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Cuppa Commerce

Cuppa Commerce

News:Tech News

Ep010:  Raphael Neff of ChessHouse.com plus Akhilesh Srivastava and Vince Cavasin of Fenix Commerce on offering an Amazon-like shipping experience

Ep010: Raphael Neff of ChessHouse.com plus Akhilesh Srivastava and Vince Cavasin of Fenix Commerce on offering an Amazon-like shipping experience

2019-05-02
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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: 

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 more

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