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Predic t ive Analyt ics

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Predict ive analyt ics : The corporate crysta l bal l

Wouldn’t it be great if your organization had a crystal ball to predict what your

customers will buy and when they will buy it? Imagine the power you’d hold in

your hands.

Unfortunately, crystal balls exist only in fiction and fairy tales. However,

predictive analytics (PA), when used effectively, is a darn close substitute.

Corporations are embracing it as a business strategy to predict trends and

buying patterns. In fact, some would argue that predictive analytics is more

valuable than a crystal ball, because it uses actual data that can be reused

across the organization for multiple purposes. Plus, when it’s effective, it

can be used to gain competitive advantage, increase sales and help your

marketing efforts reach the right people every time.

That’s why PA is one of the hottest growing topics in business technology. Since

2010, Google® Trends shows a 300 percent spike in searches for predicative

analytics.1 In addition, according to the research group IDC Manufacturing,

business analytics is now a $31.7 billion market. And the market continues to

grow; Predictive Analytics World, a thought leader in the topic, puts the annual

growth rate for predictive analytics at 8 to 10 percent.2

It’s hardly a new concept. Historically, organizations have relied on customer and

market data to improve decision making. But in the past 20 years, advances in

computer power and server capacity have provided greater opportunity for data

collection and usage. Now, the explosion of social media and mobile devices has

unleashed enormous amounts of data that can tell you almost anything about a

customer or product. Today, an estimated 2.6 quintillion bytes of data are created

per year, and 90 percent of all the data in the world has been crated in the past

two years.3 This is what is called “Big Data,” and predictive analytics is about

leveraging large volumes of data to improve, grow, and yes, predict the future.4

So what is it about? Like a fortune teller might do, it helps organizations move

beyond what happened to determine what will happen. But instead of using a

crystal ball, the process relies on extracting information from existing data sets

1 Krupnik, Yan. “Predictive Analytics—The Five Things You Need to Know.” Supply & Demand Chain Executive. N.p., 8 Feb. 2013. Web. 11 Aug. 2013. <http://www.sdcexec.com/blog/10876460/predictive-analytics-the-five-things-you-need-to-know>.

2 Siegel, Eric, Phd. “Seven Reasons You Need Predicative Analytics Today.” Prediction Impact. Predictive Analytics World, 2010. Web. 11 Aug. 2013. <http://www.predictiveanalyticsworld.com/pdf/YTW03080USEN/7-Reasons-Predictive-Analytics.pdf>.

3 Parr-Rud, Olivia. Drive Your Business with Predictive Analytics. White Paper. SAS, 2012. Web. 25 Aug. 2013. <http://www.sas.com/content/dam/SAS/en_us/doc/whitepaper2/drive-your-business-with-predictive-analytics-105620.pdf>.

4 Ibid.


to determine patterns that help predict future outcomes and trends. In fact,

predictive models and analysis are typically used to forecast future probabilities

with a high level of reliability. When applied to business, predictive models are

used to analyze current data and historical facts so companies are better able to

understand customers, products and partners, and to identify potential business

risks and opportunities.

Let’s look at an example to illustrate how predictive analytics works. If you have

a perishable product that has a limited shelf life, you want to keep the product

moving so it doesn’t rot on the shelves. You might look at past sales figures and

purchasing habits to figure out how to prepare for production and inventory. But

predicative analytics takes the data a step further. It takes the cumulative amount

of data you have on the product, and simulates what might happen if you make

changes to pricing, marketing, or the product itself, so that you can determine

sales volume and keep the product in stock without producing too much. Using

PA, you can create unlimited scenarios that tell you if sales will increase or

decrease depending on a given change.

That’s the crux of predictive analytics, only it applies to much more; it’s the

business approach taking over companies worldwide. Instead of relying on gut

intuitions, predictive analytics help companies use the past to predict the future.

