent buy items with money-saving coupons; 62 percent buy store brands instead of name brands to save money; and 58 percent buy items only when they are on sale.

In a complex retail landscape, with consumers looking for more value than ever before, fast and accurate Shopper Research insights are critical to helping both retailers and manufacturers respond to a changing marketplace and gain a competitive edge.  With deep shopper insights companies can:

• Create pricing strategies that drive growth for price-conscious demographics, channels, or regions
• Offer value-pack contents and product bundles that are most appealing to consumers
• Learn which packaging resonates with value-conscious shoppers
• Improve product selection and findability at retail
• Develop effective store planograms more quickly and cost-effectively
• Boost product movement with more effective store signage

Today’s announcement is also a great opportunity to revisit some of Dan Bot’s Shopper Research blog posts – in particular his three-part series on virtual shelf technology:

Part 1, Shopper Research: The Virtual Shelf provides an overview of virtual shelf technology, and the value it delivers by taking into account shoppers’ tendencies to make holistic purchase decisions based on competitive context.

Part 2, Virtual Shelf vs. Brick and Mortar Testing, describes the advantages of using online virtual shelf technology over traditional shopper research conducted in a real-world store environment – in terms of saving time and expense as well as allowing limitless experimentation.

Part 3, The Many Uses of the Virtual Shelf, discusses ways to apply virtual shelf technology in shopper research such as Package Testing, Product Pricing Tests, Planogram Testing, and concept testing. 

In a landscape where consumers have more choices than ever before, it's innovative shopper research – using technologies that improve respondent engagement and offer more detailed insights than ever into shopper intents and preferences – that will help companies win sales and market share.

For all the recognition of the impact of social media on new product introductions, there appears to be relatively little effort to understand the role and contribution of buzz when evaluating new product ideas.  While the traditional “Likelihood to Recommend” measure is still part of the key performance indices tracked for new product concepts, additional – perhaps more relevant – measures of advocacy are not systematically incorporated into innovation research. 

Whether or not a new product or line extension is buzz-worthy, the reality of how new products break through barriers of awareness and adoption should be incorporated into concept testing disciplines.  However, to do so suggests that we should update how we test new product ideas – in terms of how ideas/concepts are presented to consumers, what questions we ask them in their evaluations/reactions, and what variables to include when forecasting them. 

 In the innovations arena of market research, norms can provide critical insight into determining whether your company’s latest concept is likely to succeed.  But when you compare your concept test scores against a normative database of results from other studies, you need to look beyond just getting a “good” test score (as in, “will my new concept make the top 10-percentile benchmark?”). 

As you evaluate the merits of your idea against the norms for research scores achieved by other concepts, keep in mind the following issues:

This is Part 3 of a series of Shopper Research blog posts; check out Part 1 and Part 2 of the Path to Purchase series.

In previous posts I discussed the insights you can obtain by constructing a Path to Purchase for your product, and followed up with some survey questionnaire tips to ensure you successfully collect all the data you need to maximize those insights.

Today, I’m concluding this series on the Path to Purchase with tips on how to analyze and interpret your path data to maximize your product’s impact upon the consumer.

• First and foremost, don’t expect there to be one single path! Few shoppers follow the exact same series of steps, in the same sequence, with the identical needs for buying your product. Most likely there will be numerous path “segments” that share common steps. The key is arriving at segments with meaningful differences. For example, if you’re selling bottled water, you might have a segment that se arches circulars for deals and stocks up on your water by buying a case or two at a time at a grocery store. Another segment might have a much simpler path that consists of buying a single bottle at a convenience store to satisfy an immediate need.

For marketing research projects that collect product awareness and usage data, there are some ratios that are easy to calculate and which lead to deeper insights.  These simple analytics are often called “conversion ratios”.

One such ratio is Awareness-to-Trial, which tells the percentage of trial received from consumers for each point of awareness generated by a product.  “Trial” is the proportion surveyed who have ever used/purchased the product, and this can be gauged against top-of-mind awareness, total unaided awareness, and total awareness levels overall.  Here’s a simple example:

This is Part 2 of a series of Market Research blog posts on Shopper Research; check out Part 1 of the Path to Purchase series.

In a previous post  I discussed the ways that constructing the Path to Purchase for your customers allows you to build a deeper understanding of their motivations and behaviors as they shop for your products.  By understanding the components of the path, you can better strategize ways to maximize a product’s impact upon the consumer at each phase, and ultimately win the sale.

A well-designed survey program can ensure that you successfully collect all the data you need to detail your customers’ Path to Purchase – and here are some tips to help you develop your questionnaire.  

• Get consumers in their purchasing frame of mind.
  Before just diving into your survey questions, it’s important to encourage the respondent to recall all the nuances that go into a shopping experience.  Consider asking a shopper to recall a specific recent shopping occasion in which they bought a particular product.  That way, you can uncover all details of the trip – instead of a watered-down recollection of a more general shopping experience that might miss some subtle, but important, steps.  It can be useful to ask the respondent to describe the shopping trip in a detailed paragraph at the beginning of the survey, in order to jog their memory and make the most of the subsequent questions.

There’s been a lot of buzz lately surrounding Path to Purchase – the course of events or activities that consumers follow from the first glimmer of interest in a product up through the point where they actually make the payment to complete the buy. By understanding the components of the Path to Purchase, marketers are able to strategize ways to maximize a product’s impact upon the consumer at each phase to ultimately win the sale.

Like many aspects of market research, there’s a lot more to this process than meets the eye – it can be much more complex than you’d expect. While investigating your product’s Path to Purchase, you’ll uncover some interesting shopper insights along the way, such as:

Much of the Path to Purchase may be purely mental. The actual physical path through a store from the shelf to the cash register can be an important aspect of your customer’s Path to Purchase, and it’s worthwhile to understand ways to overcome barriers and facilitate their way.  However, the most valuable insights often lie in the decision-making steps that precede the retail transaction.

I’ve been reading Daniel Kahneman’s new book Thinking, Fast and Slow, and I highly recommend it to any market researcher.  Be warned, though: the implications can be kind of scary.  One of the topics the book covers is the “conjunction errors” that people commonly make – as demonstrated by the classic experiment known as the “Linda Experiment” (see page 156 of Kahneman's book). 

Linda is described (keep in mind that the experiment is very old) as “31 years old, single, outspoken and very bright. As a student, she was deeply concerned with issues of discrimination and social justice and also participated in antinuclear demonstrations.”  Participants in the experiment are asked to indicate which is more probable – that Linda is a bank teller, or that Linda is a bank teller AND active in the feminist movement.  Even experimental subjects who were highly trained in statistics tended to choose the conjunctive description (bank teller AND feminist) as more probable than the simpler, more general description, even though it is impossible for a conjunctive to be more probable than any of the components of the conjunction.