AI is Here. What Now?
Artificial intelligence (AI) has created some of the biggest business success stories of the past decade. People benefit from AI every day – from spam filters and Google Maps to mobile banking security and voice assistants like Siri.
AI-powered personalized recommendation engines drive immense business value. Amazon uses AI recommendations to drive 35% of revenue.
Amazon uses AI recommendations to drive 35% of revenue. Amazon uses cutting-edge AI to power product recommendations. Amazon processes customer data to make relevant and authentic recommendations like “recommended items” and “inspired by your shopping trends,” which people see in their shopping carts, in turn, leading them to buy more. Amazon’s personalization spans its channels – if you’re an Amazon member, targeted emails and mobile notifications reinforce your logged-in experience.
Netflix uses algorithmic predictions to keep 75% of viewers engaged. Using AI, Netflix generates a unique list of recommendations for each user, which increases viewer engagement and retention. Netflix’s recommendations influence more than three-quarters of viewer activity. Netflix also uses AI to determine which customers are likely to unsubscribe and sends users proactive suggestions to keep them engaged, saving the company $1 billion a year.
Facebook uses AI to make $86 billion a year from advertising. Facebook makes 98% of its revenue from selling ads. Using machine learning, Facebook creates targeted ad experiences that engage individuals and drive immediate value for businesses. Companies pay Facebook for ad space because they know Facebook places ads in front of users who will be interested in them.
What is AI?
Artificial intelligence (AI) is a growing branch of computer science that trains machines to simulate human intelligence and execute specific tasks (e.g. Which Netflix show should I recommend for this viewer right now?). The major difference between AI and a simpler analytical approach is that AI (specifically deep learning) can ingest more data – both volume and complexity – cut through the noise and find higher-dimensional patterns. Rather than recommending any thriller to this viewer, recommend thrillers that intersect with other preferred micro-genres, like romance or comedy, for this viewer on the weekends in the evenings.
Deep learning as a means to drive customer experience is on the rise because of the exponential growth in computing power that makes training and maintaining models faster and cheaper.
Why AI Hasn’t Transformed Distribution Yet
By 2030, AI is predicted to create $4 trillion for sales and marketing. Yet a survey from Distribution Strategy Group published by the National Association of Wholesaler-Distributors found that just 12% of distributors use AI in sales and marketing today.
The reason for the low number: It’s cost-prohibitive to properly build and maintain neural network artificial intelligence models. And distribution-specific solutions for AI aren’t widely available.
Despite this, the appetite for and urgency of AI adoption in distribution is on the rise. The same Distribution Strategy Group survey notes that two-thirds of distributors see great potential in AI to drive sales and marketing.
Pairing the right customers with the right product at the right time is a more complicated challenge for distributors than it is for B2C businesses. Distributors have more products (100,000 SKUs vs. 1,000, for example) and more sales channels (6 vs. 1, commonly).
Distribution-specific solutions are emerging that make it easier to adopt AI where it can have an immediate impact. Distributors have an opportunity to engage customers and add value as a one-stop shop for products and expertise. With AI, distributors can find gaps in wallet share and predict when a customer is due to reorder an item with a greater level of confidence. These are just two examples of the types of tasks that AI can carry out.
For example, an electrical distributor may have a diverse customer base that includes small residential contractors and matrixed municipalities. One residential contractor may be purchasing conduit and fillings from this distributor but remain unaware that they could and should also be purchasing their tools, including conduit benders from them. The right AI tool can identify those wallet-share gaps and provide a means for a distributor to put that information in front of the customer.
Using these strategies can drive a 5-10% uplift in sales for most distributors.
Consider the growing gap between giants like Amazon and retailers and distributors that compete with them. Particularly in the wake of the COVID-19 pandemic, company success hinges on a distributor’s ability to improve the customer experience and add value at every touch. To do this, they need to partner with an AI vendor to:
• Collect and centralize data from all sales channels (think ecommerce, customer service, inside and outside sales, and even branches)
• Analyze the data holistically to predict how, when and what each customer will buy
• Surface insights in a personalized and actionable way so reps and customers get what they need
Why Distributors Need Omnichannel AI
For most distributors, each sales channel operates as a separate business: Sales reps don’t know what a customer has been looking at online, customer service reps don’t know what field reps are trying to sell, websites don’t know what items customers are due to reorder and so on. This must change.
Distributors need to unify data from across their channels to start making relevant cross-sell and upsell recommendations. AI enables distributors to analyze their data and then enable each channel with the right suggestion at the right time. As a result, distributors can provide customers with a consistent and proactive experience across sales channels, which improves the customer experience overall.
