The number of decisions that a business must make in the marketing space is on the rise: which audience to target, what is the best method of communication, which marketing campaign should they receive? This complexity is compounded by each business’s unique constraints and goals. In order to stay ahead, a growing number of businesses are embracing Artificial intelligence (AI) analytics, machine learning and mathematical optimization in their decisioning models and strategies.
What is an optimization model?
While machine learning models provide predictive insights, it’s the mathematical optimization models that provide actionable insights that drive decisioning. Optimization models factor in multiple constraints and goals to leave you with the next best steps. And each step in the optimization process can add incremental value to significantly improve the overall impact of your marketing outreach — for both you and your customers.
Using a mathematical optimization software, these strategies can be efficiently applied to marketing practices throughout the consumer lifecycle to enhance targeting, increase response rates, lower cost per acquisition and drive engagement. Better engagement can lead to stronger business performance and profitability. Here are a few key areas where machine learning and optimization modeling can help increase your marketing return on investment (ROI):
- Prospecting: When acquiring new customers, advanced analytics and optimization can be used to better identify individuals who meet your credit criteria and are most likely to respond to your offers. By taking a customer-focused approach to campaign planning, you can provide the most relevant marketing messages to customers at the right time and on their preferred channel.
- Cross-sell and upsell: The same optimized targeting can be applied to increase profitability with your existing customer base in cross-sell and up-sell opportunities. Gain insights into the best offer to send to each customer, the best time to send it and which channel the customer will respond best to. Additionally, implement logic that maintains your customer contact protocols.
- Retention: Employing optimization modeling in the retention stage can help you make quicker decisions in a competitive environment while meeting your customer experience expectations. Instantly identify triggers that warrant a retention offer, and from there, determine the likelihood of the customer responding to different offers.
Gaining insight and strengthening decisions with our solutions
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