Arvind Rapaka

19 Jan 2022

AI In B2B E-Commerce


AI in B2B e-commerce

According to Statista, the global SaaS market that leverages AI and ML is predicted to reach an evaluation of $126 billion by 2025. This clearly indicates the surging interest in Artificial Intelligence and Machine Learning, therefore, attracting tech-tycoons willing to invest heavily in developing infrastructures that can better benefit from this duo. Of course, the foremost practicalities that buzz our mind once we hear “AI” are search engines, virtual assistants, and product recommendations on social media, backed by giants like Amazon and Google. However, just as B2C e-commerce, Artificial Intelligence holds just as strong grounds on the B2B aspect.

As with any emerging tech, AI isn’t the magic sword that slays every challenge in B2B companies. Although we might assume that e-commerce business applications work just the same for B2B and B2C relationships, the silver lining is much broader. One notable example is that for B2C brands, customer retention is much short-term as opposed to B2B. Therefore, B2B e-commerce has its own challenges that should, if not must, be addressed by implementing AI at its core. Interestingly, B2B companies already have rich data sources around their user interaction, tapping into which, by leveraging AI, can adapt to and analyze unique situations in such efficient ways that no human can.

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5 Ways of improving B2B e-commerce by leveraging artificial intelligence

What’s unique about AI is that it’s infinitely scalable; therefore, the more data it has, the better AI-based e-commerce personalization it can offer, further driving the ROI. Although the implementation of AI in B2B e-commerce is still in its early years, it teases its future-readiness and the potential to become the top tool in AI-based recommendations and personalized search. Let’s now look at what aspects of B2B e-commerce will be disrupted the most with AI.

1. AI-powered personalized search and e-commerce product recommendations

If you are wondering how that particular product showed up on your Amazon feed moments after you searched it on Google, you can thank AI. Personalized search and e-commerce product recommendations are faster and more convenient for analyzing shopping interests and predictive searches. As discussed in our previous article on how AI-based recommendations will save you money, AI-powered personalized search allows buyers to find products with ease instead of scrubbing through an entire catalogue of products. Like the previous example, Amazon leads, being the top AI-based e-commerce personalization engine, with 35% of its purchases recommended by AI.

2. Supply chain and inventory management

On top of AI-based recommendations and personalized search, B2B e-commerce platforms have also needed to deploy AI to predict sales and product demand. On that note, I would highlight an article by McKinsey & Company that brilliantly explains how an unstructured volume of big data can be churned down to smart data, which further can be actioned into B2B e-commerce applications. AI can determine which products might be high in demand at specific seasons or certain regions based on previous stats. Not only does it help companies better predict sales and prepare for logistics, but this backbone can also streamline the automation of supply chain management to reduce overhead.

3. Customer segmentation and personalization capabilities

With every purchase made in the B2B e-commerce space, every major company has collected some data in the form of demographic, volumetric, and behavioural dimensions. However, an important question is whether this expanding data chunk can be actioned into B2B e-commerce operations. By interpreting big data, B2B e-commerce platforms can build optimized business dynamics using AI-driven analytic solutions. Similarly, the convenience of AI-based recommendations has simplified personalized searches based on buying patterns. And since the volume of orders is higher for B2B buyers, platforms can implement dynamic pricing for e-commerce conversion improvement.

4. AI empowers your potential customers with information

An insightful study points out that 65% of buyers prefer organic product research without consulting sales reps. Now, this fact is a no-brainer for an online marketplace where AI-powered personalized search is more efficient, and most importantly, more cost-effective. What’s more interesting is that AI-powered chatbots have also allowed B2B e-commerce companies to serve their potential buyers with accurate and relevant info, thanks to enhancements in natural language processing. The more semantic search algorithms, the more precisely they can comprehend queries. The ability to answer unique questions of buyers prioritizes access to pertinent information.

