Recent advancements in data collection and machine learning algorithms demonstrate that AI is more than just a smart assistant. According to Gartner’s Emerging Technologies and Trends Impact Radar for 2021, a new breed of virtual assistance is on the way, promising brands better personalisation, decision-making ability, and by extension, increased sales.
Like any new and exciting technology, AI is getting a lot of attention in the B2B eCommerce space. As customer behaviors and purchase patterns change, B2B brands are rethinking their business strategies. While some are adopting digital selling channels for the first time, others are addressing newfound challenges with transformative technology.
Looking through the fog of AI
The definition of AI is the application of computers to process large amounts of data and predict results, thereby simplifying the experience for customers and back-office staff. A handful of B2B eCommerce brands are already using this technology to offer product recommendations, better market to customers, or predict likely outcomes using collected data.
Today’s challenging sales landscape
Last year we saw B2B sales processes change to reflect new realities. Digital interactions took the lead, replacing traditional sales models and deeming old methods obsolete.
B2B eCommerce brands hope AI-powered tools can help them leverage new situations, past experiences, and their data to navigate the unpredictable market. At the same time, many of them don’t have the right corporate culture in place and little idea of how implementing AI will benefit them.
Leaders and business owners, for example, struggle with maintaining business alignment. Since AI is inherently different from traditional software development, adopting AI strategies requires different management skills and team competencies.
Meanwhile, data officers are wary of AI projects due to the risks involved with data collection, storage, and properly feeding it into systems. Even after successful implementation, they struggle with interpreting results and driving team adoption.
Aside from implementing new processes and coordinating roles, operations find it difficult to attract talent. According to Deloitte, there’s an ongoing AI talent gap across all skill and experience levels.
Start with the right AI approach
While definitely important, the talent shortage isn’t the biggest impediment to AI adoption. According to the AI Adoption in the enterprise 2020, almost 42% of respondents identified a lack of institutional support and difficulty identifying appropriate use cases as the most significant barrier to AI adoption.
Prepare your business for AI
While AI utilises elements of software engineering, it also encompasses many subfields such as mathematics, statistics, psychology, linguistics, and others. To create better algorithms, you need a diverse team of data scientists and cultivate a whole-company, research-centered approach to innovation. Ensure knowledge is shared and your organisation understands what to expect from your AI initiative.
Maintain high quality data
AI algorithms calculate and process data supplied. If data is outdated or incomplete, the results will be misleading and the decision-making process compromised. If teams are spending time on manual data entry and data is disorganised and spread across separate tools, you’ll need to do some housekeeping first. Review systems for accuracy, completeness, and uniformness. Ensure data is high quality, unique, and without duplicates.
Look for a platform
Whether you’re looking to develop your AI strategy in-house or implement an off-the-shelf solution, make sure your B2B eCommerce platform is up to the task. For example, open-source, modular solutions with robust APIs will make it easy to integrate with other business applications, including AI-based systems. From chatbots to virtual assistants all the way to sophisticated product recommendation engines, the flexibility of an open-source system will accelerate your AI deployment.
Avoid doing too much too fast, cautions prominent data scientist Robert Magoulas in his AI in Enterprise podcast. “Companies need to start at the bottom of the data ladder, and not rush to adopt neural networks right away”, he warns. The right approach, team, and tools for the situation are the deciding factors in successful AI adoption.
Leveraging AI in B2B eCommerce
AI doesn’t just give customers a personalised and relevant website experience. The right solutions can also predict what customers will want to drive conversions and repeat purchases. There are many reasons why B2B eCommerce brands implement eCommerce AI algorithms.
Some of the most popular use cases include:
Product recommendations
Personalised product recommendations have a direct impact on conversion. At least 35% of Amazon’s sales come from an AI-powered recommendation engine powered by statistical models to recommend customers products they will be most interested in. In a separate example, the data science team at Home Depot implemented an AI algorithm for upselling and cross-selling furniture and appliances based on style, color, color finishing, brand, or other users’ purchase history regarding similar products.
Content and sentiment analysis
Machine learning can decipher text, images, and other digital files, streamlining customer self-service and back-office tasks. Natural language processing (NLP) models can pick up sentiment and identify customer’s true opinions and feelings about a product. On product pages with thousands of reviews, brands can relay to customers how many people think a product is “spacious”, “expensive”, “loud”, and so on. Similarly, Lowe’s uses digital image recognition to spot and replenish missing inventory.
Predictive analytics
AI helps recognise correlations between inventory, seasonal fluctuations, customer behavior, purchase history, and their impact on sales. Deep learning algorithms can complete or generate visuals, analyse similar products, and determine who is buying what and why. As data sets expand, so do learning algorithms, either suggest solutions or execute them automatically. Amazon’s patented anticipatory shipping model predicts how, when, and where customers purchase products and matches it with warehouses closest to vendors and customers.
Is there an alternative to AI?
Consider the time and money investment to gauge if AI is right for you. In the end, your AI solution must provide value that exceeds the costs of administering data and building a dedicated team. The good news is that technology is always evolving and there are many different ways to reach your goals.
Automated workflows. Both workflows and AI involve machines completing tasks. While AI will learn, adapt, and take different courses of action, workflows won’t change or update on their own. Still, workflows can automate multiple B2B ordering workflows such as performing credit approvals and notifying warehouses to fulfill orders.
Automated+human processes. Until technology catches up, AI won’t know you as well as you do. By combining digitised and manual processes, sometimes you can get the best of both worlds. When a solution is presented, the automation system can take action – either with human intervention or autonomously.
Customers, technologies, and processes evolve, so a flexible workflow engine will enable you to act on opportunities and introduce new business models as circumstances change. You can improve and build on your processes, promoting a culture of continuous experimentation and innovation.
Make sure AI works for you
Demand for personalised and relevant customer experiences is increasing, and so is the desire for internal teams to maintain alignment and focus. The growing interest in AI tools for B2B eCommerce is a testament to this, and we are excited to see where this technology will take us. At the same time, AI should work for you and not against you. Before taking the next step, carefully evaluate whether AI is right for your customers, industry, or business.
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