Streamlining tough choices: Applying an optimization approach to hospitality marketing

You have spent your time carefully crafting a number of campaigns for distribution to your various customer segments, and it doesn’t take you much time after that to realize that you probably can’t execute on all of these campaigns. Perhaps it is because you realize that the combination of campaigns that you have planned will exceed customer contact policies, or perhaps they won’t meet mail-house minimums or maybe you just don’t have enough time and resources to complete all of the campaigns. How do you identify which campaigns will get you the results that you both want and need? Enter the analytic-centric approach of Marketing Optimization. Marketing optimization can help you contact the right customers with the right offers at the right time, while staying within the constraints of your budget and channel capacities, all without diluting future sales or exceeding customer contact policies. Marketing optimization helps maximize your economic results by making the most of each individual customer communication.

Marketing Optimization allows you to construct a scenario that balances your objectives with your constraints with your organization’s contact policy and then identify the optimal combination of campaigns for execution. The objectives for a marketing optimization problem can be defined in a variety of ways, depending on the goals you have for your campaigns. For example, if your goal is to increase profitability, you could chose profit as the metric to be maximized. You may also consider setting an objective to minimize as a goal, such as risk or costs.

Constraints enable you to specify key marketing limits such as minimums or maximums for spending. Constraints can be set at the customer segment level and include examples such as setting budget constraints for any or all campaigns. Often, campaigns need to be a certain size to be worth executing. You can create constraints that reflect the real nature of the direct marketing world through minimum or maximum cell sizes. Outbound and inbound channels also may 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. Lastly, you may want to place an additional constraint that drives toward a threshold so that a certain return on investment (ROI) is targeted across the campaigns.

Contact policies are important for planning the number of allowable touches that the overall campaigns or brand can have on each individual customer. You can set these in a variety of ways. 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. Contact policies can also be constructed so that they allow certain types of communication more leeway, such as limit on a certain type of offer. A contact policy can also 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.

The benefits of applying optimization techniques to marketing are multiple: enhanced ROI, better adherence to customer contact policies, and improved overall operational efficiency.

  • Return on Investment:  Increasing your targeting effectiveness results in higher response rates, improved channel effectiveness, and reduced spending on campaigns. The math-based approach offered by marketing optimization techniques produce results that are superior to rules-based or segmentation approaches when it comes to prioritizing marketing offers.
  • Contact Strategy: Complex customer contact policies are required to avoid over-saturating customers and violating your organizations corporate governance requirements. Marketing optimization techniques can eliminate uncoordinated and conflicting communications while accounting for customer risk, advertising exposure and house-holding to ensure that customers are receiving the best possible set of communications across every channel.
  • Organizational efficiency: Marketing optimization techniques can show where and how changes in channel usage, target customer segments, campaign budget, and other constraints will affect the business, and highlight financial opportunities and unused capacity.

The complexity of direct marketing has expanded rapidly in recent years, particularly with the growth of digital marketing channels. Hospitality 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. 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.

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  1. […] to digital marketing in her two posts “Forecasting digital outcomes for hospitality” and “Streamlining tough choices: Applying an optimization approach to hospitality marketing”.   […]

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    We’re hospitality industry specialists at SAS, the world’s leader in business intelligence and analytical software. We’ve partnered with The Center for Hospitality Research at Cornell University to find solutions to hospitality industry challenges. In this blog we will leverage the existing knowledge of the faculty, existing research and the experience of industry peers to answer the questions that hospitality executives face every day. Questions regarding topics such as revenue management and price optimization, social media analysis, sustainability, patron/guest lifetime value optimization, labor planning and marketing automation and optimization.
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