The document discusses the limitations of traditional decision-making models in product development, emphasizing that static attribute models can create misleading trade-offs and ignore the complexities of real-life decision-making. It advocates for heuristic questionnaire designs to better capture individual consumer decisions and preferences, acknowledging that stakeholders operate under different contexts and information constraints. Such heuristic designs can enhance decision support by identifying barriers to information-seeking and validating user needs across various perspectives.

1. Trading up
from tradeoffs:
heuristic questionnaire design

2. Closed models have drawbacks
“We got him!”

3. Why should static attribute
models still drive research?
• Developed before automated data collection
• Designs based on team illusions, qualitative
research, “limited” room
• Require introspection and abstraction outside
normal life, disclaiming the “X factor”
• Create falsely dichotomous assumptions that
inadequately inform decisions
• Assume an overall “trade-off” between quality
and price, when stakeholders may not

4. When is a decision
difficult to model?
• Specifiers, purchasers and users may differ
• Manufacturers cannot easily change
attributes and levels, even with high need
• High stakes: decisions can disable or kill
• Benefits and risks cannot be reliably
generalized or projected
• Commitment time permits re-evaluation, but
decision burden for self (and often others) is
long-term: cars, colleges, real estate

5. When is a decision
a tradeoff?
• Attributes are intrinsic to the product, and can
only exist at one level at one time
• Reasonable people agree on a product’s
actual attribute levels, and are aware of those
salient to them
• Stakes and commitment times are significant;
only infrequent re-evaluation is possible
• Product data is empirical, not proxy
• Many decisions will not meet these criteria

6. “Messaging for
market segments” isn’t real life
• Product profiles often present an artificial “full
information” context
– What stakeholders don’t care about, they are less
likely to actually know
• Specifiers seldom have a complete
competitive set to consider
• People make decisions, not segments, strata
or audience groups
– Context meets content
– Mfr label says one thing, we do another

7. Human brains make
bite-sized decisions
• We use “heuristics” –decision shortcuts –
because time is short and our brains are too
small to consider everything at once
• Heuristics can be simple: “Never pay extra for
national brand peas,” or as complex as
choosing a life partner
• We always break our own rules
– Heuristics are subject to mediating factors, e.g.
anchoring and adjustment, priming; as well as
situational constraints

8. Conjoint methods are
product-centric
• Experimental designs sometimes select
profiles based on initial preferences, but
attributes/levels are still pre-fabricated
• Conjoint designs also assume:
– Attributes represent a single construct, apart from
interactions used in the experimental design
– The distance between levels represents a finite,
measurable value, that exists irrespective of any
respondent’s reference point

9. The choice task subject
is only human
• She’s focusing on a few attributes, and
making assumptions about those not shown
– To complete the task in a reasonable time within
her context
• But analysts assume that she considered all
and only the stimuli, in a zero-sum game
• Table stakes may assume false importance,
because excluded factors are the real drivers,
and/or because the levels offered were not
salient or even believable

10. Heuristic designs identify and
leverage decision drivers
• Respondents’ domains, measures and
thresholds populate and limit the stimuli (not
just profiles) presented
– No two respondents may see the same questions
or profiles
– Base sizes for simulations will differ, since those
whose benchmarks are unmet will not “contribute”
to projected interest or share
– Range of threshold values is defined by
respondents, not a priori
• Studies are cheap, fast, transparent and thus
easily integrated

11. The “voice of the customer” is
an N of 1
• Traditional respondent-level conjoint outputs:
– Profile 1 preference share = XX% and so on
– Imputed importance utilities and interpolated
preference shares for the scenarios not presented
• Heuristic studies:
– Domain/measure (“attribute”) 1 = Z, with threshold of
X, attribute 2 = C, with threshold of Y, and so on
– Preference share given respondent’s thresholds (+/- X
%) = XX%
– Multiple scenarios can be presented, all salient to the
respondent’s benchmarks

12. Heuristic designs help
optimize decision support
• Eliciting barriers to information-seeking,
consideration, selection and purchase,
including communication gaps
• Developing support to facilitate use
• Validating domains of unmet need and
benchmark(s), often contrasting user, retailer,
distributor, funder perspectives
– In one case, arguing for a subsequently successful
launch, albeit with an inferior delivery system

13. Thank you for listening!
Laurie Gelb
lmgelb@profitbychange.com
profitbychange.com