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The Value of Analytics in Customer Value by Maria Petrescu * [108]

Did you ever wonder what is the value that consumers are getting from your company’s products and services? Your customers are surely analyzing, consciously or not, the value they are receiving when interacting with a business. Organizations who do not try to find answers to these questions are not going to be able to differentiate themselves and offer superior value to customers. But if you are already working on constantly monitoring the value received and perceived by consumers then marketing analytics are a great source of help. 

Customer value analytics assumes the use of data science, technology, statistics and business processes to analyze customer response and perceptions and to understand the buying and consumption context. From this information, managers are able to draw conclusions about the customer experience and formulate strategies for improvement. 

For example, businesses can use marketing analytics to evaluate and monitor customer acquisition, customer needs, and customer profitability, to gain customer insights, and to build relationships. Analytics also help manage and improve customer lifetime value, as well as personalize the value offering for consumers in order to increase loyalty. Marketing analytics can be used in different stages of the customer value management process.
  • Customer acquisition - prospective customer behavior, customer needs, lead management
  • Customer profitability - sales, registrations, sales discounts use 
  • Customer loyalty - loyalty offer use, loyalty card data, online account use 
  • Customer engagement - complaint behavior, online interaction, word-of-mouth
In the customer acquisition phase, there are various software and analytics platforms that are being employed for lead management (such as Salesforce), to analyze prospective customer behavior offline and online (including Google Analytics), and to assess customer needs through market research. 

In the next stage, after the customer acquisition, analytics can be used to evaluate existing traditional data, including scanner data and the sales database, as well as more modern marketing promotions and analytics insights coming from the interaction with consumers online and through sales promotions. This can also contribute to an increase in customer loyalty and to further consumer insights from loyalty management analytics and digital behavior, including email management, web traffic, and pull marketing (e.g. various platforms such as Hubspot, Hootsuite, ConstantContact).  

Nevertheless, an important part often ignored by marketers in the evaluation and monitoring of customer value is related to customer engagement and especially the feedback offered by buyers on the digital platform. Because of the difficulty and the skills required in the analysis of qualitative data such as consumer complaints, reviews, recommendations, and shares, methods such as sentiment analysis, social network analysis, and quantitative analysis of qualitative data are often overlooked. In this case, simple software options such as Tableau, MAXQDA, MonkeyLearn, and Adobe Analytics can help. No matter which analytical tool is employed, the insights about consumers’ perception of value are essential. 

* Maria Petrescu, Ph.D., is an Associate Professor of Marketing at ICN Business School Artem, Nancy, France and Colorado State University, Global Campus. Her main research areas include marketing analytics and digital marketing. Dr. Petrescu has published articles in journals such as Psychology & Marketing, the Journal of Marketing Management, Public Management Review, Journal of Product and Brand Management, the Journal of Retailing and Consumer Services, and the Journal of Internet Commerce.  She may be reached at     

Further Reading
Iacobucci, D., Petrescu, M., Krishen, A., and Bendixen, M. (2019). The state of marketing analytics in research and practice. Journal of Marketing Analytics, 7: 152. 

Petrescu, M. & Krishen, A.S. (2018). Novel retail technologies and marketing analytics. Journal of Marketing Analytics, 6: 69.



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