Standard Bank wished to drive new credit card sales to non-existing Standard Bank customers. Projected lifetime value of customers was considered to measure the campaign's ROI. Measuring ROI had previously been a problem for Standard Bank, as reporting on actual sales became difficult due to POPIA compliance.
With the help of Omnisient, Standard Bank was able to upload data to Omnisient's servers, where customer IDs were encrypted for security. The data was joined using the encrypted IDs and provided an alternative, unique key, which was used to fully attribute sales to the campaign.
Standard Bank had been running paid social and Google campaigns prior to this campaign; however, the previous campaigns did not make use of the look-a-like audience. Since implementing the first-party data look-a-like audience, the Cost per Lead decreased by 74 percent, with the cost per acquisition decreasing by 88 percent.
Using a data clean room, agency Rainmaker Media overlayed Standard Bank's existing credit card users with Checkers (supermarket and convenience store) shoppers whose behaviors indicated they had a healthy credit score. This was determined by using an in-house affluence indicator, determined by average grocery basket size, types of products purchased, and their bank card.
Likewise, stability and fluctuation of monthly spend was coupled with cash-to-card payment ratios, as well as Deeds Office data on salary, marital status, and car and homeownership status. Using this data source, a Look-a-like model was built of non-banked Checkers shoppers with high propensity credit card usage.
The creative and media strategies worked together by focusing on a highly targeted audience using first-party data. The creative strategy was to promote applying for credit via the Standard Bank app, highlighting its ease, safety, and quick process. This messaging was intended to resonate with the target audience, who were non-banked Checkers shoppers with high credit card usage propensity. By targeting within social media and Google, the audience was reached on their mobiles, making it easier for them to complete the campaign's call to action of applying for credit on the Standard Bank mobile app. The media channel choices were right for the audience and ideas because they allow for precise targeting and measurement of campaign performance, based on real-time results. With previous campaigns having run on paid social and Google without the Look-a-like audience, there was a clear benchmark of historic data, which the brand could measure the campaign against.
The enabling technology of the data clean room look-a-like audience was crucial to the performance of the campaign. Considering the primary variable that was changed between historic campaigns and this one being the use of this Look-a-like audience, the company could confidently say that the 74 percent decrease in cost per lead and an 88 percent decrease in cost per acquisition came because of the audience. Likewise, without the data clean room, Standard Bank would not have been able to directly attribute any sales to the campaign.
The total campaign budget was R522,000, of which 35 percent was allocated towards media spend, all of which ran digitally on mobile, with the remaining budget being allocated towards the look-a-like model and data clean room services. The full media spend budget was allocated to mobile, as this was where the campaign's conversion would take place.
The Standard Bank Data Clean Room campaign showcases its creativity in leveraging first-party data and advanced analytics. Rather than relying on interest-based targeting, which is becoming less effective due to the decline of third-party cookies, Standard Bank focused ads on individuals who demonstrated good credit characteristics before they even filled out a lead form.
Further, the company recognized that browsing credit-related topics doesn't necessarily indicate a high credit score — like how enjoying content about houses doesn't make someone a homeowner. This approach allowed the company to strategically conserve the media spend budget, ensuring it wasn't wasted on individuals interested in credit, but who still posed a high credit risk.
A common problem faced by financial institutions was the difficulty of proving ROI from paid media campaigns. While paid media has served Standard Bank well for lead generation, due to the protection of data, paid media campaigns could not see the IDs of the leads, and therefore linking these leads to sales was considered impossible.
The Standard Bank Data Clean Room campaign proved to be a massive success. From the look-a-like audience, 224,335 potential customers were identified as the ones who would match the profile of Standard Bank's desired credit card customers. Of this audience, 1,136 were converted to actual sales. With an average customer lifetime of five years, this brought the estimated lifetime value to R3.8 million. With a campaign budget of R522,000, this would put the ROI to a phenomenal 728 percent.
The campaign was well-received, as evidenced by its incredible performance. While the broader market impact was not measured, this campaign strategy has been adopted by Standard Bank for more paid media campaigns, and the return on investment is clear.
