Over this blog series: “The Journey toward direct marketing optimization” I have covered all the topics that are part of the optimization process. As said before, however, the most impactful part in an optimization problem is to set the constraints. If a company does not have any type of boundaries when selecting and contacting customers, then there is no optimization problem at all.
As a customer intelligence advisor, I have never worked with a company that does not have some contact policy restrictions. Most marketing teams do manage customer saturation and choose not to contact their customers more than certain number of times during a period. Another frequent constraint is channel capacity, particularly call center capacity. Most companies choose this channel as it is usually more effective, but it is also the more restrictive.
In the example used in the first article “The Journey toward direct marketing optimization” I already introduced two different type of constraints: contact policy and amount of targets per offer. In this last stop before we reach the destination, I will go deeper into restrictions and how they influence the optimized results. As in SAS Marketing Optimization restrictions are classified as business constraints and contact policies constraints, I will use the same categories.
Observing Contact Policies
In order to cover this type of restrictions, I will use the same simple example used at the beginning of these series of articles.
Remember that if there were no constraints, we could have an expected profit of 265, the sum of all the possible offers for all the customers. However, the company in the example indeed has a contact policy in place. The company cannot contact the customer more than once, so we cannot select the same customer for the two available offers. In this simple example, having this restriction made us lose 105 of profit as we selected the most valuable offer for each customer (offer 1 in both cases).
It is important to differentiate between two different types of contact policy restrictions. The first one makes a customer eligible for optimization. This is relative to the target selection process and not optimization, as we discussed in the second stop of our journey, “Designing Campaigns for Optimization”.
When setting the contact policy in the optimization scenario, we are considering the contact for the period we are optimizing, not the previous ones. This is necessary because a client might be eligible for more than one campaign, so we have to decide how many times we are going to contact him/her during the period under analysis.
Contact policies can also be different by channel. Many companies we work with don't count email campaigns as a contact, so if they have different campaigns that are communicated through different channels, they can give their client more than one offer in the period optimized. The most restrictive channel is usually the call center. Most of the companies set that a customer can only be contacted once by an agent in the period.
Setting the contact policy in the scenario is very important in the optimization process. Knowing the company’s boundaries is essential as it affects the optimization results. The contact policy can be as complicated as necessary. SAS Marketing Optimization will take all the information in order to calculate the best results, with no need of complicated processes.
Managing Business Constraints
Contact Policies are not the only restrictions companies have. Many other business constraints have to be considered in an optimization problem. Continuing with our example, the next restriction we had was the minimum amount of customers per offer. Even for our very simple example, setting this restriction makes the problem more difficult to solve. By defining that each offer must have at least one customer, we have to change the solution and we are now able to get 150 as a result (offer 2 for client 1 and offer 1 for client 2), 10 less than the previous scenario.
Constraints can include, among others, budget limits, channel capacity, risk limitations, amount of offers, etc. These constraints can apply to all the customers or we could set constraints by segments, cities, or any other characteristic of the customer. For example a bank might need to limit the amount of loans offered to customers with a higher risk profile, even if they might probably the most likely to accept them.
Constraints can be “at least” or “at most”, giving the users the possibility to analyze different combinations and possibilities. For example, a Product Manager may want a minimum of customers for his offer, but also a maximum in order to control expenses. SAS Marketing Optimization will find the best number of offers between these boundaries.
In order to set certain constraints we will need particular data to input to the tool. For example if we have a budget limitation, we need to know the cost of each offer. This information can also be used when setting the optimization goal. If we need to set restrictions on channels, we need the capacity of each of them. The more information we have, the more complex we can make the scenario. However, be careful, complexity doesn't always mean better results.
All these kinds of factors are usually not considered in prioritization strategies as they are very difficult to implement. Implementing SAS Marketing Optimization can help marketers to target the best customers, with the best offer for each, observing all the business restrictions, and getting the best results overall.
We are coming to the end of this exciting journey. In the next and final stop I will explain how to put all these pieces together. However, as you might have noticed, Marketing Optimization is not only about having the best solution in place, but also about rethinking the campaign process. I will then cover this topic as well in my next article. Join me!
If you did not read the previous posts in this series, I encourage you to do so since Luciana planned them as a step-by-step journey. Marketing optimization is a very effective way to tie overall business objectives (often profitability) to marketing campaign activity because it mathematically calculates the best aggregate outcomes based on how you define them. If you'd like to dig a little deeper into how marketing optimization could work for you, I suggest you download this whitepaper, Improving Multichannel Marketing with Optimization. Among other useful content, it includes a practical checklist of seven steps to optimize your marketing.