Three Awesome, Nielsen, Quantitative Research Capabilities That are Not Named “Answers”

Three Awesome, Nielsen, Quantitative Research Capabilities That are Not Named “Answers”

When you hear the name Nielsen, or ACNielsen, you probably think about TV ratings.  I take that back.  90% of you will think of TV ratings, and the shopper insights managers will think POS Scanning and Homescan Panel.  Either way, the Nielsen company offers a lot more to market researchers than just ratings and sales data.   Over its long history of acquisitions, mergers, and divestitures, the Nielsen umbrella now covers three of my favorite quantitative research capabilities:

  • SPECTRA:  An exceptionally granular database of consumer attitudes and habits at the store-level.
  • BASES II:  A method of forecasting trial and repeat the C&U testing.
  • Affinnova:  A method of quantitative research that is an evolutionary approach to consumer concept development.

You may be familiar with these capabilities from prior to their integration under the Nielsen umbrella.  But whether you noticed or not, they are now part of Nielsen.  If you have never heard of these capabilities, read this article and get ready to call your Nielsen rep.

SPECTRA:  An Ultra-Local, Ultra-Personal Quantitative Research Database

As a shopper insights manager, the focus of your job is the shopper and the store.  Whether you are doing qualitative research or quantitative research, you always have an eye on building the shopper’s basket.  Scanning and panel are probably two of your go-to resources, but both look at regional and national data only.  Imagine a tool that could tell you all the demographic, behavioral, and product preference information you wanted about your shoppers at a STORE LEVEL.  That tool is Nielsen SPECTRA.

Whether you are trying to learn about Gordon Food Service or Walmart, SPECTRA can give you data on who shops each individual store.  If store-level data isn’t interesting to you, how about basket level data?  If you want to understand your consumer-target better, there’s nothing more telling than what’s in her basket.  SPECTRA can tell you which products she is most likely to be purchasing today — from her favorite brand of cosmetics to her favorite brand of beer.  If you need the kind of shopper understanding that panel provides at a local or individual level, SPECTRA is the tool for you.

What’s even more exciting is that SPECTRA is now available as a user-accessed portal.  Once have access to SPECTRA, you can log-in to it through the same portal that you use for Nielsen Answers.

BASES II:  A Quantitative Research Method Using C&U to Forecast Volume

In a previous article, we talked about the basic components of volume forecasting.  Further, we discussed how important trial is to the overall volume forecast.  We still think so.  However, BASES II is a methodology that is hard to argue with.  Consumers are exposed to a concept and give their typical purchase intent response.  Afterwards, you have the option of doing a Concept & Usage (C&U) test.  This means you can supply those consumers who rated the product T2B with the product to try.  These shoppers then use the product for a set period of time and then complete an additional usage survey that is used to estimate repeat for the product.

Although trial is still king, repeat does make a big difference in Year 2.  With the BASES II methodology, Nielsen has the ability to forecast Year 1 and Year 2 of launch.  Additionally, they claim that the BASES II method also helps give a more realistic read on product incrementality.  Most importantly, Nielsen describes strong after-use scores in BASES II v. their database as, “a lead indicator of longer-term survival” for the product.

In conclusion, back when I started working with BASES on forecasting projects, they were just BASES.  Now all of BASES, including their impressive BASES II capability, is part of Nielsen’s Innovation Services.

Affinnova:  A Quantitative Research Method of Evolutionary Optimization

Speaking of Nielsen’s Innovation Services, it is now the home to Affinnova.  I used to work with Affinnova back when it was its own stand-alone company too.  I’ve had many a project where Affinnova’s evolutionary algorithms helped identify a benefit space or portfolio of products that delivered wins for me and my business.  Today, the capability is no longer called Affinnova, it’s called Nielsen Optimizer.

Nielsen Optimizer is a quantitative research method that uses the same, patented, evolutionary algorithms to optimize concepts.  You create a matrix of possible idea combinations that can include hundreds of claims, images, product names, benefits, flavors, scents, etc.  If you were to create a single concept for everyone of these, you would need to test millions of concepts.  The Nielsen Optimizer generates concepts from the matrix of attributes and evaluates consumer choices in a series of conjoint tests.  Over the course of the study, the algorithm “learns” which traits are most likely to “survive” (thus the evolution reference) and begins focusing on optimizing only the strongest concepts.  At the end, the capability delivers a handful of “evolved” concepts with the strongest combination of attributes.

This may sound amazing, but, of course, the “garbage-in, garbage-out” rule applies too.  If you give Nielsen Optimizer lousy inputs, your “optimized” product will only be so good.  However, you have the ability to test that concept with BASES after it’s optimized, so you’ll have a pretty good idea of “how good” it really is.  I wonder if they ever did launch the mint product they talked about in their promotional materials.

Conclusion:

There are many capabilities under the Nielsen umbrella.  Many of these are quantitative research methods. Sometimes, the coolest methods are the ones that are least well-known.  In the case of Nielsen’s quantitative research arsenal, I’d say that’s the case with these three.

Disclaimer:  Neither Netizen Insights, LLC, nor its managers, nor its members, nor its agents received compensation or any other kind of incentives from these companies for this article.  The opinions and experiences shared in this article are those of the author and his contributors as a result of their favorable experiences with these services.

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