In a world where consumers have access to a multitude of choices, trust has become one of the most valuable currencies. Before making a purchase, buyers often seek tangible proof that the product or service is worth it. This is where “social proof” comes into play. This powerful force, based on the experiences and opinions of others, significantly influences purchasing decisions. There is now an effective tool that allows a customer to be recommended their ideal clothing size. At FITLE, we offer a sizing solution that has already proven itself.
Size selection, a puzzle for multi-brand e-retailers
Size recommendation is an essential step in the clothing sales process. Indeed, the customer expects personalized and truly tailored advice from the seller. If this is not the case, the sale does not happen. Logical!
In the case of a purchase in a store, the glance of the salesperson should allow them to suggest the most suitable size to the customer.
Indeed, the e-retailer cannot rely on simple expertise and must rely on a good estimation of size for their clientele, asking questions and offering choices that might align with the tastes and preferences of the people in front of them.
Thus, if the store is mono-brand, size recommendation is facilitated.
The customer quickly knows if the products from a particular brand tend to fit large or small, as the store offering its unique brand highlights enough elements for the clientele to know whether a certain garment is likely to fit them or not.
Thus, even online, the process remains the same. In other words: by continually buying from a brand, the final customer eventually learns the ropes.
However, for a multi-brand e-retailer, the question of size recommendation can quickly become a headache because, indeed, between customers’ sometimes inaccurate estimates and the peculiarities of the different brands presented, as well as wearing preferences… it’s not easy to navigate size prediction.
In other words: a salesperson in a multi-brand department store will have more difficulty advising their customers well than a salesperson in a mono-brand store.
A major commercial challenge
That’s why a clothing seller cannot overlook an effective size recommendation strategy.
And this is even truer for a multi-brand e-retailer. Thus, size prediction must be as effective as possible to avoid numerous issues. Otherwise, the risks for the (re)seller are quite significant. Let’s list some negative consequences of poor size prediction.
- The customer hesitates, is not quite sure of the size they should choose, and ultimately abandons their purchase. This is a lost sale.
- The customer thinks they have ordered the right size, but upon receiving the item, they realize that the garment does not fit them. They will then return the product and request a refund. It’s also possible that they will leave a negative review on the site. This results in a lost sale, a disappointed customer, return and restocking fees, and a damaged e-reputation.
- After a bad size prediction experience, it is very likely that the customer will not make further purchases on the site. But it is also possible that they will discourage their friends from using the site. Your e-reputation is affected.
Loss of customers, additional return costs, bad word-of-mouth… In a sector as competitive as online sales, this can quickly become very problematic.
That’s why it’s necessary to have a effective size recommendation system, which can help any e-retailer, regardless of their positioning.
FITLE offers you a reliable multi-brand predictive tool
With our expertise in sizing, we have developed a size prediction solution based on social proof.
How does it work in practice?
We have developed an algorithm that compiles and analyzes the morphological characteristics of all customers who have ordered from a multi-brand site.
This data includes size, weight, age, and morphology… All these elements are then associated with purchases made, product returns, and reviews left… A gigantic database is thus gradually implemented, allowing us to match a morphology to a size recommendation for each product sold on the site.
And the more orders and recommendations increase on the e-retailer’s site, the better the tool performs. Unlike mono-brand, where our tool matches the retailer’s size guide with a typical database designed for this purpose, in social proof, the concept is different.
Thus, based on the data entered by the customer, our software can recommend an ideal size. It will do this based on purchases made by customers with the same morphology. The recommendation is therefore not simply based on the size indicated by the brand, but on the satisfaction of previous customers.
The richer the database becomes, the more effective the prediction through social proof is. So that your customer can make an informed choice, our FITLE prediction solution is presented as a percentage.
This figure reflects, after data analysis by our AI, the probability that this size will suit them.
Immediate benefits
Size recommendation through social proof has already been adopted by many multi-brand e-commerce brands. It’s a rational choice, as the advantages are numerous.
The final customer feels reinforced in their choice since the size recommendation is based on data from real customers. It no longer matters that one brand fits small and another fits large.
“As a buyer, I know this garment will fit me well because people my age, weight, size, and body type have been satisfied with the size recommendation”.
With this solution, you will thus increase your conversion rate and avoid purchase abandonment caused by uncertainty about the size to choose.
Another advantage, the return rate decreases drastically. Size selection based on objective data analyzed by AI means there is very little chance that the ordered garment will not fit. Therefore, you reduce your costs and increase your customers’ satisfaction.
Retaining customers through social proof
Social proof and loyalty programs are powerful tools that uniquely complement each other in the world of marketing and online commerce.
Social proof is based on the fact that consumers tend to trust the opinions, recommendations, and testimonials of their peers when making purchasing decisions. Loyalty programs, on the other hand, aim to strengthen the bonds between customers and a brand by rewarding loyalty and ongoing engagement. When combined, these two elements create a winning dynamic. Loyal customers can become important brand ambassadors by sharing their positive experiences with their network, thereby reinforcing social proof.
In return, loyalty programs offer exclusive benefits to regular customers, encouraging them to continue supporting the brand. This synergy between social proof and loyalty programs can help businesses build stronger relationships with their clientele, boost trust, and encourage the ongoing growth of their loyal customer base.
This social proof within loyalty programs can be realized through actions performed by customers to promote a brand. For example, sending photos with the product, following the brand on different social media, or responding to a satisfaction survey. Through this, customers will receive loyalty points allowing them to benefit from advantages on their next purchases.
Social proof has revolutionized the e-commerce landscape by providing invaluable trust to consumers. In a world where choices are plentiful, reviews and experiences shared by other buyers play a central role in decision-making . Recommendations based on social proof, such as size prediction, offer consumers precise and personalized advice, thereby enhancing the quality of their online shopping experience. By wisely combining social proof with effective loyalty programs, businesses create a virtuous cycle that fosters trust, loyalty, and long-term growth.
