[Free Takeaway Included – 17 Must-Use Analytical Techniques Across Marketing Life-Stages]
When it comes to campaign creation and execution, most marketers often make the mistake of targeting a mass audience instead of targeting a specific subset which is more likely to respond.
Optimizing campaigns to achieve relevant targeting has not always been easy; in fact in many cases the process can take days or even weeks requiring teams of data scientists.
This is where advanced predictive modeling can come to the rescue. You can target relevant prospects who are likely to respond and build effective response optimization models that you can follow and implement in your campaigns.
Don’t Set and Forget - Track Response & Optimize
Most campaigns have a thin ROI margin to start with, making it easier to sabotage your campaigns with wrong targeting and wasted resources. That’s why it’s vital to optimize your campaigns based on response optimization modeling to get as much ROI as possible.
Following are the parameters you need to track to build the best marketing campaign optimization models:
- Target Segments
- Intended Action
- Marketing Channel
- Day, Time and Week of Campaign Execution
- Creative Variants
- Response Status of Customers
Download this free guide to effective analytical techniques to build response optimization models across the entire marketing life-stages from acquisition to retention and more.
Steps to Build Response Optimization Models
Once you have captured contact and response history using the above parameters, you can build statistical models to find the optimal combination of parameters that work for your business
- Process the Data – Before building the model, ensure that your data is clean and doesn’t have missing values, outliers, and is of good quality.
- Split Data into Training and Testing Data – Divide your data into two subsets – training and testing to compare and evaluate performance.
- Run Algorithms on Training Data – Build the actual model by running different combinations of algorithms on the training data.
- Test – Test the finalized algorithms on the testing data to evaluate the accuracy of the algorithms.
- Finalize the Model – Finally, use the model that delivers the greatest accuracy to find the correct range of values or factors that maximize campaign responses.
It’s recommended to utilize machine learning and predictive analytics modules to build and run response maximization models as it automatically scales and tunes any performance issues. Besides, use a stacked ensemble model to avoid performance issues and derive predictive algorithms.
How to Sustain the Efficacy of Response Optimization Models
The efficiency of optimization models tends to deteriorate over time due to several reasons. Hence you need to periodically test and assess the performance of your models using the champion-challenger approach:
- Bi-annually or quarterly, load the model building workflow with updated data
- Compare the performance of the new challenger models with updated data against existing champion models
- Deploy the better performing model as the new champion model
- Repeat the sequence periodically.
You can leverage the power of artificial learning to ensure that your optimization models give the necessary weightage to significant factors and parameters of the campaign. Using algorithms such as Shapley Additive explanation or SHAP, could help explain the recommendation made by the models and identify the influential variables of the campaign.
This is the visual representation that demonstrates how this algorithm provides an explanation in the form of weightages assigned for each parameter that influences campaign responses:
While maximizing responses to campaigns may seem hard, it certainly should not be. Once you collect the necessary data and build response maximization models, you will start seeing an increase in campaign responses.
An advanced AI-powered marketing automation solution that automates every aspect of response maximization is what you need to help you get started with building sustained champion models.
The automated platform can help you get through the data-wrangling and modeling process so you can move quickly to decision making and execute campaigns successfully instead of getting caught up with DIY statistical models for months.
Do you use response optimization modeling for your overall campaign management and execution? If so, how do you build and sustain effective response models? We’d love to hear your perspective. Do write to us at email@example.com.