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Nobody’s Unpredictable26 February 2009
Why PI is Losing Its AppealCONCEPT SCREENING:
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Secret codes?
PI:
DWB
PWB
M/MNB
PWNB
DWNB
Top Box / Top Two Box
Overstatement
Purchase Intent:
Definitely Would Buy
Probably Would Buy
Might or Might not Buy
Probably Would not Buy
Definitely Would not Buy
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Why PI?
To predict what consumers will do.
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Why so successful?
Intuitive
Easy to ask, and easy to answer
Correlation with in-market results
Consensus on wording, scale, interpretation
Databases
Why so problematic?
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Does it really predict future?
“Getting Real About Prediction in Marketing Research”
Jan Hofmeyr (Synovate, 2007)
9 studies linking PI to actual behavior (since ’66)
R2 = 0.09
“91% of the variance is not captured by PI”
“We ought to be embarrassed.”
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Guiding you down the innovation path
“Concept Testing: How to Test New Product Ideas Before You Go To Market”
David Schwartz (1987)
Offered warnings on relying on 1 number for determining which concepts should proceed along the innovation path
product can be polarizing
need other metrics too: expensiveness, believability
Need to understand why, diagnostics
Relying solely on PI can be dangerous
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Times are changing
Many more introductions
Analysis by McKinsey (’99 –’03): only 3% were “breakthrough innovations”
Many more line extensions or incremental improvements (“Me-too”)
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“Me-too” products
“Me-Too” products often score well on PI:
Strong brands, trusted
Minor changes to successful products
Easy to understand
Established categories
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“Breakthrough” products
Breakthrough products have a harder time:
Harder to understand / believe
Uncertain about benefits
Uncertain about tradeoffs
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PI problem #1:
Purchase intent advances lineextensions and incrementalinnovations, and killsbreakthrough and targetedproducts.
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Overstatement
57% ≠ 57%
Is the overstatement consistent in a category or country?
How do we assess this number? Is it really intuitive?
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Purchase psychology
"As Time Goes By: Do Cold Feet Follow Warm Intentions for Really New Products Versus Incrementally New Products?"
Anderson, David L., John G. Lynch Jr., Qing Wang (2008)
Journal of Marketing Research, 45 (June), 307-319
Consumers are less likely to claim PI for newer products
Consumers are less likely to follow through on claim
For newer products, PI must be deflated even more
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Clorox Anywhere
New cleaner that kills 99.9% of germs, but is safe to use around kids / food
Trial expectation = 10%
Trial reality = 3%
What happened?
PI: 60% Top Two Box
But safety concerns & believability problems among “DWB” consumers
Consumers may have discounted their concerns, and gave PI anyway
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What does PI Mean?
“I definitelywould buy
that product.”
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What about the database?
“Lets compareit to the
database.”
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PI Problem #2:
Purchase intent does not reflectthe competitive context.
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What drives consumer reaction?
On a concept board:
Price
Brand
Features
New! Folgers CoffeeBlah blah blah blah sdlkf lsdkfjslsoiusdls slk f f slkdfjs kjd fg sdlf ksldfk f sldflskdfoidkf f sdlksdf flsk dfk jfjf lfkf f f lksdfk jdfl sBlah blah blah blah sdlkflsdkfjslsoiu sdls slk f f slkdfjs kjd fg sdlfksldfk f sldfl skdfoidkf f sdlksdf flsk dfkjfjf lfkf f f lksdfk jdfl s
$2.49 for 1kg
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PI problem #3:
Purchase intent fails to discriminatedifferences in concept positioning.
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PI problem #4:
Purchase intent isnot diagnostic.
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Finding the good ideas
0 10 20 30 40 50 60
Concept X
Concept B
Concept C
Concept D
Concept P
Concept Q
Concept F
Concept G
Concept K
If Top Box > 25%,
then proceed to the next stage
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What does PI really mean?
PI is an outcome metric
It is a collection of many thoughts and themes:
Is this category meaningful to me?
Is the product relevant?
Does this product offer something new or better?
What is the tradeoff (price, features, process)?
Do I believe it?
How do I feel about the brand? Variants?
What do you really need to know at this stage of the innovation process? In screening, what are you really searching for? What is the decision criteria to move forward?
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Stronger approaches
Understand your true screening objective.
Identify key ideas or constructs that you can measure individually
Provide clear direction on how to improve
Take into account competitive context
That are directly related / contribute to the net behavior (sometimes about usage, not purchase)
Relevance = meet consumer needs
Differentiation = unique benefit vs. competition
Expensiveness = percieved price vs. competition
Individual attributes
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Human decision making
Jan Hofmeyr confirms:
Psycological and human decision making research indicates:
People are not mechanical calculators
People are comparative
Ambivalence is a fact of life
“…what matters is not how highly a product is rated per se, but how highly it rates relative to others.
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Final thoughts
PI is not bad
Its actually important and special in our industry
But we must use it carefully, particularly with today’s innovation environment.
There are other tools & techniques out there
Evaluate closely your innovation goals, and tools you’re using to move products through stage-gates