Discovery commerce can be leveraged to reach a wider customer base and increase your customer lifetime value. For those that are not familiar with discovery commerce, it is an online sales method that uses targeted advertising to guide a buyer towards a product using machine learning to improve targeting and relevancy. Optimizing discovery commerce creates a retail environment in which products find people, as opposed to people finding products. In the session, The Age of Discovery: Building Loyalty in the Next Era of Ecommerce, Aubrey Richie, VP of Customer Acquisition & Media at TechStyle, and Ian Simons, Head of Industry, eCommerce at Facebook, discussed how TechStyle has become a leader in this type of marketing.
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SESSION RECAP
Overview: Techstyle
TechStyle was founded in 2010 and has grown rapidly, now running six retail brands. They have since discovered that reaching their objective of “delivering access to quality and style for unprecedented value through a flexible membership program” can be best achieved via data and testing.
This slide shows Techstyle’s current company portfolio.
Relentlessly data-driven, TechStyle shares data learnings across all six brands listed above, enabling TechStyle to more efficiently and effectively implement testing and measurement across the portfolio. All brands consistently test new methods and practices to stay ahead of their competition. They have found that data can help them increase their reach via different media platforms. This has enabled them to unlock entire new waves of customers.
Discovery Commerce and LTV
After originally looking at flat CPAs, Techstyle has adapted and learned the value of cohorts, understanding payback periods and knowing what things or variables drive high-value cohorts, which has played a major role in their growth.
TechStyle implements machine learning using the data found across all six platforms. They test new and upcoming social media, advertising and other marketing trends with their customer base first. In their shift towards discovery commerce, the marketing team learned three key lessons to help them optimize customer engagement and retention:
- Listen and respond to your customers. Consider testing with a specific group to learn before launch.
- Leverage influencers’ creative in other marketing assets like email, social feeds, etc.
- Track which influencers, content and creative are driving the most impact on lifetime value and incremental reach.
Identifying customers’ lifetime value gives TechStyle the opportunity to target the right customers using the right methods. Using machine learning technology, TechStyle estimates predictive LTV. One of the first data points to help predict LTV is order value and TechStyle looks at order value across channels like influencers, platforms and ads to predict the LTV of customer cohorts. It’s important to think beyond simple acquisition costs and move towards driving discovery that will increase LTV. More money should be put towards customers with predictably longer-term value or have higher cart values.
Creative also plays a huge role in TechStyle’s machine learning, because they are able to find valuable information and data from their successes and failures. The company focuses on remaining agile and following the data when it comes to their creative and they produce a lot of creative to feed their testing efforts. Richey highlighted these points regarding their creative strategy:
- Great creative can come from anywhere, your team, creative partners and even your audience.
- Apply a micro-iterative, data-driven approach to creative to eliminate waste.
The company has hosted company parties and contests to generate creative and found that great ideas and inspiration come from almost anywhere in the company.
Since embracing discovery commerce, TechStyle has been able to grow their customer base and target those of the highest value.
Watch the 20-minute fireside chat now to learn about discovery commerce beyond this recap.
