As consumers have increased their digital spend, their expectations of online services have also grown. Speed, transparency and convenience at every step of the shopping journey are now critical differentiating factors that sway consumers from one retailer to another.

Meanwhile, the resources that facilitate a seamless delivery service are tighter than ever: available warehouse space is scarce, especially in key distribution zones near ports and major cities. Carriers are stretched thin and pass along rising costs to brand clients in the form of surcharges and shipping delays. Customer and regulatory scrutiny over business practices is increasing and the impact of delivery is too significant to avoid further focus.

On the consumer side, bracketing — the practice of over-purchasing multiples of a certain item with the intent of returning the undesired excess — is becoming increasingly commonplace. According to a 2022 survey from Statista, some 63 percent of e-commerce consumers in the US bracket their online purchases, citing reasons like unclear sizing and fit as well as an inability to try on product in-store. A higher rate of bracketing means a higher rate of returns — in 2021, US shoppers sent back about $218 billion in merchandise they bought online, up from $100 billion in 2020, according to data from the National Retail Federation.

Advantageously, machine learning and artificial intelligence (AI) solutions now represent a real opportunity to increase service standards and customer satisfaction while decreasing waste and emissions. Smart models can create smart insights, and today, depending on whether the focus is on speed, sustainability or cost, retailers and brands can model their end-to-end distribution networks. About 60 percent of retailers have said they are making changes to existing returns policies, with fewer promising free returns, according to a survey of retail executives by return management solutions company GoTRG.

What is more, advocates believe a seamless experience receiving and returning orders could garner lifelong loyalty among customers. Post-sale care is now one of the most effective levers in nurturing community and brand loyalty.

ShopRunner’s Brittanie Knezovich, Vanessa Rodriguez and Melissa Zinzi, alongside BoF’s Robin Mellery-Pratt, hosted a roundtable event at the Nine Orchard Hotel in New York, where a group of market leaders discussed how AI solutions could increase customer service levels, drive performance and accelerate the adoption of more responsible business practices.

Executives in attendance represented Fendi, Puma, Skims, Mytheresa, Mario Badescu Skin Care, Adore Me, LoveShackFancy and Diane von Furstenberg. Now, BoF shares anonymised key insights from the conversation, which was conducted under the Chatham House Rule.

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Meet Consumer Expectations Through Tech Innovation

Brands have long recognised “The Amazon Effect”, whereby consumers’ expectations of immediate, gratis delivery schemes pose a serious challenge.

“We want to deliver [product] like food, [but] there is a reason why brands do not do it. It’s actually quite hard,” said one event attendee.

“Consumers now expect [delivery] in one or two days. [...] And we are getting close to serving the customer in an instant fashion, but we are [also] moving to a time where profitability matters. We heard an interesting titbit from a consumer the other day, [who said] ‘I did not order because I wanted to order 10 sizes and try them all’, and we were talking about having lost that customer. In the back of my mind, I’m thinking, ‘do I want that customer?’ At the end of the day, we are looking to long-term value (LTV), we are looking to loyalty, we are looking to strong customers that drive your brand.”

Once [e-commerce] services and the adoption from our store associates started growing, clients were expecting the same level of service that they were having in store.

To focus on consumers who make the core of your business, it is imperative to cater to and personalise their service, which today is becoming increasingly attainable using AI.

“AI will help us tremendously in terms of zooming in on individual customers, and it will be a tremendous help in customer service, customer journey and experience — keeping a customer happy,” said one guest.

Interlink Shopping Channels to Reduce Returns

With the reopening of physical retail spaces and the plateauing of e-commerce, fashion companies are advised to use technology as a connector between channels to mitigate returns and amplify consumer experience.

One brand representative said: “We have been spending a lot of time on increasing services whereby someone can shop from a store’s assortment online. And one of the things that we realised once these services and the adoption from our store associates started growing is that those clients were expecting the same level of service that they were having in store. They did not want the four to five-day average shipping that an e-commerce client who does not have a relationship with an associate had. So we had to develop our operations [to include an] e-commerce fulfilment warehouse and shipping from store.”

A seamless omnichannel framework also affects customer LTV, as one attendee noted: “The moment we were able to decrease shipping time by 50 percent, we were able to decrease return rate by a significant amount, and the repurchase rate of clients goes up. These store loyal clients are then asking their associates, ‘Can you just send me links, I do not want to go back to the store.’ And that is when the LTV of the client goes up — when we marry the two channels.”

However, that strategy poses further logistical challenges.

“When the store has to manage 30-40 boxes a day, how do you do that?” questioned one guest. “It is getting to be ridiculous [...] For us, the most important thing is to meet our customers’ expectations. If we tell the customer [to expect delivery] in two days, let’s make sure we communicate and we deliver in two days.”

Use Deep Learning Models to Raise LTV

“The biggest cost, the biggest headache, and [what] returns challenging is reverse logistics,” said one event attendee. “When you tie up all the inventory, when you take [product] off the site, the cost of processing back [...] how do we deal with that? Do we charge for it? That is another question — is shipping important in the experience?

“We did some A/B testing about the checkout, your conversion rate is higher, but your LTV is absolutely not higher. So you may win today, but you are not winning in the long-term because it does not make a difference — it is about managing expectations.”

Managing said expectations becomes easier with AI-powered deep learning models, as trialled by one attendee: “We have implemented deep learning models to protect our delivery dates, beginning at the shop cart, and then it updates and changes as the shipping conditions change throughout the journey.”

The moment we were able to decrease shipping time by 50 percent, we were able to decrease return rate by a significant amount, and the repurchase rate of clients goes up.

The technology also helped mitigate returns, as the attendee said: “10 percent of your customer-base is responsible for 40 percent of your return. No one needs $400 of [product] at the same time, so we catch this kind of behaviour and deny these kinds of orders through our UX.”

Another guest noted that “the minute [you choose] to return a product, you are offered the opportunity to exchange. The moment you get your return label, you get the store credit back on your account, and you can place the order right away. You don’t need to wait to get the product back. [The idea of] an instant exchange is split across the board — I have spoken to retailers who are totally against it, and I have spoken to retailers who are so focused on creating another conversion, they want it in that same moment. It is an interesting conversation.”

Obtain and Analyse New Sources of Data

“One of our [main strategic focuses] is to really learn our consumer,” said one guest. “50 percent of our team in the US is personal shopping, but it is not just making recommendations like ‘Oh, you might like this dress’ — we physically are in the field.

“It is almost like a scrappy grassroots strategy where we try as hard as we can in concentrated regions from Connecticut to Arkansas to Park Slope to get to know our customers. And the more that we get to know them and they get to know us, we have been able to decrease our returns because the recommendations or the sizing that we are sending is more accurate.”

The resources that go into obtaining an intimate familiarity with your consumer base bring about ample amounts of data that can be used to manage consumers’ expectations and adapt accordingly.

“I see value in post-purchase relationships,” said one guest. “If I have the data and I understand that this person did this thing at this time, then I can anticipate those meetings as we go forward, and that is super valuable to me.

“Mass data and understanding how I can anticipate the needs of a larger community and say, ‘Okay, here are sports-attuned consumers, or here are more fashion sporty streetwear consumers,’ or whatever kind of groups that are crossing over with [our brand], that is interesting to me, and that could be tremendously valuable if we could crack into it, the conversation about AI [will move beyond] the knowledge of a good merchandiser. We could be more powerful in servicing consumers and their needs if we were collaborating and understanding what those needs were.”

This is a sponsored feature paid for by ShopRunner as part of a BoF partnership.