PA tells you what’s possible, instead of what happened. In the simplest form, it’s

about using data mining techniques to predict trends and behavior patterns.5

A recent article in Forbes® shared three corporate case studies that

demonstrate the value of predictive analytics. Best Buy®, for example,

used predictive analytics to show that seven percent of its customers

were responsible for 43 percent of sales.6 Using this data, the company

completely redesigned its stores to appeal to the buying habits of

particular customer groups. Olive Garden® is another well-known company

using predictive analytics. By analyzing data on staffing needs and food

preparation requirements, the restaurant chain was able to manage

staff more efficiently while significantly reducing food waste. Finally, the

U.K.’s Royal Shakespeare Company used predictive analytics to develop a

marketing program that increased attendees by more than 70 percent and

boosted membership by 40 percent.7 These are only some of the examples

that show the value of predictive analytics.

5 “Predictive Analytics.” Wikipedia. Wikimedia Foundation, 08 Nov. 2013. Web. 11 Aug. 2013. <http://en.wikipedia.org/wiki/Predictive_analytics>.

6 Rich, Dave, and Jeanne G. Harris. “Why Predictive Analytics Is A Game-Changer.” Forbes. Forbes Magazine, 1 Apr. 2010. Web. 11 Aug. 2013. <http://www.forbes.com/2010/04/01/analytics-best-buy-technology-data-companies-10-accenture.html>

7 Ibid.


How can your organization start using this magic crystal ball called predictive analytics? This Blue Paper® evaluates the basic principles of predictive analytics and gives you some tips on how to get started. It also gives you examples on how companies use predictive analytics to improve sales and products. So get ready to gaze into the past to foretell your future, without visiting a fortune teller or using a crystal ball. Predictive analytics is not only more accurate, it’s something that

provides clear value.

Ins ide the mind of a fortune te l ler (Or, making sense of predict ive analyt ics )

If you’re looking for a comprehensive resource on the topic, start with the book “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.” As the title implies, the book explains how you can use predictive analytics to identify clients that will buy your products as well as clients that will move on. It’s written by former Columbia University Professor and Predictive Analytics World Founder Eric Siegel, and it gives a good introduction to the power predictive analytics, as well as the potential pitfalls.

But first, it’s important to make the distinction between the complexities of predicative analytics versus simple business intelligence. In short, predictive analytics moves beyond traditional business intelligence and is far more accurate and forward looking. Business intelligence uses tools like queries, insight reports and dashboards to show how an organization is performing in the present and whether or not it is meeting targets and goals. Predictive analytics, on the other hand, moves beyond traditional business intelligence by modeling future behavior based on past behavior.8 The following diagram (Figure 1.) gives a comprehensive visual of the differences between basic reporting, analysis, monitoring and prediction. As shown, there is a direct correlation between complexity and business value. Simply put, while predictive analytics might be the most complex

form of analysis, it’s also the one with the highest business value.

Figure 1. Common types of analysis

8 “ Predictive Analytics.” Wikipedia. Wikimedia Foundation, 08 Nov. 2013. Web. 11 Aug. 2013. <http://en.wikipedia.org/wiki/Predictive_analytics>.


PA encompasses a variety of mathematical techniques that derive insight from

data with a single objective: to find the best action for a given situation. While

predictive analytics is most often used to predict the future, it actually can be

applied to any type of unknown in the past or present. This is because predictive

analytics captures relationships between variables to predict unknown outcomes.

Potential outcomes are determined by applying predictive models to data sets.

A predictive model is a mathematical equation that uses data to produce a

calculation. Credit scores, for example, are the result of a predictive model that

crunches a vast amount of data to evaluate a credit ranking for individuals.

Models give specific instructions on how to analyze data in order to deliver a

particular result.

The following figure (Figure 2.) depicts the basic concept of PA. As shown, it

inputs customer data into data mining frameworks to create a predictive model

for your business. In brief, the process takes historical data on your customers and

makes sense out of it. It combines multiple data points, like purchases, behavior

and demographics to learn from your organization’s collective experience.

Predictive modeling software leverages the principles of computer science, and is

a mixture of number crunching, trial and error.

Figure 2. The concept of predictive analytics

Generally, there are three types of models in predictive analytics. These include:

1. Predictive models

2. Descriptive models

3. Decision models

Predictive models analyze past performance to predict customer actions in the

future. Predictive models detect data patterns that answer questions about

customer behavior and performance. For example, you might use predictive

models to calculate whether or not a customer is likely to pay bills on time. Also,

predictive models can be used during live transactions. Think of your last trip

to the grocery store, did you wonder how the coupon that was printed on your

receipt reflected a brand you prefer as well as an item you regularly buy? Most

likely, your grocery store used predictive modeling to give you a coupon that

reflected your buying patterns before you even left the store.


