4 Smart Steps to Set Up E-Commerce Analytics

You must make sure your business has a solid foundation if you want to effectively harness the power of e-commerce analytics. Analytics can only be effective if it manages to combine the right tools, has clear objectives, and actionable strategies. Such analytics effectively begins with integrating key platforms and ensuring seamless data flow across all your e-commerce business operations. Here are 4 smart steps you can take to make it happen.

Step 1: Set Clear Goals

If you want to make the most of your e-commerce analytics, you must begin by setting clear, measurable goals. Right from the outset, you should decide whether you want to boost sales, reduce cart abandonment, or increase customer retention. Such specific objectives will help you focus on the data that matters most to your progress. It is such data you must diligently track to know how well you are doing. Significantly, well-defined goals act as a roadmap to chart your progress. These goals help to align your efforts and ensure your analytics strategy supports the business priorities you earlier set out.

Step 2: Choose the Right E-Commerce Tools

To achieve your set goals and objectives, you need the right e-commerce tools.  These tools will help put actionable insights for optimizing your business at your fingertips. The most popular, most preferred and commonly available of these tools are Google Analytics tools. These tools are versatile tools that help to track website traffic, user behavior, and conversions. They help provide detailed insights into where your visitors come from, how they navigate your site, and which pages drive the most engagement. Google Analytics tools are particularly very handy to provide a robust and free platform to monitor your e-commerce website performance. Data from these analytics tools help the business owner to understand customer journeys in-depth and to make the right marketing decisions as needed.

Step 3: Diligently Track Events/Activities

With your goals well set, you can begin to track relevant events that provide insights into your customers’ behaviors. You must pay particular attention to events relating to specific actions that users take on your website. These are suchlike events as clicking a product, adding items to the cart, or completing a purchase. If you diligently track these events, it helps you to bridge the gap between your goals and actionable insights. This is made possible because these events manage to reveal how users interact with your site and where they might encounter friction. If for instance you have a goal to reduce cart abandonment, you can diligently track events like “Add to Cart” and “Begin Checkout” to pinpoint where users are dropping off and identify potential causes. By directly addressing such issues as identified, it is possible for you to create a smoother customer experience. If you are able to do this correctly, it helps to improve your overall conversion rates.

Step 4: Deploy Your Data to Good Use

Even though collecting valuable analytics data in your e-commerce business is hugely important, deploying such data to good use is even more important. You can deploy your analytics data to good use by using the insights to identify trends and areas for improvement. If for instance your data shows a high drop-off rate at checkout, you can remedy this by streamlining the process a bit as well as adding some trust signals. At this point, you can even offer some percentage discounts that could help encourage completion of the process. In the same vein, if you notice a particular product page getting a lot of traffic but low conversions, there is definitely something the matter. In this kind of scenario, you can then choose to optimize the page by adding better images or making the descriptions more compelling. This is where A/B testing becomes hugely important. A/B testing enables the business owner to routinely compare different strategies and seeing what resonates with the audience. The key is to continuously test and adjust your marketing strategies based on your A/B test findings.

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.