predictive analytics: the next big thing in bi?

E-Book

Predictive analytics: the next big

thing in BI?

Predictive analytics goes beyond traditional business intelligence,

enabling users to churn through large volumes of both historical and

real-time data in an effort to build predictive models. In this eBook,

learn about predictive analytics technology and why it’s getting

increased attention from prospective users. Read about real-world

predictive analytics projects and get expert advice on organizing a

predictive analytics program and on developing and utilizing predictive

models in business operations. Get examples of predictive analytics in

action as well as insight on the potential benefits and challenges of

using predictive analytics software.

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Predictive analytics: the next big thing in BI?

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E-Book

Predictive analytics: the next big

thing in BI?

Table of Contents

Predictive analytics early adopters focus on individual customer analysis

Data mining, predictive analytics: trends, benefits and challenges

To be effective, predictive analytics software must be tied to action

Using predictive analytics tools and setting up an analytics program

Resources from IBM



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Predictive analytics early adopters focus on individual customer analysis

By Jeff Kelly, SearchBusinessAnalytics.com News Editor

Target isn’t just a name for the Minneapolis-based retail chain. The company applies that

term to business operations on a daily basis, using predictive analytics technology to target

its marketing programs to individual “guests,” as Target calls its customers.

“We are able to derive guest expectations through mining our data,” said Andrew Pole, head

of media and database marketing at Target.

Target isn’t the only company that uses predictive analytics to zero in on customer behavior

and expectations on a micro level. In fact, rather than identifying and predicting larger

market or economic trends, most early adopters of the technology are using predictive

analytics software to tailor marketing campaigns and identify up-sell opportunities down to

the individual customer level, according to speakers at the 2010 Predictive Analytics World

conference in Alexandria, Va. The goal, they said, is to better understand what specific

customers are likely to spend their money on.

Take Paychex Inc. The Rochester, N.Y.-based company’s core business is processing

payrolls for its corporate clients. Paychex also offers 401(k) services, a business it is eager

to expand. Until recently, however, Paychex sales reps were cold calling payroll clients to

see if they might be interested in adding the 401(k) services, according to Jason Fox, an

information system and portfolio manager in Paychex’s enterprise risk management

division.

The cold calling proved to be an inefficient way to sign up new 401(k) customers: Nearly

half of Paychex’s clients use the company’s payroll services but not its 401(k) offerings.

“That’s a lot of revenue to leave on the table,” Fox said.



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Predictive analytics tools point the way to prospective customers

The company decided to invest in predictive analytics technology to help identify which of its

payroll-only clients were the most likely to be interested in the 401(k) business. The

analytics routines take into account whether a client uses a competitor’s 401(k) services or

none at all, as well as its credit rating and payment history at Paychex.

With the most likely 401(k) clients identified, Paychex can then allocate its available

marketing budget to the various prospects based on their perceived value and likelihood of

signing on, Fox said.

At Monster Worldwide Inc., Jean Paul Isson and his team are using predictive analytics

technology to help differentiate the New York-based company from other online careers

sites.

In addition to its flagship job posting services, Monster offers services such as resume

mining and careers website hosting to corporate clients. Predictive analytics helps Monster

identify which services to market to which clients, said Isson, who is vice president of the

company’s global business intelligence and predictive analytics division.

Isson added that the predictive analytics software has become a crucial tool for the

company as it takes on new, and free, job listing sites in the careers services market. “It’s

the only way we can optimize ourselves,” he said.

Using predictive analytics to keep the cash registers ringing

At Target, predictive analytics technology helps the retailer maximize the amount of

revenue it gets from each customer, whether people shop online or in stores, while also

enabling the company to allocate its marketing resources more efficiently, Pole said.

With data mined from online transactions, loyalty card use and demographics databases, for

example, Target creates a profile of each customer and determines the amount of money

that he or she is likely to spend with the company in a given year.



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So if, with the help of the predictive analytics software, Target determines that customer X

can afford to spend $5,000 annually, the company tailors its marketing efforts accordingly.

And when the customer reaches the $5,000 mark, Target can stop spending money

marketing to him if it decides that any additional efforts aren’t likely to induce him to make

more purchases, Pole said.

