Data Science – The Tool for the expedition into the MarTech Jungle

In the field of marketing technologies (MarTech), we like to speak metaphorically of a jungle, because the subject proves to be a multifaceted landscape with different terrains and disciplines. Due to the sheer mass, one easily runs the risk of losing the overview. In the context of this metaphor, Data Science can be understood as a useful tool set that should not be missing for exploring the marketing technology jungle.

Having access to a lot of data is one thing. Generating added value from this data is another. When information flows into companies’ databases via a wide variety of channels, data science comes into play: data science helps to make customer data usable and to gain valuable insights for short-, medium- and long-term corporate development.  

Engaging in data science is therefore a logical consequence for anyone who doesn’t want to sit around twiddling their thumbs on mountains of data. Getting the most out of the treasure trove of data already offers concrete application scenarios in the short term – be it for improvements in online retailing, for the perfect target group approach or for general process optimization. 

 The trend toward personalization is further strengthening this development. Many online store operators have already jumped on the data science bandwagon. They are offering their customers ever better shopping experiences – thanks to sophisticated algorithms and artificial intelligence.   

Data Science explained in a nutshell  

Data Science is the targeted acquisition of data-related knowledge. The knowledge gained is used to optimize processes, products and services. The knowledge required for this is gained with the help of artificial intelligence (AI) and machine learning (ML) methods. Among other things, they are used to recognize patterns and create forecasts, for example with regard to customer behavior in the online store.   

Data Science can be used in many ways  

• Identification of customer needs  
• Improving marketing strategies  
• Increasing customer loyalty  
• Cost reductions  
• Predicting inventory levels  
• Detection of criminal activities  

The benefits of Data Science in the retail sector

 A professional approach to data brings benefits to retail companies – and to their customers. In the following, we look at the added value of data science from both perspectives.   

Customer perspective 

Customers are becoming increasingly demanding. They expect the “perfect” shopping experience both online and offline. It should be fast, uncomplicated and personal. The prices should be as attractive as possible, and the products should always be available when they are needed. If this is not the case, they want precise information about availability and stock levels. Online shoppers also value detailed information about the delivery and return of their order. The comprehensive customer data can be used to draw conclusions about customer needs using a deductive method. In addition, the data allows conclusions to be drawn about purchasing behavior, so that stock levels can also be predicted and preventive measures initiated. As a result, all the above-mentioned requirements can be met more and more effectively. Personalization included: individual product recommendations already in the right color and size or coupons as well as suitable offers at the right time.  

Company perspective  

Retail companies are responding to the dynamic developments and challenges of the times with creative strategies: Manufacturers are doing away with middlemen and increasingly going into direct sales. Virtual marketplaces are disrupting established business models. Low-priced private labels are competing with the once prominent branded products. And finally, they are trying to meet the growing calls for greater sustainability. Data science supports all these strategies in a certain way. Retailers are getting to know their customers better and better through data analysis and can respond precisely to their wishes with targeted and personalized marketing measures. Data-based forecasts can be used to match the product range more and more precisely to demand. Optimized goods logistics can also significantly reduce CO2 emissions in online retailing – an important step toward sustainable business models. 

Conclusion  

Data Science, as a tool set for the first expedition into the MarTech jungle, can be used in many ways. It creates added value for customers and companies alike and helps to holistically improve the customer experience.