Plan, prioritize and optimize communications to maximize profits
SAS Marketing Optimization helps you maximize economic outcomes by making the most of each individual customer communication. The solution enables you to increase marketing campaign ROI by determining the best offers for individual customers and delivering analytic insight into the implications of business constraints, such as channel capacity and contact policies.

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Benefits

• Improve marketing ROI. Targeting effectively means higher response rates, improved channel effectiveness and reduced spending. It also means fewer deleted e-mails and fewer unwanted direct mail solicitations. Using segmentation and rules-based approaches to prioritizing marketing offers will not achieve the same results as the math-based approach offered by SAS Marketing Optimization.
• Enhance your contact strategy. Optimize across complex contact policies to avoid over-saturating customers and violating corporate governance requirements. Eliminate uncoordinated and conflicting communications, and incorporate relevant relationship factors such as customer risk, advertising exposure and householding into the optimization to ensure that valuable customers are receiving the best possible set of communications across every channel.
• Increase organizational efficiency. Quantify where changes in staffing and budget really pay off with what-if analysis that shows you where you’re leaving money on the table or where you have unused capacity.

Features

Robust optimization formulation

• Solve a wide range of business objectives to maximize or minimize virtually any business goal – e.g., maximizing profit, minimizing marketing cost, achieving sales volume goals, and maximizing revenue and account balance.
• Account for:
  • Overall budget or a budget for any campaign or offer combination.
  • Channel availability for store, branch, call center, direct mail, e-mail, mobile or other channels.
  • ­Customer-level attributes, such as consumer credit scores or recent purchase amounts.
  • Desired minimum or maximum cell sizes for any campaign, offer or combination of offers.
  • Resource consumption at the offer level.
  • Nearly any custom customer-level criteria, such as “total revenues greater than or equal to $25 million” or “average portfolio risk score greater than or equal to 680.”
  • Categorical constraints (geography, customer attitude, etc.).
  • Householding.
  • Contact policies.

Easy-to-use interface

• Easily navigate through the optimization process.
  • ­Use wizards for frequent tasks and for integrating with SAS Marketing Automation.
  • Reuse existing work (e.g., to create, copy, rename, delete or compare scenarios).
  • Hide or expose advanced optimization functions to support the needs of different users.

Scenario analysis

• Analyze the effect of each constraint on the overall objective.
• Optimize scenarios and review the analytic output reports to determine which scenario will bring the best result.
• Run different scenarios based on constraints and optimization objectives, contact policy and offer economics.
• Import an offer solution (e.g., from a campaign management system) and compare with SAS Marketing Optimization scenarios.
• Directly send the optimal solution to the campaign execution system.

Contact strategy

• Manage contacts to avoid over- or undercontacting customers by specifying:
• How many offers to assign to each customer or household across the planning horizon.
• Number of offers during any rolling time period.
• Number of offers from a group or subgroup (e.g., make at most two cross-sell offers through e-mail or at most one rebate offer per brand). Define groups and subgroups in the project input data. Groups and subgroups might include channel and time period, product and channel, or brand and offer type.
• Blocking policies (e.g., if you make Offer A, then you can’t make Offer B for three weeks).

Reporting and analysis

• Prebuilt reports include:
• Offer summary reports and graphs showing total costs, total profit and other metrics by offer, product, channel, time period, campaign, offer group or offer subgroup.
• Optimal results by offer, product, channel, time period, campaign, offer group or offer subgroup.
• Visual sensitivity analysis for all constraints.
• Range analysis graph when two objectives are defined.
• Collaborative capabilities enable users to publish or e-mail reports of competing scenarios before execution.
• OLAP cubes are automatically created based on input data and results of scenarios. This enables more detailed analysis of results before execution.
• Rich reports that compare not only differing objectives within a scenario, but also show comparisons across scenarios to view the impact on various counts and critical measures.