Amazon.com, a company started in 1994, has a market value of about $1.3 trillion, 2021 net sales of more than $465 billion, and, according to Digital Commerce 360, accounts for more than 40% of U.S. ecommerce sales. And, it is in a massive growth sector, with online sales increasing 50.5% since 2019.
Amazon has achieved this dominance by providing the ultimate shopping convenience to the customer: Just go to its site, type in what you are looking for in the search bar, browse through the possible purchase options, and then buy with one click. Once you order, get it delivered in a day or two, have full visibility into tracking the journey of the order, and return it anytime. It is this ultimate convenience that has enabled Amazon to earn unparalleled loyalty worldwide.
B2B Online Commerce: Explosive Growth in the Face of Complex Challenges
B2B online commerce is now experiencing similarly explosive growth, with businesses from high tech to industrial parts increasingly selling online.
Start visualizing the future of B2B ecommerce and share critical data with you colleagues and clients.
In 2021, online sales on B2B ecommerce sites, log-in portals and marketplaces increased 17.8% to $1.64 trillion, from $1.39 trillion in 2020. B2B ecommerce in 2021 grew 1.17 times faster than the growth of all U.S. manufacturing and distributor sales, according the 2022 US B2B Market Forecast Report by Digital Commerce 360.
U.S. manufacturers grew their combined digital sales 12.9% to $4.104 trillion in 2021, up from $3.634 trillion in 2020, according to DC360’s 2022 Edition of The Manufacturing Report. B2B ecommerce now accounts for a bigger share of all U.S. manufacturing sales, thus U.S. manufacturers are making digital commerce a mainstream channel.
In this B2B commerce space, it is even more important to provide the Amazon-level convenience. Corporate buyers are all busy professionals, often under tremendous pressure. They need the ability to purchase online quickly, easily and by themselves, no matter how complex the products.
According to Sapio Research, the top three challenges these corporate buyers confront are limited product data, inaccurate product information and overly lengthy and complicated checkout.
B2B Customers Need to Resolve these Issues Today
Customers’ frustration levels are high. They have limited time, and they need to find products based on complex specifications on a site with millions of products. However, the primary tool available to them today is search, which is inadequate for this space. As a result, they often need to call a support line, use voice, live chat or send emails to site support – all of which takes time and is expensive for the B2B site.
Buyers need to be able to specify their product requirement spec in English, without having to fill out an elaborate form or browse through hundreds of pages looking for the right items. They need to get the exact product or part numbers, along with the necessary data sheet, drawing, installation instructions and more. And if they miss entering something in the spec, they should be confident that they’ll be prompted for the missing information.
Customer support queries also include installation, configuration and troubleshooting specific configurations.
They also involve finding replacement parts, matching or compliant parts, and mating parts. Whatever their need, they expect it to be easily doable via your commerce site using self-service tools.
Put Your Entire Product Catalog Online
Rather than preview only a few products, showcase your entire product catalog, even if it totals millions of product items. Make it easy for customers and distributors to find any and all necessary information – i.e., products or parts that meet their specs, specific features, pricing, promotions, replacement parts, compatible parts, compliance and regulations, reviews and more. The buyer should be able to compare multiple products in order to make a decision. Even compare your products versus a competitor’s products.
The Limitations of Existing Technologies
1—Customer is looking for a Product with a complex Spec: Consider a customer looking to buy a router with a specific spec: “I am looking for a router with 2 WAN/LAP ports, at least 300 Mbps system throughput, WAAS enabled and price less than $4,000.”
Search technology cannot handle such a complex query. Search finds unstructured documents based on keyword density. In the case of this query written as an English sentence, you need to be able to get pieces of information from structured and unstructured data, then perform mathematical computations on the pieces of information such as “less than 300 MBPS” or “price between $2,000 and $4,000” and then put the pieces of information together to find the answer.
You need a Deep Learning and natural language processing (NLP)-based system that can get the subsets of data from structured and unstructured data, then perform intelligent mathematical computations on it.
Similarly: “I need a router compatible with IEEE 802.11a/b/g/n/ac/ax standards and offers a speed of 1148 + 4804 + 4804 Mbps. I also need a separate network for guests.”
2—Customer is looking for Product Features, Installation Steps or Configuration Instructions — and you need to provide the exact answers: “How to configure QoS on Linksys routers?”
Existing search technology presents the customer with links to documents that contain the requested information such as datasheets, user guides, product guides and the like. But wading through these documents takes more time than the customer is willing to devote. The customer today needs specific answers, for example, in the case of the query above, the exact configuration steps and perhaps an accompanying visual extracted from the pertinent user guide.
In your B2B commerce site you might have a million products with a million datasheets and a million user guides. It is important to be able to pull up the exact answer (in paragraphs, images, tables, lists), display it with the exact formatting as in the source document, and then provide a link to the exact location of the answer in the source document.
If you have this capability, you can offer it to the customer via self-service chat or voice.
It is also critical that the self-service tool be able to pull up answers directly from your original datasheets, user guides and other reference documents. With such high volumes of reference documents and products, there should be no need to tag or customize any document. Otherwise, this project will become too expensive and impractical.
AI/ML systems can provide such capabilities with high accuracy.
3—Click-to-Buy: provide a frictionless path to purchase
Lastly, you need to make the sale. It is critical that the customer be able to buy the products very easily. You need to provide a purchase path with minimal friction — and personalize it so customers don’t need spend much time completing the order. The last thing you want is to go through all the various steps above and then make it hard for the customer to buy the product.
You should observe the customers interactions on your site and be able to transfer them to a live agent if you sense they need help (or are unhappy) — or if you think they are a likely to be a very attractive buyer. There are AI models that can help.
4—Post-purchase support issues: After placing an order, the customer should be able to easily get the status of the order, make a change to an order, or even return a received order at any time. Existing voice bots and chatbots have several limitations, which often necessitate live support that is expensive and often not desirable to customers. Email based support is usually slow.
You also need to have a continuous communication channel with customers to provide reminders about maintenance, complementary products, promotions, new product announcements and the like. Make sure you are always top of mind.
Fulfilling the Promise of B2B
The Amazon-inspired promise of B2B commerce is beginning to be fulfilled, despite its complexity. The below chart shows that the risks of inadequate support not only increase the cost of sale but may also result in losing the customer forever. Fortunately, there have been significant advancements in Deep Learning, NLP and other AI technologies in recent years. It is now possible to overcome these challenges and take care of your customers – just as Amazon does.
In my next article, I will discuss how advancements in AI (Deep Learning, NLP and CV) can be used to solve these pre-sales and post-sales issues discussed above.
About the Author:
Prosenjit Sen is a serial entrepreneur and currently the CEO of Quark.ai, an “autonomous support” platform that uses deep learning, natural language processing (NLP), and computer vision to bring automation to sales support and field support. He was previously employee No. 5 as part of the founding team of Informatica, a pioneer in online data integration technology. Prosenjit is a mentor for the Alchemist Accelerator and the Bay Area IIT Startups Accelerator. And he is the co-author of the book “RFID for Energy & Utility Industries.”
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