I have always said to my team: “The one thing we must deliver to clients is improved outcomes. If our technology can’t increase the effectiveness of client marketing then we don’t have a business. Either it works or it doesn’t.”
With this context, it’s easy to see why we put so much emphasis on benchmarking and objective analysis of the transaction data we generate. The challenge has always been separating the outcomes achieved by the AI-defined email content from the outcomes achieved by the client without the assistance of AI. Let me explain.
The algorithms that our team has developed are built around specific use cases. For example, one use case is convincing a one-time donor to become a monthly donor. Our models need a minimum of engagement with client email before they can take a supporter record and put it into a cluster of similar supporters. Separate algorithms need transaction data to find the patterns within each cluster before they can define the most effective content to write.
If a supporter does not have enough recent engagement data (e.g. email clicks and page transactions) there is no point trying to place the record into a cluster or predict responsiveness to defined email content. This is why we can’t use a general benchmark using the entirety of the client data set to measure outcomes. Therefore:
Point 1. We can only build a benchmark (pre-launch) using the same data qualification rules to select records that our models use. Otherwise, we would unfairly penalize client data by including unresponsive records in the benchmark.
Our models cluster, and re-cluster, supporters over time. The behaviour of a supporter in their first 60 days ‘on file’ is very different from the behaviour of a supporter that has been ‘on file’ for more than a year. So… our algorithms put records into a cluster and define content for 1-3 emails that get automatically sent in a short burst over a few days. There are typically 4 of these short bursts of AI-defined email over the course of 12 months. During the much longer periods in between the AI-defined email content, supporters continue to receive the client’s own marketing content. Therefore:
Point 2. Direct attribution of a conversion (AI-defined email versus client email) is most objectively done by looking only at conversions that came from the email (trackable email ID included in the transaction record). In other words, which email content prompted the engagement and the conversion.
Coming to the end now… This is why we have settled on two metrics to measure outcomes: a static benchmark of qualified records pre-launch, and an email conversion rate that compares conversions from AI-defined email content versus client content to the same supporter over 12 months. In our pilot clients thus far, the outcomes for both metrics, and for all clients, have demonstrated that the AI-defined content is producing substantially better engagement and conversions.