Unleash Omnichannel Capabilities: Main Technology Challenges
Companies that don’t prioritize off and online integration won’t thrive in the digital era. It’s not just about creating a remarkable experience; it’s about efficiency, allocating resources on behalf of the entire company regardless of channels, increasing the group’s GMV as a whole, and decreasing operational costs.
This article will consist of three parts. In the first one, I’ll point out and break down the main challenges I’ve encountered throughout the last ten years of my career. I’ve put together our customers’ main pains, not just in Europe but in the US, Asia, and Latin America.
In part two, I’ll present how VTEX is excelling in the art of deploying efficient ecommerce operations worldwide, delivering comprehensive distributed order management and marketplace capabilities.
The last part will bring some of our prominent cases, companies that have implemented VTEX and are now thriving in connecting both worlds. For example, during the pandemic, some implemented new strategies such as Uber delivery or drive-in pick-up within a couple of weeks.
Are you ready? Take a breath, and let’s start.
Inaccurate delivery time, or even absence of estimation
Nowadays, every ecommerce platform claims to provide omnichannel capabilities. Still, when analysed in-depth, most of them only offer the option to “upload a tag” to display on the front-end—for example, Shipping-from-store: 4 business days. There isn’t a function that calculates delivery time accurately; it is just a label.
This is a crucial issue because it affects the implementation of strategies such as ship-from-store. Check the example below:
Picture a company that works with shipping-from-store from two different locations.
Although Tom is near the London store, the system cannot estimate the delivery time, which is only one hour. instead, it will promise four business days.
This also affects Jade, who is in Manchester. Even though the London store could ship faster than the one in Bristol, the system’s inability to accurately calculate delivery time led it to display the worst-case scenario.
The fallback strategy is another critical sign of lack of logic.
Let’s say that Tom wants to place an order, but the London store doesn’t have the product available. In this case, the system would promise an estimated delivery time of 5 hours. So, shipping from the Bristol store to Tom in London, the worst-case of 4 days doesn’t repeat.
But, how does this impact your company? Well, on average, our customers experience a conversion rate increase of around 17%*! This means that the store brings more money to the table by accurately calculating the delivery time.
The ecommerce platform wasn’t ready to deal with multiple fulfillment locations
This is probably one of the main missing features of most ecommerce platforms in the market. Imagine the following scenario:
- Fashion omnichannel store;
- Forty physical stores across the country;
- Twenty of these stores are located in London.
You are searching online for a specific product with the following characteristics:
Style: regular fit.
Due to Black Friday, this product is out-of-stock in the main warehouse, hence the online channel.
How likely is it that one of the forty stores will have the product and ship it within one day? How likely is it for one of London’s stores, near your address, to have the product and send it to you right away? I’d say highly. In this case, you might lose sales simply due to poor communication among the fulfillment locations/providers.
Now, let’s imagine a more comprehensive case:
This time, Jade wants to buy three products from different categories, and each of them is shipped from different locations.
On the right side, we can see that the product is available for shipping, and it takes two days to be delivered.
On the left side, the desired piece of furniture is not available, so it needs to be bought. It will take 30 days to arrive at the location and four more days to be shipped to the customer—a total of 34.
The last product is not available either, but the supplier can meet the demand within two days. In total, three days.
Why is this so important? Our customers acting as B2C and Marketplace, on average, sell over 22%** from third-party locations, suppliers, sellers, and so on.
Manual procedures to split orders and allocate them
This is one of the most common problems I have seen, and it indeed is a blocker for any kind of growth since it has a significant impact on the store’s efficiency. Even in the UK, some companies still have all orders placed in a centralised system, manually checking whether they have the products available to deliver to the customer or, if not, the need to make a transfer from the central warehouse. All this is still done on the phone or via email.
In 2019, I visited one of the leading suppliers in the UK’s building and fence market. The company had more than 40 stores, and they had implemented a process similar to the described above. They had two people allocated to check the orders, call the stores, input information into the ERP, etc.
