The Properties of Predictive Analytics That eCommerce Benefits from Today

Dmitry Ioda

Dmitry Ioda

Head of the Retail&eCom Department at Andersen

Jan 20, 2022
6 minutes to read
  1. <em>Definition</em>
  2. <em>The difference from traditional methods of analysis</em>
  3. <em>The tools of predictive analytics eCommerce benefits from</em>
  4. <em>1. Build effective advertising campaigns</em>
  5. <em>2. Improve pricing</em>
  6. <em>3. Increase sales</em>
  7. <em>4. Managing resources better</em>
  8. <em>5. Reduce the incidence of fraud</em>
  9. How to integrate predictive analytics into your processes
  10. Conclusion

Several years ago, the UK's National Health Service's blood donation agencies switched to predictive analytics software. Better prediction of patient blood needs has enabled hospitals to cut transportation costs fivefold. More importantly, warehouses were able to decrease the wastage of this perishable biological material by 30%. In such a case, accurate demand forecasting means saving lives. And, although this forecasting is not always so critically indispensable, it is essential in many areas. What are the properties of predictive analytics eCommerce benefits from? What impact does it have on revenue and sales? How can companies apply these technologies? Read more in our article.


Predictive analytics is a software analysis of existing information to make quantitative or qualitative forecasts for the future. In such calculations, the program uses statistical data along with ML algorithms. How has predictive analytics improved eCommerce? Well, it estimates the demand, classifies the audience, and predicts customer behavior. That is possible by analyzing the actions of consumers in the past, their personal information, and external data from various sources.

The difference from traditional methods of analysis

The prerequisite of traditional calculations is cyclicality. This means that the scenarios repeat at regular intervals. The calculation results can be presented as follows: alt text Traditional methods are suitable for long-term forecasting or products with a rich sales history and established demand.

Unlike statistical methods, predictive analytics is used when there is no stability. This approach is well-suited for short-term and medium-term forecasts. For example, the calculation is performed for:

  • a new product with no sales history;
  • goods with no constant demand (seasonal clothing, sports equipment, umbrellas, etc.);
  • items that are only popular for a short period (for example, fashionable clothes).

Another distinguishing feature of predictive analytics is the use of a large amount of information from various sources. These systems don't rely simply on data about the product itself - such data as its cost, purchases, sales, as well as information from customer service. It also leverages the data collected from CRM platforms, social media, and apps. Some external factors influence the results of the calculations as well, such as weather forecasts, legislation, economic indicators, holidays, and data from third parties. alt text It stands to mention that the program is fueled by ML. The flexible systems are self-learning and adapt to a rapidly changing environment. This ability of software to find relationships in seemingly unrelated streams of data makes it an excellent assistant for calculations in complex and unpredictable situations on the market.

The tools of predictive analytics eCommerce benefits from

The ability of a business to anticipate future events is one of the factors for its success. With this tool, the companies predict demand. The companies can also foresee the buyers’ actions and influence them at the right moment by building their marketing campaigns accordingly. Predictive analytics tools enable companies to:

1. Build effective advertising campaigns

Do you know that up to half of all ad campaigns don't bring the desired results? Predictive analytics estimates the interaction of clients with the brand based on many factors. They are the history of past purchases, the rating given to products, navigation on the store's website, subscription to newsletters, time to purchase, and more. Then, the system anticipates further actions of buyers. This allows businesses to:

  • divide the audience into groups and send each group personalized advertising messages;
  • improve the interaction of visitors with the site, including the modification of pages in real time for each specific buyer;
  • provide the best customer sup