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Using Analytical Marketing Optimization to Achieve Exceptional Results

Using Analytical Marketing Optimization to Achieve Exceptional ResultsSAS White Paper

Table of Contents

Optimization Defined . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Prioritization, Rules and Optimization – a Method Comparison . . . . 2

Prioritization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

The SAS Approach to Marketing Optimization . . . . . . . . . . . . . . . . . . . 6

Organizational Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Standard Reports and Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . 8

Extending Optimization with Business Intelligence . . . . . . . . . . . . . . . 8

Balancing Suppression Rules and Constraints . . . . . . . . . . . . . . . . . . . 8

Success with SAS® Marketing Optimization . . . . . . . . . . . . . . . . . . . . 9

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Learn More . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

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Using Analytical Marketing Optimization to Achieve Exceptional Results

Optimization DefinedThe complexity of direct marketing has expanded rapidly in recent years, particularly with the growth of digital marketing channels . Companies today have to make difficult decisions about targeting the right customers with the right offers while staying within budget and channel capacities, all without cannibalizing future sales or saturating customers with too many messages . That is a lot to manage, particularly when multiple campaigns from one company might also be competing for customers’ attention .

Optimization resolves these complex issues by looking at problems in a holistic fashion that balances the constraints of an organization with the need to improve key metrics . Unlike traditional business-rule methods for allocating campaigns to customers, optimization allows marketers to gain critical knowledge about factors that affect the success of marketing campaigns – such as the impact of adding a new channel, the probable results of reducing a budget or the consequences of instituting a strategic contact policy .

The best way to explain the differences between traditional approaches and optimization is through example . This paper will use an example in order to:

• Showthevalueassociatedwithtakinganoptimizationapproachtodirectmarketing .

• Discussorganizationalchallengescommonwhenimplementingoptimization.

• DetailtheSASapproachtomarketingoptimization.

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Using Analytical Marketing Optimization to Achieve Exceptional ResultsSAS White Paper

Prioritization, Rules and Optimization – a Method Comparison The following example illustrates problems that can arise when companies execute customer-based campaigns where there are limits on which customers are eligible to receive offers . In cases where one customer only qualifies for one offer, the solution is simple – the customer gets that offer . The problem becomes more challenging, however, when there is a group of customers that qualify for more than one offer .

Figure 1 shows this situation . Overlapping sections in the diagram represent customers who qualify for multiple offers . When optimizing across time periods, the overlap can increase exponentially . What makes offer allocation decisions even more important is that customers who qualify for more than one offer are often the most valuable customers . Poor decisions about campaign allocation could jeopardize that value .

Campaign A Campaign B

Campaign C

Figure 1: Overlapping sections represent customers who qualify for multiple offers.

Companies approach this problem in different ways . In the following example, we will compare three approaches: prioritization, rules-based and optimization . The first thing many companies do when attempting to make a decision about offer allocation is to develop model scores that reflect the probability of response for given customers and given campaigns . These model scores, in addition to other values such as the expected

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Using Analytical Marketing Optimization to Achieve Exceptional Results

revenue from a response, make up an expected value . Table 1 shows the expected values for each customer-campaign combination . Execution of this campaign also has two constraints:

• Eachcampaigncanbesent,atmost,tothreecustomersinthelist.

• Eachcustomercanreceiveonlyonecampaign.

Prioritization

Prioritization is the most common approach database marketers take to solve this problem . Put simply, prioritization assigns an order of priority for each campaign being considered within the same time period . For example, it may have been determined that Campaign A is the best-performing campaign available . Therefore, it will get its first choice of customers and will choose customers 1, 7 and 9 because they offer the highest expected values available .

Campaign B will get the best customers remaining, and Campaign C will get the rest . Table1showstheresultsoftheprioritizationmethod.Shadedcellsmarkthecustomerschosen to receive each campaign . Using this approach, the company can expect $655 in profit for the three campaigns .