Descriptive models, on the other hand, apply to groups rather than individuals.

Instead of focusing on a single customer behavior, descriptive models evaluate

relationships between customers or products. Customers might be categorized

by product preferences and life stages to help make predictions. For example,

descriptive analytics might look at electricity usage statistics to plan power needs

across a given market or geography. Or, it may group customers based on buying

preferences to help set pricing.

Another form of predictive analytics uses decision models. Decision models are

the most advanced level of predictive analytics, and are used to describe the

relationship between all elements of a decision. This includes results of predictive

and descriptive models and any other data points that can be combined to

produce actionable options. Decision models are most commonly used offline

to develop strategy. They allow companies to simulate products to explore

possibilities that aren’t practical with live testing. For instance, decision models

will tell companies the results of lowering costs by 5, 10 or 15 percent with a high

level of accuracy. They produce mathematically optimized results for defined

scenarios as they relate to the product.

All predictive models (predictive, descriptive, or decision) are similar in the sense

that each combines a number of predictors to create a formula. For example,

maybe you want to know the likelihood that customers who recently made an

online purchase will purchase again. In this case, one of the predictors might

be “frequency.” You could look at other predictors, like time spent online,

personal income or whether or not they lived in rural versus urban cities.

These would also be predictors, and when combined with formulas, you

create a model that relies on weighted data sets that combine the formulas

to create “if/then” scenarios. In other words, they might tell you customers

who purchased an item in the past 30 days and spend more than 20 hours

online each week, are more likely to make an additional purchase in the

next week or days. The data would also be able to tell you purchasing habits

of those in rural versus urban settings. Of course, this is a simplified example; an

effective predictive model is more complex and combines dozens of predictors

that evaluate multiple aspects of the customer and his or her behavior.

Predict ive analyt ics in sports , yogurt and more

What can predictive analytics do for your organization? Of course, the results

vary by organization and are largely dependent on the quality of data that is

used. However, experts say that predictive analytics is most commonly used to

do the following:


•Predict market trends and customer needs;

•Create customized offers for specific segments and channels;

•Predict how market-price volatility will impact production plans;

•Foresee changes in demand and supply across the entire supply chain; and

•Proactively manage a workforce by attracting and retaining talent.9

According to SAP, the top five data sources for predictive analytics are sales,

marketing, customers, financials and products. If you’ve ever been on Amazon®,

Netflix® or Pandora®, you’ve seen predictive analytics at work. Those aren’t people

that are making suggestions for other items you might like to read, watch or

listen to, it’s a complex predictive analytic algorithm that pulls multiple data

points to highlight what you might be interested in next. The level of accuracy

is impressive, it’s almost as though the program reads your mind in order to

determine what you might enjoy.

In the corporate world, predictive analytics is often applied to customer

relationship management (CRM), decision support systems, customer retention,

direct marketing, fraud detection, risk management and many other areas. A

telecom company, for example, might use predictive analytics to figure out when

customers are likely to leave in order to develop customer retention strategies.

Specifically, predictive analytics can help you answer questions like:

•Which customers are most likely to terminate service?

•What sales prospects are most (and least) likely to buy services or products?

•How can I align my sales efforts with customers most likely to buy?

•What products should I try to cross-market to which customers?10

While it may be difficult to believe, there’s really no limit to how an organization

can apply data to improve sales, operations, business functions and more. You can

use predictive analytics to improve social media, marketing, banking, health, sales

and customer retention, to name a few. You can use it to produce credit ratings,

fraud risk, or to guide decision making and the outcome of sports.

Yes, sports. Perhaps you saw the movie “Moneyball” with Brad Pitt. It’s a true

story that applied predictive analytics to help a baseball team (the Oakland

A’s) select players that would help the team excel, or simply put, win. The team

manager (Billy Beane) looked at basic player analytics like home runs, earned-

run averages and RBIs, but took the data to a new level. In reality, Beane used

9 SAP. Transform Your Future with Predictive Insight. N.p.: SAP, n.d. SAP.com. 2013. Web. 19 Aug. 2013. <http://fm.sap.com/data/UPLOAD/files/Transform%20Your%20Future%20with%20Predictive%20Insight.pdf>.