Imagine what things were like when these same stores saw the sales soar five times higher during the COVID crisis. They probably allocated five times as many people just to check orders and assign them manually. We are talking about a company that sells less than £10m online each year.
But things could be worse. I remember a big pet shop in Spain that had on its checkout a tag reading “to be confirmed” for Click-and-Collect orders. They complained that less than 1% of the orders were click-and-collect, which caused them to not invest in this strategy. The truth is that they had the same internal process of receiving, confirming, and allocating orders manually.
External OMS: a leap to new problems
To solve this equation, companies generally integrate external order management systems into the checkout process. Therefore, they can automatically allocate and split items accordingly. However, this strategy brings a couple of challenges:
When adopting an external OMS, companies must rely on “real-time” calls to fetch the necessary data to proceed with the purchase.
Dealing with a vast number of requests per second is a trivial task for VTEX, CommerceTools, SalesForce, and other platforms. On the other hand, external OMS might have problems maintaining uptime during peaks.
During a conversation I had with Robert Werkema, one of our solution architects in the United States, he said:
“I was working on the implementation of one of the biggest retailers in the UK, and we needed to rebuild the checkout architecture twice within six months after the go-live just to solve SLA issues.”
Too expensive, don’t you think?.
Responding quickly may be more challenging than being online. Only having a responsive OMS is not enough. The process will have to take place in less than 3 seconds; otherwise, the checkout conversion rate may be affected.
Let’s remember that your conversion rate decreases by up to 2% for each second of slowness. It’s not me saying it; it’s this Walmart analysis. Now, imagine a Black Friday scenario in which the number of requests increases by 50 times in a matter of seconds. Will the OMS be able to handle it? I have my doubts.
In 2020, one of our main customers, with over 5 million orders a year, had performance issues during checkout. Due to the high number of items in each basket, customers were spending more than 16 seconds on average on the basket page. Whenever an item had the quantity updated, the entire basket was recalculated.
After implementing VTEX Smartcheckout, a stand-alone approach with high availability and fast response time, the average dropped to 5 seconds, and the conversion rate increased by 12.5%. Considering the size of this retailer, we can get an idea of how much money they are making now.
The combination of these two items leads us to much higher implementation and maintenance costs. This is always hidden by any vendor who says, “just integrate the platform with an OMS.” Even after the integration is done, the duplication of the infrastructure to support “real-time” calls will require much higher investment rather than a stand-alone solution.
To illustrate this example for one of our customers, I created this spreadsheet. This is a real case:
As you can see above, we used Google Analytics to understand how many times we would need to call the external OMS to get each item’s stock availability on each page. In this case, we talked with a grocery company, so the numbers of cart and checkout items are high.
Due to their internal architecture, they expected caching 70% of the requests, set this threshold themselves, so only 30% would affect them. The final number is 7,227,463.
Now, let’s imagine the opposite scenario, in which the ecommerce platform can store each item’s stock in each fulfillment location. In this case, instead of having a “real-time” request to the external OMS, requests happen only when an order is placed, or the stock is moved among locations.
After analysing this scenario, we reached the following conclusion:
The table on the right shows the average number of orders per hour within a day. We assume that each basket contains approximately 22 items, just like on the ecommerce website.
As you can see, all stores had 161,331 orders placed and a total of 3,549,282 items. Therefore, the ecommerce platform would have needed to be updated 3,549,282 times.
That led us to conclude that they would need an infrastructure to support 105% more requests, 3,549,282 against 7,227,463. I remember the time when the CFO said that the TCO would increase by approx 0.31%.
The examples I’ve shared above are real, from my personal experience with more than 500 enterprise clients worldwide. Some of them are simple but show exactly the main challenges companies face to integrate on and offline channels.
In the second part of this article, I’ll explore more in-depth what VTEX has been doing to overcome these challenges and what strategies have been implemented.
Until next time!
* Analysis based on eight online stores right after migrating from the previous ecommerce platform to VTEX.
** Analysis done with over 50 clients using VTEX platform integrated with multiple fulfillment locations.