Worried About the End of the Cookie? Turn to Your Own Data
As we countdown to our last year with browser cookies, many marketers are still concerned about achieving marketing performance in a post-cookie world.
Mobile marketers have already had to manage their data diets following Apple's termination of IDFA in 2021. But with the death of the cookie, marketers will be very limited in their ability to support campaigns by buying and integrating external data.
The reality is that most marketers already have extensive internal first-party data that they have permission to use, but it might be housed in sales or distribution and not yet widely used in the marketing efforts of the company. It's time to break down the data silos between marketing, sales, and distribution to ensure that the marketing team has access to sales data to effectively manage marketing programs, and efficiently support sales.
An important and effective data stream that the sales/distribution teams need to share with marketing is the sale of Stock Keeping Units (SKUs) per retailer/retail channel. By analyzing SKU data over time and according to the specific retailer, we've enabled marketers to understand which online ads generate the greatest sales lift at a specific location of a brick-and-mortar retail store. Based on this analysis of sales data, marketers are then able to effectively optimize their online marketing to maximize performance at specific retail stores.
This is the value of working with artificial intelligence (AI) technology. While the human eye might not catch a specific data pattern, definitely not at the level of an individual retail store, advanced technologies can uncover data patterns that improve marketing performance.
If you're a marketer reading this post, you probably have a treasure trove of privacy-compliant first-party marketing data to optimize campaign performance. The challenge is integrating all the relevant data into your marketing technology stack and working with a martech vendor to enable uncovering insights to optimize marketing performance.
Current AI technology enables optimizing campaign performance by analyzing the results achieved by the various creative variables including videos, headlines, body texts, and images. From this analysis, AI technology can compile the most effective ads based on submitted creative variables to create the best-performing ads, whether running in York Township Michigan or New York City. Based on past campaigns and performance, AI technology can also understand and recommend which ad creative will work best in each targeted location.
While human creative directors are still responsible for creating the ad creative variables, including headline and body copy, images, and videos, AI technology is assembling the ad using these creative variables. And whether the ad is targeting young adults in York, Michigan or teens in New York City, the AI technology determines the final ad creative, compiled to generate the greatest Return On Ad Spend (ROAS).
One of the attributes I like best about AI technology is that it doesn't get sentimental. Like all people, marketers have their favorites and are sometimes slow to recognize when creative fatigue sets in and an ad needs to be replaced. No such problem with AI technology. The second an ad's performance begins to wane in a specific ad market, AI technology either refreshes or replaces the ad.
As marketers, we need to break the silos between marketing and sales/distribution. All data decision-makers in the organization must understand what product sales and marketing data is available. Then, it's necessary to understand the organization's marketing technology stack, its AI abilities, and how to integrate organizational product and sales data to support marketing decision-making. Finally, by integrating regular product and sales data with other relevant data, including seasonality and marketing campaign data, the marketing technology stack will enable overcoming the end of the cookie.
If AI technology can show a marketer which online ads are achieving the best performance within a specific targeting location, then it can enable marketers to move on from their reliance on cookies.
The views and opinions expressed are solely those of the contributor and do not necessarily reflect the official position of the ANA or imply endorsement from the ANA.
Charlotte Naim is senior account manager at Albert AI.