56% of abandonments are due to inconsistent data!

and at unexpected costs, according to Baymard Institute.

The basket: where it all comes together - and often breaks apart

The shopping cart is the most sensitive moment in the e-commerce journey.

A product sheet can be perfect, a tunnel smooth, a price competitive...

However, all it takes is one wrong piece of data for the whole thing to fall apart: non-existent stock, wrong variant, price updated too late.

In other words: the problem isn't UX, it's data.

⚠️ 38% of buyers never return after an availability error.

(According to Adobe Commerce)

The unpleasant surprise scenario: 

The most common scenario is that of a consistent experience... right down to the last click:

• The website displays: Available - Red - Size 38
• Customer adds to basket: everything looks good
• At time of payment :
  • ❌ Actual out-of-stock condition
  • ❌ Variant removed from catalog
  • ❌ Price recalculated by ERP

Result: immediate abandonment.

💣 An invisible but massive problem

Behind every "unexplained" abandonment, we often find :

• A poorly synchronized SKU,
• MIP not aligned with ERP,
• Incomplete attributes (size, color, model),
• A price or stock update propagated too late.
• Incomplete attributes (size, color, model)
• Inconsistent mapping between catalog, site and API
• Indexing too slow

These are not bugs: they are breaks in the reliability of the data chain.

And they appear precisely at the most critical stage: the shopping basket.

💰 The measurable consequences of out-of-date product data

1. Loss of income

• +5 to +12% dropouts due to inconsistent data
• Up to 25% lost sales due to poor data quality

2. Additional operating costs

• Support tickets and product returns soar
• 50% of fashion returns are due to a difference between sheet and reality

3. Loss of confidence

• An error on the basket = a broken promise.
• The customer doubts, gives up, doesn't come back.

💸 Why every product error is (very) expensive

Here's how a tiny desynchronization creates abandonment:

• A SKU disappears in the ERP → the PIM doesn't update → the site displays the wrong variant → the basket detects a problem → the purchase is cancelled.
  
• A "red / 38" variation is deleted for a restocking → the product sheet remains displayed → shopping cart impossible → immediate abandonment.
  
• The price is changed in the catalog → the basket recalculates → inconsistency → loss of confidence → abandonment.
  

We're not talking about a UX problem.
We're talking about a systemic problem.

🔍 The key point: data isn't just displayed - it must be consistent from end to end

In a modern e-commerce environment :

• a missing attribute can break a recommendation,
  
• a pseudo-available variant breaks the basket,
  
• an incorrect image breaks the conversion,
  
• an erroneous SKU breaks logistics flows,
  
• a false stock breaks customer confidence.
  

Product data has become a commercial promise.
And every broken promise costs money.

🚀 The solution: Continuous monitoring, validation and correction

Unlike PIM or ERP, which store, organize or distribute data, Data On Duty continuously monitors, validates and corrects it.

Key capabilities:

✔️ Real-time detection of inconsistencies in stock, attributes, SKUs, variants
✔️ Auto-correction before a customer sees them
✔️ Permanent alignment between ERP ↔ PIM ↔ catalog ↔ front-end
✔️ Semantic verification of attributes (size, color, model)
✔️ 100% reliable product tracking

Results observed among equipped merchants:

• 80% reduction in incident time
  
• 4% more revenue from clean data
  
• drastic drop in "unexpected issues" basket abandonment
  

The aim: no nasty surprises at payment time.

✅ Conclusion: shopping cart abandonment is a DATA problem, not a UX one.

Most e-tailers are still optimizing buttons, tunnels, pop-ups and email reminders.

But the real breaking point lies elsewhere:

👉 if the data isn't reliable, the sale can't be.
👉 and if the chosen variation no longer exists, the basket is lost.

In a world where customers expect a seamless experience, and where one click is all it takes to go elsewhere, up-to-date and reliable product data is no longer an advantage - it's a prerequisite.

With an observability and self-correction layer like Data On Duty, every piece of information seen by the customer becomes: accurate, available, coherent, immediately exploitable...

And the shopping cart becomes once again what it should be: the last step before conversion, not the first place where everything breaks down.