An online plant retailer specializes in offering a diverse range of indoor and outdoor plants for urban dwellers. Driven by a commitment to sustainability, they’ve built a digital sanctuary for plant enthusiasts, from novices to experts. By integrating modern e-commerce practices with a passion for greenery, the brand has blossomed into a prominent digital space for all things plants. They saw an increase in sales after implementing numerous campaigns across several channels, including Google Ads, Facebook, LinkedIn, influencer partnerships, and email. However, their marketing team soon faced a perplexing question: Which channels were truly driving sales, and where should they allocate their marketing budget for maximum effect?
The Challenge
The fact that customer trips were so complicated added another layer of difficulty. Think about this: a customer might be drawn in by a Google ad, then be persuaded by a positive review from an influencer, and finally make a purchase after receiving a tempting email. On the other hand, someone else might just see the name on Facebook and make a purchase right away.
This multifaceted journey presented its own set of problems when it came to attribution. The brand in its quest for simplicity, had been leaning toward the last-click attribution model thus far. This approach lavished undue credit on their email campaigns, potentially sidelining other pivotal touchpoints that paved the way to the purchase for their customers.
Consequently, with this murky view, budget allocation became a lot of guesswork. The looming dread? Squandering precious advertising dollars on channels that looked effective but didn’t deliver on ROI.
The Solution
The brand adopted Multi-Touch Attribution (MTA) as their attribution model of choice so that they could learn more about their customers’ journeys and make more informed campaign placements. All of their marketing databases were merged into one convenient platform. From the many MTA choices available, they decided on a data-driven methodology that uses algorithms to identify which marketing efforts were most effective. They were able to make course corrections in real-time by monitoring the outcomes.
Findings
- The brand discovered key insights that last-click attribution had previously obscured. One of the revelations was Facebook’s crucial role as an entry point for customer discovery. Although last-click attribution underestimated Facebook’s impact, MTA data revealed that many customers initially discovered their brand through the social media platform.
- Another eye-opener was the influential role of third-party reviews in the customer decisionmaking process. While influencer reviews rarely received credit in a last-click model, MTA illuminated their pivotal role in the consideration phase. Prospective customers frequently consulted these reviews after discovering the brand and before purchasing.
- Finally, the analysis shed light on the importance of Google Ads in the conversion process. These ads frequently served as the last touchpoint before a purchase. However, MTA data revealed that these ads were most effective when customers had prior interactions with the brand across multiple channels, enriching their conversion path.
Through these insights, the marketing team gained a more comprehensive understanding of how different touchpoints contributed to their overall campaign effectiveness.
Action Taken
- Budget Reallocation: The brand increased its investment in Facebook ads by 25%, focusing on awareness-driven content.
- Influencer Collaborations: Recognizing their value, they solidified longer-term partnerships with key influencers, ensuring regular reviews and content.
- Optimizing Google Ads: They began creating more retargeted Google Ad campaigns for individuals who had interacted with the brand’s influencer campaigns or engaged with their content on Facebook.
Key Results
- 35% increase in overall sales
- 20% reduction in CAC
- 40% Quarterly growth
Conclusion
Navigating the Complexities of Digital Attribution with Multi Touch Attribution Models
