Empower Your Business With The Magic Of Machine Learning!
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Empower Your Business With The Magic Of Machine Learning!
The current behaviour related to product returns will continue and eshops need to take their returns management procedures seriously.
When it comes to online returns, the stats aren’t pretty.
Consumers return 30% of e-commerce purchases and research from Shopify reveals that 40% of consumers buy variations of a product on e-commerce with the intention of returning most of the order.
In many cases, returned items cannot be re-shelved due to the age of the product or some minor wear and tear.
This ever-increasing number of online returns causes huge costs.
While 2020 saw an increase in e-shops that tightened up their batch returns and implemented a more efficient and sustainable approach to managing returned stock, the issue is not being resolved quickly enough.
Retailers need to look carefully at how they manage returns, which will be worth $400 billion this year – not including inventory losses or replenishment costs.
The best plans include strategies before the return, using Artificial Intelligence and Augmented Reality.
53% of the returns were due to size and fit issues.
Of all returns 4% resulted when there were some similar products in the cart.
Similar product does not necessarily mean the same product in different size, color; it may be similar pattern based on the visual characteristics of the product.
An order with the same product in different colors represents 2% of the total orders returned.
In a basket size of more than five products, the return rates are 72%, while a basket with one product has a return rate of 9%.
Return behavior changes with day of the week (beginning of the week, end of the week) and time (morning, evening).
Old products are almost twice as likely to be returned as newer products.
Historical data also shows that return rates for some products are higher than the e-shop average.
These products can be 0.2% of the e-shop catalogue and have returns of 2%.
The search ranking of these products may be last or be removed altogether.
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https://mobiplus.co/ebooks/customer-rediction/
Artificial Intelligence enables real-time evaluation of the customer’s shopping cart against the individual customer profile based on preferences such as suitability, order value and purchase history to predict the likelihood of returning the product with a high degree of accuracy.
The technical control can look for:
・ How many similar items are in the basket
・ Correct size
・ What was the customer’s return rate for similar items in the past?
This information can be extended to a decision engine , with the support of AI, which can make recommendations to prevent return.
Recommendations include offering targeted promotion, recommending better products , free delivery charges even preventing purchase to consumers who serially return products.
For example, it may increase shipping costs as a deterrent or offer a coupon as an incentive to make the purchase non-refundable.
The possible set of actions are:
・ Personalized shipping costs
・ Non-returnable product with additional coupon
・ Options for trial & purchase
・ In case of refund related refund, the money is directly allocated to a wallet which can only be used for purchase again on the same platform
・ Artificially display the product as out of stock and prevent the user from placing this order
Most of these action items require the provision of a return at the basket level, while for the rest it must be at the individual product level.
A growing number of e-shops are applying Machine Learning and Predictive engineering techniques to reduce returns.
These models predict the likelihood of returns before an online order is placed by combining information such as product details , historical return rates and personalized size data.
This prediction allows automated decisions to be made about fees or penalties at the time of ordering, in order to reduce the likelihood of returns.
Artificial Intelligence (AI) is also being used more widely to ensure that customers buy with confidence and that key reasons for their returns are eliminated.
Leading Fashion and Sporting Goods companies are using AI to provide Personalised Recommendations during online and physical shopping.
This may also include. Recommendations on size, based on the customer’s purchase history.
AI is also used to give accurate delivery promises.
When next-day delivery is available, for example, AI will be able to provide stock locations in real time, helping e-shops to meet delivery promises.
Accurate forecasting of product returns before placing orders is critical for e-shops.
Finding return probabilities for millions of customers on the shopping cart page in real time can be difficult.
To address this problem we propose a new approach based on Deep Neural Network.
User preferences preferences and latent hidden product features are captured using feature embeddings based Bayesian Personalized Ranking (BPR)- based feature embeddings.
Another set of embodiments can be used for the shape and size of the body of users that have been captured, using model based skip–gram based model.
The Deep Neural Network incorporates these features to predict the probability of return.
The Artificial Intelligence in e-commerce can help your business and your e-shops to reduce up to 40% of the cost of returns.
Contact us now , to see how the mobiplus shopping recommendation platform can give you the above features and increase your revenue. 30%.
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