How AI-Powered Recommendations Grow Sales
AI-generated product recommendations in B2B should be based on data collected across all of a distributors’ sales channels. If your ecommerce recommendation engine only considers website browse and intent data to make product recommendations, it’s failing to take into account all you can learn about that customer from their cross-channel activity such as speaking with their inside sales rep or purchasing items from a customer service team.
Customer service and sales reps can lean into these insights to make smarter pitches, upsells and cross-sells. Like Amazon, distributors stand to drive a heavy percentage of revenue through AI-powered recommendations, but distributors have a key advantage: They have knowledgeable salespeople with decades of experience. In fact, a McKinsey survey found that 76% of B2B buyers still want to talk to a person before buying a new product.
How AI Makes Salespeople More Efficient
Sales reps only spend about a third of their time selling, according to the State of Sales report produced by Salesforce.
As noted above, the Distribution Strategy Group survey found that two-thirds of distributors see great potential in AI to drive sales and marketing. Instead of building manual call lists, searching for product information and taking notes, AI can point reps to sales opportunities that most warrant their attention. Conversations can move from “Do you need anything?” to “I noticed you’re buying conduit from us but have never purchased conduit benders. Can I send you some information on a few products?”
An AI-enabled sales rep can:
• Focus on the right customers based on churn risk, reorder likelihood or growth opportunity.
• Embrace an omnichannel view of what is happening with each customer.
• Spend less time being reactive and more time being proactive with relevant pitches and suggestions for complementary products and reorders.
• Have the context they need about every product in their catalog to confidently discuss it with a customer, even if it’s not a product they’ve sold in the past.
• Track what they’re pitching with the click of a button instead of taking manual notes that are hard to search and track.
If reps can use AI to figure out who to contact, when to contact them and what to pitch, all without losing that personal touch, they will provide value that an all-digital competitor can’t replicate.
Results Distributors Can Expect from AI
We’ve seen distributors across segments yield triple- and even quadruple-digit ROI with the right AI-powered product recommendations.
• 10%+ average order value on e-commerce: A large distributor of lawn and garden parts implemented AI-powered features to enhance e-commerce sales, driving a 21% increase in average revenue per customer and putting the distributor on pace to gain more than $10 million annually with a triple-digit ROI.
• +15% on inbound channels: A large industrial distributor’s customer service team, using upsell and cross-sell programs, is on pace to generate $115,000 in incremental annual revenue per rep. This distributor has a deep catalog, but reps are limited because they can’t memorize details of every product. Now, when reps interact with buyers over phone or email, a recommendation engine instantaneously scans the catalog and customer information to identify which items are likely to be sold. The reps have transformed from order-takers to order-makers without intensive training or protocol changes.
• +10% for outbound channels: A large industrial distributor’s inside sales reps are averaging $150,000 per year in incremental sales. Reps pitch more than 100 AI-recommended products to customers each week. The solution steers reps to the right customers and suggests the next best action. Instead of a generic pitch (for example, promoting an item that includes a rebate to every customer), reps offer uniquely relevant items to each person.
Finding the Right AI Solution
AI solutions are accessible to distributors of all sizes. When choosing a vendor, make sure:
• The AI solution is built for distribution (in other words, it accommodates your channel complexity, customer behaviors and SKU count).
• The vendor uses deep learning and natural language processing. Basic machine learning or business intelligence tools won’t cut it.
• The vendor secures your data, uses as much of it as possible, and helps you find and integrate more data.
• The vendor works with you to train your teams to ensure adoption and efficacy of the solution are maximized.
• The vendor ensures the AI engine is working based on the goals set at the start of the project and adjusts as needed after implementation.
Adoption is still low among B2B businesses, with less than one third of B2B companies using AI for sales. But over the next few years, the speed of AI adoption will be the difference between winning big – and losing. In a McKinsey forecast, the frontrunners in AI adoption will increase cash flow by 122% by 2030. Slow adopters will gain 10%. Non-adopters will lose 23%.
Distributors have the right data but need to act fast to benefit from early-adopter returns.
But not all AI is equal. A distribution-focused AI vendor should be able to centralize complex data, identify new sales opportunities, lower your cost of sales and gain insights with deep reporting. Distributors warrant a sophisticated approach and a vendor that understands their business and customers deeply.
Do your research; learn the ways other distributors are adopting. Then commit to integrating and training your team to transform your business with AI.