Wondering What Lies Ahead? The Power Of Predictive Modeling
Wondering What Lies Ahead? The
Power of Predictive Modeling
Wondering What Lies Ahead? The Power of Predictive Modeling.
From telecoms to finance, e-commerce to government, predictive models are
being utilized across various sectors to tackle all kinds of business problems.
Companies that have yet to benefit from this practice need to examine the ways
in which they can do so…
Using Predictive Modeling to Address Different Business Problems
For thousands of years, people have had the desire to (or claimed they could)
predict the future. This desire to foresee what lies down the road is a common
one among individuals, each of us wanting to know what our lives will be like one
day (be it in regards to happiness, wealth, health, etc.). Naturally, companies also
possess this desire, wanting to know whether certain products or services they
plan on releasing will be successful, whether their customer base will expand or
shrink based on a strategic decision, or whether their investments will pan out as
Thankfully, the rise of the digital era has partially enabled this, (with the help of
databases and the power of analytics), taking shape in the form of predictive
Predictive modeling, by definition, is the analysis of current and historical facts to
make predictions about future events. Several techniques – according to the
nature of the business problem and current conditions – can be used when
conducting predictive modeling. These include regression techniques, time series
models, decision trees, and machine learning methods, among others.
The phases of predictive modeling are rather straightforward, and involve
activities aimed at ensuring a look into the past through the analysis of various
data points will in fact help predict the future:
Companies from different sectors are using predictive modeling on a regular
basis to add value to their business in several different ways. Some examples
Telecom companies, such as AT&T, use predictive modeling to predict customer
demand for voice or data services by creating choice models. These models also
enable companies to conduct “what-if” analysis by simulating the market,
helping to predict results based on various scenarios. These ultimately help the
company impact its bottom line through ensuring the right product features and
pricing is used to ensure maximization of market share and revenues.
One of the most common usages of predictive models in the telecommunications
sector is around predicting churn, trying to understand which customers are
more likely to be leaving the company within a certain time frame. Cellular One,
a telecommunications company in Puerto Rico, is an example of a company that
does this particularly well. Every one of its customer service agents knows which
customers are at-risk, and are able to offer tailor-made benefits to these
customers during their interactions. Cellular One claims this practice has helped
it reduce churn by 33%.
Estimating Customer Lifetime Value
ING Belgium uses predictive modeling techniques to estimate the potential value
of a given customer over their entire lifetime (how much revenues and profits a
given customer can generate for the company over xx number of years). Based
on the findings for a given customer, resources are then reallocated accordingly,
such that those with high potential lifetime value (though not necessarily
valuable today) receive an added level of service.
Estimating Credit Risk
Predictive credit risk scoring is a stronghold of customer analytics in the finance
sector. Financial institutions, for example, can estimate the likelihood of a loan
being defaulted on by looking at several variables (such as income, credit history,
outstanding load balances, etc.) that directly correlate to default behavior.
Wachovia Bank uses this type of predictive modeling on a regular basis – by
looking at financial indicators, demographics, and life events (such as divorce,
loss of job, new business, children going to college, etc.), the bank changes the
way it strategically addresses each and every one of its customers.
Capital One, known as a “test and learn factory” in the financial services sector,
used predictive modeling to identify the most appropriate individuals to target
for each specific campaign that will be launched. This reflects in the over 300
business experiments that are conducted at the company each day in helping to
fine tune their predictive modeling capabilities.
Predicting Startup Success
Financial services companies are also exploring the power of predictive modeling
as it applies to new businesses, essentially whether giving a loan or startup
venture capital is wise or not. One website (Younoodle) is trying to do exactly
this – to that end, they have built up a large database of detailed startup
information of 60,000 companies and 350,000 people. Three different predictive
models have been built based on this database, models which generate scores
that signify the potential future value of the startup, its growth potential, etc.
Determining the Next Best Offer
If you are familiar with e-commerce sites such as Amazon or Netflix, you are
most likely then familiar with the “our recommendations” section. The offers in
this section are tailored based on each and every customer’s past behavior and
preferences. Thanks to an intense usage of analytics, companies are quite
successful at predicting which customer will buy what next (next best offer).
Netflix declared that from 1999 to 2006, revenues generated directly from the
practice of analyzing customer behavior and creating customized offerings
increased from $5 million to $1 billion dollars.
Junk e-mails, AKA spam, represent some 80% of all emails sent in any given day.
Predictive modeling techniques are used extensively in helping to determine
which e-mails are more likely to be junk. Companies like HP, IBM, and AT&T rely
on predictive modeling techniques to determine which emails to label as spam
when it hits their employees’ email accounts.
