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Expanding Bi’s rolE by including Predictive AnAlytics

In today’s economic downturn, organizations are looking for ways to improve the way they do business to keep ahead of the competition and grow revenue.

In a 2009 CIO Insight survey of senior managers and IT executives, respondents listed their top priorities as improving business processes, delivering better customer service, generating more business from new and current customers, and differentiating the company from competitors via IT. But faced with the challenging economic environment and reduced funding for new initiatives, how do organizations focus on meeting these prioritized objectives?

The path to success in all of these areas, traditionally, has been to use business intelligence (BI) information to make decisions. Increasingly, organizations are finding that the benefits of BI can be enhanced when complemented by predictive analysis. Specifically, more insight can be gained, and even better decisions made, by coupling business-relevant information with an easy-to-use predictive analytics solution.

A NAturAl ExtENsioN to Bi

Business intelligence provides valuable insight into the state of affairs within an organization. The information is critical to decision-making. But when combined with predictive analysis, synergies can be leveraged to improve business and operations.

Many industry analysts like to make an analogy between BI and predictive analytics by citing a quote from the famous hockey player Wayne Gretzky, who said: “A good hockey player plays where the puck is. A great hockey player plays where the puck is going to be.”

Comparably, BI tools help users know what has happened and what is happening, while predictive analytics tools help to elicit more from this information by providing an understanding of why these things happened and in predicting what will happen.

For example, BI tools can report which sales region had the highest sales, how many widgets were sold in stores in different ZIP codes, the average spending per online customer vs. in-store customer, and how many customers stopped doing business with your company last year. All of this information is essential for developing new product and services, allocating resources, investing in marketing campaigns, and so on.

Predictive analytics tools, though, can give deeper insight into why these things happened. For example, knowing the average customer spends $100 per visit to a store is one thing. Knowing that a certain 20 percent of the customers are responsible for 80 percent of all revenues and that they are more likely to buy particular products bundled together is much more valuable. Also, identifying which products influenced the purchase of others or the strength of the relationship between products purchased together would give more insight into specific buying patterns. This added level of analysis can yield valuable results. It helps you understand how that prized segment of your customer base would respond to very targeted promotions.

Similarly, knowing that the average response rate to a direct-mail marketing campaign is, say, 4 percent, an organization can decide how often to run these campaigns factoring in mailing costs and the revenue generated by a campaign’s sales. Knowing the types of customers and

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being able to correlate that with what they purchased and when they are likely to purchase again would allow an organization to target those customers at the right time with the right offerings. This would allow the company to

while ensuring customers are offered a product or service they would actually be interested in.

That’s the difference between BI and the power of BI combined with predictive analytics.

MANy ApplicAtioNs

Predictive analytics helps organizations look forward and make educated decisions that anticipate the future needs of customers. It combines known information about customers, sales, operations, or finances, with critical insight that helps solve problems, achieve business objectives, and uncover hidden patterns not easily identifiable through reports or dashboards. The combined knowledge is used to take actions that can improve business.

A traditional example of predictive analysis’ use would be to identify trends like poor customer service or customer dissatisfaction and correlate complaints to customer churn. Having insight into why customers are leaving or why they stay, an organization can take action to retain them. For instance, by surveying customers, an organization might find that 30 percent of their customers consider the price of the service to be the most important factor in choosing a company. Another 30 percent might love to receive perks and consider such offerings a distinguishing factor that keeps them coming back. And the rest might simply feel that timely and courteous service is essential.

Having this level of insight into customer likes and dislikes can help an organization make predictions about the future actions of these customers. Correlating this information with actual customer actions allows an organization to take action. For example, having identified a segment of the customer base that attaches importance to pricing, an organization might offer discounts or reduced rates if the customer signs a multi-year contract. Those who love perks might be offered free shipping, a free music download, or an extra day at a hotel.

In another area, an organization might use predictive analytics to cross-analyze sales data and marketing spending, perhaps finding that 80 percent of the sales in response to direct mail or e-mail campaign come from only

group of 20 percent in future campaigns, the organization can significantly increase the ROI of these campaigns.

Additionally, an organization might use purchasing information tied to a customer loyalty program to understand which products are purchased together, by whom, and when. Having this information, the organization can try to increase revenues by cross-selling distinct bundles to select customers. For instance, a retailer might find that customers who bought the highest-priced suits also bought shoes to match and a protective trench coat. Having that information, a store might selectively place trench coats next to the high-end suits. Or it might develop a marketing campaign that offers discounts on shoes and trench coats when a customer buys a suit valued over a certain price.

With such success from traditional predictive analytics usage, organizations are looking to expand its influence to more areas of operations and to more users. In particular, predictive analytics is increasingly being used to help identify key influencers in customer satisfaction, employee retention rates, customer churn, and other areas.

For example, Human Resources (HR) might use predictive analytics to help select job applicants. Specifically, employers want to predict which job applicants are going to make a commitment to their job. Predictive analytics can be used to show which personality traits are better predictors for worker productivity and turnover.

