Sign up to save your podcasts
Or
Share Ep010: Raphael Neff of ChessHouse.com plus Akhilesh Srivastava and Vince Cavasin of Fenix Commerce on offering an Amazon-like shipping experience
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Ep010: Raphael Neff of ChessHouse.com plus Akhilesh Srivastava and Vince Cavasin of Fenix Commerce on offering an Amazon-like shipping experience
This time I spoke with three commerce veterans who share their experience transforming how small- and medium-sized businesses offer an Amazon-like shipping/delivery experiences and compete with ecommerce giants: Raphael Neff, owner of ChessHouse.com; Akhilesh Srivastava, founder and CEO of Fenix Commerce; and Vince Cavasin, head of marketing for Fenix.
In this episode, we discuss:
How Fenix “democratizes Amazon features for the masses” and helps mid-market retailers differentiate themselves with a better delivery and fulfillment experience
Why delivery considerations/options should appear on product pages and not just in checkout, since delivery is a major purchase consideration
ChessHouse.com has experienced a 37% improvement in conversion rate, much of which Raphael attributes to Fenix
Overall, Fenix customers average:
14% improvement in end-to-end conversion (shopper on product page who submits an order)
38% improvement in cart abandonment rates
21% increase in shipping revenue
Large decrease in pre-purchase inquiries to customer service
The importance of not only presenting enticing free shipping thresholds but also offering “reasonable” shipping upgrades for speedy service
Up to 40% of retailers using Fenix see customers paying for Fenix-calculated, enhanced low-cost shipping, even when a free option is available
A firm delivery date is attractive versus an ambiguous range (“5-7 business days”)
Testing various price points – while also considering internal delivery costs – is key to finding the sweet spot with your customers
How Fenix uses machine learning to augment data available from the retailer to determine the probability of shipping dates being accurate
The retailer has control of many parameters, based on weather events and seasonality and similar factors
It takes about 1 year of data to get accurate predictions
Ways Fenix can provide additional data about actual performance, which often drives operational improvement and improves bottom-line results
Why Fenix takes a consultative, collaborative approach with each retailer to ensure Fenix is leveraged for maximum impact
View all episodes
By Cuppa Commerce
5
11 ratings
This time I spoke with three commerce veterans who share their experience transforming how small- and medium-sized businesses offer an Amazon-like shipping/delivery experiences and compete with ecommerce giants: Raphael Neff, owner of ChessHouse.com; Akhilesh Srivastava, founder and CEO of Fenix Commerce; and Vince Cavasin, head of marketing for Fenix.
In this episode, we discuss:
How Fenix “democratizes Amazon features for the masses” and helps mid-market retailers differentiate themselves with a better delivery and fulfillment experience
Why delivery considerations/options should appear on product pages and not just in checkout, since delivery is a major purchase consideration
ChessHouse.com has experienced a 37% improvement in conversion rate, much of which Raphael attributes to Fenix
Overall, Fenix customers average:
14% improvement in end-to-end conversion (shopper on product page who submits an order)
38% improvement in cart abandonment rates
21% increase in shipping revenue
Large decrease in pre-purchase inquiries to customer service
The importance of not only presenting enticing free shipping thresholds but also offering “reasonable” shipping upgrades for speedy service
Up to 40% of retailers using Fenix see customers paying for Fenix-calculated, enhanced low-cost shipping, even when a free option is available
A firm delivery date is attractive versus an ambiguous range (“5-7 business days”)
Testing various price points – while also considering internal delivery costs – is key to finding the sweet spot with your customers
How Fenix uses machine learning to augment data available from the retailer to determine the probability of shipping dates being accurate
The retailer has control of many parameters, based on weather events and seasonality and similar factors
It takes about 1 year of data to get accurate predictions
Ways Fenix can provide additional data about actual performance, which often drives operational improvement and improves bottom-line results
Why Fenix takes a consultative, collaborative approach with each retailer to ensure Fenix is leveraged for maximum impact