Fast fashion is an incredibly competitive space where economies of scale rule. Businesses face the difficult task of achieving this scale with increasing customer acquisition costs and competition. The stores' competitive advantage increasingly lies in their ability to productize well and have a great website experience. 

‍

In tomorrow's world, where everyone will be competing with similar ads and the same kind of data with no cookies, the site experience and the product offering will be the only two differentiators that the merchants in the fashion industry we'll be able to have. The key competitive driver for many apparel stores is to dial into their store experience to know that the cold traffic they are guiding to their pages is more likely to convert.

‍

Furthermore, stores face constant pressure from customers who provide better products for a lower price. And ultimately, this will not be sustainable. So, what Apparel stores need, in this case, is the ability to showcase the superiority of their products and have the customer impression of the products be superior. 

‍

In this case study, we will tackle all merchant problems one by one and share how they are solved for this specific case. Please note that this is for this particular case, and your case might differ as an approach. However, the principles and process would remain the same. We believe that by getting results for one merchant and having a working, proven methodology, we will be able to get results for the other merchants. 

‍

While our goal is to optimize the store as a whole, there are a few items that deserve special attention in this case study. 

‍

1. One of the most important parts for this client was to have a smooth product discovery process for the customers. This process was specifically important because the client needed multiple products versus orders for them to maximize profitability. The shipping costs from China were too high for single-product orders, so they needed to have multiple products. 
   ‍
2. People purchasing multiple products increased the likelihood of finding a product they liked and the category where they understood their sizing. 
   ‍
3. This was not only important for buyers purchasing multiple products. It was also vital for single-product purchasers. 
   ‍
4. We wanted to create a site where shoppers would love to visit and discover new products regularly. 

‍

With these vital goals, we focused on three areas on the site:

1. Showcasing categories on different pages. 
2. Optimizing for search discoveries and discoverability 
3. Optimizing category filters 

‍

Apparel store merchants who are not yet household names will have to drive cold traffic to their stores first. Cold traffic, by its nature, is difficult to convert because these people are skeptical and often just curious. Moving these people over to the line, where they become buyers instead of just browsers, takes a lot of finesse, effort and understanding of these customers.

‍

The good thing here is that we are not alone in this situation. Many merchants have struggled with these issues and have come close to solving them or have already solved them. To improve the conversion rate, CRO Gurus adopted a two-line approach. If you looked for so-called “easy usability wins” that we could reason with commonly agreed CRO principles, that's where low risk and easy to start testing.

Our goal was to put these tests live as soon as possible so we could start learning from our experimentation efforts.

‍

Simultaneously, we put together a competitor analysis where we looked at what the competitors are doing and what they are doing differently. Then, we analyzed the differences between what they were doing that our client was doing better and what competitors were doing better than our client. After that, we move to merge it for one ultimate high-converting store.

It is important to note here that the goal of this competitor analysis was to get initial ideas on the table.

‍

In many cases, we cannot precisely know what will work for this store but can operate in probabilities. So, our goal at this initial stage was to put together a set of high-probability bets that we could then showcase to the client team that conversion and optimization work when done correctly. And it has the promise to keep this going. 

This approach also benefits the client since they will get initial results that build momentum and excitement within the team. When we get everyone on board, we will then be able to drive better results. 

‍

After phase one of the formulation of the testing plan, we dived deeper into more analytical test suggestions based on data. By having data-driven suggestions, we can better predict how those tests would end up performing. And we can also have a higher hit rate with our successes and failures.  

During this second stage, we also looked into technical issues from the store to determine whether the store was performing perfectly on all the different browsers, operating systems and devices to eliminate any bure occurring on major devices. 

‍

We start getting test data in part three of the conversion rate optimization process. From test data, they can further accelerate their learning. Our tests are generally based on either something self-evident from a CRO standpoint or hypothesis-driven testing, where we will test different assumptions. These assumptions can be beneficial in outlining the path of the business going forward. 

‍

Our most important finding from this early stage is that some leftovers from general Shopify themes are causing friction. By simplifying the store, we were able to improve it. In this case, it was not about what to add but what to remove.

