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5 Tracking Best Practices

Late last year we wrote a few pieces about tracking best practices.  These pieces really struck a nerve.  As 2017 gets rolling and many of you are probably thinking about and evaluating your research needs and objectives for the year we thought it would be helpful to revisit and expand on the theme.

As a company that’s very involved in the tracking space we have a lot of opportunity to hear directly from clients and prospects what’s working for them and what’s not.  What we hear repeatedly is a lot of dissatisfaction in the research community with trackers.  We hear things like;

“too big”

“bloated”

“measuring too much”

“too expensive”

“not actionable”

etc.

None of this is new but it seems to be at a fever pitch recently.  As such we’ve been fielding a lot of questions about what we recommend as best practices.

We have 5 best practices that we consistently offer up to our clients, that we have proven to represent a best in class approach to tracking – a sentiment shared by many thought leaders in our industry.  Most of these can be found in a paper that our own Frank Findley, EVP of Research presented at the ARF Re!Think Conference last year.  (contact us for the full paper)

1. Utilize One Primary Aggregate Measure

Utilize one primary aggregate measure.   The best in class choice for this measure is our Brand Preference.  This is supported by many independently validated research projects, most recently the MASB work presented in this paper.  The three most important features of this measure are its ability to capture the impact of all other ‘equity’ measures, take into account competitors and data collection at the individual respondent level.  Brand Preference is the cornerstone measure of our Brand Strength Monitor service.

tbsm

2. Supplement This With The 7 Other Measures We Have Proven Work Across All Other Categories

Once you’ve established this, then you should use the other 7 measure that we typically see work across all categories to help explain the data. They are:

  1. Awareness Unaided

  2. Brand Loyalty

  3. Value

  4. Purchase Intent

  5. Brand Relevance

  6. Awareness Aided and

  7. Advocacy

3.Customize For Your Specific Category

Then what we do for clients is customize their trackers to address specific category needs.  For example; convenience might be an important measure for one category but not another.  To gauge this, each element can be analytically compared vs the aggregate measure to calculate a derived importance.  The strengths and opportunities for the brand can then be easily found by crossing derived importance vs. brand performance on the attributes (see matrix example below).  This type of analytics is usually done once per year or every two years as category drivers tend to be steady in the absence of disruptive changes in the category.

prefmatrix

4. Track Continuously or Less Often Supplemented With “Deep Dives”

Collectively; the brand preference, the seven cross-category measures, and the category specific measures can be arranged into a score card and tracked over time.  Currently 70% of our clients collect this data continuously while 30% do waves (typically two per year).  For those that collect it continuously, the data itself is typically rolled up monthly with ‘deep dive’ reports going to management quarterly.

5. Harness The Data To Run Segmentation Analyses

Along with the scorecard, there are also generally segmentation type analyses where performance on KPIs is used to find and qualify consumer clusters or to monitor trends on quickly growing consumer groups.  For example, monitoring millennials has become standard.  This chart from the MASB, Brand Investment & Valuation Project demonstrates this point:

sensitivysubgroups

So, if you’re like many that we’re hearing from recently, and that with whom we’ve already shared this thinking with, this may be a revelation.  If you’d like to read more about this, please contact us – we’re happy to share a full white paper with you and we can discuss your particular needs.

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