AI optimises campaign performance at WWF and drives ROI

So how do you optimise marketing spend, while retaining brand values, and at the same time deliver better results?

In its mission to advocate for endangered species and for the environment, the World Wide Fund faces challenges familiar to all brands.

The WWF customer experience is something I outlined in a recent webinar on AI and marketing Why AI will power CX in the world after COVID-19.

By optimising its marketing through the smart use of machine learning and AI, the WWF was able to address the issues of cost and purpose successfully.

The approach the WWF took was to marry the offline data generated from their printed material and campaigns - and the attribution from that - with their digital performance.

By taking the offline data that underpinned their print campaigns and marrying it with online data, it was able to develop a cohesive single source of truth.

And by having its data house in order it was able to apply predictive modeling to determine which channels would offer the best performance for different target groups.

The results speak for themselves.

For instance, WWF now knows that some donors like to be contacted monthly, while others like to be mailed quarterly or during a specific month of the year. Through more thoughtful, targeted mailings, created using advanced SAS modeling, WWF improved revenue for multiple campaigns by 25 percent.

And that increase in net income, also came with big improvements on the cost side, as they sent out 500,000 fewer pieces of mail.

They were able to cull the print runs because they could identify which channels perform best - and which combinations of channels could easily perform the best.

Not only did they save money (and trees) on the print side, they were also able to lower external consulting fees.

According to Mac Mirabile, WWF’s Director of Strategic and financial analysis, “Overall, we’ve found that by communicating with our members individually, understanding the cost structure and making sure we optimise all of our marketing efforts, we can raise the same amount of money for our conservation mission with much less expense. That means WWF and its members are more efficiently helping to protect the planet.”

This experience of the WWF is a very practical example of the application of machine learning to attribution, and predictive analytics produces a very real result.

AI recognises objects and patterns and via machine learning, it can learn patterns, and understand context.

For marketers determined to improve customer experience by making better decisions based on data, this is a great proof point that AI provides a competitive advantage.

If you would like to learn more about the WWF experience you can read it here.