With average cart abandonment rates falling anywhere between 55 and 72%, it’s no wonder checkout optimization is the number one concern for ecommerce marketers. But redesigns and A/B tests often fail to move the needle because they focus only on checkout design, and ignore the psychological reasons customers are abandoning their purchases.
In this deck you will learn:
*A systematic process for optimizing your website that addresses the FUD (fears, uncertainties and doubts) surrounding the purchase process
*How to perform a heuristic evaluation on your checkout process for design and usability
*Tips for breaking out of your testing rut
5. 44% Shipping/handling too high
41% Not ready to purchase
27% Wanted to compare prices
25% Price higher than desired
24% Want to save for later
The top 5 reasons are non-design / usability issues
6. 14% Didn’t want to register
12% Felt site was asking for TMI
11% Checkout too long/confusing
11% Website too slow
10% Not enough information
The next 5 reasons are design / usability issues
14. • “For whatever reason, a free shipping offer
that saves a customer $6.99 is more appealing
to many than a discount that cuts the
purchase price by $10.”
--David Bell, Wharton
School of Business
15. Cart abandonment spikes when
cart total is low and when shipping
charges are close to the cart total
It also spikes near the $100, possibly
due to the “triple digit” mark
60. inline validation
• 22% increase in success rates
• 22% decrease in errors made
• 31% increase in satisfaction rating
• 42% decrease in completion times
• 47% decrease in the number of eye fixations
(easier to visually process)
– Source: Etre / Luke Wroblewski
74. • What are you measuring? Conversion rate or
profit?
• How were they presented? Above below fold?
Labeled?
• Did you use the correct price points? What
were the merchandising rules?
75. Positive or negative results depend on how well
you’ve nailed it with the treatment design
What might be influencing your
analysis?
76. takeaway
• Optimization starts with in-head factors, not
on-page factors
• Form your testing hypothesis with user testing
first, then heuristics
• Start with radical redesigns and work from
there
• Interpret test results wisely