Do you ever wish you could see into your business’s future? Knowing which products will likely sell out, when customers are about to churn, or when to staff up your customer support department could transform your decision-making process. With that sort of crystal ball, you could permanently transform your business.
Predictive analytics makes seeing the future possible by using past and real-time data to forecast future trends. Learn the benefits of predictive analytics in ecommerce, including specific use cases to enable smarter decision-making—and see how they can help your business build a brighter future than you’ve ever imagined.
What is predictive analytics in ecommerce?
Predictive analytics is a data analysis technique that helps businesses forecast future growth opportunities and pain points. By using these predictive models, you can stay ahead of the curve—from anticipating product sellouts to nudging a customer to repurchase at the right time. With predictive analytics, you can proactively anticipate challenges or opportunities, rather than react to issues as they arise.
Here’s how predictive analytics works:
1. You collect historical and real-time customer data from sources like your ecommerce sales platform and customer relationship management system.
2. Machine learning algorithms use data mining to identify relationships within the data, and statistical algorithms uncover trends from these relationships.
3. The analytics software makes forecasts based on these trends, fine-tuning the predictions over time as more data is collected.
4. Businesses use predictions to make data-driven decisions.
Benefits of predictive analytics for ecommerce
Predictive analytics is a powerful tool, and it can help you improve operations across your business. Here are a few of its benefits:
- Fewer stockouts and overstocks. Leveraging predictive analytics can help you optimize inventory levels to ensure warehouses are stocked during peak seasons. This avoids backorders, which can lead to buyer frustration, while helping you eliminate unnecessary storage fees.
- Decreased cart abandonment. You can predict which customers are most likely to abandon their carts, then send reminders or discounts to encourage these customers to complete their sale.
- Higher conversion rates. Predictive analytics can help you segment your audience for marketing campaigns and offer personalized messaging for customers, helping you encourage conversions in the short term and improve customer lifetime value over the long term.
Uses for predictive analytics in ecommerce
- Inventory management optimization
- Personalization
- Marketing campaign improvement
- Customer service optimization
- Churn prevention
- Financial planning
- Price optimization
Here are six uses of predictive analytics in ecommerce:
Inventory management optimization
Having capital tied up in inventory that won’t move during slow periods isn’t ideal. Neither is selling a product on back order and forcing customers to wait for it.
Predictive analytics uses historical sales data to predict when sales demand will increase or decrease so that you can optimize inventory management. This allows businesses to restock products during sales spikes and decrease inventory when demand dips. This not only avoids sellout or overstock issues but also saves money in inventory holding costs.
Personalization
Have you ever wondered how Netflix recommends movies you’re interested in? Or how your favorite clothing store recommends products that fit your personal style? You can leverage predictive analytics to deliver more personalized experiences to customers by analyzing past purchase history and behavior.
Personalization is now an industry standard. 73% of respondents to a 2024 Salesforce study agreed that companies treat them as unique individuals, up from just 39% last year. Quick adaptation to and anticipation for your customer needs are both essentials.
You can also use personalization to improve customer lifetime value. Let’s say you run a skincare company and use predictive analytics to find that customers who purchased a certain face lotion are likely to repurchase after three months and are also likely to buy a certain face wash. You could send those customers a targeted email that reminds them to replenish their lotion and recommends the face wash to capitalize on this organic trend you’ve spotted.
Marketing campaign improvement
You can use predictive analytics to improve your marketing efforts by focusing on audience segmentation. You can use historical data and current trends to predict customer behavior among different customer subsets, then send the appropriate marketing materials to different audiences.
Imagine you’re designing a marketing campaign for your new clothing line. You could use predictive analytics to anticipate that repeat customers will likely convert from text messages and new customers will likely convert from emails. You can then send texts and emails to these different customer segments, allowing you to optimize your conversion odds without inundating your audience with marketing materials.
Customer service optimization
Many businesses are seasonal, with peaks and lulls throughout the year. Predictive analytics not only helps predict this seasonality but enables businesses to act strategically. If a business knows they have a busy period coming, it can prepare by scaling customer service operations accordingly.
For example, during the Black Friday or holiday season, you might expect increased sales volumes and new customers, which can result in higher help desk ticket volumes. You can anticipate this demand and hire extra customer support staff to improve customer satisfaction and maintain a seamless customer experience.
Churn prevention
Predictive analytics helps businesses identify customers at risk of churning and take proactive steps to retain them. Predictive analytics uses data like purchase history, cancellation frequency, or negative complaints to identify at-risk customers.
Suppose a subscription business uses predictive analytics to find that churn is most likely to happen at the nine-month mark or at certain times of the year. The company can then anticipate which customers are most likely to churn and send them personalized campaigns in order to improve customer retention.
Financial planning
At its core, predictive analysis is about forecasting—and one of the most important ways a business forecasts is by predicting revenue in order to allocate resources. With predictive analytics, you can project future revenue based on past trends, considering seasonality or market conditions. For example, you can use predictive analytics to forecast a low revenue stream in January and February after the peak holiday season.
Proper forecasting facilitates better cash flow management, allowing you to budget payroll, marketing, or inventory holding costs. When you know how much revenue to expect, you can make more informed decisions on how much to spend.
Price optimization
Predictive analytics helps businesses tailor pricing strategies to specific customer segments or individuals. For instance, you can analyze customer behavior to identify price-sensitive shoppers and offer them a discount.
You can also predict how price fluctuations might affect future demand and act accordingly, like by reducing the price of a bikini during winter and increasing the price during summer. In some cases, businesses use dynamic pricing to adjust prices based on real-time data, such as hotel or flight ticket prices.
For example, Tamburlaine Organic Wines increased its sales by 88% by working with a data analyst and using Shopify Scripts to alter pricing based on membership lengths, sales trends, and customer behavior. This ultimately allowed the company to optimize pricing for its customers and provide relevant product recommendations and promos.
Predictive analytics ecommerce FAQ
What are the types of analytics in ecommerce?
There are four main types of analytics in ecommerce:
- Descriptive analytics
- Diagnostic analytics
- Predictive analytics
- Prescriptive analytics
How can predictive analytics increase sales in ecommerce?
Predictive analytics helps ecommerce businesses increase sales by providing valuable insights that allow companies to optimize dynamic pricing, offer personalization, decrease cart abandonment, and increase conversions. Businesses can ensure they don't run into overstock or sellout issues by optimizing processes like inventory management.
Operationally, predictive analytics helps anticipate when to increase resources like customer service staff in order to deliver a seamless customer experience. Predictive analytics can also uncover trends in customer churn, allowing businesses to act proactively to retain at-risk customers.
What are the challenges of predictive analytics in ecommerce?
Predictive analytics relies on clean data. A challenge some e-commerce businesses might encounter is inconsistent data, which can result in inaccurate predictions. Implementing predictive analytics can be expensive for smaller businesses, requiring extensive technical expertise. There is also a risk of over-reliance on algorithms that fail to account for market trends, unexpected events or nuances in customer preferences.
Predictive analytics lets you guestimate future demand, potential churn, marketing effectiveness, and more.