By looking carefully at the customers chosen for each campaign, you can clearly see thatthereisroomforimprovement.Specifically,itislogicaltothinkthatCustomer1should have received Campaign B, which would have resulted in an improvement of $20 . Campaign selection based on this type of reasoning is shown below .

Customer Campaign A Campaign B Campaign C

1 100 120? 90

2 50 70 75

3 60 75 65

4 55 80 75

5 75 60 50

6 75 65 60

7 80 70 75

8 65 60 60

9 80 110? 75

Table 1: Results of the prioritization method.

Expected Return:

655

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Using Analytical Marketing Optimization to Achieve Exceptional ResultsSAS White Paper

Rules

Based on what we learned about prioritization, the logical question becomes: Why not give each customer the offer that will result in the most revenue? This question describes the rules-based approach . This approach establishes rules that look at each customer in order to determine the appropriate campaign for that customer .

In our example, Customer 1 will get Campaign B, Customer 2 will get Campaign C and so on . This seems like a major improvement over prioritization, and in some cases it is . However, the drawback of this approach is that if revenue opportunities exist further down the list of customers, the marketer may not be able to target them because of constraints . Remember that each campaign can go to a maximum of three customers . Because of this constraint, Customer 9 cannot get Campaign B, even though it would be a better choice . The rules-based approach would result in a $715 profit for this organization .

Customer Campaign A Campaign B Campaign C

1 100 120 90

2 50 70 75

3 60 75 65

4 55 80 75

5 75 60 50

6 75 65 60

7 80 70 75

8 65 60 60

9 80 110? 75

Table 2: Results of the rules-based approach.

Expected Return:

715 Improvement:

+60

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Using Analytical Marketing Optimization to Achieve Exceptional Results

Optimization

The use of operations research techniques enables the best allocation of customers to campaigns . This method takes opportunity cost into account with the knowledge that extending an offer to any particular customer could prevent a better offer from being presented . Evaluating all combinations simultaneously will result in the best possible solution . In this case, a profit of $745 was achieved using the same customers and the same campaigns . This represents an improvement of more than 13 percent over the prioritization method .

Customer Campaign A Campaign B Campaign C

1 100 120 90

2 50 70 75

3 60 75 65

4 55 80 75

5 75 60 50

6 75 65 60

7 80 70 75

8 65 60 60

9 80 110 75

Table 3: Results of mathematical optimization.

While a detailed look at the mathematical methods for optimization is not within the scope of this paper, it is important to note two things . First, this simple example does not reflect the enormity of typical marketing optimization problems . Many companies face similar situations with millions of customers, dozens of campaigns, complex constraints and sophisticated contact policies . When the scale of the problem increases, so does the opportunity for improvement . Many large organizations have seen improvements of greater than 25 percent .

Second,thecomputationalpowernecessarytosolvesuchcomplexproblemstraditionally has been a bottleneck . Intensive research by a team of operations research scientists and domain experts has yielded a breakthrough algorithm that solves large-scaleproblemsefficiently.Duetotheseinnovativeapproaches,SASallowsmarketersto solve these problems in a time frame that is reasonable and flexible enough to fit the objective .

Expected Return:

745 Improvement (business rules):

+30 Improvement (prioritization):

+90

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Using Analytical Marketing Optimization to Achieve Exceptional ResultsSAS White Paper

The SAS Approach to Marketing OptimizationAs mentioned above, any optimization exercise will consist of an objective, a set of constraintsandacontactpolicy.SAS® Marketing Optimization allows marketers who know nothing about optimization techniques to construct a scenario with these three components and then optimize campaigns for execution .

Objective – The objective for a marketing optimization problem can be defined in many ways, depending on the overall goals of the campaign . If the overall goal is to increase profitability,themarketercanchooseprofitasthemetrictobemaximized.SASprovidesflexibility in the goals of the campaigns so that the optimized value can be the result of an equation of two or more metrics . In other cases, the marketer might set an objective to minimize, such as risk or cost .

