In the world of digital marketing, data-driven decision-making is key to maximizing return on ad spend (ROAS). Marketers are constantly optimizing their campaigns to achieve the best results, whether in terms of ROAS or cost of customer acquisition (CAC). However, one digital marketer recently experienced a situation where over-optimizing a campaign led to disastrous results. The marketer, who had been in the industry for over 30 years, decided to share this case study as a warning to others.
The marketer’s business, Restaurant Furniture Plus, had been steadily growing its ecommerce operations through Google Ads. By increasing their advertising budget year-over-year, they were able to scale their revenues. They focused on high-level metrics like ROAS and Cost Per Lead (CPL) to guide their campaign management. However, when they upgraded their CRM system to better connect with Google Ads, they decided to bring in a more sophisticated marketing agency to optimize their campaigns based on CAC rather than CPL.
The agency’s plan was to connect the CRM data directly with Google Ads to track which ads led to actual buying customers. However, this decision backfired, causing their CAC to double and their ROAS to be cut in half. Upon further investigation, they realized that by changing the primary data point for optimization from leads to customers, they severely limited the amount of data being sent to Google, which stifled the algorithm’s ability to work effectively.
The fix was simple: they reverted to optimizing for leads data rather than transaction data, which allowed the algorithm to function properly again. The campaign’s ROAS and CAC returned to historical levels once they stopped choking the algorithm with limited data. The lessons learned from this experience included the importance of providing Google with enough data to work with, the possibility of even experienced marketers making mistakes, and the dangers of over-optimization in marketing campaigns.
Overall, the case study serves as a cautionary tale for marketers to find the right balance between optimization and over-optimization in their campaigns. By ensuring that Google has enough data to work with and avoiding excessive tuning of their campaigns, marketers can avoid potential pitfalls that could derail their advertising efforts. This experience highlights the need for continuous testing and iteration in digital marketing, as well as the importance of learning from mistakes to improve future strategies.
