Archive for August, 2011

70% of all product decisions are made at the shelf…..no, wait 80% of product decisions are made before i go to the store….huh?

Thursday, August 4th, 2011

If one reads the research behind these two claims carefully and interprets the results across the studies, one can come up with what seems to be a reasonable, additional view of actual consumer behavior.  Both sets of statistics could be about right, and perhaps the studies should be read as complimentary.

Most consumers are not entirely loyal to a specific brand in a category.  Most have a set of acceptable substitute solutions that they chose from based on:

  • availability
  • price
  • other promotions or incentives
  • other factors likely to impact the current purchase (“Fred’s mom does not like my brand”)

Traditional grocery lists (perhaps all shopping lists) serve primarily as “reminders” to the consumer triggering an in-store scan and grab for the product of primary choice or select from a largely pre-determined set of substitutes.    So, the list while not explicitly calling out products, does evoke a specific customer centric set of product choices for each list entry.  The list determines a small set of products, the final choice is made at the shelf based on the conditions or circumstances laid out above.

listorshelf.jpg

 If I am correct about this, an I see no reason to doubt myself ;-) , this should critically influence how application and ecommerce platforms and loyalty service providers think about building list capabilities.

Back when I designed the MyWebGrocer online grocery shopping product selection display, I chose alpha by brand/size as the hierarchy to be used.  After we had launced and run for a bit I looked at the impact of this display hierarchy on consumer product selection behavior.

I worked with Lisa Selip at Lowes Foods and looked at market share comparisons between online customers and in-store customers on toilet tissue.  I picked that category because it tends to be purchased frequently, universally and there is not a huge switching penalty between brands.  The result was surprising and probably a bit dismaying for brand managers.  The market share for the first products displayed in the category was almost double the share generated on those same items in-store.

My conclusion about these seemingly disconnected studies,  is that while a product might be on a consumer’s mental or actual list , loyalty to that selection is generally quite tenuous.  Meanwhile influence is and can be exerted at the shelf to move a consumer from a primary selection to a pre-selected substitute.  It is much harder to move them to an item not on their evoked list at all.   The implications on the designers, developers and advertisers who wish to gain access to that consumer list are far ranging and complex.  The rewards for figuring it out are monumental.

Usage Decay Rate on Mobile Applications Over 90%, Missing or Irrelevant Product Data The Cause?

Thursday, August 4th, 2011

One of the mobile application measurement services recently reported that the use of mobile applications generally fell off by over 90% after the initial post-download burst of enthusiasm.

In some ways this dramatic dropoff should not surprise as many low cost, reusable products and services (I-Pod songs, Happy Meal Toys) get scant attention after initial use.

Still, this 90+% decay curve is a staggering statistic and one that should give marketers pause.  Clearly in order to earn ongoing consumer usage an application must be:

  • Useful, having some utility meaningful for that consumer
  • Easily accessed
  • Easy to run (simply input in CPG that means either  a list of options salient to the user or the ability to scan a picture or bar-code for self selection)
  • Rewarding.  The application needs to have a high probability of returning data relevant to the person operating the application.

With regard to this last point,  a study run by GS1 UK examined consumer usage of mobile applications  involving consumers attempting to gain product information by scanning or taking pictures of products on the shelf.  The consumers were hoping for information  regarding:

  • health and wellness guidance
  • promotional offers
  • other product information

Over 90% of the usage attempts resulted in either no data or in images/data inconsistent with the actual brand data according to the manufacturer.

I am of the opinion that the only appropriate gauge of product image and data accuracy is one that begins with how relevant the product image and data are to the consumer when they visit the shelf.   If the product displayed in the application is not the product found on shelf then the data fails.  We call such data Consumer Relevant Product Data.  The gap between the data available for mobile and online applications and the package the consumer sees on the shelf, is called the Consumer Relevant Product Data Gap.  

ShelfSnap with two partners, are engaged in a much more in depth and ongoing review of this Consumer Relevant Product Data Gap.  We will extend the research to compare what the consumer sees in their application to the actual product on shelf (vs. the manufacturer’s impression of which product is current).  We will also test how consumer purchase behavior is affected by the disparity.   The report will cover:

  • The impact on shopper behavior
    • Sales
    • Dwell time
    • Brand – Banner perception
  • The significance of the problem in terms both of:
    • The percentage of products affected by the gap
    • The severity of the differences.

 

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So far our findings indicate that the  gap between the images that manufacturers think are current and the packaging the consumer actually sees on the shelf is well over 50%, regardless of the source of the images used in the consumer applications.   Mobile, online and even product specific paper coupon efforts are handicapped by this impediment.   Of that there can be no doubt.  This is an important set of findings and one of which marketing buyers need be aware.