Forrester Research began tracking predictive analytics and machine learning solutions three years ago. That’s a sudden sneeze since the dawn of computing, yet still so much has changed since then. We saw Prince and David Bowie leave the stage. There’s a whole new crew in the White House. Not to mention that the stock market is pushing into and out of territories it’s never been.

Could software solutions have predicted these changes? Maybe—if they were set up with the right data. While Forrester isn’t using machine learning to compare different market solutions, what the analysts did track in “The Forrester Wave ™: Predictive Analytics and Machine Learning Solutions, Q1 2017” is 14 vendor solutions. SAP BusinessObjects Predictive Analytics ranks among the top three in the leader category, markedly separating itself from every company on the list.

What makes SAP unique? The enviable close proximity between SAP business applications deployed by customers and SAP machine learning solutions.

SAP is the largest enterprise application vendor in the world. With so many deployments across 40+ industries and 12+ lines of business, the chances are quite high that the core business data that machine learning solutions need resides in an SAP system. That in turn, streamlines and speeds the deployment of predictive and machine learning insights into business processes and apps.


Predictive Analytics and Machine Learning Team Up with Enterprise Apps

Once machine learning is embedded into business apps, business users can put their hands on insights that will help them take actions to create positive business outcomes. Here are examples of the big and small changes that occur when machine learning is embedded in apps:

  • Retailers and consumer products companies are better understanding market segments and forecasting demand.
  • Manufacturers are raising the level of quality controls and reducing waste on production lines.
  • Telecommunications providers are reducing customer churn and increasing revenue through campaigns that cross-sell and up-sell customers.
  • Police departments are identifying which neighborhoods need more patrol cars and staff to protect vulnerable areas.
  • Hospitals are standardizing the most effective care-based procedures to improve surgery results.

Predictive analytics and machine learning, integrated with enterprise apps, fuels hundreds of thousands of positive business outcomes like these.

Customers See Positive Results from Embedding Machine Learning in Apps

Every business can benefit from automation knowledge work, and machine learning opens that door. SAP BusinessObjects Predictive Analytics automation lets business analysts step into projects usually reserved for highly skilled data scientists. When automated predictive analytics and machine learning  techniques are applied to business data, the user can prepare data, create models, and manage deployments through templates and workflows—without writing code. Every department in the enterprise can leverage machine learning.

It’s possible to embed these models where they are needed—in databases or in business processes and applications so business people can enable better decision-making.  Companies are making a business process more profitable or competitive and spotlighting new ways of doing business and new opportunities for growth.

  • A cable company, for example, is now identifying customer-related issues, including propensity to purchase, likelihood of churn, and prospective credit risks. Having a better understanding of customers has led to a 28% reduction in customer churn. In another example, a bank wanted to discover hidden risks and stopped 60% of fraudulent transactions by applying machine learning to its business data.
  • Utilities companies have turned to machine learning to predict asset maintenance. By tracking water usage, water departments are automating when they send technicians out to homes or businesses for inspections. They are staying ahead of cracks and fissures that lead to overflows and waste. Similarly, water treatment plants are tracking pollution measurements and automating responses to improve water quality and availability.
  • Clothing brands are identifying untapped opportunities with their customers and running what-if scenarios on forecasted sales. Machine learning models can identify that customers who purchase a specific shoe, for example, are extremely likely to purchase a running jacket if they have a $15 off coupon. The brand can send the coupon to the shoe buyer, recommend a jacket, and become part of the shoe and jacket upsell campaign.

That’s the incredible power in automating these processes with machine learning.

SAP—The Tie that Binds Machine Learning and Business Apps

Other machine learning solutions don’t have the ready-made-connection to business data, and they struggle to deeply integrate with the business apps. SAP is uniquely positioned to provide the tools, platform, and applications for machine learning across industries and lines-of-business. Without that tightly coupled integration, machine learning solutions cannot deliver insights and actions needed to improve business outcomes.

As Forrester states, “This puts SAP in a unique position to create tools that allow business users with no data science knowledge to use data-scientist-created models in applications. SAP’s solution offers the data tools that enterprise data scientists expect, but it also offers distinguished automation tools to train models.”

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