10 Kardon, Brian. “Predictive Analytics: The Power Behind Next-Gen Marketing.” Predictive Analytics: The Power Behind Next-Gen Marketing. N.p., 20 Mar. 2013. Web. 15 Aug. 2013. <http://cmo.com/content/cmo-com/home/articles/2013/3/20/predictive_analytics.html>.


predictive analytics to analyze hundreds of detailed statistics from every play and

every game to predict future performance and production. Some of the statistics

that Beane and his team used were obtained from game footage that used video

recognition techniques. Beane and others used this data to select players that

would have the greatest impact to the team by relying on future predictions of

performance. His strategy worked; by the end of the season, the A’s were ranked

second in the league. Beane’s legacy is historical; it is now standard practice to

apply analytics to a wide variety of sports that assist with decision making. But as

he said in a recent interview, his idea was not new: “We robbed ideas from Wall

Street and academics outside our business who said there was a different way of

doing things.”11

You can even use analytics to get ahead in specific markets, like yogurt. Have

you noticed that Greek yogurt is taking the market by storm? In 2007, Greek

yogurt accounted for less than 10 percent of the U.S. yogurt market. Today, it

represents 28 percent of all yogurt sales and is a $7 billion industry. Moreover,

sales continue to grow while demand for non-Greek yogurt declines.12 Estimates

show within the next three years, Greek yogurt sales could account for more

than half of yogurt sales.

The yogurt giant Dannon® realized the market held vast potential, and

that it needed to figure out a way to remain competitive. As a result, the

company used predictive analytics to gain a competitive advantage in the

Greek yogurt wars. How? In 2011, the company rolled out a predictive

analytics tool from IBM® that increased its accuracy in forecasting and

inventory planning from about 70 percent to 98 percent.13 Basically, the tool

used sophisticated algorithms to evaluate data on historical, regional and

market data. Dannon used this data to rollout successful promotions that

gave the company competitive advantage over other well-known brands.

How to make a crysta l bal l

Now that you have an idea of what predictive analytics is about, it’s time to figure

out how you can do it. What are the main ingredients to successful PA? Can you

do it yourself without hiring a fortune teller? Like many business functions, you

can develop the skill in-house or hire external vendors and consultants to help.

Let’s explore both options.

11 Bolen, Alison. “A New Way of Thinking about Predictive Analytics.” Business Analytics. N.p., Oct. 2012. Web. 15 Aug. 2013. <http://www.sas.com/knowledge-exchange/business-analytics/building-an-analytical-culture/a-new-way-of-thinking-about-predictive-analytics/index.html>.

12 Waxer, Cindy. “Inside the Greek Yogurt Wars: Dannon Taps Predictive Analytics.” Computerworld. N.p., 12 Aug. 2013. Web. 14 Aug. 2013. <http://www.computerworld.com/s/article/9241522/Inside_the_Greek_yogurt_wars_Dannon_taps_predictive_analytics?pageNumber=1

13 Ibid.


It’s not impossible to develop predictive analytics in house, but it’s not easy, either.

Figure 3. shows the predictive analytics lifecycle and gives you an idea of the

functions and steps that are required to implement PA. Some of the key players

are the business manager, business analyst, information technology, and data

mining expert. Each of these players has a key role in the process.

Figure 3. Predictive analytics lifecycle.

Figure 3. outlines not only the resources involved in PA, but the process it entails.

To begin, it’s important to note that the PA process is cyclical and ongoing, it

never really ends; it’s about constant refinement and repurposing. Once you

develop and deploy a PA model, it can be reused, refined, and updated to reflect

changes in the market, product or customer base.

Formulating and identifying the problem is the first step in the process, and

perhaps the most critical. It’s important to clearly define why you want the data

and how you plan to use it. Without a concise objective, data is really just a bunch

of numbers that don’t make sense. By identifying and formulating the problem

you want to solve, you give it a purpose. In this stage, a business analyst or similar

resource evaluates the data to create scenarios and hypotheses related to the

data. In many organizations, input for this stage comes from business functions

like customer service, direct marketing, fraud detection and risk management, to

name a few.

