5 Ways Predictive Analytics Helps You Stay One Step Ahead in Ecommerce

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What would you do if your e-commerce store could see into the future? Your competitive advantage would be untouchable. You don't quite have access to that powerful of a tool, but predictive analytics comes close. 

This solution looks through your data sources and uses a model that predicts likely next steps. For example, do you know what characteristics indicate a customer is close to making a purchase? What would you do if you knew?

Explore these five use cases that improve everything from your inventory management to your customer experience.

1. Inventory Management

Your customer found what she was looking for, and is ready to buy. There's only one problem–it's out of stock. EConsultancy reports 25 percent of your abandoned carts come from consumers frustrated by unavailable inventory. Predictive analytics helps you keep appropriate inventory levels by tracking customer demand, overall supply and other metrics.

2. Promoting What's Hot

New trends explode overnight, thanks to the viral nature of social media. Your must-have products change rapidly, and failure to adapt puts you at a competitive disadvantage. Predictive analytics helps you spot hidden trends, so you're one step ahead off your rivals. Do you know what your customers will want next?
 

3. Improved Customer Experience

The global marketplace often makes it difficult to compete purely on price, but your customer experience serves as another important differentiator. One of the best-known examples of predictive analytics creating seamless processes for customers comes from Amazon. Each customer sees personalized content throughout the website, such as the recommended products relevant to their interests. The company also holds a patent for anticipatory shipping, which uses analytics insights to ship products to fulfillment centers before customers place orders. The shipping time drops drastically when the products reside at the closest warehouse rather than one across the country.

4. Price Changes

What would happen to sales if you changed the price on a popular product? What would happen to your margin and average order value? Trial and error may have a devastating effect on your business–or may provide a huge windfall. Predictive analytics uses machine learning to look at all previous changes, sales of similar items right now, behaviors of the customers looking at the item over time, and your traffic patterns to estimate the likely outcome of price changes before you make the change. Use this data to optimize your pricing strategy and gain a better understanding of the factors influencing your sales.

5. Spot Losers

Do you have pallets of inventory that simply won't move from your warehouse? Even the most experienced ecommerce store owners sometimes end up with product losers. The earlier you identify these troubled products the faster you can free up that working capital and move on to more profitable products. How much are you losing? Look outside those top 100 sellers–at the bottom 10% of sellers, to identify troubled products. Which products are there that seem like they shouldn't be? Could you have known this earlier if machines were monitoring performance and you could tell at a glance how far off your sales were from expectations–without opening Excel?
 

Predictive analytics puts your e-commerce data to work. Are you relying solely on ad hoc analysis and intuition? Or is your data driving your business forward, continually improving your assortment, pricing, promotions and overall growth and profitability?

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