Data will talk to you if you’re willing to listen to it.
“Every day, we create 2`.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone” says IBM.
Data has grown out of its avatar as information in bits and bytes form. Today, statistical tools help businesses visualize data in bar graphs, charts, trends, curves and many other forms that quicken decision making.
Unfortunately, despite the increase in data creation and accessibility, enterprises are yet to leverage it as a strategic benefit. Too many businesses underestimate the power of data to give insights. especially eCommerce stores where every single inch of the website creates and archives some of the data.
Of course, most eCommerce store owners follow the good habit of taking MIS reports based on top 100 eCommerce metrics.
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 a 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?
Techniques Used For eCommerce Predictive Analytics
There are quite a number of statistical modeling methods which can help in eCommerce predictive analytics. Here are a few techniques that can help you develop insights about your eCommerce business.
- Linear Regression
- Logistic Regression
- Correlation analysis
- Time series regression analysis
- Repeated Measures ANOVA
- Binomial Logistic Regression
- ARIMA model
- Point-Biserial Correlation
Imagine a big LED display in your office with the most important predictive metrics, wouldn’t that be good? It will give a periodic display of your most important metrics that you should track and stay updated. Compare them against benchmarks and whenever there is a deviation, corrective measures can be taken.
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. With their capabilities, you must be able to find insights out of chunks out of data even if they are scattered and heterogeneous in nature.