Constraints – Constraints enable marketers to specify key marketing limits such as minimums or maximums for spending . Constraints can also be set at the customer segmentlevel.Suchconstraintscaninvolve:

• Budget. Setthebudgetconstraintsforanyorallcampaigns.Inaddition,budgetconstraints can be created at the individual communication level .

• Cell size. Very often, campaigns need to be a certain size to be worth executing . Marketers can create constraints that reflect the real nature of the direct marketing world through minimum or maximum cell sizes .

• Channel capacity. Outbound and inbound channels often have limits, whether in terms of the total hours a call center can handle or the number of pieces a mail house can send out .

• Custom. Constraints can be constructed such that they enforce a variety of specific limitations . For example, geographic constraints may dictate that a certain number of customers are contacted within a certain region . There may be additional constraints that ensure a proper ratio of high value to low value customers are contacted across campaigns .

• ROI. All campaigns can have an additional constraint that drives toward a threshold so that a certain ROI is targeted across the campaigns .

Contact policy – Contact policies are important for planning the number of allowable touches that the overall campaigns or brand can have on each individual customer . These can be set in a variety of ways:

• Maximum contacts. A limit can be placed on the number of touches per customer for the overall cycle . For example, an organization might say that each customer can be contacted only twice per cycle . This can be maintained at the overall level or the individual customer level .

• Group/subgroup. Contact policies can be constructed so that they allow certain types of communication more leeway . A credit card company may want to limit the amount of a certain expensive offer, for example .

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Using Analytical Marketing Optimization to Achieve Exceptional Results

• Time period. It is important also to optimize across time . A contact policy can beconstructedthatlimitsthenumberofoffersinanygiventimeperiod.So,acustomer could be restricted to three communications in January and two in February . A rolling time period can limit that same customer to, for example, four communications over any two-month period .

As marketing organizations mature, they may start with a simplistic contact policy, such as an overall limit on all customer contacts, and then graduate to a more sophisticated strategy . It is critical to consider capabilities that will allow the most flexibility . In addition, customer-level contact policies, when applied, add more complexity to the underlying algorithm, making it critical to have an optimization engine that can handle this load .

Organizational ConsiderationsDespitepowerfultechnologyforsolvingcomplexmarketingoptimizationproblems,sometimes the hardest part is overcoming the organizational challenges associated with implementing optimization techniques . There are some difficult questions to be asked . Product or campaign managers are often rewarded for the performance of their product orcampaignratherthantheperformanceoftheentireorganization.So,intheexampleused above, Campaign A has a higher profit using prioritization than using optimization . If the campaign manager for Campaign A is rewarded based on the performance of only that campaign, there will be resistance to change . The overall profitability of marketing activities needs to be aligned and communicated effectively for an optimization process to be successful .

Another advantage of using optimization in marketing is that it can serve as an impassionate arbitrator among campaigns . Optimization doesn’t play favorites when deciding which campaigns will get the best customers, but the organization needs to be committed to letting the numbers speak for themselves . This approach is consistent with the overall trend toward more analytic methods in marketing .

SAScanhelpinthiscollaborativeprocessthroughtheuseofaninformationdeliveryportal . As optimization scenarios are run, the results can be viewed through this Web-based portal . In fact, as a best practice it is valuable to explore many different scenarios before putting the results of the optimization into the finished campaign . The portal summarizes and aggregates results by campaign and communication to ensure that key objectives are being met and that key stakeholders are aware of the potential impact of campaigns .

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Using Analytical Marketing Optimization to Achieve Exceptional ResultsSAS White Paper

Standard Reports and Sensitivity AnalysisAnother important aspect of using optimization is the ability to gain insight into each constraint’simpact.Uponrunninganoptimizationscenario,SASMarketingOptimizationgenerates a set of reports that includes an objective summary report, campaign/communication summary reports and graphs, a constraint summary, and a sensitivity analysis . With the constraint summary, the user can identify which constraints are limiting the overall objective and by how much . An opportunity cost of five dollars for budget constraint, for example, would tell the user that increasing the budget by one dollar would increase the overall objective by five dollars .

