Today’s businesses are increasingly affected by a convergence of multiple forces: social media, the cloud, business networks, big data, analytics, and more. These bring with them a huge amount of valuable data and information, much of it available in real time. Yet as SAP cloud senior vice president and chief marketing officer Tim Minahan points out, we’re reaching the point when “real time” is no longer good enough. For example, if you hear in real time that your manufacturing plant has gone down, or your biggest customer just defected to a competitor, it’s already too late. Instead, to stay ahead, companies must become predictive businesses.

 

So what is a predictive business? One able to anticipate future events with a high degree of accuracy, then assess potential responses and implement the best ones quickly and effectively. And while multiple resources can help you attain this goal—such as the cloud, mobility, and business networks—predictive analytics is the most important. At the Ariba LIVE 2014 conference, James Tucker, senior director of business network strategy and marketing for Ariba, and Will Caseber, director of value engineering for Ariba, led a session on why predictive analytics matters so much—and how you can use it to drive success for your business.

 

“In the future, businesses will be expected to possess the talent, tools, processes, and capabilities to analyze past business performance and events to gain forward-looking insight to drive business decisions and actions.” Lawrence Maisel and Gary Cokins, Predictive Business Analytics: Forward Looking Capabilities to Improve Business Performance

 

What is predictive analytics?

As Tucker explains, “Predictive analytics is like preventative medicine for business.” It uses various techniques from statistics, modeling, machine learning, and data mining to analyze current and historical facts to predict future events. In business, predictive models use the patterns and relationships found in historical and transactional data to identify risks and opportunities, helping companies make informed, intelligent decisions.

 

Why does predictive matter for your company?

The ability to make good predictions can literally make or break your organization. And while you may have a business scorecard, you need to ask: is my scorecard smart enough? If you’re still using traditional reporting solutions that answer questions about what happened last quarter, the answer is no. Though historical reporting has value, it’s a look in the rearview mirror—telling you what has happened rather than what will happen. Predictive analytics flips that equation, enabling you to look forward so you can avoid wrong decisions and make the right ones to gain strategic advantage. For example, predictive analytics might help you:

 

  • Identify hidden revenue opportunities within your customer base
  • Retain your high-value customers, employees, and partners with the right retention offers
  • Enable your call center agents to delight customers with the best next-step recommendations
  • Build long-term customer/employee/partner relationships with intelligent interactions

 

If you think all this sounds a bit futuristic, think again. Innovative predictive technologies are already enabling many companies to achieve these goals and more. For example, SAP Predictive Analysis helps organizations use the power of predictive across multiple business functions, and SAP InfiniteInsight automates modeling and deployment tasks to make predictive analytics available to users in diverse operational environments. “These tools make it easier for end users to define predictive models for their particular areas,” Tucker says.

Who’s using predictive today?

Industries

  • Utilities use smart meters to effectively track consumption in neighborhoods and homes, then compare that with environmental data to predict upcoming energy needs on the grid and take corrective action as needed.
  • Healthcare organizations use predictive to map patient outcomes for specific treatments and anticipate where the market’s headed on new technologies and protocols—enabling them to staff appropriately, invest wisely, reduce hospital readmittance, and cut costs.
  • Retailers employ mobility and location tools to predict and drive what consumers will purchase--for example, stores can text passersby with sale alerts about the specific items they most want to buy.
  • Telecoms use predictive to monitor customer usage trends and get notified when consumption falls so they can take steps to secure shaky accounts.

 

Functional groups

  • Sales teams use predictive to assess the likelihood of a deal closing in a given quarter, prescribe corrective action if needed, and correctly forecast revenues. Mobility tools give sales reps on-the-spot awareness of the probable price a customer is willing to pay.
  • Staffing/HR targets the best candidates through predictive. For example, the Navy SEALs use a sophisticated model to identify those applicants most likely to make it through the strenuous and costly BUD/S training, increasing success rates while protecting their investment.
  • Business planning, forecasting, and budgeting are predictive models and core, data-driven activities that every business uses. They also help companies gain competitive advantage, identify new revenue opportunities, increase profitability, improve customer service, and drive operational efficiencies—all named as top predictive benefits in a recent Ventana Research survey.