Some of these findings are quite surprising to the upper management because they had largely fallen in love with the store functions, but thankfully, they were open-minded enough to see and believe the A/B testing results. Thus, we eliminated some elements on the page, such as gift notes on the cart and the model information on the product page. 

Results from removing the gift notes on the cart page:

‍

Results from removing the model information on the product page:

‍

This phase was a great start for the optimization program because we could prove early, quick wins. As a result, we got free hands to do further optimization. 

‍

Many stores are struggling not only with converting visitors but also with getting these visitors to buy for any significant order value. When Apparel stores often manage to make a sale, it is for one or a maximum of two items. It is a problem because it is challenging to cover the customer acquisition cost with this pricing.

Also, it leads to the lifetime value is lower than it would be in other cases. To improve the average order value, we implemented several different things. 

‍

‍

The first thing we did to improve the average order value was a free shipping threshold. We want them to set this threshold to be quite ambitious because these products were shipping from China. And as a result, their shipping costs are very high. This problem puts a lot of pressure on average order value, which was generally the most important metric for this store. 

‍

So we focused the majority of our efforts on average order value optimization. Nothing different than free shipping price points. We found that the best threshold for us was to use 140% of the store's average order value as the free shipping threshold. 

The idea was to turn it off for those that are only looking for one item since that audience was unlikely to be valuable to us, and we didn't want to focus on just the smallest of sales or be neutral on the first order. The goal was always to make money from the first orders and then have those customers return if they were happy. However, the second order was always treated as a bonus. 

‍

The second thing we did to improve the average order value was to have a discount and tiers. The idea was that the more a person added to their order, the higher the discount. This approach uses the fear of missing out principle, where customers feel they are missing out on a discount by not purchasing more. We found this especially powerful in our target age group of 16 to 24 year old females. 

‍

The third thing we did to improve the average order value was a basket analysis where we identified the products that were not only selling well but were selling for higher average order value. And then, we prioritized these products for the store so that the most revenue-generating products were not necessarily the ones that were showing up first but the ones leading to the higher average order value. 

‍

Additionally, we looked into the advertisements to see which ads converted to the highest average order value possible so that the customer didn't just optimize for the return on ad spend but also for those customers to purchase for high value. Lastly, we implement upsells to help boost this store's revenue further from its current base. Other things on our roadmap are bundles and very specific cross-selling opportunities where there is a perfect product match with another product. 

‍

Due to the client's business model, the client faced a lot of negative reviews. The challenge here was not the store experience but the shipping times and the product quality. Their audience was an audience that is used to having things quickly and used to certain product quality. 

Our client is a low-cost provider, and as a low-cost provider, they do not have all the possibilities to improve their product to the quality of their leading brands. Because of this, we need reputation management. Otherwise, the negative reviews would ruin their conversion rate.

‍

We saw that in review services like Trustpilot, the client was piling up with negative reviews, and the customer reviews on these sites were not balanced because many happy shoppers did not leave a review. We were facing a situation where all these negative reviews were negatively affecting the business to the point where they had a Facebook ad account suspension due to these negative reviews.

This issue was urgent to fix. We have outlined the things that we did to remedy this situation. 

1. The first thing was to put the client in touch with a reputation management expert who helped in removing some of the unjust negative reviews about the brand from popular review sites and also generate positive reviews on these sites so that the perceived experience would be improved. 

‍

2. We took an approach where we would funnel the positive customers into these public review sites to leave a review and funnel the negative, unhappy customers into places where they could leave a private review with feedback.
   
   As a result, we built a honeypot for the negative ratings so that they would not go public, and we were able to showcase the positive ratings so that they would go public. This approach improved the client's relationship and reputation with the low-cost new customers, and the Google searches were way more attractive for the client site reviews. 

‍

3. The third approach we took with this client was to have their own review page. Once they have their own review page, they generally outrank the different service providers offering reviews, such as Trustpilot and others.
   
   Because of this strong domain authority, we advise the client how to best focus this page so that it's optimized for these particular keywords and shows up on the first page of Google. Additionally, we generated a system that would use Google's rich snippets to showcase these results straight in the Google search.
   