Target also uses predictive analytics to determine how much of a marketing investment is

required to get a particular customer to buy a certain product. For some customers, a $1

coupon might be enough to get them to buy dishwasher soap, while others might need only

half of that to induce a sale. With that kind of information in hand, Pole said, Target’s

marketing department can decide which customers are worth marketing to in given

situations.



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Data mining, predictive analytics: trends, benefits and challenges

By Craig Stedman, SearchBusinessAnalytics.com Site Editor

Predictive analytics software is getting increasing amounts of attention from technology

users, vendors and analysts. The advanced analytics technology is designed to enable

organizations to mine data and build predictive models that can help them analyze future

business scenarios, such as customer buying behavior or the financial risks of proposed

corporate investments.

Until now, data mining, predictive analytics and advanced business modeling technology has

been used almost exclusively by highly skilled – and highly paid – statisticians,

mathematicians and quantitative analysts. But that’s changing as business intelligence (BI)

and analytics vendors offer more user-friendly predictive analytics tools – or is it? In this

interview, conducted via email, Forrester Research Inc. analyst James Kobielus assesses the

current state of predictive analytics software and provides an overview of predictive

analytics trends and the potential benefits and challenges of using the technology.

There’s a lot of talk about predictive analytics being the next big battleground in

the business intelligence market. Do you agree? And if so, why is that? Yes, I agree.

The core BI market has become quite crowded with vendors providing solutions that do a

great job of supporting rich analysis of historical data. It would be a gross oversimplification

to claim that the traditional BI market has become commoditized. However, vendors all over

the BI arena are looking to new types of advanced analytics applications as a way of

avoiding the “me too” syndrome of look-alike offerings that blur into each other and fail to

differentiate in a way that can justify a premium price.

Predictive analytics is a natural evolution path for BI offerings, and it’s something that many

users want but have often needed to obtain separate from their current BI tools. Predictive

analytics can play a pivotal role in day-to-day business operations. If they’re available to

information workers – not just to Ph.D. statisticians and professional data miners –

predictive modeling tools can help business people continually tweak their plans based on



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what-if analyses and forecasts that leverage both deep historical data and fresh streams of

current-event data.

From a general standpoint, is predictive analytics software ready for broader use?

Or are there limitations that need to be addressed first? Yes and no. Yes, Forrester is

seeing an impressive new generation of user-friendly predictive analytics tools that are

geared to the needs of the mass market of information workers and other nontraditional

users.

But no, traditional predictive analytics tools are still very much the province of a specialized

cadre of statistically and mathematically savvy modelers with an academic background in

multivariate statistical analysis and data mining – although most of the established

predictive modeling vendors have made great progress in rolling out more user-friendly

visual tooling. Still, I had to reflect the current state of the industry when I published my

Forrester Wave report on predictive analytics and data mining tools in early 2010. I didn’t

put a huge emphasis on features geared to business analysts, subject matter experts and

other “nontechnical” information workers. The core problem with today’s offerings is that

many of them remain power tools with a steep learning curve and a commensurately high

price.

What’s happening with predictive analytics software? Can you give us an overview

of the key technology trends that you’re tracking? The key trend is the move toward

user-friendly, self-service, BI-integrated predictive analytics tools that encourage more

pervasive adoption. Another trend is the move toward integrating more predictive analytics

functionality into the enterprise data warehouse, through in-database analytics. That’s an

approach under which data preparation, statistical analysis, model scoring and other

advanced analytics functions can be parallelized and thereby accelerated across one or more

data warehouse nodes. In-database analytics also enables flexible deployment of a wide

range of resource-intensive functions – such as data mining and predictive modeling – to a

cluster, grid or cloud of high-performance analytic databases.

We’re also seeing the growing adoption of open frameworks for building predictive analytics

models for data mining, text mining and other applications. The principal ones are

MapReduce and Hadoop, which have been adopted by a wide range of vendors of analytics



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tools and data warehouse platforms. In the coming year, we’ll also see the beginning of an

industry push toward an open development framework for inline predictive models that can

be deployed to complex event processing (CEP) environments for real-time data streaming

applications. Still another trend is the embedding of predictive analytics features in

customer relationship management (CRM) applications to drive real-time “next best offer”

recommendations in call centers and multichannel customer service environments.

