How Much Could Claims Automation Save in Peak Season?
How can e-commerce companies make peak season even more profitable? Claims automation.
With order volumes up 2-3x in peak season, the chances of lost, damaged or delayed deliveries also increases. For instance, Sendcloud found that the likelihood of lost packages rises 41% in November and 21% in December.
Unfulfilled orders lead to unfulfilled revenue potential, but automating claims enables you to recoup costs and enhance customer satisfaction.
Take our quick 3-step test to find out how much claims automation could save you this peak season.
Step 1: Look at sales data from peak season 2024
Your potential savings are directly linked to your sales volumes. For instance, e-commerce companies that ship lots of mid-value items (i.e. fashion, electronics etc) via standard parcel delivery couriers stand to gain much more than those shipping a few bulky high-value items via specialist couriers (i.e. furniture, gym equipment etc).
To estimate your potential savings, first find out:
The number of orders sent out for delivery in peak season 2024.
The percentage of these shipments that were lost or damaged.
The average value of the goods shipped.
Example: online fashion business
An online fashion business receives 200 orders per day and ships 6,000 packages per month with an average value of £50 per item. With 6.5% of UK deliveries encountering a delivery issue, the business loses out on revenue from 390 packages (£19,500) each month.
But during peak season, its orders double (400 per day / 12,000 per month) while shipping reliability falls (by 41% in November and 21% in December). As such, the business loses out on revenue from 2044 lost or damaged packages across peak season (totalling £102,200).
Step 2: Look at previous claims history
From Evri to Royal Mail, almost all major couriers offer compensation up to £100 per shipment (depending on weight and goods value). But each courier has different rules, requirements and claims forms, which can be complex and time-consuming to fill in manually. As a result, it's easy to make a mistake - i.e. missing documents, missed deadlines, incorrect inputs etc - leading to wasted time and a low claims success rate.
To work out your claims efficiency, estimate:
The time spent on claims processing during peak season.
The success rate of those claims.
The total £££ recouped by those claims.
Example: online fashion business
Like many mid-sized e-commerce companies, the online fashion business still relies on a manual claims process for lost or damaged deliveries. It therefore takes 10 minutes per claim to:
Review the courier-specific restrictions, item exclusions and claims deadlines.
Compile evidence to support the claim.
Input data into the courier's claims form.
The online fashion business has a respectable claim success rate of 33%. During peak season, that would lead to:
2044 claims x 10 minutes = 3,400 hours (or 141 days of the customer team's time).
33% success rate of 2044 claims = 675 successful claims (or, at £50 per claim, £33,700 saved).
Step 3: Compare the results with Claimit
Given the time spent on manual claims compared to the success rate, it's no surprise that many e-commerce businesses don't see the point in making claims for lost and damaged shipments - simply writing off the cost as a business expense.
But with Claimit's AI-powered platform automatically submitting claims according to each courier's Ts & Cs, we've reduced time spent on claims by up to 80% and have a claims success rate of 90+% in 2025 so far.
For example Claimit helped:
Nutrition brand Bulk reduce claims time by 80% and improve its claim success rate by 4.4x.
Fashion business Oh Polly save 60% of claims processing time and increase its claims success rate by 20%.
Homeware store Emma Bridgewater improve its claims success rate to 80% and save £10k per month.
Example: online fashion business
If the online fashion business used Claimit during peak season, it would recoup 90% of the value of its lost or damaged shipments (£91,980) without burdening the customer team.