 

While there’s still a ways to go before predictive becomes the norm for every business, inroads are visible in many areas (see sidebar). Tucker likes the story of the Oakland A’s. As depicted in the movie Moneyball, the A’s successfully used predictive analysis to identify and fill the positions needed to produce a championship team—despite having the lowest payroll in all of baseball. “It’s a fantastic example of how an organization used predictive models to identify the right talent required to achieve their objectives,” Tucker notes.

 

Best practices to help your company become predictive

The key to becoming a predictive business is accessing the right data and creating the right models to optimize results. The good news? Much of this data already exists and is available to you. The following best practices will help you use it to advance your predictive journey.

 

  • Know where you are now. To project where your organization is going, you need a solid understanding of where it is today. This involves developing both internal benchmarks—is company performance where you want it to be, and where you said it would be?—as well as external benchmarks: are you leading or lagging compared to competitors? Then establish clear metrics or key performance indicators (KPIs) to track your progress and identify corrective actions as needed. “We’re starting to see more development and industry alignments on standard sets of KPIs,” Caseber notes. “It’s very useful to develop your own goals using the same sets of benchmarks that are available externally; this makes it much easier for you to evaluate your performance against peers.” Follow these steps:

 

1) Standardize metrics: Get internal agreement on the metrics you'll use, and make sure you have access to the same metrics for competitors to establish a basis for comparison.


2) Collect and normalize: Identify where and how you'll obtain the data you need, then eliminate size/scale biases to ensure your assessments are valid. For example, large companies have bigger budgets and more opportunities for savings than small ones, so comparing savings as a straight financial amount isn't useful. Instead, even the playing field by looking at savings relative to the amount of investment.


3) Define segments: Determine the industry or peer groups that will make your comparisons meaningful. "If you're a financial service business, does it make sense to compare your operations to a small manufacturer in China? Probably not," Caseber explains. More relevant selections will strengthen the value of your results.


4) Compare performance: Critically assess your performance to identify desired activity outcomes. This involves 1) understanding what competitors in your defined segments are doing differently to achieve results that exceed yours, and 2) developing an action plan and best practices based on that information to help you attain objectives.

 

“Value comes only when the insights gained from analysis are put to action to drive improved decisions.” James Taylor, Smart (Enough) Systems

 

  • Use a comprehensive approach. Tucker compares the reliability of results gained from a single-source model to a diversified one like that of Sears Holdings, which uses 500 KPIs fed into a series of predictive tools for understanding risk and revenue. The takeaway? “Don’t put all your eggs in one basket,” he advises. Instead, be comprehensive when creating your model.
  • Set goals and outline steps to execute your strategy. Caseber emphasizes the importance of written goals when designing and implementing your predictive model. “It’s amazing how many companies don’t have a clear understanding of their goals when they enter into a program like this,” he says. And be sure to outline a sequential plan for deploying your model—identifying which actions you’ll take first to drive use and get the system live, and which should come later.
  • Measure your performance regularly. Once you’ve set your goals, review your performance against them on a periodic basis. “It’s important to basically ask the question every three months, every six months, ‘Where are we against our plan?  How are we performing?’” Caseber says. “Michael Hammer made the point that ‘you can’t improve what you don’t measure,’ and you can’t fix what you can’t see. Metrics are what you use to help you see.”
  • Continually evaluate and revise. “The first time out of the chute, the system doesn’t always work 100%, so make sure your model outputs are actually producing something reasonable and realistic,” Caseber says. If percentages or results don’t look right, the model may need revision. And avoid slavish clinging to your initial metrics; as conditions change, so should your model.

 

“At a time when companies offer similar products and use comparable technology, high-performance business processes are among the last remaining points of differentiation.” Thomas Davenport and Jeanne Harris, Competing on Analytics: The New Science of Winning

 

Where can I learn more?

For additional insights and information on the value of becoming a predictive business, watch the full session on the Ariba Slideshare site or listen to Tucker’s comments in this brief interview from Ariba LIVE.