by Kim S. Nash

Business Intelligence Meets BPM: Using Data to Change Business Processes on the Fly

Jun 17, 2010
BPM SystemsBusiness IntelligenceEnterprise Applications

When business intelligence is used to inform business process changes, companies find new ways to save money and connect more closely with customers.

Actions reveal more than words, we know, and companies are watching carefully, using business intelligence (BI) and analytics tools to figure out what’s happening in their markets. But it isn’t just what makes a consumer buy a product or respond to an e-mail promotion that companies want to understand. They’re also putting business operations—where efficiency can make the difference between profit and loss—under the microscope.

By using insights from analytics to improve business processes, CIOs can help managers feed updated intelligence into their decision-making routines almost continuously. A marketing campaign can be adjusted in hours in reaction to uptake on a website. A logistics process, such as trucking equipment to construction sites, can be adjusted to changes in the price of fuel.

As analytics tools become more powerful, analysis happens faster, enabling business changes that make or save significant amounts of money, says Rick Roy, senior vice president and CIO of CUNA Mutual Group, a $2.8 billion insurance company. CUNA Mutual has pored over customer data to identify which of its products are selling, why and to whom. Using insights from analytics to guide business process changes is a “dramatic productivity enhancer,” Roy says.

Sixty-five percent of 335 IT leaders we polled recently say business intelligence and analytics have spurred a business-process change in the last year. However, they also indicate that there’s more work for IT to do. For example, just 41 percent say their analytics and business process management (BPM) tools are closely integrated.

If analytics and BPM tools were integrated, they could be used to map how best to change business processes given a particular insight from the data, says David White, a senior research analyst with Aberdeen Group. But for now, CIOs like Roy have to work with their business partners to make such determinations. Connecting analytics and business processes sometimes means creating data warehouses to collect information from multiple systems in order to study it. IT leaders also find themselves promoting old-fashioned conversation across departments.

Companies where CIOs enable the best use of predictive analytics tools and techniques—including tying them to BPM initiatives—are better at financial forecasting, retaining customers and eking out more operating profit than companies that haven’t caught on, says White. He recently studied 159 organizations that actively use predictive analytics and determined, for example, that best-in-class companies retain 93 percent of their customers, compared to laggards who retain just 66 percent.

Competitive pressure—along with substantial, measurable financial gains—will lead more CIOs down this road, says Tim Fleming, CIO of the Industrial Technologies sector at Ingersoll Rand, a $13 billion heavy-equipment manufacturer. “It’s not easy, but you want to do it,” he says. “You have to do it.”

How Data Enables Change

The insurance industry may have been among the first to connect analysis tools—in the form of fraud detection—with automated workflow technology—systems for claims processing. But meshing analytics with other kinds of enterprise software can produce new ways to engage customers or make operations more efficient.

CIOs who can give business users the means to connect analytics and BPM can cut a path to faster and more fruitful decision making, says Fleming. “Data starts to tell a story. We can help them find that story,” he says. And rewrite the ending.

Fleming’s division at Ingersoll Rand has used analytics to flush out and correct problems in such varied processes as order management, global inventory and invoicing. Fleming has dedicated the biggest portion of his IT staff to analytics, he says, because the company has found it so powerful.

Doing analytics well has helped raise the profile of IT at Ingersoll Rand. The company recently replaced a mix of manufacturing and financial systems with Oracle’s ERP suite. Historically, IT released new reporting capabilities twice a year. But by updating these capabilities quarterly, then every two months, end users caught errors and spotted trends sooner than they used to. “This gets you back in front of your user groups very frequently, so they’re seeing the value of IT,” he says. “It’s not a Christmas present once a year; it’s multiple birthday parties.”

For example, business people were able to identify on-time delivery problems in the Asia-Pacific region and Europe. Through analytics, Ingersoll Rand discovered the use of some incorrect data about supplier lead times. In the past, the company had relied on knowledge of factory employees to identify a problem like this, Fleming says. Also, managers, thinking about efficiency, sometimes waited until the end of each month to enter all the details about some orders. “You don’t get that service order in, we can’t invoice. If we don’t invoice, our accounts receivable balances will be lower than they should be. Then we have a forecasting issue,” Fleming says.

After getting accurate lead-time data to the factories, the division changed its service-order process so managers now enter data every week. Revenue forecasting improved measurably, Fleming says. Among respondents to our survey, accounting departments topped the list of beneficiaries of analytics-driven business change. (Read more about the survey in “Cloud Analytics Picks Up the Pace.”)

Those kinds of outcomes allowed Fleming to get funding to double the analytics team in his division to about 15 full-timers who help business groups plan and implement analytics projects. “Business made a decision to make a higher investment in it because they saw results.”

