How Ogee used LTV predictions and predictive acquisition audiences during growth
2 min read
Founded in 2016, organic skincare brand Ogee relies heavily on social media for brand awareness and acquisition. Between 2018 and 2019, the brand was using Facebook Lookalikes and, as a result, saw a surge of new customers. However, Facebook didn’t give them the crucial insights needed for improved audience targeting and personalization.
As the customer base grew, Ogee wanted to focus on high-LTV customers to maximize their ROAS. So, they teamed up with Faraday to learn more about their customers, predict customer lifetime value, and target high-LTV audiences on Facebook.
Enhancing first-party data to understand customers outside of purchases
Ogee’s customer base quickly evolved as the brand grew. To understand more about their new customers, Faraday dug deeper into the data, enhancing Ogee’s first-party data with rich third-party attributes to give the brand a more holistic picture of who their customers are, outside of their past transactions. Key findings included that many customers were college graduates, upper middle class, and had a history in buying beauty products.
Using LTV predictions to identify best customers
To understand which types of customers were still worth going after, Faraday predicted the customer lifetime value (LTV) for three distinct groups of customers, segmented by their date of first-purchase. Faraday predicted how much each group would spend over the following 6 months and over the following 12 months, using a probabilistic model that took into account each group’s transactional history. Calculating LTV can be a challenge for many brands, but it’s a crucial metric that influences revenue forecasting, ad spend, and fundraising efforts. Traditionally, the margin of error for these calculations is pretty high, but Faraday came in with an impressive 7% MOE when predicting Ogee’s customer LTV.
Propensity models targeting high-LTV customers
To continue to attract the kind of high-LTV customers growing their brand, Ogee worked with Faraday to leverage propensity models, scoring the entirety of the U.S. to see who would most likely become a high-LTV customer. The top 1% of the scored group comprised the custom Facebook audience pushed from the Faraday platform. Ultimately, the predictive audience outperformed Ogee’s traditional Facebook Lookalike audiences. Spending their Facebook budget on predicted high-LTV leads lowered Ogee’s cost-per-result and significantly lifted the brands ROAS on Facebook.