Actions reveal more than words, we know, and companies are watching carefully, using business intelligence 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.