Analytics tools integrated with customer-relationship management (CRM) or e-commerce systems can also lead to better processes for engaging with customers.

In the past year, CUNA Mutual has used analytics to understand members of the credit unions it serves. The company provides financial products to 7,000 credit unions, and, through the credit unions, to individual members. The number of credit-union members has grown by 14 percent since 2000, which CIO Roy attributes to people seeking stable alternatives to big banks. But CUNA Mutual’s primary customer base is shrinking: The number of credit unions in the United States has dropped 24 percent since 2000, mainly through acquisitions. To keep the remaining credit unions, and therefore itself, growing, CUNA Mutual has to know what moves credit-union members—the end customers—to buy a new product or service, Roy says.

In 2009, the company launched an analytics project called Voyager, which uses Microsoft’s SQL Server database alongside analysis tools from CA Technologies and SAP BusinessObjects to segment its credit union customers by variables such as product, profitability and demographics. The first step was consolidating customer data from sales and marketing systems, as well as systems used by its credit unions, into a single data warehouse. Then business analysts explored the data with canned reports and iterative queries on the fly.

“They were able to complete in a couple hours using multiple queries an analysis that before would have taken one to two weeks to complete,” Roy says. “It’s game-shifting change.”

Business analysts were startled to find that half of CUNA Mutual’s $2.8 billion in revenue comes from three of its 12 customer segments. Now the company wants to build financial products to attract the other nine. To appeal to Generation Y consumers, for example, CUNA Mutual is developing more Web and mobile access to products it offers to its credit unions. The company also built software that automatically offers life or disability insurance to people after they take out a loan, over whatever channel the customers used to close the deal—phone, website or in-person.

The new thinking has CUNA Mutual moving away from doing three or four large marketing pushes per year to 12 smaller ones focused on producing specific results: selling a particular product to new customers in a given demographic, for example, or gaining more profit from an existing customer segment. “You run the marketing, test it and use what you learn to start new campaigns,” Roy says.

New Ways to Save

While CUNA Mutual looks outward, at customer dynamics, Welch’s, the grape juice and jelly cooperative, uses analytics to make internal operations, namely transportation, more efficient. During a 2007 upgrade of Oracle’s enterprise resource planning suite, Welch’s saw it needed newer tools to enable more flexible queries using multiple dimensions of data, says Kevin Kilcoyne, director of customer operations at the family farmer-owned co-op. The organization does manufacturing and marketing for the National Grape Cooperative Association.

Welch’s wanted to collect every data element from each year’s 40,000 orders and bills of lading, sweep it into a database and look for patterns to highlight where it could save money on transportation. Welch’s auctions its transportation business to trucking companies every year.

At the time, Oracle’s reporting tools couldn’t perform the in-depth analysis Welch’s wanted quickly enough, Kilcoyne says. To prepare for the auction, it took about 30 hours to cull a year’s worth of data and then a few months for analysts to study it to decide how to formulate each bid request. Analysts consider routes, kinds of transportation available and fuel pricing trends, along with carriers’ limitations and performance statistics.

Because so much time was involved, Welch’s was not always able to bid out all of its distribution routes; typically it bid out only about 60 percent of them each year. The balance went largely unanalyzed and, therefore, unoptimized, says Bill Coyne, director of strategic sourcing.

Welch’s hired Oco, a software-as-a-service vendor, to provide analytics and data warehousing. Oco accesses Welch’s ERP system through a secure Internet connection, skimming the data from order fulfillment and other modules and putting it in a data warehouse at Oco. The process takes about half an hour, Coyne says. Each morning, Welch’s analysts tap into Oco to draw the fresh data to their own PCs. They can study it in pre-written reports or formulate their own queries.

The time saved on data collection and analysis enables Welch’s to bid out all of its transportation routes and do it more than once per year, Coyne says. Welch’s can also tweak those routes to save money. For example, the company discovered that if it relocated some of its distribution points, it could use rail instead of trucks, further reducing fuel use and rates as well as carbon emissions. All told, Welch’s cut 12 percent to 15 percent from its $50 million in annual transportation costs, Kilcoyne says.

Conversations With Customers

As Kilcoyne and Coyne learned, modern business intelligence and analytics tools can extract data from enterprise software, populate pre-built statistical models and quickly produce insights that used to take weeks. “In the past, doing predictive analytics needed a PhD in statistics to build a model and interpret results,” says Aberdeen’s White. But newer analytics tools “hide the underlying statistical nerd details,” he says. “Business people don’t have to worry about how the sausage gets made.” Many tools, have models built in for such complex analyses as segmenting customers based not simply on demographics and the products and services they buy, but also on less cut-and-dried information, such as how they behave at a website or what comments they make during call- center interactions.

