Marketing 0

REFLECTION QUESTIONS
Think about how better data management can improve
marketing practices in your organization. What is the low-
hanging fruit?
How do you segment the market for your products and
services? Create a roadmap to implement segments-of-one
marketing in your organization data.

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CHAPTER 9
Predictive Marketing: Anticipating
Market Demand with Proactive Action
Following the 2001 Major League Baseball season, the Oakland
Athletics lost three key players due to free agency. Under pressure to
replace the free agents with limited budgets, the then–general
manager Billy Beane turned to analytics to assemble a strong team
for the following season. Instead of using traditional scouts and
insider information, the A's used sabermetrics—analysis of in-game
statistics.
With analytics, the A's discovered that underrated metrics such as
on-base percentage and slugging percentage could be better
predictors of performance compared to more conventional offensive
stats. Since no other teams are recruiting players with these qualities,
the insights allowed the A's to recruit undervalued players and
maintain relatively modest payroll. The remarkable story was
documented in Michael Lewis's book and Bennett Miller's movie,
Moneyball.
It attracted the attention of other sports clubs and sports investors
around the world. John Henry, the owner of the Boston Red Sox and
Liverpool Football Club, was one of them. Mathematical models were
used for the rebuilding of Liverpool. The soccer club, despite its
fantastic history, was struggling to compete in the English Premier
League. Based on analytics, the club appointed manager Jürgen
Klopp and recruited some players onto the team that would go on to
win the 2018–2019 UEFA Champions League and the 2019–2020
English Premier League.
These stories epitomize the essence of predictive analytics. It allows
companies to anticipate market movement before it occurs.
Traditionally, marketers rely on descriptive statistics that explain
past behavior and use their intuition to make smart guesses on what
will happen next. In predictive analytics, most of the analysis is
carried out by artificial intelligence (AI). Past data are loaded into a

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machine learning engine to reveal specific patterns, which is called a
predictive model. By entering new data into the model, marketers
can predict future outcomes, such as who is likely to buy, which
product will sell, or what campaign will work. Since predictive
marketing relies heavily on data, companies usually build the
capability upon the data ecosystem they have previously established
(see 
Chapter 8
).
With foresight, companies can be more proactive with forward-
looking investments. For instance, companies can predict whether
new clients with currently small transaction amounts will turn out to
be major accounts. That way, the decision to invest resources to grow
the specific clients can be optimal. Before allocating too many
resources into new product development, companies can also use
predictive analytics to help with the filtering of ideas. All in all,
predictive analytics leads to a better return on marketing investment.
Predictive modeling is not a new subject. For many years, data-
driven marketers build regression models to find causality between
actions and results. But with machine learning, computers do not
need a predetermined algorithm from data scientists to start
uncovering patterns and models on their own. The resulting
predictive models coming out of a machine learning “black box” are
often beyond human comprehension and reasoning. And this is a
good thing. Marketers are now no longer restricted to past biases,
assumptions, and limited views of the world when predicting the
future.