Visits, visitors, events, conversions, orders, time on site, page views, subscribers, average order value, etc. are just a fraction of the e-commerce KPIs and metrics that every online retailer should keep an eye on. All these pieces of data are just the perfect fit for predictive analytics and sales forecasting.
Its huge amount of data which might be modeled in order to project future demand and overall e-commerce performance. This is leading to crucial benefits pushing online retailers further in terms of effectiveness, profitability, and performance.
Let’s say you are selling clothes and your best seller are t-shirts. It seems your range of t-shirts is pretty good and for your customers, they are a reason to visit your store. The peak of t-shirt orders is somewhere in the last weeks of April and the first couple of weeks in May. Your store is also offering shorts in different styles and colors but for one or another reason they are slow movers.
Having this in mind you might try to create a nice and shiny bundle of t-shirts and shorts. Or to give a specific discount for shorts to every t-shirts customer.
Improved inventory and resources management
This benefit is heavily related to sales forecasting and demand prediction. Too much stock will fill your inventory and will lock cash resources. Insufficient availability means slower delivery, resulting in a lack of customer satisfaction and decreasing customer lifetime value. Finding such tiny balance is a tricky game where predictive analytics is really helpful. Forecasting future sales is a key instrument for clever inventory and resources management.
By knowing how many you are going to sell, your cash flow management becomes a much easier process. Relying on historical business data and its modeled future performance forecast gives a chance for better planning in so many directions – financial, sales, marketing, advertising, staff, etc. Any of this verticals might be cash blowers if they are not perfectly optimized for your e-commerce business. Revenue forecasting is solving this particular issue.
We already have discussed how automated product recommendations and suggestions are turning into reality in a dedicated blog post some time ago. They are a result of predictive analytics efforts and shifts online shopping from information to recommendations. This way your online store is able to increase the conversions, i.e. sales, with the same amount of traffic. Your promotions and cross sale offerings will be much more attractive, which will help you build customer loyalty. By achieving this you`ll extend customer lifetime value, one of the crucial KPIs of any e-commerce business.
Predictive analytics analyzes pricing trends in correlation with sales information to determine the right prices at the right time to maximize revenue and profit. Pricing is managed using a predictive model that looks at historical data for products, sales, customers, and more. Based on this model, the price for a given product and customer can be predicted at any given time. Amazon is a heavy user of predictive pricing.
If you link pricing trends with sales information and extract the correlation, you`ll be able to determine the right prices at the right time in order to maximize revenue. The predictive analytics modeling is able to create an algorithm that looks at historical data for products, sales, customers, and more. Based on this, the price for given product and customer might be predicted at any given time. Guess which e-commerce website is a heavy user of this approach? You are right – Amazon.
Well, right now you might benefit with A4Everyone sales forecasting tool a4RetailStores.