5. Implementing AI into e-commerce automation

By actioning AI-based software into e-commerce automation, there’s no denying that complex or somewhat routine tasks can be efficiently automated. This will save manhours throughout the workflow and provide consistent information to different departments, that too, in real-time. The ability to deliver real-time insights across major channels in an e-commerce workflow can assist B2B enterprises in better optimizing their business systems. On top of real-time business insights, buyer behaviour can tackle inconsistencies across departments spanning from service teams to inventory management and maintain an effective personalized shopping experience for customers.

However, there are still major challenges worth tackling. Remember when I said, “AI isn’t the magic sword that slays every challenge in B2B companies!” AI, especially in B2B sectors, has its unique obstacles. Most importantly, cost-effective alternatives often exist! And given the scale of B2B, how could execs not look for an affordable approach?

Challenges to AI in B2B e-commerce

AI applies as an algorithm or SaaS add-on that churns big volumetric data into smart data, which further can be actioned into B2B dynamics to address business challenges and predict possible outcomes. While AI seems like an “endgame solution” to major enterprise roadblocks, humans must still develop, train, customize and test AI to perform desired actions, which in practicality, may take years to complete. Raising the concern even higher, AI is confined within existing platforms and processes. Many initiatives begin strong but gradually fade away, given that AI, at an enterprise-scale, is still costly, complicated to implement, and often lacks valid use cases or expertise.

Challenge 1: Justifying the cost in e-commerce conversion improvement

Over 65% of buyers have been observed to switch brands because of an unsatisfactory experience, thus, naturally inclining valid use cases for AI towards customer experience. Other aspects in B2B that justifies the need for AI are personalized search, AI-based recommendations, buying patterns, and sentiment analytics. For example, major B2B players like Amazon invested heavily into creating its AI-based recommendation system to interpret its customers.

Challenge 2: Churning big data into actionable smart data

This article by McKinsey and Co gives an insight into the importance of smart data in the age of big data. However, that’s where B2B e-commerce enterprises continue to struggle. One recent report from O’Reilly points out that 15% to 10% of AI developers have reported problems with inconsistent or unactionable data. What’s being missed out by major enterprises is that raw data cannot be actioned; instead, it requires filtering, analysis, and interpretation.

Challenge 3: Building an overseeing team of AI and ML wizards

If an enterprise decides to develop its AI infrastructure from the ground up, it cannot miss out on the importance of data scientists. An experienced team of AI wizards must include AI architects and machine learning engineers to oversee the entire AI project. What’s holding back enterprises from deploying their dedicated team is the expense of keeping this team operational. In addition, the lack of expertise poses an incredible challenge in hiring professionals.

Challenge 4: Varying expectations with AI and risk management

As an emerging technology, AI is still an overly-used buzzword, with industry leaders bearing different concepts and expectations of what it can do. In order to avoid such a mismatch of ideas, stakeholders must be incorporated into devising an AI strategy to get them involved. Similarly, the evolving nature of AI and ML also imposes privacy, security, and safety concerns with vast pools of raw customer data. If unregulated, AI can lead to reduced revenue or even legal action.

The Future of B2B e-commerce is Still AI

If you’re concerned about the risks associated with implementing AI into your B2B operations, you’d be relieved to know that the benefits far outweigh the risks. Artificial Intelligence is all about the integrity and value it brings into

  • Improving upon an omnichannel B2B e-commerce strategy 
  • Predicting stockings and logistics, along with inventory management 
  • Identifying recurring business operations which can be automated 
  • Improving B2B customer experience by refining AI-based recommendations 
  • Quickly answering unique sets of customer queries across multiple channels

Can my B2B e-commerce business benefit from artificial intelligence?

How can medium and small-scale B2B companies can benefit from AI? That’s where CartUp AI leads! Regardless of the scale of your B2B e-commerce business, actioning AI in your operations is just a few clicks away. At CartUp AI, we leverage AI-based recommendations to drive your e-commerce conversion improvement. Visit CartUp AI and book your demo to make the transition to AI-based e-commerce personalization.

— Arvind Rapaka, Founder & co-CEO

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