Once this sort of information is available, the marketer then needs to determine how muchtoincreasethebudget.Sensitivityanalysishelpswiththisdetermination,sinceitcanshowtheappropriaterangeforwhichconstraintsummaryinformationisvalid.So,for example, if the budget was $100,000, the marketer may be able to increase the budget to $125,000 before the incremental benefit becomes negligible . Again, there is tremendous value associated with creating multiple scenarios and experimenting with the outcomes of different configurations of budgets, constraints and contact policies .

Extending Optimization with Business IntelligenceInadditiontothesestandardreports,SASMarketingOptimizationcantakeadvantageoftheenterprisereportingcapabilitiesoftheSAS®9 platform . These include such capabilities as ad hoc reports, Web-based reports and an information delivery portal todistributereportstostakeholders.SASalsorecognizesthatMicrosoftExcelisthede facto standard for many marketing analysts and has built a seamless integration betweenSASandExcel,sothoseuserscanstayintheenvironmentmostcomfortablefor them .

Balancing Suppression Rules and Constraints

Given the enormous value that optimization provides, should organizations be optimizing every offer? At one extreme, the organization would let optimization decide all offers; all eligibility and contact policy rules would be left completely up to mathematics . At the other extreme would be to let all decisions be made arbitrarily, based on gut feel or business rules .

The ideal situation, of course, lies somewhere in the middle of these extremes . The exact balance depends on the organization . There will always be occasions for which the predictive model was not designed (optimization would not work in those cases), and there will always be value that can be added with more embedded analytics . An intelligentintegrationbetweenSASMarketingOptimizationandthepredictivemodeling,campaignschedulingandcampaignmanagementcapabilitiesofsolutionssuchasSASMarketing Automation can help achieve this balance .

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Using Analytical Marketing Optimization to Achieve Exceptional Results

Success with SAS® Marketing OptimizationSAShasexperienceusingmarketingoptimizationtosolvetheuniquebusinessproblems in a number of different industries . For example:

• ANorthAmericancatalogretailerwantedtofocusonbeingsmarterabouthowitmanaged the cost structure of its different channels . Having multiple call centers, direct mail and email channels available, the retailer did not know how to spread offers, or combinations of offers, across these various channels . By leveraging anexistingmodelingeffortusingSAS,thecompanywasabletoexploittheknowledge it had derived about these different channels for significant campaign performance improvements .

• ANorthAmericanfinancialservicesinstitutionwantedtomovebeyondstandardsolutions for database marketing to lift returns from marketing campaigns . ThiscompanyhasworkedwithSAStocombinepredictivemodelingwithSASMarketing Optimization to create the best multichannel offer selection and targetingsolutionintheindustry.UsingSASthecompanyincreasedexpectedROIfor a recent campaign by 50 percent and has analyzed more than 70 offers all at once for a variety of products and more than 3 million customers .

• AEuropeantelecommunicationscompanyhadestablishedcomplexbusinessrules for prioritizing cross-sell offers . This process of prioritization was largely inefficient and led to a suboptimal offer allocation . By combining business rules and constraint-based optimization, this organization has dramatically improved the prioritization process .

SummarySASMarketingOptimizationcanefficientlyhelpmarketersdeterminewhotocontactwith which campaigns in a complex marketing environment where customers could qualifyformultipleorcompetingoffers.Throughtheuseofadvancedanalytics,SASsolves this problem in a manner that is superior to traditional prioritization or rule-based systems . An interface designed for marketers makes it easy for users to enter objectives, constraints and contact policies . The resulting information is readily available for what-if analysis and can be executed seamlessly when integrated with a campaign managementapplicationsuchasSASMarketingAutomation.

Learn MoreFor more details about marketing optimization: sas .com/marketingoptimization

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