White Paper: Amazon's AI Powered Secrets to Success

White Paper: Amazon's AI Powered Secrets to Success

INTRODUCTION

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In 2015, Amazon launched an online B2B marketplace called Amazon Business. The enterprise racked up $1 billion in sales in its first year, and continued growing at an amazing rate. By 2018, Amazon Business recorded over $10 billion in sales. In 2019, RBC Capital forecasted that it would make $52 billion in sales by 2023.

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If distributors are going to fend off this B2B juggernaut, they’ll have to understand how it works first. This whitepaper will explain how Amazon uses a 3-pronged AI approach to dominate markets. Further, it will clarify how these AI-based strategies – product recommendations, search relevancy, and audience monetization – can be translated into winning strategies for distributors.

PRODUCT RECOMMENDATIONS

Amazons Product Recommendation Strategy

One of Amazon’s most effective sales strategies is using AI-powered product recommendations to engage customers and increase revenue. You might recognize these as offers like “recommended for you,” “products you might like,” “frequently bought together,” or “customers also bought.”

Amazon uses cutting-edge AI to process customer data and make personalized product recommendations.

As customers move through their shopping experience, Amazon predicts what items each individual is most likely to buy. With each new page, the e-commerce giant offers a fresh batch of personalized product recommendations.

This starts on the homepage, where Amazon uses individualized recommendations to draw each shopper in. Features like “recommended items” and “inspired by your shopping trends” engage customers, and significantly decrease the company’s bounce rate (the percentage of customers that do not click beyond the home page).

Amazon’s bounce rate is currently listed at 35%. Comparable competitors like Walmart and Target, on the other hand, are listed at 50% and 45% respectively.

This means that Amazon’s recommendations don’t just directly increase sales, they also draw customers into the shopping experience, creating further opportunities to make smart pitches.

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Amazon's Product Page Recommendations

Once Amazon has drawn a customer into the shopping experience, they use more product page recommendations to further increase spending. Amazon product pages don’t just feature product information, they also offer more personalized recommendations like “frequently bought together,” “similar products,” and “featured items you may like.”

These personalized offers help the company keep customers engaged. The average Amazon customer clicks through 9 pages and spends more than 7 minutes shopping. The average Walmart and Target shoppers, on the other hand, spend just five minutes online while clicking on five pages each. Thus, Amazon’s AI recommendations turn online browsing into an opportunity for cross-selling, up-selling, and other additional sales.

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Amazon's Checkout Recommendations

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Once a customer is finally ready to buy, Amazon pursues one last round of recommendations. At the checkout page, Amazon includes promotional sections labeled “recently viewed” and “saved for later.” This allows customers to easily add items to their cart that they may have missed while shopping.

As a result of all these efforts, Amazon converts 13% of web visits into sales. That’s almost 7 times the industry average. Of course, Amazon’s astounding conversion rate is not just the result of its checkout page recommendations, but of its thoroughly personalized experience. Using AI, Amazon has created a superior web funnel, that pushes more visitors to sales at every possible opportunity.

AI Product Recommendations Recap

When all of these recommendations are added up, they generate truly incredible returns. In total, Amazon’s AI recommendation engine fuels 35% of customer purchases. Given that the company does more than $140 billion in sales, calling this a multi-billion dollar strategy is something of an understatement.

Distributors can benefit greatly from AI-product recommendations too. These automated product pitches work because they automatically answer the age-old sales questions: Who is going to buy what, and when? In order to get the most out of AI recommendations, however, distributors must consider their unique sales structures.

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Why B2B and B2C Recommendations are Different

There are a few key differences between Amazon and distributors. Instead of simply copying Amazon, distributors must consider their unique strengths and weaknesses before applying some of the AI techniques that Amazon uses. Two key things for distributors to consider are how they can use different types of product recommendations, and how they can leverage their multi-channel sales structures.

Amazon buyers are likely to make impulse purchases or buy products for one-off situations. B2B buyers, on the other hand, are more likely to make regular reorders or to buy items for specific projects. Distributors strive to help customers find the complementary or substitute products they need. With the help of AI, distributors can analyze customers and predict likely purchases to execute profitable upsells, crossells and add-ons.

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Why Distributors Must Connect Channels

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In order to start making these recommendations, however, distributors will need to connect all of their channels. This means that inside sales, outside sales, e-commerce, customer service, marketing, and counter sales must all work together as part of a winning AI strategy.

