Ecommerce has been growing at a faster pace than traditional shopping, and there’s no sign of it slowing down.

Research indicates that eCommerce sales will account for 16% of the total retail market in 2022– a figure far higher than the previous year’s eCommerce retail market share (13% in 2021). By 2022, eCommerce sales are expected to reach $5.4 trillion.

An enormous amount of data is generated each second, which companies are using worldwide for several purposes like web log analysis, traffic analysis, and customer profiling.

The exponential increase in big data presents tremendous possibilities for businesses.

Benefits of Using Predictive Forecasting for Ecommerce

Predictive forecasting can be used to forecast expected sales for eCommerce businesses with confidence.

This means that businesses are better equipped to make smart decisions, like how many items they should stock or whether they can afford to lower prices without sacrificing profits.

Additionally, this would help businesses effectively allocate their resources and adjust their business strategies accordingly.

Top Predictive Forecasting Trends For 2022

Predictive forecasting data suggests that a number of key drivers and technological shifts will shape the future of eCommerce in 2022 and beyond.

Here are the top predictive forecasting trends for 2022:

Competitive Website Homepages: The first is that retail websites must become more competitive on their homepages for both new and returning visitors. This will require new technologies and site improvements, such as improved personalization and localization, the ability to add products dynamically throughout the sales funnel, and an increased focus on the shopping experience.

Demand for Content Marketing and SEO: The second driver that will be shaping retail websites is the increasing importance of content marketing and personalization for SEO. In 2017, Google began using machine learning technology to process user experiences on eCommerce sites and present them more prominently in search results. This trend is likely to continue growing, which will increase the importance of sites’ content marketing efforts and user experience. Leading eCommerce agencies such as Bing Digital leverage data to upscale their client’s revenue using a combination of marketing analysis techniques. In the future, eCommerce marketing companies that will use data will always have an upper hand as compared to their competitors.

Automated Recommendations: A third driver will be the increasing use of automated recommendations based on previous shopping cart activity or searching behavior. These technologies would suggest items like: “People who bought this also bought these items,” “Customers who viewed this item also viewed these items,” or even more advanced solutions, such as “Most popular products recommended based on past purchases.”

Rise of Mobile Devices: The shift from a PC to a mobile device world is perhaps one of the most significant in recent times. In 2014, mobile surpassed PC Internet usage in the UK, and in 2016, search from a mobile device surpassed that from a PC for the first time. This shift will continue to grow as more consumers purchase smartphones and tablets. Consequently, retailers must design their websites to accommodate smaller screens and optimize speed. Mobile page load speeds have been an increasing concern for both consumers and search engines, so this is an area that many retailers must focus on improving.

Voice Assistants: Predictive data shows that consumers are becoming more comfortable with voice assistants such as Amazon’s Alexa, Google Home, and Apple’s Siri when shopping online. Therefore, retailers will be challenged to accommodate these new technology preferences in the future. While the technology is still in its infancy, approximately 40% of US consumers already own a smart speaker and more say they are likely to purchase one soon. The growth of voice assistants will impact how consumers search for products and consider purchases. This new technology will influence their purchasing behavior by providing product recommendations based on previous online searches and product purchases.

Machine Learning and AI: Another important technological shift is the continued growth of machine learning and artificial intelligence. For example, it is predicted that in 2022, AI will create more jobs than it will destroy. This suggests an increased need for employees with digital skills to manage these technologies in eCommerce stores to increase their online effectiveness.

One-day Delivery: One final key factor shaping eCommerce is the growth of one-day delivery. This is the result of several factors, including consumer demand for ever-faster shipping speeds and improvements in logistics technology that allow retailers to fulfill such orders. One-day delivery is already available in certain cities and regions worldwide and will become more common as eCommerce gathers pace.

Examples of Predictive Forecasting

1. In 2014, Google analyzed over 100 of the most popular Android apps and uncovered some interesting stats about how they use data. For example, Google found that most apps determine a user’s location almost instantaneously when the app is opened but then stay in the background for the rest of the day, gathering data on other habits. They also found that apps pull out personal information more often than you might think – even if it’s only done to provide a more personalized experience.

2. In 2015, Facebook implemented an artificial intelligence software called “DeepText” to analyze and understand all of its users’ posts in real-time, as well as who those posts were being shared with. This was used to help prevent the spread of violence or terrorist propaganda on their website by detecting any dangerous language.

3. In 2015, Google introduced “RankBrain” to its machine learning program to help it understand search result intent better. The idea was that if RankBrain could detect one user’s intention by recognizing their pattern of previous searches, it would be able to make accurate predictions on future searches. This proved effective for Google over the next few years, and the program has since become one of the most important parts of their business.

4. In 2016, Netflix launched a new feature called “Suggested TV,” which provides recommended TV shows based on your viewing habits. This is done through predictive forecasting by tracking your recent searches to get an idea of what kind of shows you’re watching and what you might be interested in.

5. In 2017, Google released a video talking about how they use predictive forecasting to better predict people’s flow on their website. For example, suppose a user is looking for information on a local restaurant. In that case, Google will analyze data from previous searches to determine where this person is located and show results for nearby restaurants. This allows Google to display relevant and timely information, which is important for businesses.

The above examples of predictive forecasting in the eCommerce world have been used over the years to improve website rankings, recommendations from streaming services, product development decisions, advertisement relevance, and more. There are many other examples that can be used to show how predictive forecasting has been used to improve businesses across all industries.

Challenges in Using Predictive Forecasting for eCommerce

The biggest challenge for companies using predictive analytics is to figure out which data they should be collecting and how it should be used. The type of information that would be useful for a specific company depends on their interests and what they hope to achieve through forecasted data.

Another challenge is dealing with large amounts of data. It takes a lot of planning and time before you even start gathering the data required for effective predictive forecasts. Once you have everything you need, you have to put it all together and use special software to make sense of it all. This is especially necessary if your business has a lot of customers or a high number of different products to keep track of.

Conclusion

In 2022, predictive forecasting for eCommerce will have many different applications within all areas of business operations. Combining the information from customer searches with historical data from previous purchases and other variables will help companies make better decisions about their inventory and what products they should develop next.