How to Profit Predictably with Wilde.ai
Featuring Clint Dunn
Why there's no such thing as an average customer?
Mitko Ivanov opened up discussion on why he believes there is no such thing as an average customer, a thought echoed by Clint Dunn, who concurred that averages in eCommerce are not necessarily dependable. This is attributed to the reality that not every customer will return for future purchases making averages an unrealistic measure of the customer experience. Dunn emphasized that understanding each customer individually is paramount for truly effective business operations. Mitko revealed that the origin of his question was none other than Peter Fader, a renowned author. Fader frequently pointed out the inaccuracy of general averages in his works, yet the title of the specific book remained elusive during the conversation. They concluded on the note that understanding e-commerce on a deeper level, rather than relying on averages like Order Value and Lifetime Value, would create a better and realistic scenario.
Open Discussion
Mitko Ivanov kicked off the discussion by questioning where average ecommerce business owners and DTC founders should focus their time when it comes to metrics and data. Clint Dunn suggested the importance of being able to understand each individual customer in order to effectively predict lifetime value and churn, as well as personalizing the customer experience according to the value they bring to the brand. Following this, Mitko discussed Clint's background in predictive analytics and his work with Wild AI, before moving onto Clint's interest in working with retail brands and their challenges. Clint described the excitement of being able to analyze large data sets, providing insights into ecommerce trends, and exploring how different brands are growing. In terms of case studies, Clint described one successful test in which a fashion brand used Wild AI's prediction models to reduce the number of win back emails they were sending out, resulting in a significant increase in profitability. Another test showed the detection of significant revenue loss due to automatic cancellations of a large cohort of a brand's most valuable customers, reflecting the importance of lifetime value predictions in identifying issues.