For most distributors, each of these channels operate as separate businesses; Sales reps don’t know what a customer has been looking at online, customer service reps don’t know what items buyers have previously purchased, websites don’t know what items customers are due to re-order, and so on. This must change.

By breaking down data silos and connecting channels, you can gain a 360-degree view of every customer. Once your omnichannel system is up and running, any interaction across any channel will inform your understanding of a customer. This complete view of a buyer will enable you to make targeted pitches like never before

Distributor Product Recommendations in Action

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AI recommendations create a personalized shopping experience for every customer. These pitches can be presented as “recommended items,” “complete the cart,” and “customers also bought.”

Customer Service Customer service reps can use AI to figure out what to pitch to unknown buyers. This turns regular service calls into sales opportunities, as reps will be able to make upsells, cross-sells, and add-ons for any customer. When distribution customer service reps use these AI recommendations, they can increase average order value per customer by more than 10%.

Inside Sales AI doesn’t just predict what customers want to buy, it also predicts when and how they want to buy it. With AI, inside sales reps will gain a data-driven workflow that maximizes productivity and profitability. We’ve seen distributors increase revenue per product pitched by 10x using AI recommendations.

Outside Sales Many outside sales reps already know their customers well enough to make smart pitches. However, with COVID and other digitizing forces keep reps out of the field, an AI assistant can be extremely helpful. With AI product recommendations, outside reps can gain a better understanding of all customers and make effective pitches from home.

Branches If Amazon can turn a checkout page into a sales opportunity, distributors can do the same with a checkout desk. With AI-supported order entry, reps at the counter can make smart add-on suggestions on the fly to increase customer satisfaction and revenue.

Marketing Marketing should be personal. AI can gain a singular view of every customer and automatically coordinate marketing content. This means that future customers will only engage with personalized and relevant content, keeping you at the top of their minds.

SEARCH RELEVANCY

Amazon's Search Relevancy Strategy

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Amazon uses AI recommendations to predict what customers are most likely to buy. Unsurprisingly, Amazon’s ability to figure out what customers want without them telling it is very valuable. But what if a customer does tell them what they want?

When a customer uses the Amazon search bar, they are basically declaring their interest in a product. It is natural to think that search doesn’t matter very much, and that when a customer starts looking for an item online, they are probably going to end up buying it no matter what. However, the data clearly reveals that this does matter. In fact, it is worth more than $10 billion to Amazon.

When you search for something on Amazon you are four times more likely to buy it than when searching on comparable sites.

In fact, Amazon enjoys an amazing 12.9% conversion rate when customers use their search bar.

Rivals like Walmart and Best Buy, in contrast, convert only 2 to 3% of customer searches into sales. Why is Amazon search so good?

Amazon's Search Relevancy in Action

Let’s say you want to buy Allbirds shoes online. Allbirds has a business-to-consumer approach, and does not sell shoes on third party sites. If you search on Amazon, or Walmart, or really anywhere but the Allbirds website, you won’t actually find these shoes. But, there’s a difference in how you won’t find them.

If you search “All birds” on Amazon you won’t find Allbird sneakers, but you will see close matches.

If you search “All birds” on Walmart however, you will be presented with bird seeds and avian themed books. Amazon returned relevant results, while Walmart did not.

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Amazon's Search by Numbers

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Search relevancy is a complex science with a simple goal: use a customer’s search input to deliver relevant results. Amazon uses cutting-edge AI to understand the meaning of customer searches, the relevance of possible results, and the context of searches. This makes Amazon search far more effective than competitors.

Last year, Mike Roberts, the CEO of an analytics company, published a truly shocking report on the search gap between Amazon and its competitors. This revealed that 42% of Amazon searches result in a click. That might seem low until you realize that only 16% of Walmart searches end in a click. Comparable sites like Etsy and Best Buy do even worse with just 13% and 12%, respectively.

If Amazon search performed like everyone else, they would convert on 3x fewer search sales, and miss out on nearly $800 million every month. Over a year, that means their search is worth roughly $10 billion.

How Distributors Can Enhance Search

The fact that Amazon searches are literally 3 times more likely to end in clicks and sales is no accident. According to LinkedIn, 841 employees at Amazon specialize in “search relevance.” This means that Amazon has nearly one thousand software developers and engineers researching and creating ways to improve search engine relevance. In contrast, Walmart only has 54 employees in that category.

If distributors want to gain Amazon-style search results, they need to make Amazon-level investments in technology. Grainger, one of the most technologically adept distributors, currently has 10 employees specializing in “search relevance.” On a relative basis then, Grainger actually puts more emphasis on search relevance than Walmart.

