How do you personalize ads for people who’ve never made a purchase, or visited your website? The Home Depot’s retargeting technology paired with machine learning could analyze successful customer journeys and contextual influence factors to improve their prospecting campaigns.

To improve their prospecting The Home Depot developed a data strategy that would predict what products a new prospect would be most interested in.

For years, The Home Depot have been perfecting their data-driven marketing strategies, personalizing the customer journey with high-interest audience retargeting campaigns. The Home Depot looked to translate this success into personalized prospecting campaigns.

One of the main challenges with prospecting campaigns is that they're less profitable than retargeting campaigns. The common industry ROAS target for retargeting campaigns is two dollars returned for every one dollar spent. Although they can see a return on ad spend many multiples higher than that.

This is easily attributable as people are more likely to buy products they've already expressed interest in. To improve their prospecting, The Home Depot developed a data strategy that would predict what products a new prospect would be most interested in. To build new customer interest and drive online purchases would require using a technology with enhanced creative capabilities in combination with their robust business data including: sales and promotions, product, SKU and pricing data, and website engagement data.

To execute a large scale programmatic always-on prospecting campaign, the team began by looking at the technology that increased the ROI on their retargeting campaigns. Using the same intent tracking, they analyzed successful customer journeys and contextual influence factors including: location, time, weather, events and promotions, and sales trends.

This data was activated in 2 ways, first by creating cohorts of users similar to The Home Depot’s customers, and secondly by automating in-ad product recommendations with predictive intelligence. Product recommendations were done in real-time with dynamic creative connected to their eCommerce API, dynamically serving up-to-date pricing, promos, and ratings alongside every product. Every standard IAB ad was an extension of The Home Depot’s online store.

Home Depot’s technology partner created a shopper intelligence engine via their Customer Data Platform that analyzed successful customer journeys and contextual influence factors. The dynamic, data-powered templates used a custom API integration and interactive shoppable features allowing users to shop in ad, transforming their display ads into a storefront personalized for every user.

A simple product carousel could highlight multiple products that auto rotated (or could be controlled by the user) - increasing the odds of a relevant product catching the consumer’s eye, while machine learning kept improving the recommendations and the return on ad spend throughout the campaign.

The results proved the power of the Home Depot’s customer journey-driven tech stack. The campaign outperformed the industry average return on ad spend of two dollars in revenue for every dollar
spent by forty-five times the benchmark.

Overall, the campaign delivered a year-over-year increase in return on ad spend of almost 900% over the internal benchmark, with the desktop portion of the campaign seeing over 500% year-over-year
increase in return on ad spend.

Home Depot saw its biggest increase with audiences on mobile. Fueling the campaign’s dynamic prospecting with sales data helped the brand generate nearly 1,500% year-over-year increase. Proving that with the
right technology and 1st party commerce data you can unlock superior performance
from your prospecting campaigns.