   We advise the client to use Google ads to capture the first paid position so that the review sites would not appear on the first mobile view. The client fully occupies the two positions with Google showing review stars without the visitor clicking on the results. 
   ‍

This experience proved that sometimes the best way to improve the conversion rate and the client's results is to improve what's not on the page and to focus on third-party proof that will be critical for the versus journey. 

‍

‍

One of the key ways Apparel stores gather and justify the high customer acquisition cost is by making their money back from future orders. Due to the relatively poor quality of the products, the client is struggling with getting repeat orders. We had cases where the delivery was taking a very long time, and the product quality was questionable. 

‍

As a result, we needed to create returning buyers with the same kind of offers as first-time buyers. We knew that the returning buyers would not necessarily be more convinced of the client than the first-time buyers. The returning visitors would know that it's not a scam site and would not likely be looking for reviews since they already had their own experience. However, we needed to ensure that their store's value proposition remained for these visitors. 

‍

The idea here was to think beyond the second sale and build long-term loyalty. As a result, we decided to launch a VIP program that would have points for loyal customers. The idea with this one was that as the customers purchase more from the store, they could grow their recurring discounts and get better deals. 

‍

For the client, this also worked well since apparel is a very size-specific industry with many returns. So when people knew their sizes and were happy with the pieces of clothing they purchased for the first and second orders, they were way more likely to keep them, and as a result, these orders were more profitable than they were on average. 

‍

Our average order value strategy of bundling different products together also worked fine here because when customers tried several different clothes on and found their size on a certain piece of clothing, the pricing would be so attractive that they would return and purchase again for those items. In a way, the customers were positively trapped into buying versus seeing and shopping at the client store, and the value of these customers grew over time. 

‍

In this case, it worked particularly well because there is a lot of seasonality in the clothing industry. People often circle for themes such as winter, spring, Halloween, Christmas, etc. A lot of that strategy's key points were to create an ongoing buzz of new products that would be launched and featured on the site so that people would be curious to come back and search for what's new.

We wanted to get people into the dopamine loop of continuously finding new affordable products they called impulse buy for attractive offers at their time bound and then get more value that way.
‍

We knew there were better ways to showcase top categories within a key basis. So we implement that learnings from the competitors or the client side. On the search form, we implemented a new search solution for the client so that they could showcase suggested results and be able to understand common typos so that people would still be able to find products even with misguided searches.

We optimized for microcopy on these searches so that people knew what kind of things they would be searching for, increasing the search bar usage further. 

These findings were great news for the client because we know from experience that search bar users typically convert 4x to 5x better than known and bouncing known search users. Additionally, we looked into our categorization to ensure that we not only feature the best-selling categories but also consider the profitability and the likelihood of returning from those product category purchases and becoming an ongoing customer of the store. 

This way helped us take a long-term view with lifetime value in mind and further helped us guide clients to categories where they were more likely to be satisfied, leading to an improved reputation as a side project of this change. 

‍

With price-sensitive buyers and tight margins, getting pricing right can make or break a business. Too many merchants just assume cost + margin pricing without properly testing it. We’ve consistently found that getting pricing wrong by 10-20% can already make a huge difference and often is the difference between a stagnating & growing business.

With our price testing, we aim to understand all the elements that go into the customer lifetime value and profit margins and find the best possible combination of the right price for the right optimized long-term profitability of the client. This idea is based on the understanding that not all purchasers are as valuable as others.

‍

Merchants have largely different marketing structures based on how likely the people buying are to return to the store. Generally speaking, the more likely people are to come back to the store, the less the initial margin matters. And the more likely they are to return to the store, the more likely we should be to sacrifice the margin on the original initial order. 

This case was largely reversed for this client, leaving us a simpler model than an average apparel store merchant, where much of their revenue is generated from a long-term perspective. One challenging thing about price testing is that getting an adequate sample size with large collections can be very difficult.

This is why we group these tests into different parts to test a collection with a different pricing multiplier than another collection. This way, we can see the relative performance of different target groups. This test leads us to find the client's optimal overall price points while keeping their long-term profitability in mind. 

