Using Analytical Marketing Optimization to Achieve Exceptional Results WHITE PAPER

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1 Using Analytical Marketing Optimization to Achieve Exceptional Results WHITE PAPER

2 SAS White Paper Table of Contents Optimization Defined... 1 Prioritization, Rules and Optimization a Method Comparison... 2 Prioritization... 3 Rules... 4 Optimization The SAS Approach to Marketing Optimization Organizational Considerations Standard Reports and Sensitivity Analysis Extending Optimization with Business Intelligence Balancing Suppression Rules and Constraints Success with SAS Marketing Optimization... 9 Summary... 9 Learn More.... 9

3 Using Analytical Marketing Optimization to Achieve Exceptional Results Optimization Defined The 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: Show the value associated with taking an optimization approach to direct marketing. Discuss organizational challenges common when implementing optimization. Detail the SAS approach to marketing optimization. 1

4 SAS 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 2

5 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: Each campaign can be sent, at most, to three customers in the list. Each customer can receive only one campaign. 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. Table 1 shows the results of the prioritization method. Shaded cells mark the customers chosen 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 that there is room for improvement. Specifically, it is logical to think that Customer 1 should 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 ? Expected Return: ? 75 Table 1: Results of the prioritization method. 3

6 SAS 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 Expected Return: Improvement: ? 75 Table 2: Results of the rules-based approach. 4

7 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 Expected Return: 745 Improvement +30 (business rules): Improvement +90 (prioritization): 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, the computational power necessary to solve such complex problems traditionally has been a bottleneck. Intensive research by a team of operations research scientists and domain experts has yielded a breakthrough algorithm that solves largescale problems efficiently. Due to these innovative approaches, SAS allows marketers to solve these problems in a time frame that is reasonable and flexible enough to fit the objective. 5

8 SAS White Paper The SAS Approach to Marketing Optimization As mentioned above, any optimization exercise will consist of an objective, a set of constraints and a contact policy. 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, the marketer can choose profit as the metric to be maximized. SAS provides flexibility 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 segment level. Such constraints can involve: Budget. Set the budget constraints for any or all campaigns. In addition, budget constraints 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. 6

9 Using Analytical Marketing Optimization to Achieve Exceptional Results Time period. It is important also to optimize across time. A contact policy can be constructed that limits the number of offers in any given time period. So, a customer 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 Considerations Despite powerful technology for solving complex marketing optimization problems, 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 or campaign rather than the performance of the entire organization. So, in the example used 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. SAS can help in this collaborative process through the use of an information delivery portal. As optimization scenarios are run, the results can be viewed through this Webbased 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. 7

10 SAS White Paper Standard Reports and Sensitivity Analysis Another important aspect of using optimization is the ability to gain insight into each constraint s impact. Upon running an optimization scenario, SAS Marketing Optimization generates 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 much to increase the budget. Sensitivity analysis helps with this determination, since it can show the appropriate range for which constraint summary information is valid. 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 Intelligence In addition to these standard reports, SAS Marketing Optimization can take advantage of the enterprise reporting capabilities of the SAS 9 platform. These include such capabilities as ad hoc reports, Web-based reports and an information delivery portal to distribute reports to stakeholders. SAS also recognizes that Microsoft Excel is the de facto standard for many marketing analysts and has built a seamless integration between SAS and Excel, so those users can stay in the environment most comfortable for 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 intelligent integration between SAS Marketing Optimization and the predictive modeling, campaign scheduling and campaign management capabilities of solutions such as SAS Marketing Automation can help achieve this balance. 8

11 Using Analytical Marketing Optimization to Achieve Exceptional Results Success with SAS Marketing Optimization SAS has experience using marketing optimization to solve the unique business problems in a number of different industries. For example: A North American catalog retailer wanted to focus on being smarter about how it managed the cost structure of its different channels. Having multiple call centers, direct mail and channels available, the retailer did not know how to spread offers, or combinations of offers, across these various channels. By leveraging an existing modeling effort using SAS, the company was able to exploit the knowledge it had derived about these different channels for significant campaign performance improvements. A North American financial services institution wanted to move beyond standard solutions for database marketing to lift returns from marketing campaigns. This company has worked with SAS to combine predictive modeling with SAS Marketing Optimization to create the best multichannel offer selection and targeting solution in the industry. Using SAS the company increased expected ROI for 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. A European telecommunications company had established complex business rules 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. Summary SAS Marketing Optimization can efficiently help marketers determine who to contact with which campaigns in a complex marketing environment where customers could qualify for multiple or competing offers. Through the use of advanced analytics, SAS solves this problem in a manner that is superior to traditional prioritization or rulebased 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 management application such as SAS Marketing Automation. Learn More For more details about marketing optimization: sas.com/marketingoptimization To read more thought leader views on marketing, visit the SAS Customer Intelligence Knowledge Exchange: sas.com/knowledge-exchange/customer-intelligence To get fresh perspectives on customer analytics from marketing practitioners writing on the SAS Customer Analytics blog: blogs.sas.com/content/customeranalytics 9

12 About SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 65,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world THE POWER TO KNOW. SAS Institute Inc. World Headquarters To contact your local SAS office, please visit: sas.com/offices SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright 2013, SAS Institute Inc. All rights reserved _S113023_0913