Key to game-changing decision making is the ability to detect and respond to market changes, taking into account historical knowledge. DirecTV uses analytics to save customers who want to cancel their television service. The company started the program two years ago when it sought to cut churn rates.

If a regular call-center agent can’t persuade a customer to stay, the agent will let her go, promising to discontinue service in 24 to 48 hours. During this cooling-off period, a specially-trained agent calls the customer, armed with the knowledge of why she wanted to cancel and a series of proposals designed to change her mind. The proposals are ranked according to the likelihood that they will work, says Jack Gustafson, director of business intelligence at DirecTV.

To someone who cited a competing offer from Verizon, DirecTV will offer a better deal. To someone who complained about technical issues, DirecTV offers free service, support and perhaps upgraded hardware.

How hard agents press depends on how valuable the customer has been to DirecTV, Gustafson says. “There are some people we just do not want to lose.” About 60 percent of customers who want to depart are deemed worth trying to save, he says. The company uses tools from Teradata and SAS to analyze past behavior, evaluating data such as the average annual revenue the customer represents, her payment history and how many pay-per-view shows she buys.

The program works so well that DirecTV now turns around its “called to cancel” data from inbound calls to its special agents four or five times a day, rather than overnight. Every customer saved is one less customer the company has to try to win back weeks or months later—an expensive process, Gustafson says, that can involve mailings, e-mail and telephone calls as well as sending someone out to reinstall the service. “When the customer first calls, they have a certain mind-set: They want to cancel,” he says. “When we call back, they’re unprepared. It’s a little psychological advantage we have.”

When Coca-Cola began to focus in earnest on using analytics in online marketing 10 years ago, one push was to understand whether their efforts were driving consumers to their website. The site would then lightly tailor pages based on customers’ actions during past visits, says Doug Rollins, group director of loyalty CRM measurement at the $31 billion beverage company. A new visitor who entered a Diet Coke promotion might have been offered that option more prominently next time.

Now, though, the My Coke Rewards program has helped the company develop more in-depth knowledge about loyal customers. The inside of every bottle cap is printed with a 12-digit code that customers can text or type into a website or desktop widget to accumulate points that can be exchanged for prizes and other awards. Those who opt in to e-mail marketing receive regular offers to gain more points, as well as other marketing pitches. Each is customized based on segments created from demographic information and behavior collected by the site. On average, 285,000 customers visit per day, entering an average of seven codes per second. Information embedded in the codes may include a region or location where the bottle was sold and whether it had special packaging, such as an Olympics logo, that Coca-Cola uses to tailor its pitches.

A 43-year-old woman, for example, may receive an e-mail touting a “Family Roadtrip” sweepstakes, including a $5,000 gift card. A 20-something man might get a message offering an extra 10 points if he enters three more codes within a week. After four years, My Coke Rewards is among the longest-running marketing programs in Coca-Cola’s history. And as the program has grown, the company has changed the way it runs in response to insight from analytics, Rollins says.

For example, at first the program focused on Coca-Cola, Diet Coke and Coca-Cola Zero drinkers. Now the company cross-sells and upsells other brands, including water and juice products inherited in corporate acquisitions. Doing so entailed shifting how each business unit approaches its marketing, he explains.

Coca-Cola uses the FICO Precision Marketing Manager suite of statistical analysis tools to study data from its websites. Marketers look at which come-ons elicit the most and best responses, says Thomas Stubbs, Coca-Cola’s interactive marketing director in global IT. Coca-Cola also exchanges data with companies that supply prizes, including Nascar, Nike and Sony. “As technology has evolved, we’re able to do more and have a relevant dialog with customers, not just push our ideas out there,” he says.

Limited-time promotions don’t teach a company as much about its customers as ongoing interactivity, Rollins says. FICO’s business rules-management software helps determine in real-time what material to present to consumers on which platform. The company has learned that watching behavior is more meaningful than reading questionnaires Web visitors are asked to fill out, he says. For example, some consider Diet Coke a woman’s drink. (The company even developed Coca-Cola Zero—in its black can, proclaiming “REAL Coke taste”—to appeal to both men and women.)

“A man might not want to admit that he’s a Diet Coke drinker. He will say in a survey that he prefers Coke. But we see he enters only Diet Coke PINs and market accordingly.”

The idea is not just to save business but to create new business. Successful projects spark new ones. Analytics tools help companies create more money-generating interactions with customers and shave costs from internal operations. CIOs should connect analytics technologies with ideas about refining business processes, says Aberdeen’s White. “Meld them together and that’s very powerful.”