As we contemplate possible futures for marketing
research, it’s “Follow the data!” MR has traditionally helped sellers connect
with buyers. Today’s connected consumer is changing this relationship – and
thus the role of MR and its integration with other functions in the organisation.
Some consumer behaviour has always been
routine, but digital assistants and subscription services based on the Internet
of Things may soon make the “first moment of truth” the last chance to acquire
a customer. Someday, sell-bots will market to buy-bots. Customer service will also
be delivered in part or wholly by chat-bots. Insights traditionally provided by
MR will be “baked into” such systems which will also “learn” from and adapt to
new situations.
As recent concerns about automated online
ad placements and “brand safety” illustrate, however, there are potential
pitfalls. The technological imperative to engineer out the variable human
element for “frictionless” buying or service delivery harbours a “field of
dreams” assumption: If you automate it, they will (still) come. What is missing
is the value consumers place on experiences.
While the Internet did “disintermediate” some categories, it did not change
human nature. The travel agency that once stood on the busiest corner in
Harvard Square is long gone – replaced by online eyewear retailer Warby
Parker’s new brick and mortar store. One woman’s search costs is another woman’s
fun experience.
Change
Just as ubiquitous data and automation are
changing consumer behaviour, so too will MR change. Traditionally, we ensured
the relevance and objectivity of our findings through labour-intensive
qualitative research for discovery and carefully designed samples and
questionnaires for validation. Unfortunately, consumers’ perceptions, attitudes
and intentions did not always predict their subsequent behaviors.
Today, entire “found” datasets from online
search, service transactions and other behaviors can be analysed automatically
for both discovery and validation. MR may thus shift from designing samples and
attitudinal questionnaires toward correcting the potential biases built into
such datasets and the unintended consequences of implementing results of automated
analyses.
Consider these challenges/opportunities:
- Machine learning may yield
spurious as well as relevant correlations. How can market researchers help data
scientists distinguish the two – and improve their models?
- “Black box” models can create
“lift” in targeted marketing – until they don’t. Lacking explanations of why
the model used to work, it may be hard to decide what to do next.
- Whereas theory-based findings
can yield insights (e.g., consumer personas) that managers can share and extrapolate
to new contexts, purely inductive black box models may not.
- Black boxes may also become echo
chambers: Analysing the behavior of existing
customers may merely optimise for existing
customers and not inform actions needed to expand the franchise.
- Similarly, such models may end
up targeting only customers predisposed to respond or in late stages of the
decision funnel, creating endogeneity that confounds causal interpretation.
- The training sets required for
machine learning of “found data” may not be representative – resulting in biases
that can be exacerbated by applications of the algorithm.
MR must remain “the voice of the customer”
– even when that customer is Alexa. We will need to focus on when, why and how consumers
cede some or all of their decision-making. As automated “insights” are baked
into service delivery and other systems, we must ensure that all current and prospective customers
have a voice. Accordingly, the MR function will need to be more closely aligned
with – and even “owned” by – the product/service design and delivery functions
of the organisation. As sampling, data collection and analysis are automated,
researchers will need to add value in more strategic interpretation and
communication of findings where managerial discretion is still required. Hence
the need for effective storytelling, no matter what the source and technique
that yielded the insights. As consumers and marketers increasingly connect
experientially, we should share our findings with managers in more immersive
ways (e.g., using augmented or virtual reality).