Case Study: Reviving Revenue Through Optimal Contact Strategies and CRM Experimentation for RBX Active

In today’s competitive landscape, direct-to-consumer (DTC) brands face unique challenges, particularly when growth begins to plateau or decline. Our case study focuses on an established athleisure brand experiencing a troubling trend: a -12% year-over-year revenue drop. As the brand has successfully expanded its audience beyond millennials into an older demographic, it must pivot its strategies to ensure sustainable growth.

“When working with brands, we recognized that the first-party data they collect—their customer file data—is one of the most valuable assets at their disposal.”
- Cara Manion

Brand Overview

RBX Active is a DTC athleisure brand known for its high-quality yoga pants, hoodies, and tees. The pandemic propelled significant growth for the brand, but as the market stabilized, so too did its performance. With Black Friday and Cyber Monday (BFCM) approaching, there was an urgent need to reverse this trend and reignite momentum.

Go-to-Market Strategy

The company has traditionally focused on prospecting, targeting specific audiences through Facebook media and email marketing. While these efforts successfully drove customer acquisition, our study highlights the opportunity for enhanced retention strategies.

Currently, retention initiatives rely on hand-picked audience selections, primarily targeting individuals who visited the website but haven’t opened an email in the last six months. This approach presents an opportunity for improvement, as the brand is still exploring how effectively these efforts drive incremental performance. By incorporating data-driven insights into retention strategies, the company can strengthen customer loyalty and maximize long-term value.

Attribution Challenges

RBX Active utilized Facebook’s reported metrics and their own analyses to track performance, but there was some confusion regarding the impact of their media spending on sales. This uncertainty led to questions about the effectiveness of their audience targeting and the return on investment.

We engaged with them around September, aiming to run the test at the end of October or early November. This timing was strategic, as it allowed us to activate insights for the critical Black Friday/Cyber Monday period. With these important sales periods approaching, anxiety about making the right decisions increased, highlighting the need for clearer attribution and more reliable performance metrics.

Objectives

The primary goals of this initiative were to:

1. Understand the incremental impact of Facebook media on customer retention and revenue during the BFCM period.

2. Identify a contact strategy that maximizes customer engagement and increases revenue contribution from existing customers.

3. Optimize media spend to ensure a more efficient allocation of resources during peak sales periods.

4. Reverse the current -12% trend in customer retention, focusing on strategies that effectively re-engage lapsed customers and drive sustained revenue growth.

By addressing these goals, we aim to create a robust framework for improving customer loyalty and maximizing the effectiveness of marketing efforts during critical sales periods.

Methodology: RFM Analysis and CRM Experimentation

When we engaged with the client, there was a prevailing assumption that their audience selections were effective. However, our data analysis revealed otherwise, indicating significant opportunities for optimization. To address this, we deployed a two-pronged strategy.

First, we focused on the RFM (Recency, Frequency, Monetary) model to gain a clearer understanding of the composition of their customer file. This foundational analysis allowed us to identify key segments and behaviors within their audience.

Next, we conducted a benchmarking analysis comparing customer behavior during the 20 days leading up to Black Friday and Cyber Monday (BFCM) in 2023 versus 2022. We specifically examined the change in revenue per customer ($/Customer) and uncovered a concerning trend: the lapsed segment—customers who hadn’t purchased in over 18 months—was declining by 35%. This decline emphasized the urgent need to re-engage this group, particularly since many customers acquired during the pandemic were not returning for subsequent purchases. From a P&L perspective, this scenario is problematic, as it is costly to acquire customers only to lose them shortly after. These insights laid the groundwork for our targeted re-engagement strategies.

Testing

After performing the RFM analysis, it became clear that lapsed buyers were not returning, necessitating a focused effort to re-engage them. Our hypothesis was that the brand's hand-picked, conversion-optimized audience targeting strategy on Facebook might be leading to less incrementality. This approach typically targets individuals more likely to convert, which tends to favor recent buyers and overlook those who had lapsed.

To test this, we aimed to understand the impact of Facebook’s conversion-optimized campaigns compared to reach and frequency (RF) campaigns. At a high level, the conversion-optimized approach performed well, as anticipated. However, a deeper dive revealed that some segments responded more favorably to RF campaigns, while conversion optimization did not yield the same results.

Based on these insights, we recommended a targeted approach: select segments that performed best with conversion optimization while employing RF campaigns for segments that included a significant number of lapsed customers. This dual strategy aimed to effectively re-engage those who hadn’t purchased in a while while leveraging the strengths of conversion optimization for high-performing segments.

It's worth mentioning that the client had some initial reservations about running a fully RF campaign, which is why we proposed a balanced approach that combined conversion optimization with RF strategies. This allowed us to explore the strengths of both methods.

We also considered the implications of opt-in and opt-out strategies. By integrating RFM analysis with opted-in/out data and campaign objectives, we developed a variety of targeted segmentation options. This framework provided a flexible set of contact strategies, enhancing the potential to re-engage lapsed customers while effectively optimizing media spend across different segments.

The Results

RBX Active experienced a successful Black Friday and Cyber Monday (BFCM) by implementing a targeted approach. As a result, there was a 10% increase in revenue per customer ($/customer) from the lapsed segment. Overall, the brand achieved a remarkable turnaround during this period, realizing a 16% increase in total revenue, effectively reversing the previous trend of -12%.

This success can be attributed to a well-crafted contact strategy tailored to micro-segments. By leveraging insights from the RFM analysis and strategically optimizing media spending, RBX Active not only re-engaged lapsed customers but also maximized overall revenue. This outcome underscores the effectiveness of a data-driven, segmented approach to customer retention and engagement.