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Most business executives understand that pricing is a powerful lever that can be used to close deals and drive profitability. Unfortunately, however, knowing the importance of price optimization doesn’t make implementing best-in-class price management capabilities any easier.

Often, companies set rates and fees at the corporate level, then ask branch offices to enforce product pricing parameters. But faced with complex interrelationships among key drivers – such as customer demands, product offerings, and pricing strategies – branch pricing managers still turn to long-established business practices and ad hoc calculations to negotiate and close deals. Old rule-of-thumb practices and gut-feel experience prevail – but these methods are not enough to accurately gauge the price sensitivity of customers and consistently offer the best product at the right rate with the right fees.

Advanced Mathematical Models Help With Deal Pricing

What banking, retail, and utility pricing mangers need is a better way to support local pricing decisions. They need a tool that allows them to identify and use influential drivers – so they can negotiate and close the deals that will drive portfolio performance and profitability.

Advanced analytic tools can address the complexities of modeling multiple rates, fee structures, and customer scenarios, and can help support real time price optimization during negotiations. Models can evaluate key customer and product variables, so users can optimize product pricing for the best results.

Decision Support Helps Close Deals

A deal decision support system enables pricing managers to negotiate a product origination or renewal with higher awareness of the impact of their decisions on key performance indicators (KPIs). Users choose a pricing variable to adjust during optimization, then examine the effect of that adjustment on a defined KPI. imageFor example, a model might use customer information such as down payment, credit score, and price sensitivity to calculate the probability of winning different mortgage scenarios by adjusting fees and rates. As part of the negotiation process, the user can also set a limit on the maximum possible variation of each KPI from the optimal values.

Deal decision support model parameters should include the following information: 

  • Variable: The optimization pricing variable that can be changed during the negotiation process
  • KPI: The key performance indicator calculated during the deal scenario
  • Number of points: The number of deal scenarios to determine on each side of the optimal prices
  • KPI range definition: Set of thresholds on the deal negotiation. These include:
    • Range type – Absolute or relative values used to calculate ranges on KPIs
    • Maximum up – For a relative range, the maximum up is a percentage of current KPI value. For an absolute range, actual movement value can be provided directly in the currency or factor of the KPI.
    • Maximum down – For a relative range, the maximum down is a percentage of the current KPI value.

For example, the illustration shows the maximum up and down movement as 15% of the current KPI value of “Mortgage Production.” The number of deal scenarios to generate is 10, derived by varying the variable “rate” from the selected optimization scenario.

Final deal scenarios are shown in the pricing event detail screen as rows and variable values for each product or service. The following information is available for pricing managers:

  • Variable driver values around the optimal rates
  • Relative weight – the probability of winning a deal relative to the optimal rate
  • KPI values for the deal negotiated rates

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For example, in the figure above, annual profit, mortgage production volume, and unit sales for each pricing product is shown for each change in the variable driver value. For a five-year adjustable-rate mortgage, with a loan to value (LTV) ratio of less than 65%, the optimal rate is calculated to be 4.78. Any discounts on top of the optimal rate result in loss of annual profit, with an associated increase in unit sales and mortgage production or volume.

With these deal scenarios in hand, the pricing manager now has the information to decide the maximum discount that should be provided for this particular pricing segment – one that increases the probability of winning the deal while meeting all the organization’s objectives. The pricing manager can also check similar pricing segments for cannibalization effects, then present the customer with a deal that may be more beneficial.

SAP Experts Provide Focus and Guidance

Performance and insight optimization services from SAP can help create deal decision support systems that improve decision-making using industry-specific focus and line-of-business guidance. SAP leverages proprietary, leading-edge mathematical models to obtain business insights from large volumes of data. These models capture key product attributes and provide accurate, quantitative insights into customer behavior, while taking into account all major factors influencing demand and supply. SAP solutions are engineered to maximize the value in data, transforming it into tangible benefits.

SAP services shed deep insights into the key pricing decisions and opportunities that drive differentiating value for banking, retail, and utility organizations.

For more information, visit us online at www.sap.com/PredictiveAnalytics .

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