Why should prospective users be interested in predictive analytics? What are the

potential benefits or competitive advantages that companies can get from it?

Business is all about placing bets and knowing if the odds are in your favor. Business

success depends on your company being able to predict future scenarios well enough to

prepare plans and deploy resources so that you can seize opportunities, neutralize threats

and mitigate risks. Clearly, predictive analytics can play a pivotal role in day-to-day

business operations. It can help you focus strategy and continually tweak plans based on

actual performance and likely scenarios. And, as I noted in a recent Forrester blog post, the

technology can sit at the core of your service-oriented architecture strategy as you embed

predictive logic deeply into data warehouses, business process management platforms, CEP

streams and operational applications.

The grand promise of predictive analytics – still largely unrealized in most companies – is

that it will become ubiquitous, guiding all decisions, transactions and applications. For the

technology to rise to that challenge, organizations must move toward a comprehensive

advanced analytics strategy that integrates data mining, content analytics and in-database

analytics. We’ve sketched out a vision of “service-oriented analytics,” under which you

break down silos among data mining and content analytics initiatives and leverage these

pooled resources across all business processes.

You may agree that this is the right vision but have doubts about whether there is a

practical, incremental roadmap for taking your company in that direction. In fact there is,

and it starts with reassessing the core of most companies’ predictive analytics capability:

your data mining tools. As you plan your predictive analytics initiatives, you should avoid

the traditional approach of focusing on tactical, bottom-up, project-specific requirements.

You should also try not to shoehorn your requirements into the limited feature set of

whatever modeling tool you currently happen to use.



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On the flip side, what kind of challenges or issues should people consider and be

prepared for when they’re weighing a possible deployment of predictive analytics

software? The learning curve, complexity and cost of predictive analytics tools are the

principal challenges. Also, if you’re committed to deploying sophisticated predictive

analytics, you’ll need to hire specialized, expensive talent to handle data preparation and

cleansing, build and score predictive models, and integrate the models and their results into

your BI, CRM and other application environments. And if you decide to integrate your

predictive analytics initiatives with your data warehouse through in-database analytics,

you’ll need to bring the groups who handle those functions together and get them speaking

a common language.



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To be effective, predictive analytics software must be tied to action By Jeff Kelly, SearchBusinessAnalytics.com News Editor

Like many advanced and emerging technologies, predictive analytics software has a certain

degree of coolness associated with it. When corporate and business executives see that the

technology can accurately predict which customers are likely to buy what products, they get

excited.

But what good is a prediction if companies don’t do anything with the insight? Not much,

according to Dr. Eric Siegel, president of consulting firm Prediction Impact Inc. and

chairman of the 2010 Predictive Analytics World conference.

As predictive analytics starts to gain more traction and deployments increase, the

technology must be used to tie insight to action to be truly effective, Siegel said. Companies

must devise business rules that trigger specific actions when predictions are made, he

added.

Insurance companies, an early adopter of predictive analytics technology, are a good

example of this, Siegel noted. Insurers use predictive analytics software to determine the

riskiness of taking on a particular customer, he said. The potential risk is then tied directly

to the price of the insurance policy being offered to that customer.

At a retail organization, connecting predictive analytics to action could mean triggering

marketing campaigns based on a customer’s likeliness to purchase a certain item or service,

Siegel said. At financial services companies, the technology could be used to identify

potential fraud and then prompt an audit.

Whatever the industry, predictive analytics software used in isolation doesn’t do anybody

much good. But that’s not all that companies considering predictive analytics projects need

to keep in mind, according to other speakers at the conference, which was held in

Alexandria, Va.



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Predictive analytics demands significant data prep work, user buy-in

There is significant prep work that must go into a successful predictive analytics initiative,

said Paul Coleman, director of marketing statistics at retail giant Macy’s Inc. He estimates

that getting data prepped before even applying predictive analytics technology to it is about

80% of the job.