In other words, the objective in this stage is to identify a single, high-priority

business problem that needs to be solved. Ideally, a company will already have

the data to evaluate the problem available, so the effort will generate results

quickly to prove the value of using predictive analytics. Maybe you want to know

if customers are more willing to purchase your product if you lower the price, or

if certain customers are likely to pay bills on time. These are examples of business

scenarios that predictive analytics uncovers. As noted in the previous section, you

can develop scenarios that are predictive, descriptive or decision based.


If you are unsure where to begin, experts suggest that companies start with the

marketing function. With marketing, you can analyze price points or advertising

levels to see how it will affect sales. Maybe you want to uncover customers that

are most likely to respond to brochures or coupons, but whatever you decide, the

objective needs to be clear. While there are an abundance of factors that can be

analyzed, it’s important to stay focused on one or two outcomes. Other items that

are easily analyzed include competitors’ pricing, customer churn rates or incidents

of fraud on your website. Most companies have ready-made data on these topics.

Data preparation, exploration and selection are the next phases in

the PA lifecycle. Preparing and selecting the data is a critical piece,

but it’s probably also the trickiest. Here is where you uncover trends

and patterns in behavior to identify predictors. For example, a predictor

might be customers who spend 20 percent more time on your website,

order more products. You also need to evaluate the data to identify

relationships. For example, perhaps you noticed that customers in

Florida order more of your product than those in colder climates. This

is an example of a relationship that can be examined in greater detail

using PA.

Next, a data mining expert or statistician will engage in exploratory analysis of

the data and begin the process of descriptive segmentation. This means the data

will undergo another round of analysis, using complex mathematical analysis and

algorithms. This is where you might use the data to test “if/then” scenarios. For

example, if customers spend at least 10 hours online and they live in Florida, what

is the likelihood that they will purchase more of your products? Is there a direct

correlation between the data points? The predictors and relationships extracted

in this phase are fed into the model that will be used to make future predictions.

This information will help build the predictive model.

Some organizations use a team of cross functional experts to analyze and explore

data and provide insight into the predictive analytics model. Data engineers, data-

savvy business managers and analysts can provide business as well as analytical

feedback to assist with model creation. A team-based approach can help you find

and explore historical data sources, make hypotheses for business problems that

are identified, and develop and test analytical models.

Once you’ve identified the business objective, model, and data you want to

use, it’s time to involve information technology. In short, you need some type

of technology solution that will help you analyze the data. It isn’t necessary

to purchase an expensive application if your needs can be met by another,

light-weight analytic application. If you’re just starting out and Microsoft®


Excel can meet your needs, start there. Chances are you’ll need to migrate to

a more sophisticated platform in the future, but until then, it is okay to use

something basic. As you progress, you will probably want to check out some of

the technology offered by IBM®, SAP® and SAS® to engage in more sophisticated

analytic analysis.

Alpine Data Labs®, an organization that specializes in predictive analytics,

provides helpful tips on how to select a tool for predictive analytics.14 For

example, something that is easy to set up and low cost is optimal if you’re just

getting started. It’s also a good idea to select something that lets you start

generating insights quickly without major changes to the infrastructure. The tool

that you use should also have a data agnostic so that you can leverage all existing

data sources from multiple locations. Also, something that is easy enough for

non-data scientists to use with a highly visual interface is optimal.

In the final stage, predictive analytics uncovers valuable insights that can be

incorporated into business operations and strategic decision making. This is what

you’ve worked hard to uncover, the results and action items that can help you

improve customer satisfaction, products or marketing strategies. In reality, this

is where the real work begins. This is where you translate data into action to

provide value. Remember, predictive analytics only tells you what might happen

in the future, how you decide to act on this knowledge is the final chapter in the

predictive analytics story. Accordingly, it’s important to communicate the results

with the rest of the organization. This will help build momentum and excitement

for future predictive analytics projects. Along the way, you’ll build skills and

confidence that will help with future projects, too.