Improving Care Services
New York City Health and Hospital Corporation uses predictive modeling to
predict disease related risks for each of its members. According to the risk scores
assigned to each member, NYCHHC then prioritize the high risk groups to take
preventive / proactive actions to lower the possible clinical consequences. With
limited resources, predictive modeling allows maximized effectiveness of disease
Predicting Equipment Failure
The US Army has created several predictive models for the purpose of estimating
how and when the various equipment it has on hand will fail. With such models
in place, the US Army operation planners can more effectively manage required
resources by answering how long they can rely on any given piece of equipment,
or, when they should start seeking a replacement. With the value of equipment
on hand in the hundreds of billions USD, the predictive models are of significant
Optimizing Customer Service Levels
The Canadian Automobile Association (CAA) uses predictive models to optimize
its customer service levels. Using results of a member-based survey around
overall satisfaction levels in regards to emergency roadside services, CAA built
predictive models to determine which customers need to receive an added level
of service to prevent membership cancellation, thus allocating capacity-
constrained resources to the customers that most need the assistance.
Organizing Air Traffic Systems
Airlines predict airspace performance with the help of analytics. Continental
Airlines used predictive models so they can better organize and manage air
traffic in United States, especially in territories where weather challenges are
How Companies Can Get the Most Out of Predictive Modeling
Designing predictive models that work is not as simple as 1-2-3. It requires that
the company has a strong analytical team is in place, that it is truly dedicated to
collecting data, and that it can take time to reap the benefits.
Some principles companies should adhere to in their trek to benefit from
1. Increase awareness across the organization about what can be achieved with
In many companies, analytics is perceived to be in the focus of BI or IT
departments. On the contrary, analytical models (including the predictive ones)
need to be owned and triggered by the business, in such units as marketing or
sales. Executives need to make sure that employees in such departments have a
vision about what can be achieved with analytics, and how it can help them in
their day-to-day business activities.
Business analytics workshops can be held to help achieve this, with business
intelligence experts sharing best practices with business units, showing how
companies have used predictive modeling to impact the bottom line. This can
then be followed by a roadmap building session, defining when and how
predictive modeling will be used in the business units.
2. Define a data strategy to clarify which data should be collected, calculated,
The single indispensible element of predictive models is data. A rich set of data
fields with a high level of granularity can enable several different
analyses/models to be generated – without the data little can be done. Thus,
having a data strategy in place (specifically focusing on which data needs to be
collected at what regularity) is of critical importance for ensuring predictive
models can be generated.
Long lists of forms with too much detail frustrate customers where as a very
limited number of data fields limit the potential benefits of predictive analytics.
Rather, based on the desired predictive model, a company can work backwards
to determine what data fields are needed at what detail. This is a step that can
be built into the predictive modeling roadmap, as an early but vital step.
3. Manage data quality to keep it high
Predictive models, like with other analytical models, solely rely on data.
Successful predictive models can only be designed with data that is of high
quality. Companies should measure the quality of their data regularly and
understand the root causes of possible data quality defects. The level of quality
of the data should also be assigned to one individual or team, with data quality
targets / KPIs in place to ensure acceptable levels are achieved such that
predictive models and their continuity can be ensured.
4. Ensure a data-driven decision making culture is in place
To ensure that predictive models can be created, a data-driven organization
needs to be in place, one that relies on facts rather than intuition in their
decision-making process. As such, from top-down, a culture needs to be built
into the organization that emphasizes the importance of utilizing data in all
business activities to the greatest extent possible. Such a culture should even be
reflected in job descriptions and performance reviews, whereby employees are
made responsible for ensuring data is tapped into regularly, valued by the entire
organization at every level.
5. Establish a test and learn environment
While establishing such customer analytical models such as predictive ones,
establishing a test and learn environment is of critical importance. Models need
to be tested over and over to ensure they become as accurate as possible in
predicting whatever it is that wants to be predicted. “Rushing to market” with a
predictive model just because it’s ready is not recommended. Rather, the model
should be deployed behind the scenes and fine-tuned if necessary based on its
accuracy. An example of this would be a model that is meant to predict customer
churn – a company should check to see that the model accurately does actually
predict churn before building strategies based on its predictions. Otherwise, the
wrong strategy could be applied to the wrong customer based on flaws in the
6. Conduct pilots and spread the results to get buy-in
The most effective way to achieve and accelerate momentum around predictive
models is through demonstrating bottom-line business results achieved because
of the models. Companies should be communicating the learnings and achieved
levels of success with pilots across the company. Any type of business case
showing the potential success of large-scale rollout of the model can help with
this. Predictive models stand to succeed once employees embrace them, which
can ultimately be achieved through demonstrating bottom-line impact.
Be it small-scale models that help predict call volumes to those that predict a
customer’s potential lifetime value, predictive models have proven their worth
time and time again in practically every sector on practically any issue.
Companies need to strive to ensure they make these models a part of their day-
to-day business to the greatest extent possible.
About Forte Consultancy Group
Forte Consultancy Group delivers fact-based solutions, balancing short and long term
impact as well as benefits for stakeholders. Forte Consultancy Group provides a variety
of service offerings for numerous sectors, approached in three general phases -
intelligence, design, and implementation.
For more information, please contact
Forte Consultancy Group | Istanbul Office