Business Intelligence

Almost all areas of business today can benefit from mining historical data to enhance decision-making and none more so than in the area of pricing. The biggest challenges are having the right tools, automating the analysis processes and presenting the findings in a way that can inform the business when it needs it most, in some cases in real-time.

At KSS we categorize business intelligence, or pricing pnalytics, into three areas:

“Passive” business intelligence

Using reporting tools and analytical engines such as Crystal Reports, Business Objects XI, OLAP and data exports to Excel to “slice and dice” historical data to uncover key trends. The term “passive” reflects the need for the user to have an idea of what they’re looking for. Some examples in pricing include:

• Identifying and quantifying seasonal variations in customer demand
• Correlating causal factors, e.g. competitor A strategy appears to be to follow cost of product whereas competitor B is tracking another competitors prices
• Variations in price sensitivity over time – customers are more sensitive early morning/late afternoon than other times of the day, weekends are more price sensitive than weekdays

“Reactive” business intelligence

Using BI components such as dashboards, alerts, and data visualization tools to report changes in key performance measures or underlying trends, as they happen. Reactive tools, such as those listed below, can give early indication of changes, in some instances in real-time, conferring competitive advantage by allowing companies to respond promptly to changes.

• Role-based dashboards
• PriceNet Maps
• KSS Visualizer
• Alerts

“Proactive” business intelligence

Analyzing historical data to predict potential future performance, trends and so on, also referred to as “predictive analytics”, is set to be the next big wave of BI activity. KSS systems support a more proactive approach through the use of:

• Demand forecasting to quantify impact of price change on volume, profit and margin
• Time-series forecasting for longer-term volume trends
• Competitor modeling – price elasticity analysis, competitor tendency, main marker identification and tracking
• Scenario and “what-if” analysis for assessing changes to strategy, pricing tactics and so on 

All of the above BI areas are supported within KSS pricing systems by addressing the following:-

1. Efficient data gathering mechanisms – standardised interfaces/integration avoids custom interface development and minimizes cost of ownership
2. A single source of data with efficient access and appropriate archiving strategy so that users can query over longer periods if needed
3. Data cleaning and validation to ensure low quality data is prevented from “spoiling” subsequent analysis
4. A suite of analytical, reporting and other BI tools to query the data
5. Automated creation and distribution for regular, standard format reports
6. User training to develop the skills to use the tools to their best effect, interpret their output and communicate to other stakeholders

How does BI in pricing benefit the business?

• Provides quantified basis to challenge and enhance current pricing strategy and tactics
  • Insight into customer price-volume sensitivity
  • Competitor modeling and price elasticity analysis
  • Demand forecasting
  • Rapid creation and comparison of alternate pricing scenarios
• Quickly alerts stakeholders to issues with key performance measures
  • Volumes, margins, profits, running rates and so on 
• Provides rapid drill-down analysis capabilities to identify root causes of performance issues
• Highlights changes in competitor and/or market conditions
  • Competitor changing pricing strategy
  • Shift in market dynamics as a result of new competition entering market, existing competitors rebranding or leaving a market etc
• Real-time alerts to tactical changes demanding a response
  • Competitor price changes
  • Cost changes
  • Shifts in local traffic, competitor site closure, local events changing demand patterns etc

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