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3 examples of how ecommerce brands use artificial intelligence

With the growing popularity of new Hollywood releases such as Ex Machina (2015) depicting intelligent, human-like machines doing amazing things, it can be hard to think of artificial intelligence as anything else other than science fiction and stuff of the future.

But artificial intelligence actually comes in many forms, and its uses in online e-commerce can revolutionize the way e-commerce brands attract and retain customers.

As the e-commerce marketplace continues its rapid rate of growth of around 20% per year, it’ll be ever more important for e-commerce stores to be ahead of the competition to cut down on their costs and be more efficient.

New AI software like Metisa can now save you both time and money by automating complex processes previously done manually. They can analyze the behavior of customers and use this information to create specific, personalized content for them. They can realize trends and patterns exhibited by customers that would’ve otherwise been missed.

Clearly, AI is the solution. Here are 3 ways that e-commerce brands are already using AI.

Generating Product Recommendations To Increase Sales Conversions

Brand: Blacks
Result: 277% increase in conversion rates

Blacks, an outdoor retailer, used previous customers’ purchasing patterns to create a ‘What customers ultimately buy’ recommendation section.

Compared to the average site conversion rate, the conversion rate at Blacks was increased by 277% when a visitor selected one of these product recommendations.

User-Behaviour analytics is a recently developed technology that reveals insights into the behavior of Internet users online. Each time an Internet user performs an action online - clicking, browsing, purchasing - data is generated that allows us to predict users’ future actions, trends, and preferences.

This kind of information is gold for e-commerce brands. Being able to predict a user’s preferences and future actions allow e-commerce stores to tailor their offerings to current demands.

Automating Marketing to Improve Customer Retention

Brand: PaperStyle
Result: 330% increase in average revenue per email; 244% higher email open rate; 161% increase in click rate

Data captured by user behaviour analytics can also be used by marketing automation systems to segment customers and send automated, targeted campaigns to them.

Take a look at the case of PaperStyle.com, who increased their average revenue per email by 330% after employing a marketing automation campaign. The invite and stationery specialist worked with Whereoware to create an automated marketing campaign, targeting their customers that were brides, brides-to-be, or friends-of-brides.

Based upon their customers’ actions on their website and emails, PaperStyle segmented customers through any one of three criteria:

  • Customers who clicked on a wedding link in any PaperStyle email;
  • Customers who purchased wedding or bridal shower products; or
  • Customers who have visited a wedding-related page on the PaperStyle.com website.

Through these triggers, PaperStyle sent tailored emails at just the right times to these customers with relevant marketing messages. For example, a bride that had purchased wedding favors earlier would receive a tailored email regarding “thank you” cards a few days later. A friend-of-a-bride that had just purchased bachelorette tiaras earlier, for example, would instead receive a tailored email regarding wedding gift ideas.

The results of the automated marketing campaign were clear. PaperStyle saw that they achieved an astounding 244% higher email open rate than average, as well as a commendable 161% increase in click rate.

Demand Forecasting to Lower Logistics Costs

Brand: Moleskine
Result: Reduction of 15% in working capital and 70% improvement in sales forecast accuracy

Moleskine, an e-commerce store selling notebooks and journals, found itself having to balance a high innovation rate against lengthy lead times. For them, demand forecasting was vital to their business. Using SO99+ from ToolsGroup, the software was able to use quantitative and qualitative data to generate reliable demand forecasts and optimize inventory management.

With SO99+, Moleskine achieved an astounding 70% improvement in sales forecast accuracy, reducing lost sales and lower inventory. Working capital was also reduced by 15%, increasing profitability.

According to the Institute of Business Forecasting and Planning, consumer product companies could save on average $3.52 million a year by reducing under-forecasting errors by 1% and another $1.43 million a year by reducing over-forecasting by 1%. Why? The answer is ineffective inventory management.

Especially within growing e-commerce businesses stocking slow-moving, ‘long-tail’ products, it is notoriously difficult to manage sudden spikes in demand leading to lost sales, or sudden droughts of purchases leading to ‘dead stock’.

With increasingly complex supply chains and the expectation of even faster delivery times, no longer is it realistic for most e-commerce businesses to expect human planners to analyze market trends under ever-changing scenarios.

It’s because of this that e-commerce brands are increasingly relying on statistical analysis and machine learning to predict future demand.

Conclusion

As some e-commerce brands have already demonstrated, artificial intelligence’s role in e-commerce is becoming essential. Its capability to target and personalize campaigns to customers as opposed to the previous method of sending universal emails has already been proven to be extremely effective, and the growing importance of statistical analysis and machine learning in demand and trend forecasting just can’t be ignored. If you’re an e-commerce store owner and have not yet considered employing the wonders of AI, then you may just be missing out on the opportunity to get ahead of the game and hugely grow your business.

Justin Yek

Justin Yek

Partner & cofounder at Altitude Labs, creator of Metisa, former investment banker, public speaker, hobbyist musician

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