“Building [predictive data] models is at least as complex as your business,” Coleman told

attendees. And, he cautioned, “the models are only as good as the data” that goes into

them.

Jean Paul Isson agreed. Isson, vice president of global business intelligence and predictive

analytics at Monster Worldwide Inc., said data governance and data quality are key to

successful predictive analytics projects.

At Monster, for example, company executives first had to decide on the definition of

“customer,” Isson said. Initially, they came up with seven possible definitions. Not until they

agreed on a single one could the provider of online job listings and career management

services move forward with predictive analytics, he added.

Isson also said that internal change management is important when deploying predictive

analytics technology. He noted that most predictive analytics initiatives fail not because of

faulty predictive data models but from a lack of executive buy-in and poor end-user

training.

Marketing executives who are hitting their numbers will likely be reluctant to adopt a new

technology such as predictive analytics, Isson said. As a result, he advised, it’s important to

show them how the technology can improve their success rates and then train them on how

best to use the associated tools.

Jason Fox, an information system and portfolio manager in Paychex Inc.’s enterprise risk

management division, told conference attendees that finding and enlisting subject matter

experts from business operations was crucial to the company’s predictive analytics

initiatives.



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“We identified subject matter experts to ensure that business conditions were met,” Fox

said. He also sought out champions of the technology in Paychex’s sales department –

people who could tout the benefits of the predictive analytics software to their colleagues

and help boost end-user adoption.

Technical obstacles to predictive analytics success

There are also technical factors to consider, Coleman said. Data contained in flat files, for

example, is relatively simple to model for predictive analytics but then difficult to change,

he warned. Data in relational databases, on the other hand, is more flexible to work with

but can be limited by data volume constraints, according to Coleman.

Companies should consider the type of data that they plan to exploit and how it’s stored

before starting a predictive analytics initiative, he recommended. Those factors might also

play a role in determining the type of workers that a company hires to oversee its

deployment and use of predictive analytics software.

In the end, however, all of the required efforts are worth it because of the business insights

that can be gained through the use of predictive analytics tools, the conference speakers

agreed.

“Inside this data, there’s a customer in there someplace,” Cole said.



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Using predictive analytics tools and setting up an analytics program

By Rick Sherman, SearchBusiness Analytics.com Contributor

Business intelligence (BI) software has become widely used – even to the point of being

pervasive in many organizations. But for the most part, predictive analytics tools are still

used by only the most sophisticated data-driven enterprises.

In addition, whereas IT groups typically develop BI dashboards and reports for business

users, predictive analytics models usually are created by a handful of highly skilled end

users. It can be an eye-opening experience for IT workers to realize that the people who

build predictive models are more data-savvy and technically oriented than they are. In fact,

predictive model builders often view the IT staff merely as data gatherers whose purpose is

to feed their data-hungry models.

The industries that pioneered the use of predictive analytics software are insurance,

financial services and retail. Companies in those industries share the need to understand

who their customers and prospects are, how to up-sell and cross-sell products and services,

and how to predict customer behavior (including bad behavior through processes such as

fraud detection.) Predictive analytics tools can help in all of those areas. Other industries

that have benefited from the technology include telecommunications, travel, healthcare and

pharmaceuticals.

Across industries, there are common approaches that can be taken in building the required

predictive models, selecting technology and staffing up for successful predictive analytics

projects.

Building predictive models is a combination of science and art. It’s an iterative process in

which a model is created from an initial hypothesis and then refined until it produces a

valuable business outcome – or discarded in favor of another model with more potential.

Developing and then using predictive models involves the following tasks:



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1. Scope and define the predictive analytics project. What business processes will

be analyzed as part of the initiative, and what are the desired business outcomes?

2. Explore and profile your data. Because predictive analytics is a data-intensive

application, considerable effort is required to determine the data that’s needed for

the project, where it’s stored and whether it’s readily accessible, and its current

state.

3. Gather, cleanse and integrate the data. Once the necessary data is located and

evaluated, work often needs to be done to turn it into a clean, consistent and

comprehensive set of information that is ready to be analyzed. That process may be

minimized if an enterprise data warehouse is leveraged as the primary data source.

