The ROI of Predictive Marketing: Improving Net-New Customer Acquisition With Artificial IntelligenceApril 13, 2018
In a recent blog, we took a look at the ROI of optimizing campaign performance for existing prospects with response modeling. In this article, we’re exploring the ROI of net-new customer acquisition using look-alike (clone) modeling and how you can identify an entirely new—best—audience for first-time campaigns.
For years, the status quo has been marketers picking targeting criteria based on anecdotal evidence, or often using some basic analytics and a handful of standard variables. This method of traditional filtering can increase your campaign’s odds of success to some degree over random if you’re using a list which has been manually or anecdotally filtered really well.
If you have a highly differentiated product and you know your customers well, you can probably choose a few filters and improve your campaign targeting slightly. Unfortunately, you’re only human, it’s hard to think in more than three dimensions and you will often bring in a subjective bias to the filters that you choose. You can’t easily analyze 650+ consumer variables in order to objectively decide which ones are the most important without a serious data science team, which is costly and time consuming. And even then, technology is better suited for the job.
This is exactly what the Reach predictive marketing cloud does. It’s able to wrangle billions of data points across 650+ variables and millions of consumers and then create ideal predictive models, in just a few minutes.
The ROI of Predictive Customer Acquisition
Let’s take a look at how using predictive data science to identify targets, rather than using anecdotal filters, can help you reach the right people and likewise ensure you achieve positive ROI on your customer acquisition campaigns.
In a recent example, a customer wanted to run a direct mail campaign to 100,000 net-new prospects. [Fig. 1.0] For the acquisition campaign, the customer planned to run a mailing of 100k names with costs of $5,000 for their target list (at $50 CPM); $62,000 for print costs (at $0.62 per piece); $28,000 for mailing (at $0.28 per piece); and about $1,000 (at $0.01 per piece) of other marketing-related expenses. With this cost structure, running a profitable acquisition campaign would mean achieving an acceptable cost per sale of $125.
In this instance, a filtered list provided the customer with roughly a 2.1X increase over random [Fig. 2.0], using basic filters like age, marital status and state, and equated to about a 1% response rate. However, even with 2.1X lift, the campaign wouldn’t have been profitable with traditional filters. They would not have been able to get a positive campaign ROI with basic filtering, which is often the case because there’s still too much targeting waste.
Instead, in order to get value out of the acquisition campaign, the customer used predictive marketing to identify net-new consumers who look most like their current customers. With a predictive-modeled list, the customer received a 5.9X increase over random compared to the 2.1X increase with anecdotal filters.
With predictive look-alike modeling, the customer ultimately saw an increase in their conversion rate to 1.25% [Figure 2.0], as well as an increase in their revenue, and a decrease in customer acquisition costs [Fig. 3.0]. Using Reach’s look-alike models to acquire net-new customers, this customer took their campaign ROI from negative -6.72% to positive 12.41% ROI and made the campaign profitable.
Make Customer Acquisition Campaigns Profitable with Predictive Marketing
Advanced consumer targeting isn’t just a nice-to-have, its expected by modern marketers and their agencies. Basic filters will point you in the right direction, but will only get you so far in reaching your best audience. Reach’s platform makes it easy for modern marketers to shift the focus of their marketing efforts to the net-new prospects who most resemble current customers.
We offer a self-service predictive platform made specifically for marketers that allows mid-size brands and their agencies to get all the benefits of ultra fast, scalable predictive marketing, without the overhead of a large data team, and without the added cost and hassle of installing huge customer predictive analytics platforms.
Brands and agencies can get started in minutes using super fast predictive marketing with the Reach predictive cloud. With zero implementation, you can start optimizing your campaigns now to save money and create real ROI.