The quest for emerging customer needs
It's time to wake up
and discover your customer.
By David G. Bakken
The following scenario may sound familiar to
you. A financial services company wants to be first to market
with new products to meet the "emerging needs" of affluent
customers. In brainstorming sessions, the marketing team
generated a dozen or so variations on the firm's current offerings
for the affluent market. They also generated a list of customer
needs that might be fulfilled by each of these new
offers. The company hired a market research firm to conduct
a needs-based segmentation study. Survey respondents rated
each of the potential needs on an importance scale and also
evaluated the appeal of a few new product concepts derived
from the ideas generated in the brainstorming session.
Of course, the team captured current financial behaviour and demographic information to help target any new products
resulting from this effort. The analysis revealed distinct
clusters of respondents differentiated according to the importance
placed on the various needs. Moreover, reasonable correspondence
existed between the needs-based clusters and the
appeal of the different product concepts. The product team
set about developing test versions of the product concepts
with the greatest appeal to clusters representing high potential
value to the firm.
While most of the participants regarded this effort as a
success, the vice president of marketing was disappointed. She
had hoped that the research, which required a six-figure
investment, would find the "below the radar" opportunities-
the "truly new" affluent customer needs that will define
the financial services market for the next several years.
Instead, research revealed a few small opportunities based on
evolutionary changes in existing product offerings.
How Do NEW CUSTOMER NEEDS ARISE?
We can better understand why market research failed to
identify "below the radar" emerging needs if we first consider
how new needs emerge. Customers buy in order to
improve, or at least maintain, their well-being. An existing
market, comprised of a set of customers for a product or service
category, represents a value exchange framework where
the benefit or impact on customer well-being from the available
products or services consistently matches customers'
desired improvements. Current customers' needs are usually
satisfied by existing products and services. Within existing
markets, customer needs tend to center on performance
improvements in existing products and services rather than
radically new solutions.
New customer needs emerge outside the existing value
exchange framework, usually as the result of some change in
customer circumstances that alters the current state of wellbeing.
Environmental changes (e.g., regulatory changes, energy
shortages), demographic trends, and customer maturation all
may trigger changes that give rise to new customer needs.
However, no one-to-one correspondence exists between
changes in customer circumstances, the needs they give rise to,
and the new products or services that will satisfy those needs.
(See sidebar on page 32.)
FINDING EMERGING CUSTOMER NEEDS
The first, and perhaps most important, mistake the financial
services company made was to focus on the existing value
exchange framework reflected in the firm's current offerings.
The company defined new customer needs according to benefits delivered to existing customers by relatively minor changes
to existing products. This is the process companies use to
improve products and services incrementally, better meeting
existing customer needs. It's also possible the company focused
on finding "emerging" needs in the existing market-albeit
unintentionally-because most of the market research tools
available are better suited to existing than to emerging markets.
Rather than brainstorming product enhancements and then
hypothesizing customer needs that might be fulfilled by those
enhancements, the team in our example would be more likely to
meet its objective by identifying potential changes in customer
circumstances based on environmental changes, economic conditions,
and demographic trends. Once these changes are identified,
the team can hypothesize the needs that the changes in customer
circumstance might create. The final step in this process is
matching these needs to the financial services marketplace. If
there are no possible solutions for these needs within the financial
services domain, the exercise stops. If there are matches
between the hypothesized needs and possible financial services
products, the team can initiate market research to verify the
hypothesized linkages.
The marketing team made a second mistake by focusing
only on affluent customers. Many organizations pay greater
attention to their most valuable customers, but one reason these
customers are valuable is the close match between their needs
and the current value exchange framework. These customers are
unlikely to be a good source for emerging needs that aren't
served by that framework. When conducting the first step outlined
above, it's important to cast a wide net and to consider
changes in the circumstances of many different potential customers.
(See sidebar on page 33.)
I
EXECUTIVE SUMMARY
In today's rapidly changing markets, companies have more
impetus than ever to find emerging customer needs that will
translate to new profit opportunities. Historically, market
research has done a poor job of detecting emerging needs
and identifying those profit opportunities. New customer
needs originate upstream of the marketplace in changing
customer circumstances. Unfortunately almost all research
is directed at finding new needs within an existing value
exchange framework, using tools that aren't well-suited for
revealing emerging customer needs.
LIMITS OF TRADITIONAL RESEARCH TOOLS
The marketing team for the financial services firm in the
case study relied on what they believed to be "best-practices"
market research. The research firm interviewed a representative
sample of affluent customers selected initially using random
digit dialing. They pretested the questionnaire using a
concurrent protocol (think aloud) procedure. And they used
appropriate analytical methods to identify the different needs segments. However, if the VP of marketing had clearly communicated her desire to find the below-the-radar emerging needs,
the team probably would have relied more on innovative qualitative
techniques, including customer case research and lead
user analysis. As Berstell and Nitterhouse discuss in the Fall
2001 issue of this magazine, customer case research can be
a valuable tool for predicting purchase decisions. (See
Additional Reading, page 34.)
Some of our standard market research practices actually
decrease the likelihood of finding emerging customer needs. In
order to understand how commonly practiced research techniques,
particularly our sampling and analytical methods, can
obscure emerging needs, we need to consider our notions of randomness.
First, we expect that any sample of observations will
vary to some extent from the total population. Moreover, the
expected variation is a function of the size of the sample-the
larger the sample, the smaller the expected random differences
between parameter estimates and population values for measures
of interest.
However, an emerging need is likely to be relatively rare in a
sample of observations, and our inferential statistics will treat
the odd response that might indicate an emerging need as part of
the sampling error. Assuming our sample does in fact contain a
handful of respondents who have experienced some change in
circumstances that creates a new need, we're unlikely to place
much faith in that finding unless there are enough cases to be
"statistically significant."
In similar fashion, multivariate statistical techniques detect
the strongest patterns in the data. Values with low frequency are
lost in the background noise. Outliers-cases with extreme values-
may be indicative of emerging customer needs, yet those
cases are problematic for many statistical techniques.
To find emerging customer needs, we must change our thinking
about sampling and statistical inference. Instead of probability samples, we might employ targeted convenience samples
based on hypothesized changes in customer circumstances.
Instead of describing our samples with statistics that reflect central
tendencies or overall variability, we might pay more attention
to understanding extreme or low frequency observations.
We need to apply reverse statistical thinking to the search
for emerging customer needs. Rather than rely on statistical
inference to identify patterns in the data, we should look for
individual observations that conform to our hypotheses about
the patterns of customer circumstances or demand-creating
conditions that presage new customer needs.
Bayesian methods promise to improve our ability to detect
unusual individual preferences or needs. For example, Allenby
and Ginter have demonstrated the use of hierarchical Bayesian
analysis to identify customers with extreme preferences for specific
product features. This approach might be used to identify
individuals with unique needs as well.
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