Descriptive Analytics

Connecting Markets and Customers

Creating an accurate customer sales forecast requires an understanding of all aspects of the customer. Sources of customer data include those generated from S4 systems, from customer surveys, web data, POS data, IRI, and AnyERP systems.  Below are techniques to describe, analyze and forecast customer behavior and sales.

TekMetrix 5 Step Process:

• Collect and verify data
• Explore data
• Develop and apply descriptive or predictive models
• Optimize
• Make decisions

Use Cases for Descriptive Analytics

• Integrate customers with markets through analysis and decisioning
• Provides solution tenants for systematically collecting and interpreting data
• Delivers the information needed for actionable decisioning

Types of Descriptive Analytics:

• Create synergy between data and management decisions
  • Exploratory research - "why",  why are sales dropping, why is my inventory slowly turning?
  • Descriptive research - "who", "what", segments?
  • Causal Research - for well-defined problems - "will buyers purchase?"
• Mobile surveys
• Focus groups
• Active data collection. best practices and modeling
• Survey design, questions types, best practices, focus groups: 
  • Itemized category
  • Paired comparison
  • Price                                                 
  • Comparative, conjoint
  • Ranking
  • Likert analysis, scale
  • Continuous
• Passive data collection:
  • POS data - link marketing tools to product sales in SAP, anyERP
  • Social media
• Web data collection
• Nielsen data
• RENTRAK
• Etsy
• Causal data collection - correlation (relationship between variables) versus causation (one variable producing an effect)
• Predictive validity, Net Promotor Score (NPS) using the regression coefficient R² measures how well data fits a particular model and is used to illustrate how well the metric NPS predicts customer satisfaction. The higher the R², the better the model. The key NPS question is "How likely is it that you would recommend a product or service to a business colleague or friend?
• Reliability measures
• Links customers and products through decisions
• Descriptive analytics provides actionable decisions
• Product customer SKU analysis and forecasting, P&L, AOP, Balance Sheet, Cash Flow
• SAP modeling, S4, BW4, HANA SAC, PaPM, IBP
• Integrate market research data and or POS data into operational and customer data model
• Validates and creates reliability in data models and analytics supporting SCF Financial Planning and Analysis (FP&A)
• Supply chain analytics (inventory, costs, schedules, procurement, HR, conversion costs, sg&a, sales, customers, logistics, planning and analysis) is a category of descriptive analytics deployed in SAP S/4, BW4 and data consuming tools

Research and References:

Apostle Model - Harvard Business Review

Satisfaction Loyalty Relationship

Customer Satisfaction and Profitability