Many distributors do not employ search relevance engineers. For most distributors, it also may not make sense to do so. For those companies, an AI vendor like Proton or well-engineered e-commerce platform can provide search relevance.

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What Enhanced Search Will Do For Distributors

The primary effect of enhanced search relevance will be increased e-commerce revenue for distributors. Customers will also greatly enjoy the ability to easily locate products online.

Given that this feature increases Amazon’s online revenue by roughly $10 billion annually, and that Amazon does a little more than $140 billion in online sales, we can estimate that enhanced search delivers a 5-10% boost in ecommerce revenue.

Of course, enhanced search is more of a spectrum than a binary, and distributors should expect different returns based on the quality of their AI.

Distributors can also expect a slight revenue boost outside of e-commerce. Using the underlying technology that powers search, counter sales reps could locate products and find suitable substitutes to increase customer spend. Telesales reps or traveling salespeople could also use this tool to figure out the best products for customers.

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A Hybrid Model: Staying Human With AI

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Distributors have a key advantage over Amazon. They have tons of knowledgeable salespeople and decades of experience. Online only retail may work for B2C, but B2B is still heavily dependent on human contact as 76% of buyers want to talk to a person before buying a new product.

As distributors evolve, they must maintain valuable rep-to-customer relationships while improving their technological infrastructure. This means that technology should not be used to replace reps, but to empower them instead.

With tools like Proton, distributors can give reps the technological backing they need. If reps can use AI to figure out who to contact, when to contact them, and what to pitch, all without losing that irreplaceable personal touch, they can win in the modern marketplace.

Instead of imitating Amazon, distributors must strive to take the best of Amazon’s AI tactics and combine it with what they already do best.

AUDIENCE MONETIZATION

Amazon's Advertising Strategy

Amazon uses AI recommendations and AI-enhanced search to sell many items. It also collects billions in advertising fees to do it. In fact, Amazon racked up roughly $14 billion in advertising revenue in 2019 alone.

With multiple years of +40% growth, Amazon advertising is quickly eclipsing all other channels.

But, this lucrative business is entirely dependent on the previously discussed AI powered features.

Amazon uses AI to figure out what customers want. This enhances customer experiences and creates customer loyalty. Amazon then monetizes the customer attention that it owns by selling billions in advertising spots to third-party vendors.

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Amazon Advertising In Action

If vendors want to get their products in front of customers, they’ve got to go through Amazon. When consumers begin to search for a new item they intend to buy, 66% of them start on Amazon. Plus, 89% of all consumers report that they were more likely to buy products from Amazon than any other e-commerce platform. This means that Amazon owns customer attention all the way from browsing to buying.

If you search for something on Amazon, you are likely to see products tagged with a small “sponsored” label. This means that the vendors selling those products paid to have them appear on your screen.

Amazon also runs “sponsored brand” advertisements. This means that vendors and companies can pay to be more substantially featured when customers are searching for products. For example, if you searched “headphones” on Amazon, you might see a special banner featuring all Bose headphones. If you clicked on that banner, you may be taken to a specially designed, Bose-sponsored, landing page.

Finally, Amazon also charges vendors to run special display ads and video ads. These do not always directly appear while customers are shopping, but can pop up when Amazon customers are browsing around on their Kindle’s or watching Amazon’s Fire TV. Clearly, it pays to own your customers’ attention.

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Why Amazon Advertising Works

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Amazon also makes it very easy for advertisers to feel comfortable using their service. As Amazon’s recommendations and search features demonstrate, the company is excellent at using AI to figure out what customers want to buy. This means that advertisers can trust Amazon to pitch their products to the right customers.

Amazon’s CFO Brian Olsavsky recently explained:

“what we’re focused on really at this point is relevancy — making sure that the ads are relevant to our customers, helpful to our customers. . . we use machine learning, and it’s helping us to drive better relevancy.”

When it comes to advertising, attention and relevance are a winning combination. The CMO of Goat Consulting recently called Amazon “an advertiser’s dream,” explaining that the company yielded conversion rates of 20 to 30%. Facebook advertisements, in contrast, tend to yield between 1 and 10%.

As it turns out, being an “advertisers dream” pays very, very well. Over the last several quarters, advertising has been one of Amazon’s quickest growing revenue streams. Most recently, Amazon’s second quarter advertising revenue grew by over 40% to $4.22 billion. If Amazon can sustain this pace, it will haul in $20 billion in advertising revenue by the end of 2020.

