By Prem Bhatia, CEO & Co-Founder, Graas
In today’s fast-paced eCommerce world, the ability to accurately predict future trends and anticipate customer behaviour can make all the difference between staying ahead of the competition and falling behind. Predictive analytics is a powerful tool that can do just that, giving businesses the insights they need to make smarter decisions, drive sales, and play a key role in driving industry growth.
However, despite the growing buzz around predictive analytics, most eCommerce companies barely scratch the surface of its potential. Most companies today rely heavily on dashboards to review past performance. While this is important, it only answers the question of “what happened.” The more critical questions are “why did it happen?” and “what will happen next?” To answer these questions, eCommerce businesses need to move beyond traditional analytics and simple data analyses, and venture into predictive analytics.
One most common application of predictive analytics is in demand forecasting. Knowing what the demand for your product is and when it is expected to spike and dip can be a game changer. Creating accurate forecasts is critical, particularly since competition is fierce, and margins can be razor-thin in eCommerce. Today most companies are comfortable doing basic time series analysis, which simply extends past trends into the future. But as eCommerce gets more and more complex, it is important to start integrating more variables into this, such as
⦁Ad Spend: What is the company spending on advertising and what is its impact on sales.
⦁Competitive Intelligence: Understanding what competitors are doing and how the market is trending.
⦁Customised Calendars: Factoring in specific dates and events that could influence sales, such as holidays or promotional periods.
By overlaying these factors onto traditional forecasting methods, one can achieve more accurate and actionable predictions. Sophisticated models can help simulate different scenarios, which in turn can directly influence business strategy. For example, you could run simulations to see the outcome of two different strategies: a) Increasing ad spend by 10% every day of the quarter versus b) Concentrating the spend on specific sale days. You could also run brand wise simulations to identify which products would benefit most from increased marketing efforts. Insights such as these can help bridge the gap between predictions and strategy.
Another impactful application of predictive analytics is in dynamic pricing. By leveraging real-time data, eCommerce businesses can adjust their pricing strategies based on demand forecasts, competitor pricing, and customer behaviour. This ensures that prices remain competitive and aligned with market conditions, optimising sales and maximising profit margins. For example, if an eCommerce brand identifies a surge in demand for a particular product, it can automatically increase the price to capitalise on the trend while also offering discounts on slower-moving items to clear inventory.
Product bundling is yet another area where predictive analytics can help refine strategies. By analysing purchasing patterns and customer behaviour, businesses can create intelligent product bundles that appeal to specific customer segments and thereby increase the average order value (AOV). For example, if a customer frequently buys sports equipment, the eCommerce business can offer a bundle that includes related accessories at a discounted price. This increases AOV and enhances the overall customer experience by offering relevant and convenient options.
As with any new initiative that pushes the boundaries, with predictive analytics too there can be resistance to adoption. So, not only is it important to build robust and accurate predictive models, but it is also important to effectively communicate their value and implications to all stakeholders and decision-makers. This is crucial for ensuring that insights are translated into actionable strategies that drive business success.
To truly catalyse growth in the eCommerce sector, businesses need to set new standards in predictive analytics that can have a transformative impact. This means moving beyond traditional dashboards and historical analysis, and adopting newer approaches that help gain a deeper understanding of the factors driving their performance and make informed decisions about the future.