How Online Sizing Technology Can Boost Conversions: Interview with Nicole Yazolino of Fit Analytics

Ecommerce has steadily developed for decades, and at the start of 2020, the industry looked to maintain its consistent progress. But given the pandemic, online shopping not only increased this year, it grew at warp speed. Experts have long predicted that people would increasingly shop online; the surprise came when this consumer shift took place over months rather than years.

Now retailers are working harder than ever to help people understand 3D products in a 2D selling environment. For fashion and apparel merchants, online sizing technology can facilitate customers in finding the best fit and finalizing their purchase. Further, not only do ecommerce sizing tools help convert more buyers, they can also reduce returns as people are happier with their purchases.

To help retailers understand the impact of online sizing technology, we interviewed Nicole Yazolino, the Global Communications Manager for Fit Analytics, one of the leading developers of ecommerce sizing tools. We’re grateful to Nicole for sharing her expertise and insights about this technology with us.

Note: We also thank Fit Analytics Founder and Managing Director, Sebastian Schulze, for contributing to some of these answers as well.

Command C: In light of the pandemic, fewer people are shopping in person. How is this affecting the adaptation of online sizing technology?

Nicole Yazolino: The adaption of sizing technology actually looks good in the face of the pandemic. The retailers and brands that made it through the initial shock wave have understandably been forced to adjust their retail strategies to ecommerce. We did see a slight drop in purchases at the beginning of the COVID crisis, but in general, we have seen a positive increase in user engagement over the same time period last year.

Companies with liquidity issues are finding it challenging to make new investments at the moment. Those that are in a stronger financial position are thinking hard about their retail strategies. They are preparing for a different shopping environment post COVID-19. For these companies, now is the time to review new opportunities and invest in ecommerce technologies like Fit Finder to stay ahead of the competition.

In general, there is still a level of uncertainty given the economic impact of a future we don’t yet know. However, companies understand now more than ever that they need to increase their market share from offline to online. They have to adjust to the new normal. This shift has been accelerated by the current pandemic.

Command C: What kind of background work should retailers do before implementing a sizing feature into their online store? What do they need to know?

Nicole: Choosing the right sizing solution can be overwhelming, but it is actually a logical process. The most important step at the start is for the retailer to ask, “What problem do I need to solve?”.

The possible reasons are diverse – increasing add-to-cart and buying rates, reducing returns, improving overall customer experience, and demonstrating innovation. Next, retailers and brands need to map out their requirements such as features, integration type, and reporting options.

Once those questions are answered, retailers and brands can make a short list of potential solutions. Then reach out to them for demos and more information. The most critical part of choosing the right solution is to check references. Many providers promise huge impact numbers and even client references without any backing. It’s essential to really challenge the impact numbers and logos that a provider presents to you. The easiest way to do this is by checking a retailer’s website PDPs and code to see whether a sizing solution provider is implemented and being used or not.

Command C: How does sizing assistance help reduce returns? Do you have any statistics to share on this?

Nicole: A sizing solution absolutely helps reduce returns. When a customer receives an item that is the correct size, they are more likely to keep it. On average, Fit Finder reduces returns for shops by 4.4%. However for some shops, the impact on returns is much higher – in some cases up to 15%.

Foot Locker EU and their partner shops in Germany are a great example of the returns impact of Fit Finder. Results from a 6 month A/B test showed that shoppers using Fit Finder on Foot Locker EU’s site had a 13.55% reduction in return rate as compared to shoppers using a static size chart. For Foot Locker’s subsidiaries SIDESTEP, the return rate decreased by 6.61%. For Runners Point, the return rate decreased by 7.39%. You can read more about this in the full case study.

Command C: How do you see the relationship between providing sizing help and customer loyalty?

Nicole: Customer loyalty is correlated with how comfortable a shopper feels with a certain brand or shop – and many factors contribute to this. Very important among those factors is whether or not a shopper is happy with the items purchased. A company can make all the UX, payment, logistics, and returns optimizations they want, but if the customer doesn’t like the clothing they receive, they simply won’t shop with that retailer again.

This is where sizing support plays an important role. When a person gets a size recommendation, the apparel is more likely to fit them. By default they will be more satisfied with their purchases.

But this isn’t the only benefit of a sizing technology. Retailers get shoppers who have never purchased their brands every day. For customers, not knowing how a brand fits can be a real deterrent for conversion. When companies implement sizing technology, they are welcoming all customers, new and old. They’re showing their commitment to ensuring these shoppers have the best experience possible.

Command C: As a developer of online sizing features, how do you strike the balance between getting information from the customer and the speed of the process?

Nicole: We do extensive user testing in both A/B test and focus group scenarios to determine the ideal mix of gathering information and user journey length. We get direct feedback from testers about their experiences using our solutions. In addition, the results from our A/B testing – where we test two or more different versions of the solution against each other – provide hard data supporting which sizing journey length performs better.

The user journey in sizing needs to ask enough questions to give the shopper the feeling that they are going to indeed receive a personalized size recommendation. In our experience, a medium-length journey performs best. If the journey is too short and only asks a few superficial questions, the users tend not to trust it. They don’t take it seriously.

Our A/B tests confirmed that users engaging with a more thorough sizing journey had higher conversion rates than users exposed to just three questions. From two test groups, one on desktop and the other on mobile, totaling 380,000 consumers, shoppers who experienced a medium-length user journey had a 14.4% higher conversion rate on mobile. They had a 6.5% higher conversion rate on desktop than those who received a size recommendation after just a few questions.

Shoppers who completed the questionnaire that asked about height, weight, age, fit preference, body shape, and reference brands, had the sense that enough information was gathered to give an accurate recommendation. These shoppers were more committed to conversion and were ultimately more confident with the provided recommendation.

Command C: How do you determine size accuracy?

Nicole: The data provided by each Fit Finder user, as well as purchase and returns records, are added to our data network pool and drive the size recommendations. Our entire customer base benefits from the aggregated Fit Finder user data. The algorithms also include purchase and returns data from over 17,000 brands and 20 million items. Differing size charts are not a hindrance because the recommendations work on a personalized product level, matching shoppers with items purchased and kept by other customers with the same body-type and preferences.

Command C: How can customers be assured that their sizing information is kept private?

Nicole: All of the information we collect from shoppers using Fit Finder is completely anonymized. It’s not able to be traced back to individuals. Shopper inputs are saved for them in a cookie which they can remove from their browser at any time. After a period of inactivity, the cookies are automatically cleared from our side. When the information enters our algorithms to become part of our recommendation engine, it’s simply pieces of anonymous information. The original user’s privacy is protected.

Command C: Retail companies have had to adjust to a new reality this year. What retail developments from 2020 do you think are here to stay?

Nicole: Pre COVID-19, the forecasted industry consensus put a 5-10 year timeline on the evolution from brick and mortar to online. The rapid shift of economy due to COVID-19 and subsequent consumer behavior has reduced 10 years to just a matter of months.

COVID-19 and the global changes in 2020 have forced consumers and retailers alike to adjust the way they do retail. Shoppers have learned to rely on online shopping even more. Those who may have been hesitant and preferred the in-store experience over ecommerce have gotten more used to shopping online.

Retailers have had to adjust to an increased load on their ecommerce operations. This is from warehousing and distribution to placing priority on a seamless user experience that eliminates as much uncertainty for the customer as possible. In the future, more people will shop online. Retailers will eventually have a larger ecommerce operation than brick and mortar. Getting customers to virtually match with the perfect garments will become top priority. Physical stores will become less about shopping and more about the experience of being there: more events, special guests, in-store cafes, community built around a brand, etc.