PAPER
TOPIC IDEA:
My paper topic is on data mining and predictive analytics as a
marketing tool. The study of how consumers
ACT in the marketplace can often be very different from what consumers think
and say about what they want. What are
the implications of these revelations? When
a company gains insights about their customers in such a way, are they invading
our privacy? Maximizing profit? Taking advantage of our subconscious? or simply
providing a service that eliminates superfluous advertisements and gives us
what we really want.
People say they eat healthy, but they buy junk food. People think they have a discerning taste
when it comes to wine, but they often can’t tell the difference between a $50
bottle and $10 bottle. People think they
aren’t affected by advertisements, by packaging, by brand names, by price, but each
influences purchase decisions in conscious and subconscious ways. If predictive
analytics can give a company insight into how a consumer is going to act
without necessarily understanding why
consumers act the way they do, is that risky? What is the company missing if
they don’t analyze the qualitative alongside the quantitative?
WHY
THE TOPIC INTERESTS ME:
The topic interests me because in my previous job, I worked for a
startup marketing / public relations firm that primarily used ‘micro-targeting’
and other predictive analytics to allow companies to spend their marketing
dollars in the most effective way possible.
Instead of analyzing purchase history and other customer data, we used
extensive survey data correlated with demographic research and other consumer
data (subscriptions, vehicle ownership, donations, etc) in order to extrapolate
customer insights to the population at large.
The idea of a corporate ‘big brother’ is foreign to me, but very
real for some of my friends. Personally
I could care less what a company thinks they know about me…as long as they are endeavoring
to send me fewer coupons for crap I’m not interested in, I’ll be happy. On the other hand, I do want to understand
where those ‘pessimistic’ friends of mine are coming from and what really
worries them about Target knowing they like crunchy peanut butter…or that their
wife is pregnant.
In addition, I simply find it fascinating to know how these
companies engage in predictive analytics, what they use it for, and how
effective it can be…especially when it discovers behavior that a consumer has
difficulty articulating an explanation for.
CONNECTION
TO CUSTOMER INSIGHTS / EXPERIENCE:
Analyzing large amounts of data to give us insight into customer
behavior (and how and why they consume the way they do) can give us a unique understanding
of how best to design a valuable customer experience that can sometimes be in
opposition to what customers think
they are looking for. Data mining is
essentially the study of massive amounts of data that reflect actions: a
purchase, a subscription, the use of a coupon, the frequency of visits, the
frequency of purchases, the consistency of purchases, the duration of product use,
changes in shopping habits, and all sorts of other information. If a company can analyze that data in a
meaningful way, they can gain a better understanding of who their customer is
and what they really want (not just
what they say they want).
A Duke University study on the subject estimates that “habits,
rather than conscious decision-making, shape 45% of the choices we make every
day”. Other studies describe ‘habit’ as
a conservation of brain energy during which we go into an auto-pilot state
where we no longer weigh the pros and cons of a decision, we simply follow a
routine. If this is the case, designing
a valuable customer experience must be highly adapted to facilitating ‘habitual’
purchases as well as creating new ‘habits’. If a company can achieve this level of analysis
and translate it into revenue without ‘freaking out’ its customers, it will be massively
successful.
REFERENCE
ARTICLE:
An interesting article on the subject was recently published in the
New York Times by Charles Duhigg called “How
Companies Learn Your Secrets”
Hi James - This is a really interesting topic to me. I think you make some good points and raise some good questions here. I have a sense of how you might organize the paper, but I'll definitely be curious to see how you develop your outline since there are a number of ways to shape this. One question I always have a predictive analysis is how can we use it in a way to make sure that we are still drawing in customers who may not always follow the predicted pattern (or is it ok, in the name of efficiency, to just let them go?). I might be able to explain this notion better in-person, so remind me if you think about it. Let me know if you want to chat along the way.
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