How to use artificial intelligence and data analytics to improve your B2B ecommerce

If you are an entrepreneur running a B2B ecommerce business, you are probably looking for new ways to improve your sales and increase your market share. Artificial intelligence (AI) and data analytics can be two very useful tools for achieving these goals. However, it's important to understand that AI is not a magic bullet that solves every business challenge. In fact, AI in B2B ecommerce has its challenges, and more cost-effective alternatives often exist.

Artificial Intelligence for eCommerce.

Artificial Intelligence can be used in many different ways to improve your customers' shopping experience. For example, you can use AI to personalize your website content based on your customers' preferences. This way, your customers will only see products that are most relevant to them, which will increase the likelihood of purchase.

AI is fueling search engines, virtual assistants, and e-commerce product recommendations. For B2B e-commerce businesses, AI can be a tool for greater personalization, improved decision-making ability, and gaining a competitive advantage. However, deploying AI as a business solution is a costly, complex, and uncertain endeavor. Many AI initiatives start strong and then slowly fade away. Leaders and AI advocates fail to identify valid use cases, lack necessary data, don't have the right people, or face a combination of these challenges.

Data analytics for eCommerce.

Data analytics is another useful tool for improving your B2B ecommerce. You can use data analytics to monitor your customers' buying trends and to identify the products that are selling best. In this way, you can adjust your sales strategy to maximize your sales.

AI success primarily relies on the ability to draw insights from big data, yet data is an area where companies continue to struggle. According to an O'Reilly report, 15% to 20% of AI practitioners cite problems with missing or inconsistent data. The amount of data depends on the use case and algorithm complexity. By some estimates, an algorithm requires at least 10 times the amount of data as the number of examples for each parameter in the model.

Alternatives to AI

You might not need AI to solve your problem. There are other digital alternatives that can do the job for less. For many e-commerce functions, a rule-based workflow automation engine can deliver the same results at a reduced cost. For example, a product recommendation algorithm might not need AI, and neither does a personalized quoting or checkout process.

Automated workflows and AI both involve computers to perform tasks. While workflows don't change their behavior, AI will teach itself and take different courses of action. In B2B ecommerce, automated workflows can perform many important functions such as seeking approvals and providing order status.

Conclusion.

In summary, using AI and data analytics can be very useful in improving your B2B ecommerce. You can use AI to personalize your website content, automate sales processes, and analyze data about sales and customer preferences. However, any AI solution must provide value that exceeds the costs of administering data and building a dedicated team. Otherwise, you are working for AI; it's not working for you. Make sure you have a valid use case and the resources to invest to see the project through. Consider alternatives such as automated workflows. The simplest solution is often the best.