Conjoint analysis in survey research is outmoded: static, closed, attribute-based in a real-time, turn-on-a-dime, conversational world. Heuristic methods offer a cheaper, faster, more actionable framework for both qualitative and quantitative work. This deck briefly outlines the quantitative framework.

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