Like many other terms, Marketing Optimization (MO) can hold different meanings for different marketers.  For online marketers, it means developing marketing campaigns that do A/B testing on emails and microsite pages to see which one’s generate the most opens, click-thrus, conversions, etc.  Those emails and web pages that underperform are eliminated in favor of the best performers.  For other marketers, MO means optimizing your communication strategy across campaigns and marketing channels to improve response, customer loyalty and profit.  It is the later meaning that this article will focus on.

Initially, optimization was used as a way to mathematically determine the optimal allocation of scarce resources. The concept has been borrowed by business analysts to aid decision-making.  Optimization has been used in the areas of the manufacturing supply chain, airline revenue yields, and financial investment risk assessment. More recently, the concept is being adopted by marketing.

Every day, marketers face realities like competing business goals, campaigns, channels, budget constraints, and product managers with myopic views, to name a few.   Large companies are often faced with campaign calendars that may not represent an ideal communication plan with its customers.  The below diagram illustrates this phenomenon for an electronics retailer.



As you can see, campaigns and customers associated with these campaigns can easily overlap.  If you are a prospect in each of these campaigns, will you feel overwhelmed by the number of contacts?  If you are a marketer with limited budget, how should your prioritize your spend across campaigns to generate desired response rates or ROI?  With multi-LOB companies with many products and services, it makes great sense to employ some degree of intelligence into the marketing equation to ensure a win-win outcome for companies, LOB’s, and last of all but not least, customers.

MO across campaigns and channels typically relies on the development of business rules, the utilization of sophisticated mathematical algorithms, or both.  Most software applications that use mathematical algorithms typically use linear or non-linear algorithms that attempt to maximize an objective function (e.g., response rates, profit), while imposing constraints.  Constraints may include: budgets, minimum/maximum number of offers per customer and/or campaign, channel capacities, etc.  While very powerful, optimization algorithms are problematic to use.  They require statisticians that build customer response and valuation models, as well as profitability models.  This takes time and money.  Then there is the issue of ensuring the algorithms actually find the global minimum (cost) or maximum (response rate) as desired.  The image below helps to visualize this issue.



It’s possible for algorithms to find “local” minimum/maximums that lead to sub-optimal marketing outcomes.  That being said, in the hands of the right practitioners, mathematical optimization can create significant marketing ROI.  So, short of the required expertise and/or budget, what are marketers to do?

More recently, software vendors have tackled this issue via the development of business rules that marketers can build.  Business rules can work within and across campaigns to optimize your communication plan.  Examples of business rules include:

• No more than 2 weekly communications via any channel to a customer, to minimize fatigue
   
• Make the best of multiple potential offers based on profit, revenue, or likelihood to purchase, as examples.
   
• If a customer may be touched by multiple campaigns in the next month, only communicate with them about the two campaigns with the highest priority.

These types of rules can easily be developed using a point-and-click interface like that found in Aprimo’s Contact Optimization module. 

So, business rules are easy to create and use -- there must be a downside, right?  Yes, there are tradeoffs associated with simplicity and ease-of-use.  Some of those include:

• We are really not optimizing an outcome from a mathematical point of view.
   
• Business rules support “subtraction”, i.e., supporting the imposition of a maximum number of touches, offers, etc.  Linear and non-linear algorithms can do that, but they can also impose minimums like, the number of offers or contacts per campaign.

Keep a look out for my next article that will continue this discussion and provide some real-life case studies.