Your MIS reports will tell you where you are with your eCommerce business, but the bigger question one might have is ‘What will be the future of my eCommerce business?’
Some of the questions eCommerce predictive analytics can help you answer:
- List of customers who will have a higher propensity to purchase from us again?
- Top 5 products which might need change in Reorder level to avoid revenue leakage?
- How many subscription cancellations we might have in the forthcoming quarter?
- What kind of impact will the current social media image have on our future revenue?
There are quite a number of statistical modeling methods which can help in eCommerce predictive analytics. Here are a few and how they can help you in your eCommerce business.
- Linear Regression
- Logistic Regression
- Correlation analysis
- Time series regression analysis
- Repeated Measures ANOVA
- Binomial Logistic Regression
- ARIMA model
- Point-Biserial Correlation
Linear Regression can help you find the relationship between two attributes e.g your marketing budget and sales conversion. Logistic Regression model can help you predict consumers’ propensity to buy from you or cancel existing subscription.
Time series regression analysis can help you recognize patterns and trajectory in your overall eCommerce business or a category or specific product. Repeated Measures ANOVA can help you predict how your consumers would behave to repeated promotional offers over time. You can use ARIMA method to predict short-term demand for a product or category based on past data.
Imagine a big LED display in your office with the most important predictive metrics, wouldn’t that be good? Inputs from predictive analytics can help scale your business and stir towards the right direction.
Most eCommerce platforms don’t have built-in predictive analytics; you either have to build one ground-up or go for an independent eCommerce reporting tool which can be customized to your needs. Whether you buy or build, make sure the solution is scalable and can integrate with existing systems. Big Data technologies like Apache Cassandra, Spark, MongoDB can help you manage and process a lot of structured and unstructured data quickly and enable real time predictive metrics.