Predictive Analytics World is a well-known leader on the topic, and its

website provides a wealth of information. A quick visit can get you up

to speed on the tools and methods most widely used for success. You can

also watch videos and access resources that describe how organizations

effectively use analytics to drive growth and profits. There are even online

training courses and workshops that can help you learn more. If you’re

more of a hands-on learner, Predictive Analytics World offers conferences

and seminars across the country, too. Its annual conference is well attended

by a number of high profile organizations that mastered the art of

predictive analytics with impressive results.

If you’re more of a visual learner, check out the introductory video by David

Smith from Revolution Analytics. He gave a 20-minute presentation (complete

14 “Cross-Functional Predictive Analytics: Three Steps to Getting Started.” Alpine Data Labs. N.p., 2012. Web. 15 Aug. 2013. <http://www.alpinedatalabs.com/get-started.html>.


with slides) at the annual Strata conference on Big Data in 2013. It’s an

informative video that’s not too complex, and the overview might help you

gain a better understanding of the topic. If a short infographic is more your

style, check out the one SAP created that details where and how companies

most commonly use predictive analytics. If you want to calculate the potential

return on investment for your predictive analytics efforts, SAP also developed a

tool that can do the estimate for you. SAP’s Predictive Analytics Calculator asks

customers a series of questions to determine if predictive analytics is right for

your organization.

What i f you want to hire a fortune te l ler?

Some experts claim that organizations are better off outsourcing predictive

analytics because it is time consuming, cumbersome and complex. There are

hundreds of business analytics providers that can help you with part or all of your

analytic needs, the difficulty lies in selecting one that’s a good fit.

Naturally, the type of vendor or service you require depends on your goals and

objectives. There are vendors that provide scoring models, as well as providers of

credit reports and credit scores. Companies like Experian and Fair Isaac® are two

of the well-known organizations that use analytics that assist with debt recovery

or fraud protection, among other services. Acxiom™ is another analytics company

that provides marketing insight for 47 of the Fortune® 100 brands. According to

its website, the company evaluates more than a trillion data transactions each

week to populate its marketing and consumer databases.

If customer relationship management (CRM) is your primary focus, there

are a number of packaged solutions providers that could help. Oracle®

provides solutions for multiple industries and business functions. Other

leaders in the field include IBM® SPSS Statistics, MATLAB®, Agnoss

KnowledgeSTUDIO® and SAP®. This is just for starters. In truth, you’ll have

to do your homework when selecting a vendor for predictive analytics.

While these vendors vary in scope, size, and pricing, they share one important

trait: They focus on generating new data, insight and foresight. The applications

explore data to find insight to produce computational and probabilistic

techniques. They might produce decision trees, scoreboards, dashboards,

relational or multi-dimensional analytic processing, but they all aim to figure out

what the market and customers will do.

However, it’s important to realize that although technology is vital, companies

that find success with predictive analytics do more than just purchase and


implement technology. Successful organizations embed predictive tools into daily

business functions, and teach employees how to use and implement data for key

decision-making. They also nurture and develop analytical talent to ensure that

the investment in analytics capabilities assess the organizational health as well as

future potential.15

The legal and ethical impl icat ions of predict ive analyt ics

You can’t discuss predictive analytics without asking when, and if, it’s too much.

Meaning, how can you apply the methods and tools without making customers

feel like they are being watched? There’s a fine line between being helpful

versus pushy; and companies must find a happy balance when they approach

customers with so called “helpful” suggestions. While some customers appreciate

the reminder that they left items in their shopping carts, others might find it

annoying and off-putting to your brand or organization.

In addition, the question of privacy rights is leading to increased legislation.

Most Americans accept the fact that purchasing behaviors and activities are

tracked through websites, apps and customer loyalty programs, to name a few.

But how this data is used is sparking intense debate on whether or not there

should be more legislation in place to monitor commercial data mining in the

global community. Last year, both the United States and the European Union (EU)

proposed to give their citizens greater control over commercial data-mining. In

2012, the EU passed legislation that completely reformed data protection rules to

strengthen online privacy rights. Specifically, under EU law, personal data can only

be gathered legally under strict conditions, and organizations that collect and

manage personal information must adhere to privacy rights of data owners.16

In the United States, the future of data mining is currently being debated on

Capitol Hill. Specifically, there is debate surrounding data mining techniques,

and whether or not it infringes on personal privacy rights. The American

system has a number of federal and state privacy laws that separately govern

the use of personal details in areas like patient billing, motor vehicle records

and education. However, some legislators believe there are serious gaps in

consumer protection, particularly when it comes to data collection online.