‍

One of the most important touch points in the buyer's journey is their touch points over email. Because of this, we don't want to just focus on generating revenue from the site as we would be completely ignoring the possibilities that we have in the ongoing market, as well as to save orders with abandoned cart emails and introduce people to the brand with welcome sequences. 

‍

According to Klaviyo analytics, the store made 20% of its revenue on flows and 4% from campaigns leading to a total email revenue of 24%. This data was mostly driven by development series, which contained a discount code and attributed a fair bit of revenue to the flow. 

‍

Unique discount code implementation

To better protect the client against discount code app suggestions. We implemented unique codes for discounts so that people would not be able to share them and use them without subscriptions. This also stops discount aggregators such as Honey or Microsoft Edges native coupon integration from sharing the codes, thus protecting margins.
‍

Smart sending 

Third major change we made for that account was to implement smart sending for the price of abandonment flow. This setting enabled us to skip through some emails that had already received a discount code. And it also ensured that we were not pushing too hard on emails because there might be overlap with the other emails in the sequences. 

‍

Additionally, they added a second email to this flow to increase revenue if customers didn't bite at the first opportunity. This second email enables the brand to be on top of mind with minimal downside. So people who were browser abandoning are unlikely to return to the store otherwise. 

‍

VIP flows

The fourth thing we noticed in the account was that the client didn't have a specific VIP flow. And this was one thing we wanted to do holistically where the site would align with the email flow. And the VIP part would not just exist as an email but also as a VIP program on the site. This email gave birth to the loyalty program that was later on expanded to be its own section on the site.

Many people who have purchased will have a transparent way of seeing how they can benefit from the VIP program, which improved the mobile conversion rate by 0.6%. 

‍

We implemented a few new email flows because the client was missing out on revenue and results by not having these few critical flows. The flows implemented were: 

1. Feedback flow. There's a lot of revenue lost by not utilizing the existing customer better. They had a previous engagement with the brand purchasing the product. However, these customers had gone cold, and we didn't properly manage customer retention and lifecycle marketing for these clients. 
   
   As a result, the store was leaving big piles of money on the table, and we decided to implement a feedback flow to encourage lapsed customers to re-engage with the brand. For this, we implemented a discount ladder which gave people an incentive to come back even if they had first used a discount and purchase.
   
   Now they have a similar offer; they will make their second or third purchase. We decided to justify this with various sensible reasons so that they didn't discount the brand value from its current state. 

‍

2. Post-purchase flow. This flow can impact the business because these emails help increase customer retention by turning the subscribers into loyal repeat customers, which shifts this by setting the right expectations and encouraging people to understand that there's care and attention put into that brand. 
   
   By sending emails right after the customer makes a purchase, the engagement shows how valuable and appreciated they are for the business. And we can also showcase other recommended products and eventually request feedback. This email flow also helps alleviate some negative reviews, as it is more difficult to be critical of someone or something you have a relationship with.
   
   So by letting the customers know that we care and be up trying they are less likely to judge us harshly in later reviews. 

‍

3. Cross-sale flow and reviews. We mapped out a strategy for selling products related to each other that the customer has just purchased or is buying. And these products generally belong to different product categories but are complementary. Klaviyo has a built-in dynamic revenue flow, which helps display the previously purchased products.
    
   The customer can then easily see what those products were and find related products from the site itself. Our long-term goal is to bring this to the flow, where we seek solutions to enable cross-selling or post-purchase emails better. 

‍

We created an SMS subscriber segment to identify better people who had opted into SMS to see the effectiveness of that opt-in and all the subscribers at once. The result combined with our welcome series, where new customers would also receive SMS to welcome them to the community.

Both special care in today's SMS to create a good impression because we know it will be one of the most opened SMS from the brand, and as a first SMS, we wanted to make sure that it doesn't hurt our brand reputation. 

‍

Additionally, we collected an SMS consent checkout because getting people to sign up at the checkout was one of the best ways to grow the SMS subscriber list. And it was an easy way to ask for SMS consent from our customers and potential customers without intruding on their shopping experience.
‍

We also upgraded the abandoned cart flows to include SMS because this is an excellent tool for sending reminders about abandoned items, and it's more likely to be opened than email, given our target demographic. Unfortunately, we can only send this to the opt-in subscribers. So there was a split into flows depending on which category they would belong to. 