The Future of Amazon

While it’s easy to get excited about Amazon’s advertising business, it’s critical to remember that it is just one part of an extremely profitable puzzle. Without an AI enhanced shopping experience that continually builds customer loyalty, attracts almost undivided attention, and reveals customer preferences, Amazon would not be able to advertise at all.

As time wears on, Amazon is inching closer and closer to achieving a sort of totalizing AI-platform. The tech giant is not just owning customer attention in terms of B2C shopping, but has made moves to expand into B2B sales (Amazon Business), video (Amazon Prime TV) and music (Amazon Music). As Amazon gains more customer attention and data, it also gains more opportunities for sales and advertising.

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The Future of Distribution

Distributors can use AI to capture revenue in the same way.

With excellent search and product-recommendation functions, distributors can gain the kind of customer attention that manufacturers are willing to pay for.

Of course, the first step towards doing this is implementing an omni-channel AI system. Distributors need to capture and utilize all data points from all customers to start accurately predicting what customers want to buy.

With this information, distributors can create a win-win-win, for customers, distributors, and manufacturers.

Customers will win because they’ll get to enjoy more relevant and personal shopping experiences. Manufacturers will win because they’ll get to promote their products to the right customers. And distributors will win big because they’ll have established themselves as the indispensable and lucrative link between customers and manufacturers.

It’s hard to quantify just how big this will be. If manufacturers spend 5% of their revenue on marketing, and distributors sell at 25% gross margin, that means that distributors will capture $3.75 in additional profit per $100 of volume. That’s a 15% margin boost, if all ad dollars are funneled through distributors.

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Why Early Action is Necessary

The key to creating this kind of profitable advertising pipeline is owning customer attention. While early technological adopters always win big, AI tends to produce even more pronounced effects.

In a McKinsey simulation, for example, early AI adopters were predicted to increase cash flow by 122% by 2030. Slower adopters were only predicted to see a 10% increase in that same time. Meanwhile, companies that did not adopt AI at all were predicted to shrink by 23%.

This reflects the fact that companies that use AI to lock in customer attention will grow dramatically over the next decade. But, companies that cannot capture customer attention will fade away. This is because AI tends to enact virtuous cycles called data network effects. This describes how AI performance and customer growth play off of each other beneficially.

If you can use data and AI to enhance your customer experience you will gain more customers. This will give you more customer data and more revenue, which can be used to create a better customer experience. As these cycles repeat, big winners emerge. Distributors must make AI a top priority.

Companies that can use AI to improve experiences and monetize customer data today will enjoy growth throughout the decade. Firms that fail to invest in AI today will put themselves at risk of losing customer dollars and customer data.

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EXECUTIVE SUMMARY

Amazon uses AI to create billions of revenue in 3 ways:

  • AI Recommendations match the right products with the right customers at the right time to drive roughly $50 billion in incremental sales for Amazon.
  • Search Relevancy helps customers find products they want and facilitates substitutions if those products are unavailable. Search drives another $10 billion for Amazon.
  • Audience Monetization cashes in on customer loyalty and AI selling skills. Advertisers will pay $20 billion this year to get their products in front of the right Amazon customers.

Distributors can w in w ith AI by updating Amazon’s tactics in 4 ways:

  • Create an omnichannel sales structure. Distributors have more data sources because they sell across multiple channels. By sharing data across different customer touch-points, distributors can beat Amazon and create superior experiences online and in-person.
  • Update AI recommendations for B2B sales. Amazon nudges customers towards impulse buys, but distributors can’t do that. Instead, B2B sellers must help customers fill out regular re-orders, make complete project orders, and find good up-sells, cross-sells, and add-ons.
  • Use AI to empower humans, not to replace them. Many B2B customers enjoy talking to sales reps before buying. With AI-powered product and workflow suggestions, reps can leverage these interactions into better customer experiences and stronger sales.
  • Monetize your manufacturers. If vendors pay to advertise with Amazon, manufacturers will pay to advertise with AI-enabled distributors. With AI, you can charge manufacturers to pair their products with the right customers.

Distributors need to find the right AI solution as fast as possible:

  • Move Early: Early adoption is the difference between 122% and 10% gains. In order to win, distributors must beat competitors to market. The clock is ticking.
  • Pick a Good Vendor: Acquiring AI capabilities through a vendor will be much faster and cheaper than building AI tools in house. But, if you're going to pay for a vendor it's important to choose the right one. While there are many AI vendors out there, Proton is the only one that is purpose built to provide omnichannel AI for distributors.

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