A few years ago, data science technologies were considered cutting edge, with only the most innovative companies implementing such solutions. Nowadays, implementing data science is the norm and those not leveraging this opportunity are missing out on potential revenue. 

 

Transformation of Traditional Stores

 

Consumers now have access to products and services tailored to their needs thanks to the personalization that e-commerce offers. Online retailers are focused on improving their digital properties by tweaking layouts, adjusting content flow, and creating customized user journeys to maximize profits.

 

By leveraging data science, businesses can uncover hidden revenue opportunities. When factoring in elements like location, competing prices, regional income, and consumer purchasing patterns, retailers can adjust prices in a way that is best suited for their business.

 

Customer sentiment analysis is another key area where data science can provide rapid results. By analyzing massive amounts of data from social media, reviews, comments, and feedback, retailers can learn which brands or products to prioritize and which to discontinue. 

 

Affinity analysis enhancement (market basket analysis in retail) is a by-product of data science. For example, cart analysis might tell a retailer that customers often purchase shampoo and conditioner together, so having both items on sale at the same time would not create a significant increase in revenue. However, a promotion involving just one of those items would likely drive sales for the other item. 

 

Programmatic Advertising

 

Businesses can no longer deliver the same message to a large-scale audience. Instead, it is critical to target specific segments by sharing offers with buyers who are most likely to purchase them. 

 

With today’s complex customer journey, a digital business may experience numerous interactions with just one user across display, search, social, or app. All these interactions can take place on multiple devices, which makes targeting even more difficult.

 

With the advancement of machine learning and deep learning technology in advertising platforms, companies can be utilized to target ads with incredible accuracy. By analyzing data points from multiple data sources, marketers can gain access to a holistic view of their customers. 

 

Recommendation Engines 

 

For many retailers, recommendation engines are among the most used tools because they can give an in-depth look into the interests and goals of customers and help predict trends. 

 

Recommendation engines are complex machine learning components and deep learning algorithms designed to keep track of customer segments, analyze behavioral patterns based on data, and improve the digital experience for customers. 

 

Social Network Profiling 

 

Let’s take a look at a few examples. 

 

  • Instagram & Facebook 

 

Instagram and Facebook use data science in order to target sponsored posts, which can vary anywhere from clothing to trendy gadgets or popular foods. Since Facebook and Instagram are owned by the same company, they work together to compile the most comprehensive profile possible for each user. Search algorithms convert likes, comments, and interactions into predictions about which items users may be more inclined to buy. 

 

  • Airbnb 

 

Airbnb has completely revamped their search function with data science. They switched the algorithm from prioritizing top-rated vacation rentals located around center cities to prioritizing neighborhoods with a high density of Airbnb bookings. This ensures that users can access places in the city they visit based on the principle of “strength in numbers.”  

 

About the author

Sebastian Stan

Sebastian is a journalist and digital strategist with years of experience in the news industry, social media, content creation and management, and digital analytics.



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