Predictive analytics might also be used to retain talented employees by helping predict if an employee is likely to leave based on the types of services they consume from the company such as training, taking advantage of 401k plans, or the number of vacation days taken. Armed with this information HR managers can target top performers with programs designed to increase their investment in the company and hence their likelihood of staying.

Predictive analytics can also bring value to other areas of operations, such as manufacturing. For example, organizations could use predictive analytics to help identify and predict equipment maintenance for it products, ultimately increasing customer satisfaction. By analyzing

Predictive analytics tools can give deeper insight into why these things happened

increase the effectiveness of their marketing promotions 20 percent of its customer base. By selectively targeting this

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data collected by systems such as an aircraft’s health and usage monitoring system and flight maintenance log records, an aircraft manufacturer can determine the relationships between how the aircraft is being operated and maintained and the consumption of parts. The deeper understanding of these correlations will allow the manufacturer to take proactive action to reduce direct maintenance costs or even improve manufacturing processes.

liMitAtioNs styMiE usAgE

Such potential benefits derived from predictive analytic solutions are getting the attention of many organizations. In fact, a 2008 IDC BI and Analytics Survey found that predictive analysis tools were the number-two priority for purchasing within the next 12 months (second only to business activity monitoring tools).

Why the growing interest? According to IDC, the median ROI for BI projects using predictive technologies was 145 percent, compared with a median ROI of 89 percent for projects without them.

Forrester Research recently forecasted predictive analytics and data mining will also grow at a rapid pace, more than doubling in growth to nearly $2.2 billion within the next five years.

While many have noticed the synergies that might be gleaned by combining BI and predictive analysis, predictive analytics tools have not been as widely embraced as BI tools. There are several reasons for this.

Predictive analytics tools are often designed for analysts. They assume a high-level knowledge of statistical analysis methods. In particular, an analyst would be needed to determine which mathematical tools to apply to a problem: linear regression, a chi-squared distribution, something else? As such, many tools are difficult for business managers and others to use.

Additionally, many predictive analytics tools require special programming skills. Users must not only know which formulas are appropriate for doing a specific analysis, they must then know how to create and enter the formula to analyze the relevant data. Some tools require the use of programming languages like C or C++; others might rely on the statistical analysis programming language R. This puts these tools out of the reach of the majority of business users. And in some cases, IT must get involved, making many predictive analytics efforts rigid and not flexible enough to meet rapidly changing market conditions.

Another shortcoming with many predictive analysis tools is that they are standalone tools. This complicates matters in two ways.

First, getting access to information can be difficult. BI solutions often provide a means to access relevant information for decision-making. If the predictive analytics tool does not integrate well with a complete BI solution or does not accommodate data access and data mining, the user will have to obtain the information for analysis in a brute-force manner.

Second, if the tool is standalone, it might not provide an easy way for the results of the analysis to be shared, viewed, or made part of a decision-making workflow. But being part of a solid BI infrastructure makes it easier to share the results with the right users who need the information to transform the business.

BriNgiNg prEdictivE ANAlytics iNto thE Fold

SAP offers a way to overcome these limitations and make predictive analytics a part of the normal business decision making process.

Its SAP BusinessObjects Predictive Workbench helps uncover trends and patterns to solve business problems, anticipate business changes, and make forecasts.

SAP BusinessObjects Predictive Workbench integrates with your existing data environment – as well as SAP BusinessObjects Enterprise environments – and it allows for efficient discovery of important and predictive findings.

At the heart of the offering is an easy-to-use visual workflow interface. Using Predictive Workbench, business users can quickly create analysis routines that draw on specific

Organizations are looking to expand predic-tive analytics influence to more areas of operation and to more users

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datasets. The interface is point-and-click; no coding is required. The tool can help you determine the best model to use for a particular project. Additionally, the Workbench supports the entire data-mining process, making it easier to put this solution in the hands of more data analysts.

The results of a predictive analysis routine can be easily shared with users who need the information to make business decisions. And in turn, these models created by an organization’s analysts can be run by users themselves so they might apply the analysis routinely to new data as it is acquired. As an example, the results can be visualized in dashboards, reports or mobile devices making it easier to share these insights across an organization.

SAP BusinessObjects Predictive Workbench can easily be added to a company’s repertoire for business decision-making. In particular, it does not necessitate the rip-and-replace approach some other predictive analytic solutions require. SAP BusinessObjects Predictive Workbench works

with existing BI solutions and can output findings in a format that is easily used and shared.

When combined with the SAP BusinessObject XI platform, SAP BusinessObjects Predictive Workbench gives organizations the predictive analytic muscle needed to stay competitive in today’s economy.

In particular, SAP BusinessObjects Predictive Workbench allows organizations to:

• Overcome the limitations of traditional solutions and make predictive analytics a part of the normal business decision making process

• Uncover trends and patterns to solve business problems, anticipate business changes, and make forecasts

• Efficiently discovery important and predictive findings by way of an easy-to-use visual workflow interface

• Enable easy sharing of information with those who must make timely decisions. n

For more information, go to:

http://www.sap.com/

the results of a predictive analysis rou-tine can be easily shared with users who need the information to make business decisions