How Data Science is Driving Profitability in the Ecommerce Industry

Data collection methods have undergone a revolution in recent times. Prior to the 1990s, market research served as a “single” source for collecting data on product preferences and demand for the product at different price points. Based on this, the companies would then decide on prices. Survey data is structured to answer certain specific questions and is therefore called “designed” or “structured” data.

The collection method is intrusive, expensive and limited, so it is no wonder that response rates to these surveys have declined over the years. Now opposed to it are modern methods of data collection which involve collecting data from multiple sources. The search engines of e-commerce companies use Machine Learning (ML), deep learning algorithms, store data on individual customer behaviors, analyze their consumption habits and also provide recommendations to customers (nudge) based on this data. The data thus collected is for the most part non-intrusive, continuous, dense and organic. In short, with each click of the mouse (pad), the information trail is captured and stored in digital space. In addition to this, social platforms such as Facebook, Instagram, etc., are also used to collect the data on consumer behavior and likes, etc. It can also be pointed out at this point that big data is also complemented by small survey data in some cases, especially in areas of public policy.

Analyzing big data using data science provides “insights” to businesses. Usually, companies, depending on their stage of development, set their goals either as “profit maximization” or “sales maximization”, which involves three major decisions: (i) the quantities to be produced and sold of various products/services (product mix); (ii) pricing strategy; and (iii) cost reductions.

Data science and sales maximization: with the help of data science, companies try to increase “revenue” through measures such as retaining old customers, adding new customers through recommendation alumni, recommending products to said customers, selecting an optimal product line, and through advertising. This becomes possible thanks to the big data captured by companies when a consumer searches for a product, buys it and shares information (positive or negative) about it via social networks or any other platform. A company’s profitability can also be increased by cross-selling complementary products associated with the main one, or by enticing the consumer to buy the latest version for a little extra money.

Data science algorithms are able to decipher the different attributes and thus correlate products and match them to consumer tastes. Quite often, a consumer would find that a search for a product yields a recommendation for a similar product. The behavior of the consumer, whether they buy it or not, provides the company with historical purchase data in a non-intrusive way that can be used for marketing strategy. Companies often use “recommendation engines” for prediction purposes. Creating customer profiles including their location, type of products they are interested in, type of effective nudge, etc. has become possible through the integration of data sources from customers’ purchase history. consumers and social media. The task of the product team is to identify the optimal range of products and the quantities that need to be supplied at different times. Data science empowers e-commerce companies with advanced predictive and prescriptive models.

Data Science and Pricing Strategies: Data science uses algorithms and machine learning (ML) for “dynamic pricing,” i.e. setting optimal prices. Usually, pricing algorithms combine the use of AI (artificial intelligence) and ML, which makes it possible to set prices for different target groups. This is done based on market trends, fluctuations in demand, consumer behavior, purchasing power and a host of other factors. Dynamic pricing algorithms also consider the relationship between price and quantity demanded, and it can be observed that periods of high demand are associated with higher prices. Thus, prices can vary over time during the same day, depending on the algorithms used, which can thus allow firms to maximize their profits. It can also work in reverse and cause consumers to seek out times when demand is not so high to reap the benefits of lower prices.

Data science and cost reduction: By using big data and data science, companies are also trying to reduce production costs through other methods. On the one hand, data science enables companies to predict efficient supply chain patterns for timely and cost-effective delivery of products, provides data on the location of consumers, producers and transportation costs, warehouses, inventory, etc., while on the other hand, it is used for modeling purposes to forecast efficient supply chains and deliver the right products to the right places at the right times. This analysis and prediction reduces costs, which again maximizes profits.

In summary, data science has revolutionized the way consumer information can be collected, processed and analyzed. Decisions about product mix and price costs can be made based on various variables dynamically, and most importantly, all of this can be achieved non-intrusively for different target groups using different nudging strategies.



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The opinions expressed above are those of the author.



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