Generally, we wanted to reserve SMS messages for the abandoned cart and post-abandonment flow. So keep our SMS reputation good, and they'll prevent carriers from placing us into a suspicious sender category and filtering the messages they are sending. 

The overall goal of the SMS strategy was to use it sparingly since it is much more intrusive to inclined customers and focuses on providing unique value to SMS subscribers. As a result, we focused on utilizing sales for flash and limited-time sales, seasonal promotions and products, alerts for new releases, early access to sales and releases etc and exclusive and special informational messaging. 

‍

The vast majority of stores are smart to start with just one site, language & experience because this approach helps to validate the business model & to move ahead quickly. Eventually, though, this approach may become quite limiting, and with the increasing traffic, there will be a growing upside to offering more tailored experiences. 

Also, the store grew, and our upside of adjusting styles to different continents & being able to offer local experience started to increase rapidly. We calculated that there were certainly some conversions lost due to being unable to offer local currencies at the checkout (a technical limitation specific to this client's business), as well as the stylistic differences.

With our in-depth analysis, we noticed that the best-selling products varied between the US and Europe, where the European market was more about Fashion (especially UK, Italy & Spain), and the US market was more casual, with more emphasis on BOHO & LA style. This ultimately dictates the optimal approach between the continents.

The US generally prefers a more laid-back, simple and wearable outfit. At the same time, Europeans focus more on sophisticated  & striking clothing.

‍The main benefits of the multi-store approach for this client:

1. Offering a perfectly local experience.
2. A/B testing with two stores simultaneously (given sufficient volume).
3. Purchase power parity pricing opportunities.

‍

After launching a sitewide A/B test on the cart page and the product page where we removed any elements that causes friction and delays on the customer buying journey such as additional information box and model details, we got the following results:

‍

‍The additional information box was removed, therefore users could focus on the finalizing the purchase, adding more products to their cart.

‍

Also, from a transactional perspective, because we removed friction, users made more transactions on the cart without the additional information box.

‍

We eliminated elements that keeps user attention away from focusing learning more about the product.

‍

While it may seem that a lot has already been accomplished for this store, we still believe there is a lot to do to get this store from 8 to 9 figures. We still have multiple things to implement from our optimization playbook, and we want to keep up the momentum beyond what we would consider obvious changes & improvements.

‍

‍The next significant opportunities on our roadmap are:

1. Maximizing the country sites for increased conversion rate.
2. Launching a proper A/B testing program (so far, we've purposefully been very lean with this approach).
3. Utilizing lifetime value modeling to move beyond the dependence on first-time orders for us to achieve higher LTV & more recurring customers.
   ‍

We are yet to tackle some of the more obvious opportunities, such as:

1. Email popup A/B testing.
2. Price optimization & testing.
3. Region-specific improvements.
4. Menu navigation menu improvements testing.
5. Account page improvements for the logged-in section (LTV optimization)
   ‍

Overall, this engagement shows how powerful the post-traffic optimization approach can be when implemented in synergy with the business goals. We've found that the best results require involvement on the CMO or CEO level for quick decision-making & a competitive organization willing to move proactively toward improvements.

‍

During this engagement, we worked together with the CEO meeting him bi-weekly with our sprint-based approach. This engagement has been a game changer for the business, enabling them to compete with major fast-apparel retailers such as Shein & Fashion Nova and helping them to establish and economies of scale.

‍

Going forward, the client must be able to utilize this growth which enables them to achieve a lower cost structure, helping them to keep their prices competitive to attract more customers, thus enabling them to drive more traffic to increase the speed of this loop.

‍

In our experience, apparel is one of the most competitive, hardest-to-master industries, so we are pleased with the results. However, the competition is fierce, and it is a rapidly evolving world, so we believe that this optimization effort is not something that should ever stop.

‍

If you're interested in working with us to help grow your store, we'd be delighted to explore if it could be a match. Just fill in your details on our application page, and we'll contact you within one business day.