We have finally arrived to our last stop in this Journey Towards Direct Marketing Optimization. If you have read the entire series of articles, you are now ready to understand what optimization means and how to perform it. For those who have not read the previous articles, I suggest you to do so before reading this last one:
- The journey toward direct marketing optimization
- Optimization step 1: identify the best customers
- Optimization step 2: designing campaigns
- Optimization step 3: setting the goal
- Optimization step 4: setting the right boundaries
In the first article, I explained what Campaign Optimization means in a direct marketing context. Then I talked about how to differentiate customers through propensity scores or response rates. Segmenting customers by any of these measures is essential in order to make decisions about the best customers to target. In the second piece, I explained how to plan campaigns in order to optimize them all together. It is very important to understand that our optimization scenario should include all the eligible customers, before current prioritization rules. After selecting the targets, we stopped to analyze what kind of goals we can set when optimizing. Having the right information to calculate the optimized value is critical. As optimization would not be necessary without constraints, I described which the most common ones are and how to handle them. So now it is time to put everything together and arrive to our Optimized destination.
No need for complex coding
As said in the first article, to calculate an optimized scenario considering all the factors we have discussed, it is necessary to use complex optimization algorithms. However, there is no need to program them. SAS Marketing Optimization is a solution with a user-friendly interface to simulate and evaluate different scenarios. Our solution is aimed at marketing analysts that need to spend more time analyzing possibilities rather than building complex queries or decision flows. The user only needs to input data and then he/she will be ready to simulate scenarios. This input data is no other than planned campaigns/offers and eligible customers for them. Additional data can be used in order to evaluate scenarios that are more complex.
Business constraints and contact policies can be loaded as a table or written in the same user interface. This is very flexible as users can evaluate different possibilities without changing any code.
Simulate and analyze scenarios
Optimization is an analytical process, whereas prioritization is usually an automated procedure. Before arriving to an optimized final solution, several scenarios are usually analyzed. A scenario is a set of constraints and contact policies restrictions over an eligible universe. Each scenario is an optimized solution, considering the constraints we are setting.
Going back to our very simple example in the first article, we could work on 3 different scenarios:
- No restrictions at all: all the eligible customers can receive all the offers (no contact policy or business restrictions in place). This is usually called “Base Scenario” and we use it just to have an idea of the potential value of all our offers. Of course, this scenario is unrealistic.
- Contact Policy Constraints: we use the same information as above, but add as a constraint that we can contact at most one time each customer. There is no need to input the data again, we just save the previous scenario with a different name and add the restrictions we need. We will get the optimal solution, given the new contact policy restriction.
- Contact Policy and Business Constraints: to the previous scenario, we add a new business rule “at least one customer per offer”. As before, we use the “save as” capability and testing scenarios becomes just a matter of selecting or deselecting constraints. Again, this is another optimized solution for the constraints set.
If we extrapolate this to a real world problem, we could have many different constraints (budget, channel capacity, contacts per segments, etc.) and analyze what would happen if we loosen or tighten the restrictions.
Once that we discovered the best solution, the result is a list of customers and the offers we should send to each of them.
Optimization needs commitment
Probably while reading the series of articles you have identified more than one obstacle to apply optimization in your organization. Most organizations that embrace optimization get on board to change processes at the same time. Although this might sound discouraging, in our experience, we have found that it is indeed a very useful exercise. Many clients have discovered during assessment sessions that most of the prioritization rules they have in place are outdated. They just keep on using them because they are used to it and are afraid of changing the automated processes. However, when asking them about the value they are missing they have no possibility to answer.
In order to make the transition smoother, we recommend starting by optimizing only a small group of campaigns. Usually, the most critical campaigns are the ones that use the call center as channel, as they are the most restrictive ones. After simulating several scenarios, we arrive to the most convenient solution and customers are targeted.
Probably the optimized solution is not the one that a Product or Segment Manager was expecting. For example, the amount of customers to contact in each offer might not match the expected by the offer owner. It is useful to bear in mind that optimizing is about finding the best overall results, not for each product in particular. This is why optimization needs the commitment of all the participants and from the management above all.
I hope you have enjoyed our journey towards direct marketing optimization and that you had taken a souvenir from each of our stops. I will be glad to be your Captain if you decide to start this trip - or any of my colleagues. Do not hesitate to contact us if you would like to discuss how to get on board.
Editor’s note:
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.
