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E-commerce analytics have greatly evolved with the advancement of data analytics technologies. This evolution enables consumer brands in the e-commerce industry to improve their productivity and profitability. Tools that incorporate AI, Machine Learning, and Workflow Integration are now being used to expedite processes, enhance decision support, and operate at greater speed and scale. In this article, we will explore the development of e-commerce analytics and the importance of continued investment in platform development for digital shelf analytics vendors to maintain a competitive advantage for their customers.
The 4 Levels of Data Analytics
Data analytics can be classified into four levels: descriptive, diagnostic, predictive, and prescriptive analytics. Each level provides valuable insights that help e-commerce leaders make data-driven decisions.
Descriptive analytics focus on understanding what happened in the past. It provides scorecard-type dashboards that track the performance of your SKUs in online stores. These dashboards reveal the factors that positively or negatively influenced your online sales. By analyzing this data, you can gain insights into the current state of your business and identify areas for improvement.
For instance, let’s consider a scenario where sales of a product are slow on Retailer X despite high traffic to the product landing page. On the other hand, Retailer Y has similar traffic but higher sales. By analyzing the data, you may discover that the content on Retailer X’s product page is of poor quality, negatively impacting conversion rates. Alternatively, you might find that the product is not available for immediate shipping on Retailer X but is readily available on Retailer Y. Descriptive analytics allows you to understand the reasons behind these trends.
Diagnostic analytics help you uncover why something happened. By drilling down into the data, you can identify the root causes behind certain outcomes. This level of analytics enables you to discover the factors that led to a particular result.
Continuing with the example above, diagnostic analytics would help you understand why Retailer Y is experiencing higher sales despite similar traffic to Retailer X. It might reveal that Retailer Y has a more compelling product description, better user reviews, or more prominent placement on the website. Diagnostic analytics helps you delve deeper into the data and uncover the underlying reasons behind specific outcomes.
Predictive analytics focuses on forecasting what might happen in the future based on historical data. By analyzing patterns and trends, you can make informed decisions that positively impact future outcomes.
Suppose you have a seasonal product with a limited life cycle, and you want to minimize waste by producing just enough to meet demand. Predictive analytics can analyze historical sales data, identify patterns, and provide insights into future demand. This allows you to make efficient production decisions and avoid excess inventory.
Prescriptive analytics go beyond predicting future outcomes and provide recommendations on what actions to take. This level of analytics empowers e-commerce leaders to make better decisions while reducing human effort.
Advanced analytics tools that utilize Artificial Intelligence, Machine Learning, and Workflow Integration can automatically execute actions and measure their impact. These tools continually improve and refine their recommendations, enabling consumer brands to make smart decisions and optimize their operations.
Evolution of E-commerce Analytics
Over the years, e-commerce analytics tools have evolved to keep up with the changing landscape of data processing and analysis. What used to be a simple task of harvesting and processing data from multiple online sources in the early 2000s has now become a sophisticated process.
Today, consumer brands can leverage advanced and automated decision-making tools, such as digital shelf analytics and e-commerce automation suites. These tools enable brands to extract valuable insights from vast and complex data sets. Workflow integrations further reduce manual effort and increase productivity for e-commerce teams.
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E-commerce analytics have become indispensable for consumer brands in the online marketplace. The four levels of data analytics – descriptive, diagnostic, predictive, and prescriptive – provide valuable insights that help e-commerce leaders make informed decisions and optimize their operations.
Investing in platform development for digital shelf analytics is crucial to maintain a competitive advantage in the rapidly evolving landscape of e-commerce. By leveraging AI, Machine Learning, and Workflow Integration, brands can speed up processes, improve decision support, and achieve greater productivity and profitability.
To stay ahead in the e-commerce industry, it is essential for consumer brands to embrace advanced analytics tools and technologies. By doing so, they can unlock valuable insights and drive their success in the digital marketplace.