E-commerce Market Research

With the prevalence of big data, data science and market research are becoming increasingly interconnected disciplines. Since 2014, the number of companies that utilize big data analytics in their market research has grown from 31% to 46%. Combing these two disciplines enables companies to benefit from the available data about their industry and use it to make informed business decisions. This is especially true for the growing E-commerce industry.

History of E-commerce

Since its emergence in 1982, the E-commerce industry has grown to $6.3 trillion in 2023. This industry is comprised of such large companies as Amazon, eBay, Kroger, Apple, Etsy, and many others. However, numerous small and mid-size businesses are also a part of this market. With such a large and diverse industry, it’s crucial for businesses to conduct their own market research, or hire a professional, to ensure the market research insights are tailored to their organization.

Data Science and Market Research

The backbone of both data science and market research is, unsurprisingly, data. Both data science and market research projects begin with collecting data. In market research, there are two main data collection methods: primary research and secondary research. Primary research involves surveys, questionaries, interviews, and focus groups. Secondary research is less personal and can utilize government statistics, industry statistics, and industry reports.

After collecting data, it can then be used to conduct market research. One such market research technique is identifying industry trends. Industry trends can be divided into three different types: intermediate trends, long-term trends, and secular trends. Intermediate trends are short-term trends that represent sudden changes in the marketplace. Intermediate trends are often triggered by economic, social, or political actions and events. Long-term trends are those that last from a half to a full year. This type of trend is less influenced by short-term changes and is often based on market growth, supply, and demand. The final industry trend type is secular trends. These are the longest-lasting trends and can range from ten to thirty years. Economic and societal changes have the most impact on secular trends.

Trend analysis is a common technique in data science. From a data science perspective, it involves identifying a need for the analysis. Next, a time frame should be chosen for the analysis. Data must then be collected and charted using a data visualization tool. After charting the data, it can be reviewed to identify trends in the data, such as seasonality. In the trend analysis below, the chart shows that data analyst jobs are on the rise, while data scientist and data engineering jobs are stagnant or on the decline. More sophisticated trend analysis tools, such as fitting an equation to the data, can also be used to further the analysis.

Fig 1. Trend Analysis https://www.interviewquery.com/p/data-science-job-market-disappearing

Another key aspect of market research is analyzing market opportunities. Market opportunities are based on several factors, such as market size, sales data, funding trends, production costs, marketing costs, and retail prices. Understanding market size helps businesses know the maximum size of their target audience. Sales data allows companies to make predictions about profits. Knowing production costs, marketing costs, and retail prices factor is essential for predicting revenue.

Data science offers many techniques to aid in analyzing market opportunities. It can be used to provide visualizations of key data, aiding decision-making. Regression analysis is a helpful tool for predicting future data, such as sales data, production costs, marketing costs, and retail prices. Additionally, optimization tools can improve marketing plans, helping marketing budgets go further. Lastly, machine learning can be used to perform predictive analyses of market size and funding trends.

The importance of customers in the E-commerce industry should not be underestimated. Talking to existing and potential customers is an ideal way to gather data that can inform business decisions. Often, the data customers provide in surveys and interviews will be in the form of qualitative data. Qualitative data is non-numerical, as opposed to quantitative data which is based on numbers.

Data science offers many different approaches to utilize and understand qualitative data. Content analysis reviews text for certain key words and counts the occurrence of these terms. This technique transforms qualitative data into quantitative data that can be utilized more easily. Thematic analysis reviews text to identify and interpret patterns within the data. Narrative analysis groups related topics and concepts to help researchers better understand customers’ needs and pain points.

While data science and market research are not synonymous, these practices have a high degree of overlap. By utilizing data science techniques, the results of a market research study can be improved. These results can then be used to make informed business decisions for companies in E-commerce and other sectors.