How Amazon Vendors Can Use AI-Optimization to Increase Sales

With the use of predictive AI, companies can set coupon promotions, ad spends, and price points to prevent sales slumps.

Written by Rohan Thambrahalli
Published on Jul. 06, 2020
How Amazon Vendors Can Use AI-Optimization to Increase Sales
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In 2019, e-commerce sales made up more than 10 percent of retail transactions for the first time in history. At the time, this was an impressive figure. With a modest accelerating growth rate, analysts were predicting a bright future for digital retail.

Flash forward to 2020 and the world we know — both online and offline — has fundamentally shifted. While many industries are still reeling from major changes to their work flows, the situation in online shopping is much more positive.

E-commerce sales have shot up as much as 200 percent in some countries, as consumers living under lockdown orders purchase nearly all of their goods online. And with Amazon accounting for nearly 50 percent of online sales, vendors working on the platform are in a strong position to increase revenue during this surge.

The changes happening in our retail environment will not be short-term. Online orders in March of 2020 were up 21 percent compared to 2019. Many of the shoppers who seldom or never purchased goods online are now more familiar with the e-commerce shopping process, and are likely to maintain their new habits for the foreseeable future. This widespread adaptation to an e-commerce-first approach to shopping requires the right forecasting technology for vendors to find success.

These AI-backed systems don’t only improve sales through accurate predictions of demand volume based solely on historical sales data. They can also provide stabilization. By improving predictive analysis, vendors can avoid sales slumps and take better advantage of peaks (by maximizing their inventory).

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Predicting and Planning in a Pandemic

It’s easy to look at the remarkable growth in e-commerce in 2020 and think that retailers have it made. Yet this sort of viewpoint neglects the bigger picture. For example, even though Amazon has seen sales volumes spike 26 percent this year, the company has also seen profits fall by 29 percent.

What could account for such a remarkable gap? It comes down to a lack of preparedness. Though, given the circumstances, Amazon is hardly to blame. When the company gears up for a major sales event, like Amazon Prime Day or the holiday season, they allocate a bevy of resources toward advertising, logistics, and manpower in order to fulfill every order with one-to-two day shipping times.

The pandemic took retailers by almost total surprise, and Amazon was no exception. Unfortunately, there’s no way to go back and redo the first months of 2020; however, vendors can and must look to the rest of the year and beyond as an opportunity to future-proof their sales.

One way to do this is by leveraging tech not only to evaluate historical sales data, but to take stock of several key variables essential to planning for the future. Displayed products, sales volume, and ad clicks can be tracked and compared with inventory volumes to better plan for any spikes that may occur. When coupled with historical data, such as sales rank and a product’s out-of-stock rate, the insights gained from modeling these data points are strengthened.

In a way, retailers are already familiar with the process of forecasting sales based on future insights. Vendors know to stock particular products and run certain promotions in the run-up to holidays like Mother’s Day or Halloween, for example. These calendar dates are regularly occurring and affect almost all retailers in one way or another, even if their products aren’t specific to the holiday.

Where modern e-commerce planning goes further is in predicting events that aren’t written into every mass-produced calendar. In response to COVID-19, for example, some products like bread makers and kettlebells have seen a tremendous boom in sales, leading to shortages. At the same time, sales of clothing and footwear have fallen dramatically. Predicting these ebbs and flows in demand based on geographic and historical customer data helps contribute to better ad spends on the most in-demand products.

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How AI-Backed Data Research Works

Every customer transaction produces valuable data that retailers can use to benefit their sales and marketing efforts. Unfortunately, most companies don’t utilize this data to its fullest extent, with up to 73 percent of data worldwide going unused for analytics purposes.

Information regarding your customers’ geographical location and other demographic statistics can greatly inform your sales strategy. This approach to marketing is doubly important in an age of accelerated online shopping. More and more vendors are finding their digital shelves depleted, particularly when they stock essential goods. Others may not have seen sales spikes yet, but are likely to see a return to steady demand as quarantine lockdowns are eased worldwide.

Determining the right sales strategy essentially requires the use of artificial intelligence applied to historical data. There are simply too many potential variables and data points for even an experienced marketing team to handle. With AI implementation, marketers gain a better perspective of what data matters. They know, for example, when to offer coupons and discounts in a way that’s cost-effective and optimized toward improving sales.

For example, a company that ships to several regional hubs can look at the shipping volume in each distribution center over time. Sales may peak and fall seasonally depending on local events, or for demographic reasons, with one type of customer purchasing more of a product at certain times. There may even be no evident explanation for why sales rise or fall at certain times, but by looking at the data and confirming that these spikes really do happen, vendors are better equipped to shift their marketing strategies as a result, by increasing or decreasing ad spend to meet these changes in demand.

The AI takes care of organizing and plotting this data in a way marketers can apply toward improving sales. For example, users can view sales rank and sales volume in a graph using historical data. By using a coupon promotion during a predicted slump, these vendors could potentially spur sales. But knowing the right price point for such a promotion is a challenge. With a diversity of data points at its disposal, a predictive AI can suggest the right price point to maximize sales. Better yet, once the promotion is completed, vendors have another strong data set to use for predictive analytics, allowing the AI to improve iteratively over time.

Historical data is exponentially more important now that online sales have taken precedence over in-store retail. The slow global reopening of brick-and-mortar stores, coupled with the world’s reinforced comfort with online shopping across every category of goods, has ensured that the accelerated growth of e-commerce will be a long-term trend. Now is the time to start tracking and analyzing this data as a historical benchmark for future marketing efforts. You may not know what the end of 2020 will look like, but you do know what the past says about your sales. Using that information (alongside AI tools that help organize, evaluate, and display this data) is the key to finding a path forward in an e-commerce world driven by customer intelligence.

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