Existing SAP users have a special interest in SAP Predictive Analytics, which incorporates SAP Infinite Insight and SAP Predictive Analysis software. This is why it is best for customer-focused analytics. SAP also understands that building the actual model is just a small part of the journey. For that reason, they provide a wide array of facilities for data preparation, data modeling, visualization, scoring, and above all model management. SAP application suites are in use in many large corporations and businesses around the world. Among those who would welcome the analytics capability include those who want to develop predictive models for predicting or anticipating customer behavior. SAP’s HANA database technology can offer a total solution for the real-time analysis of various events originating from customers, devices or any other activity.
SAP Predictive Analytics – Benefits and Features
Predictive Analytics has a number of features and benefits that are discussed below to help you make the decision whether to adopt the software or stick to what you are currently using.
Business Application and Integration
The most obvious benefit is the total integration of the entire predictive analytics process with the SAP infrastructure. Many businesses use this application to manage their business applications. Large insurers with millions of customers can use this application to analyze customer behavior and carry out more targeted customer marketing. If your company is experiencing high product return rates, using a combination of Predictive Analytics and SAP HANA can help to significantly reduce returns whilst helping you more accurately target customers with offers. Lastly, customer churn rates can be a major concern for large corporations such as large mobile phone operators, but using this application can help reduce churn rates and help present customers with more relevant up-sell or cross-sell opportunities. SAP customers can derive more value from predictive analytics technologies, as the application range across customer service, finance, planning, machine maintenance, predicting product failure, and much more.
Technology
SAP essentially presents three predictive model avenues. The InfiniteInsight platform is customer focused and uses structural risk minimization methods to make the models more reliable. On the other hand, the open source language R can create models addressing any business problem, whilst HANA features high performance algorithms.
Better view of the future with predictive analytics
To compete in today’s marketplace, you have to use all data types. Your business can look for untapped opportunities and unmask hidden risks buried beneath Big Data or the Internet-of-Things, real-time. Quickly develop complex predictive models for data mining to provide insights that will ensure that you are always ahead of the team. No matter the industry you operate in, your success may largely depend on real-time predictive insight, not gleaned from generalized reports and data. Successful companies know the true potential of predictive analytics applied across business applications, processes and line-of-business solutions in order to sustain competitive advantage. This application will provide your organization with insights that promote real-time understanding of it operations whilst confidently anticipating what is to come, and helping make more profitable business decisions.
Automated data preparation allows faster and more accurate results
This application simplifies the entire predictive modeling process. SAP Predictive Analytics automates the steps involved in transforming data into a format that most analytical engines can easily process, even before building a predictive model. Traditional data preparation involves many manual and repetitive activities that are prone to human error. The application connects to virtually any data source, including unstructured sources like text files, spreadsheets, and proprietary file formats like SPSS and SAS, with the data encoded automatically.
Competitive advantage through predictive modeling
SAP Predictive Analytics offers a predictive modeling environment that suits users and multiple levels. Business analysts can take advantage of guided analysis that automates the development of sophisticated predictive models for all data-mining function in just a few days. Processes can be automated, as it delivers accurate, robust predictive models. The software can securely handle much input attributes automatically.
Advanced, intuitive data visualization
This is for greater insight, including intuitive ways to explore data through visualization. Visualization forms include distances, cluster sizes, density, variable comparisons, outcome probabilities, a decision-tree viewer that can zoom in and expand or contract the tree, parallel coordinate charts that support numeric and categorical variable selection, and scatter plot matrix just to mention a few. The application also provides an integrated collaborative environment to allow sharing, enhancing, and publishing of predictive models and outcomes with less time and effort.
Real-time decision making with predictive scoring
Predictive scoring is very important for a business to realize the real-time impact from using analytics. The application’s open interface can help generate predictive scoring for various target systems while directly embedding the results wherever needed.
Higher productivity with predictive model management
Business analysts can ask even more “what-if” questions to come up with numerous predictive models. Its browser-based, single-sign-on environment coupled with its user-friendly scheduling interface specially designed for business analysts helps automate model management for analysts to try out various scenarios, schedule model refresh, deploy scores instantly, and manage models by exception. Easier model management means your business analysts can get more work done while you gain key insight into your business.
Real-time predictive insight
Companies can no longer dwell solely on offering the best product and service without uncovering hidden employee, vendor, partner, and customer trends and insights. They have to anticipate behavior and take proactive actions to exceed customer expectations.
Conclusion
To a large extent the data preparation task has been addressed in the new release to simplify this time consuming and tedious task. Users can accomplish most tasks without recourse to code. To gain a feel of data, data virtualization plays a key role in the initial phases of model development. The visualization capability presents a powerful array of visualization tools and can gain access to multiple sources of data. Data scientists and business users alike can use the data modeler to build predictive models. The model management capability allows automatic schedule of model refresh, including support for “what-if” questions. SAP Predictive Analytics also support network and link analysis and can be used to check links between customer and social network participants.