The corporate environment is always in a state of transition. Uncertain and rapid changes are inevitable – and are directly influenced by key sales and marketing metrics. An increase in competition, diverse product offerings, increasing consumer awareness, a growing customer base, new sensitivities, new preferences, new choices and other factors are directly proportional to sales and revenue metrics. Understanding the needs and desires presented to use, we need to prepare for this new landscape well in advance through a calibrated and systematic planned and analytical approach to help anticipate consumer spending behavior and new trends.
Predicting existing consumer behavior is the critical factor towards success. Accurate prediction and understanding of customer behavior can help corporations retain and win customers, improve sales, and extend or create longer-term relationships with business partners. The SQL Server offers predictive analysis through data mining, empowering users with usable, actionable insight across the organization.
The SQL Server, alongside the Excel Data Mining Add-ins, offer data-mining capabilities that help corporations make informed, intelligent business decisions. This white paper explores examples that include churn analysis to estimate the number of business partners that are in danger of being lost, market analysis to show business partners’ distribution-scattered segments in terms of high- and low-value, and purchase patterns of business partners.
WHY PREDICTIVE ANALYSIS? Data-mining technologies use analytical models to discover latent behavior and patterns in data. From there, the behavior and patterns are applied to future trends and behaviors. Predictive analysis is the methodology, although adoption is not much seen – since it requires investment in infrastructure, data warehouses with comprehensive business intelligence solutions and trained, qualified resources. We’ve detailed the significant components and stages of data mining below:
SQL Server Analysis Services provides out-of-box data-mining extension, without any additional cost (a benefit to any bottom line). Microsoft Technologies is known for its favorable TCO (Total Cost of Ownership), as compared to other proprietary technologies available. The combination of included software tools, skilled resources, development effort and agile delivery timelines assure users of a competitive advantage.
Finally, we have to examine the six major challenges for successful actionable analytics with predictive analysis:
• Data-mining solutions are based on a mature “Business Intelligence” foundation
• Data governance – and quality – are critical inputs towards successful implementation
• Skilled human resources (data analysts or statistician) input are required for exceptional development of mining models, structures or algorithm choice. Advanced statistical know-how is needed; the process demands qualitative data analysis based on behavior, patterns and trends
• The preservation of non-volatile historical data, or data archiving, is necessary for accurate prediction with minimum standard deviation
• Data warehouse/marts need to be in sync with operational data store OLTP (LOB) applications and business systems
• Optimum IT infrastructure investment is a critical factor towards success.