But external and unstructured data is often used to augment warehoused

information, which can add to the data integration and cleansing work.

4. Build the predictive models. The model builders take over here, testing models

and their underlying hypotheses through steps such as including and ruling out

different variables and factors; back-testing the models against historical data; and

determining the potential business value of the analytical results produced by the

models.

5. Incorporate analytics into business processes. Predictive analytics tools and

models are of no business value unless they’re incorporated into business processes

so that they can be used to help manage (and hopefully grow) business operations.

6. Monitor the models and measure their business results. Predictive models

need to adapt to changing business conditions and data. And the results they’re

producing need to be tracked so that you know which models are providing the most

value to your organization.

7. Manage the models. Prune the models with little business value, improve the ones

that may not yet be delivering on their expected outcome but still have potential,

and tune the ones that are producing valuable results to further improve them.

With a typical BI project, business users define their report requirements to the IT or BI

group, which then identifies the required data, creates the reports and hands them off to

the users. Similarly, in predictive analytics deployments, a joint business-IT team must

scope and define the project, after which IT assesses, cleanses and integrates the required

data. At this point, though, predictive analytics projects deviate from conventional BI



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projects because it is the users – for example, statisticians, mathematicians and

quantitative analysts – who take over the process of building the predictive models.

The IT or BI group re-enters the picture after the models have been developed and start

being used by business and data analysts. For example, IT or BI teams might incorporate

the predictive analytics results into dashboards or reports for more pervasive BI use within

their organizations. They might also take over the physical management of predictive

models and their associated technology infrastructure.

To run predictive models, companies require statistical analysis, data mining or data

visualization tools. Typically, predictive analytics software and other types of advanced data

analytics tools are used by experienced analytics practitioners who are well versed in

statistical techniques such as multivariate linear regression and survival analysis.

Most BI vendors sell integrated product suites that include query tools, dashboards and

reporting software. But if they offer predictive analytics software, it tends to be sold as a

separate and distinct product. While that’s starting to change, the predictive analytics tools

now being used primarily come from vendors that specialize in statistical analysis, data

mining or other advanced analytics.

Predictive analytics tools turn the BI software selection process on

its head

Compared with a typical BI software evaluation, where the IT or BI group drives the

software selection process while soliciting input and feedback from business users, an

evaluation of predictive analytics tools is turned upside down – or at least it should be.

Ideally, the statisticians and other users who build the predictive models take the lead in

evaluating the predictive analysis tools that are being considered, with IT providing input on

the software’s potential impact on the organization’s technology infrastructure. In this case,

the users are likely to be the only ones who understand the statistical or data mining

techniques they need and whether the various tools can support those requirements.



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Predictive model builders and users must have a strong knowledge of data, statistics, an

organization’s business operations and the industry in which it competes. Companies, even

very large ones, often have only a small number of people with such skills. As a result,

predictive modelers and analysts are likely to be viewed as the star players on a data

analytics team.

The typical organizational structure places predictive analytics experts in individual business

units or departments. The analysts work with business executives to determine the business

requirements for specific predictive models and then go to the IT or BI group to get access

to the required data. In this kind of structure, IT and BI workers are enablers: Their primary

tasks are to gather, cleanse and integrate the data that the predictive analytics gurus need

to run their models.

In conclusion, the critical success factors for successful deployments of predictive analytics

tools include having the right expertise (i.e., predictive modelers with a statistical

pedigree); delivering a comprehensive and consistent set of data for predictive analytics

uses; and properly incorporating the predictive models into business processes so that they

can be used help to improve business results.

About the author: Rick Sherman is the founder of Athena IT Solutions, a Stow, Mass.-

based firm that provides data warehouse and business intelligence consulting, training and

vendor services. In addition to having more than 20 years of experience in the IT business,

Sherman is a published author of more than 50 articles, a frequent industry speaker, an

Information Management Innovative Solution Awards judge and an expert contributor to

both SearchBusinessAnalytics.com and SearchDataManagement.com. He blogs at The Data

Doghouse and can be reached at [email protected].



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