This article originally appeared on SME World.

A lot has been written about the growth of the eCommerce sector and often success is equated with numbers. Conversion rates, website traffic metrics and revenue numbers dominate the decision-making process. But for brands, the growth path today is beyond just monitoring numbers on a dashboard.  

Consumers now have the power of choice. The shopping landscape has changed a lot, with consumers taking control and navigating diverse online marketplaces, social media and more at their fingertips. Understanding the reasons behind dynamic shifts in behaviour is crucial for the success of an eCommerce brand. While sales numbers provide a basic understanding of your eCommerce performance, they don’t tell you “why did this happen,” and “what will happen next if you make some changes”.

Game of understanding  

This is where predictive analytics comes in. It allows eCommerce business owners to decrypt this code and find insights into what drives customer decisions and helps anticipate their next move. By finding the hidden patterns behind consumer behaviour and market trends, eCommerce is evolving from a numbers game to a game of understanding.  

Personalization, over the years, has helped eCommerce brands generate sales numbers and become an essential part of the industry. But creating these bespoke experiences across multiple channels comes with a challenge: data fragmentation.  

The culprit? It’s the sheer volume and variety of data bombarding eCommerce businesses. We're not just talking about the usual purchase history or website browsing behaviour (first-party data). Businesses today also use second-party data (shared insights from partners) and third-party data (aggregated consumer information). This data, sitting in silos across different platforms and systems, must be cleaned and brought together to be used to generate more sales.  

When we have a clean and structured dataset (historical and real-time data), AI-powered predictive analytics algorithms can identify hidden patterns and trends that would otherwise remain buried. 

Personalise experiences  

By analysing purchase behaviour, social media interactions, historical sales data, and market trends, predictive analytics can anticipate future customer actions with accuracy. It saves brands from recommending winter coats to a customer browsing on a rainy day. Based on past purchase history, brands can send a targeted birthday discount. These personalized experiences strengthen customer loyalty and generate meaningful engagement and conversions.  

And predictive analytics is not just limited to personalization. Based on the past data, it can predict that your inventory for a particular SKU is likely to go out of stock in the next 3 days. This allows business owners to adjust inventory levels proactively, avoiding lost sales and frustrated customers. It can also help you adjust prices by analysing at which price customers are more likely to purchase a product. This helps you remain competitive and profitable.  

Not long ago, Lululemon ended with $1.7 BN of unsold inventory. Even All Birds had a 20% margin drop owing to the $11.6 MN inventory's value dropping below its book value. On the other hand, about 23% of brands lost buy-box on Amazon last year due to stock-outs. All of these losses are because brands could not gauge the demand for their products.  

Demand forecast

With predictive analytics, eCommerce brands can accurately forecast the demand. So they can prevent stock-outs and excess inventory, meaning there’ll be no lost sales due to stockout, less capital tie-up, and storage costs.  

Demand forecasting also helps brands manage cash flow effectively by having the right amount of stock and avoiding unnecessary spending or stock-related disruptions. Brands that leverage demand forecasting are better equipped to manage their inventory assets more efficiently and also help determine if they’ll survive and dominate the market segment or not.  

There are many eCommerce brands out there that engage in price wars with their competitors. If their competitor is selling something at a 20% lower price, they blindly follow them and reduce the price, which hurts their profit margins. Predictive analytics offers a better solution – dynamic pricing.  

Dynamic pricing algorithms work on vast datasets and consider factors like demand fluctuations, inventory levels, and even individual customer preferences to suggest prices that maximise revenue while also maintaining competitiveness. It allows brands to offer targeted discounts and promotions to specific customer segments based on their purchase history and preferences.  

This helps build loyalty because if someone’s purchasing a lot from the brand, they get lower prices compared to one-time shoppers. And because loyal customers get lower prices, they’ll come back for more in the future. So, you can stay ahead of your competitors by adjusting pricing instantly to respond to competitor moves, market fluctuations, or changes in demand. 

Loyalty the key

 Losing customers is costly. After all, nearly 80% of the brand’s profit comes from 20% of retained and loyal customers. Therefore, it’s crucial to identify those at risk of churning so that you can intervene before they jump ship.  

Predictive analytics analyses customer behaviour to identify customers with decreasing purchase frequency, reduced engagement, or comparison shopping behaviour, which indicates a high churn risk.  

When brands notice high-churn risk, they can implement personalized incentives, loyalty programs, or proactive customer support to re-engage at-risk customers and nurture loyalty. They can recommend relevant products to customers based on their purchase history and preferences, which helps improve website navigation and reduce bounce rates. By retaining high-churn-risk customers, you can increase their lifetime value, boosting overall profitability and growth.  

Predictive analytics isn't just about sales and marketing. It can also optimise the brand's internal operations, like analysing equipment usage data to anticipate potential breakdowns before they occur, reducing downtime and maintenance costs.  

Brands that analyse past delivery data can predict peak delivery periods and optimise logistics routes. This ensures customers get their packages on time and brands pay less transportation costs.  

In eCommerce, if brands want to stay relevant and dominant, they must look beyond the surface-level data. By leveraging predictive analytics, they can find hidden insights, anticipate trends, and make data-driven decisions that go far beyond just boosting sales. From optimising inventory and pricing to building customer loyalty and streamlining operations, predictive analytics allows brands to deal with the complexities of eCommerce with confidence.

Authored By Prem Bhatia, Co-Founder and CEO, of Graas.