There is a strong belief that Congress should enact a baseline consumer

privacy law.17

15 Rich, Dave. “Why Predictive Analytics Is A Game-Changer.” Forbes Magazine, 1 Apr. 2010. Web. 19 Aug. 2013. <http://www.forbes.com/2010/04/01/analytics-best-buy-technology-data-companies-10-accenture.html>.

16 “Protection of Personal Data RSS.” Protection of Personal Data. N.p., 16 July 2013. Web. 27 Aug. 2013. <http://ec.europa.eu/justice/data-protection/>.

17 Singer, Natasha. “An American Quilt of Privacy Laws, Incomplete.” The New York Times. N.p., 30 Mar. 2013. Web. 27 Aug. 2013.


Recently, the Obama administration called for Congress to enact a “consumer

privacy bill of rights” that would apply to industries not already covered by

privacy rights. These could include data brokers, which are companies that collect

details on an individual’s likes, leisure pursuits, shopping habits, financial status,

health interests and more. In brief, the legislation would give Americans the right

to some control over how their personal data is used, as well as the right to see

and correct records that companies hold about them. Naturally, these kinds of

legislative changes would have significant impact on the ability to use predictive

analytics, and the data that is required to produce results.

Why is the topic so heated? Imagine if a company used predictive analytics to

figure out you were pregnant, and then told your parents before you had a

chance to tell them yourself. It sounds unbelievable, but that’s exactly what

happened between a teenage girl and Target®.18 Like many organizations,

Target collected historical data on its clients, many of which related to

purchasing habits. Target also extracted historical data on customers that

signed up for baby registries and cross referenced the data against women

with similar purchases to predict the buying patterns of expectant mothers.

The idea was simple, if Target could figure out which women would soon be

buying diapers and bottles, it could start marketing directly to these customers

to secure future purchasing opportunities.

In fact, Target’s analytics model was so effective that it could

identify about 25 products that, when analyzed together, allowed

the organization to assign each shopper a “pregnancy prediction”

score. The tool could also estimate a due date, and the corporation

subsequently sent coupons that were timed to specific stages of a

pregnancy. An article in Forbes suggests that these types of predictive

analytics contributed to Target’s revenue growth from $44 billion in

2002 to $67 billion in 2010.19

So, when the father of a teenager noticed she was receiving coupons for baby

clothes and cribs, he contacted his local Target to find out why they would

approach a young woman for such products. Turns out, the daughter had yet

to notify her family of her pregnancy. Naturally, the story prompted a series of

ethical debates on corporate responsibility for customer privacy. After the incident

hit the news circuit, Target declined further comment.

18 Hill, Kashmir. “How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did.” Forbes. Forbes Magazine, 16 Feb. 2012. Web. 19 Aug. 2013. <http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/>.

19 Ibid.


In a more recent example, a company in England was banned from tracking

people as they walk the streets of London.20 An advertising firm was using a

network of high-tech trash cans to determine where people went in London’s

financial district. The trash cans used sophisticated technology to tap into

smartphone and Wi-Fi signals in order to see where people went and how

long they stayed. It almost sounds like a scene from a sci-fi movie, but it

actually happened.

The objective was to use the information for enhanced marketing techniques.

For example, if the data showed that people spent 20 minutes at Starbucks®,

that data could be used to enhance marketing for a competitor with well-

placed billboards or signs. Or, if there appeared to be a lot of tourists passing

through, advertising could target museums, plays and sightseeing events. The

goal was to use the trash cans to find out more about the people that visited

the area in order to determine what kind of marketing efforts might have the

greatest impact.

In this case, the City of London Corporation ordered the firm to stop using the

trash cans to collect Wi-Fi signals. But it raises a host of questions regarding

methods that monitor customer behavior. In reality, data that tracks electronic

footprints of customers on the Web is not vastly different from physical

footprints. Corporations frequently measure how long you stay at a website,

whether or not you purchase something, and where you go afterwards. The

difference lies in the expectations of privacy. Customers probably expect to be

tracked online, and know that companies survey Web activity, buying trends or

frequency. However, an individual walking the streets of London probably has

a reasonable expectation to privacy, and does not expect that their actions and

movements are tracked.

