7 Effective Ways to Use Predictive Analytics to Improve your E-Commerce Sales

Technically, predictive analytics is a data analysis technique in e-commerce business. It helps businesses forecast future growth opportunities and pain points. E-commerce businesses that use predictive analytics models do manage to stay ahead of the curve by deploying the technique to anticipate product sellouts and nudging their customers to repurchase at the right time. The technique allows business owners to proactively anticipate challenges or opportunities instead of reacting to issues as they arise particularly as related to sales. Predictive analytics makes seeing the future possible by using past and real-time data to forecast future trends as they’ll affect sales. If well deployed, predictive analytics techniques can help business owners to permanently transform their businesses and set them on the path of profitability.

Here are seven effective ways to use predictive analytics to improve your e-commerce sales:

1. Financial Planning

Effective forecasting depends hugely on predictive analysis. For any e-commerce business, one of the most important ways to make business forecasts is by predicting revenue in order to properly and adequately allocate resources. With predictive analytics, it is possible to project future revenue based on past trends taking into account the seasonality or market conditions. You can use predictive analytics to forecast what time and season to expect low or high revenue streams and to plan for it. With proper forecasting, you can do better cash flow management. This allows you to adequately budget payroll, marketing, or inventory holding costs. You can manage your business better if through forecasts, you are able to know how much revenue to expect. With such vital information, you can make more informed decisions on how and when to spend and how much to spend in your business.

2. Personalization

You can leverage predictive analytics to deliver more personalized experiences to your customers. This you do by diligently analyzing their past purchase history and behavior. With such information, you can recommend products that fit the personal styles and needs of your customers. It is such products they are most likely to purchase. You can use predictive analytics to locate customers who purchased certain types of products in the past, then recommend same products or something that complements the products to the customers. Such information can be passed to these customers by way of targeted emails. These mails will help to remind these customers to replenish their stock and to recommend other products that complement the ones they’ve purchased earlier.
 
3. Marketing Campaign Improvement

If you focus on audience segmentation, you can use predictive analytics to improve your marketing campaigns. It simply involves the use of historical data and current trends to predict customer behavior among different customer subsets. What you do after that is to send the appropriate marketing materials/messages to different audiences based on the segmentation. You can have very effective marketing campaigns if you are able to use predictive analytics to anticipate that repeat customers will likely convert from text messages and new customers will likely convert from emails. Craft the right texts and messages to suit these marketing channels. You can get the best results by sending these messages in form of texts and emails to the different customer segments you’ve identified since it allows you to optimize your conversion odds without inundating your audience with too much marketing materials.

4. Price Optimization

Predictive analytics helps e-commerce businesses tailor their pricing strategies to specific customer segments or individuals. You can for instance carefully analyze customer behavior to identify price-sensitive shoppers. You can get such shoppers to convert if you offer them a discount. Predictive analytics can also help you to predict how price fluctuations might affect future demand based on seasonal influences. With such vital information, you can act accordingly and take good advantage of when it is right to cut prices (for instance at end-of-year sales) and when to raise prices on your products/services.

5. Customer Service Optimization

Many e-commerce businesses are seasonal. They habitually have peaks and lulls all year round. Predictive analytics not only helps predict this seasonality but enables businesses to act strategically. If you are a business owner and you know when the busy period of your business is approaching, you can adequately prepare to meet the rush by scaling up your customer service operations in tandem. With predictive analytics, you can anticipate a surge in demand then engage extra customer support staff to meet the surge. This way, it is possible for you to significantly improve customer satisfaction and maintain a seamless customer experience.

6. Churn Prevention

With predictive analytics, e-commerce business owners can identify customers at risk of churning then take proactive steps to retain them. Predictive analytics relies on valuable customer data like purchase history, cancellation frequency, or negative complaints to identify “at-risk” customers. With predictive analytics, you can anticipate which customers are most likely to churn. Once these customers are identified, you can deploy your marketing skills by sending them personalized marketing messages to retain them. That significantly helps to prevent churn.

7. Inventory Management Optimization

To have a significant portion of your capital tied up in inventory that’s not moving during slow periods isn’t healthy for the overall growth and survival of your business. Selling a product on back order and forcing customers to wait for it isn’t healthy either. Technically, predictive analytics uses historical sales data to predict when sales demand will increase or decrease. This vital information can help you to optimize your inventory management. Good inventory management allows businesses to restock products during sales spikes and decrease inventory when demand dips. This management technique not only helps your business to avoid sellout or overstock issues but also helps to save money in inventory holding costs.