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Don’t Ask What They Want, See What Decisions They Make

banks and credit unions have learned the importance of research. From customer
satisfaction surveys to analysis of big data, a regular regimen of industry
research is integral to brand assessment and strategic planning for an
organization’s future. But unfortunately, most questions used in banking
industry research do not effectively tell you how consumers decide which banks
or credit unions to use.

“Only by understanding what consumers would sacrifice can you truly understand what matters to them in choosing a bank.”

In an ideal
world, we’d all like to live just down the street from a bank with the highest
level of personal service, the lowest fees on accounts, a robust online banking
platform, the highest rates, and the largest ATM network. And that’s just for
starters! If you ask consumers how important each of these factors are in
deciding which bank to use, they will usually say that ALL of them are highly

“Only by understanding what consumers would sacrifice can you truly understand what matters to them in choosing a bank.”

So how do
consumers really decide which bank to use? They’re going to have to make some
trade-offs. They might decide that a bank’s ATM network is the most important
criterion for them. Or maybe they like to bank in person at a nearby branch
where they are treated like family. This is the power of conjoint analysis–it demonstrates
the real-world trade-offs consumers make when considering a decision. Only by
understanding what consumers would sacrifice can you truly understand what
matters to them in choosing a bank.

To conduct
conjoint analysis, you begin by coming up with three to five attributes you want
to test against each other. These attributes are the criteria you think
consumers are likely to weigh when selecting a bank, and should vary based on
the competitive landscape in your market area.

Next, you
assign levels to each of these attributes. For example, the levels for location
might be “located nearby” and “located farther away.” For personal service, the
levels might be “excellent personal service” or “average personal service.” It
is important not to assign levels that represent extremes; the choice you are
providing to consumers highlights the advantage they stand to gain–the exceptional
over the merely average.

At this
point, you take these attributes and, using a statistical software program, combine
them into a limited number of scenarios. The scenarios describe the possible
configurations of these exceptional and average attributes. You then present
consumers with each scenario and ask them how likely they would be to use the bank
described by each scenario. For example, you might open by asking the consumer
to imagine that they have moved to a new state and there are a variety of banks
they can use. The first bank is farther away, has excellent personal service, average
fees on accounts and services, and a larger ATM network. The next bank is located nearby, has excellent
personal service, low or no fees on accounts or services, and a smaller ATM
network. You proceed through each of the possible combinations, asking the
consumer after each one how likely they would be, on a 10-point scale, to use
the bank described by that scenario.

collecting the data, you derive a rating for each of the attributes, and this
rating shows us the relative importance of each attribute. By presenting
consumers with a series of such scenarios, you get them to think actively about
the trade-offs they are willing to make and their answers reveal the true
underlying preferences that guide their banking decisions. Finally, in analyzing
the data, you can conduct a market segmentation analysis based on the
attributes that are important to different types of consumers. This analysis
can show you, for example, how large the ATM-driven segment of the market is, as
well as the prevailing demographic characteristics of consumers in this segment.