But what about when predictive analytics is used to prevent crimes, is that still an

infringement on individual rights? Believe it or not, predictive analytics is used by

police departments in Illinois and Ohio to predict where and when violent crimes

are likely to happen so they can stop them before they start. (Remind you of the

movie Minority Report at all?). In Chicago, for example, the police department

uses data models that are updated with criminal activity on a daily basis.

Predictive analytics in the Chicago Police Department evaluates a large number of

variables, like 911 calls and the type of crime in order to uncover crime patterns in

the city.21

20 Satter, Raphael. “The Big Story.” The Big Story. N.p., 12 Aug. 2013. Web. 14 Aug. 2013. <http://bigstory.ap.org/article/uk-firm-must-stop-tracking-people-trash-cans>.

21 Rosencrance, Linda. “Using Predictive Analytics to Fight Crime.” TIBCO Spotfire’s Business Intelligence Blog, 13 Oct. 2011. Web. 19 Aug. 2013. <http://spotfire.tibco.com/blog/?p=8148>.


Likewise, the Ohio Bureau of Criminal Identification and Investigation

(BCI&I) uses predictive analytics in combination with geographic profiling

to stop serial crimes like robberies and car thefts. The analysis at BCI&I tells

crime professionals not only when a crime is likely to occur, but where it

is likely to happen. It’s used in conjunction with software that predicts the

location of a criminal that may be hiding from law enforcement officials.22

Illinois and Ohio are not the only cities using predictive analytics to fight

crime. In reality, cities and governments around the world use real-time

data collection and predictive analytics to reduce crime rates and integrate law

enforcement agencies. IBM developed a Crime Information Warehouse to help

agencies integrate data that can be used to better understand criminal patterns.

Americans seem to have a higher tolerance for sharing this type of data post-

September 11th; however, it comes dangerously close to challenging Fourth

Amendment rights that apply to probable cause and reasonable suspicion. In

short, there’s a lot to figure out, and as predictive analytics continues to infiltrate

society, corporations will be challenged to ensure that efforts to predict customer

behavior uphold privacy and civil liberty standards. In addition, any changes to

legislative changes could have a significant impact on the ability to collect and

extract data that can be used for predictive analytics.

The crysta l bal l that pays you back

While the ethics of predictive analytics may be up for debate, the value it provides

is not. Research clearly shows that companies that use predicative analytics

benefit financially and competitively. According to SAP research, 68 percent of

organizations that use predictive analytics realized a competitive advantage.23 The

same research shows that 86 percent of companies that use predictive analytics

report that it has a positive impact on the organization.

Similarly, Predictive Analytics World claims that 90 percent of organizations that

use predictive analytics report a positive return on investment (ROI). According to

another report, predictive analytics initiatives show a median ROI of 145 percent.24

The same survey reported that users of predictive analytics achieved a one percent

improvement in operating profit margins and an increase in customer retention

of six percent. There’s no limit to statistics that show the bottom line value of

using analytics, the proof of value is apparent.

22 Ibid.23 “Transform Your Future with Predictive Insight.” Predictive Analytics. SAP: The Best-Run Businesses Run SAP,

2012. Web. 11 Aug. 2013. <http://tinyurl.com/lz79mgb>24 Siegel, Eric. “Seven Reasons You Need Predicative Analytics Today.” Predictive Analytics World. IBM, 2010.

Web. 19 Aug. 2013. <http://www.csiltd.co.uk/PDFS/BI/Predictive%20analysis.pdf>.


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But what if you don’t embrace predictive analytics? Research shows that

companies that have yet to adopt predictive technologies experienced

a two percent decline in profit margins, and a one percent drop in their

customer retention rate, on average. To put it bluntly, the data suggests

that while other companies are using predictive analytics to thrive in the

future, if you don’t, you’ll be left behind wondering what happened. So

why not get ahead of your competitors, and discover what the future

might hold using the new crystal ball for corporations: predictive analytics.

predictive analytics - 4imprint learning center · “predictive analytics: the power to predict...

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