Why online retailers are missing hidden sales opportunities

In the eCommerce industry, I personally hear from founders, CMOs, and CFOs on a monthly basis, and the general gist is that performance marketing is the one area where they don’t have a forensic understanding of how budgeting contributes to their bottom line.

Retailers are often frustrated and locked in a bidding war for items that they generally predict consumers will want to buy. Business leaders are exasperated by slow data analysis and ineffective campaigns that generate zero revenue. As budgets tighten, the future looks bleak for online sellers using the same limited levers to sell products. So what can be done?

Act on the intangible

Optimizing for tangible factors is a game that all consumer retailers are playing. Typically, this will focus on seasonal trends – retailers will make sure to compete with the best ad strategy accompanied by engaging, search-optimized copy on items like swimwear and kiddie pools during the summer months, for example. . This has arguably become a zero-sum game for retailers as CPC (cost per click) increases with more Google ad buyers in the market. This strategy can only take you so far.

Intangible factors cannot be predicted and are impossible for humans to plan for. Unexpected weather changes, a viral TikTok video, or a celebrity spotted wearing a particular item of clothing can instantly change what consumers want to buy. Unexpected trends don’t fit perfectly into the agency’s work schedule; it is necessary to act in real time before the moment of purchase is lost. This is where AI comes in.

Machine learning technology has the ability to closely monitor changes in Google search trends and match that information with retailer inventory data to automatically make the best business advertising decisions for retailers in real time. Processing thousands of data points in seconds is something humans simply can’t do – unlocking new growth opportunities for retailers beyond paying more for ineffective advertising.

Humans just can’t do it all (and that’s okay)

Agencies can certainly be supportive, but when it comes to using a platform like Google to sell products online, there’s a lot to manage: multiple levers to pull, along with reports and metrics to closely watch and adapt. That’s why freeing up time and reducing effort for an internal team or agency by eliminating manual processes and automating data extraction, analysis, and optimizations is a no-brainer.

Do you know the distribution of the marketing budget in your inventory? Which SKUs generate revenue? What percentage of your inventory is Google ignoring? Could the budget be better allocated? If you work in retail and can’t confidently answer these key performance questions and know how to make improvements, then something’s wrong.

In my opinion, if retailers plan to go through this period of inflation with the same tools and techniques they have been using for years, then they plan to fail. AI provides a new competitive advantage in a tough market and cannot be ignored.

What the data tells us

According to McKinsey 2021 State of AI SurveyBased on responses from more than 1,800 executives, AI adoption and impact continue to grow. 67% of all respondents saw an increase in revenue and 79% saw a reduction in costs (a significant increase from 44% last year).

Specifically within the e-commerce industry, machine learning technology enables retailers to match their inventory data (supply) with consumer data (demand) in real time to automate advertising decision-making and drive better business results. . This may sound obvious, but this level of data connection is still not the norm. At Upp, we exposed that retailers are spending money profligately, with up to 40% of ad spend generating zero revenue.

CPCs increase with demand during forecast sales periods, often resulting in a loss of items, for example, fans and air conditioning units during the recent heat wave. However, one of our clients, an online marketplace, saw an 89% increase in revenue and a 113% improvement in P&L thanks to an unexpected increase in apparel sales that we capitalized on. While fans and air conditioning drove GMV (Gross Merchandise Value) achievement, it was wardrobe sales that drove profitability achievement due to low CPC (higher margin, lower ad cost), a hidden opportunity which was discovered by machine learning.

Without AI this was simply impossible…

Similarly, charles tyrwhit recently achieved a 42% increase in monthly UK orders and a 14x return on investment by automating ad optimization. With machine learning technology working at the SKU level, retailers like Charles Tyrwhitt can target shoppers with great precision and show them the exact product they’re looking for, precisely when they’re looking for it.

Our industry-first AI performs millions of micro-optimizations dynamically, balancing unexpected trends with changes in advertising costs, all within a framework of business goals set in the Upp platform. A real-world example of this is Poundshop, which increased its Google Shopping impressions by 375% while maintaining its ROAS target, by acting on hidden opportunities and unexpected trends that traditional retail performance marketing can’t capture.

The best way to predict your future is to create it

Reinventing the way retailers trade, make decisions and act is critical right now, especially in a world where disposable income is shrinking and uncertainty is high. In the highly competitive landscape of eCommerce, retailers need to think smarter and integrate AI to scale and automate their retail performance.

In the words of Abraham Lincoln, ‘the best way to predict your future is to create it’. With this in mind, it is easy to see that AI technology has the power to process data much faster than humans and even learns and optimizes on the job. Most businesses have realized that Google Shopping (soon to become Performance Max) is an essential channel for retailers to sell products and offers a huge opportunity for e-commerce growth, but many are still learning how. apply a data-driven approach. The best advice I can share is to take advantage of the technology at your fingertips and you will reap the benefits in what is a new era for retail.

Drew Smith is CEO and co-founder of up.

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