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Consumer Analytics
DHL case scenario Analytics with Tableau
DHL is an international air express and logistics company who serves a wide range of customers.
To be able to effectively manage such a diverse customer base, DHL has implemented a sophisticated customer segmentation and loyalty management system. The focus of this system is to assess the profitability from its customers, reduce customer churn, and increase share of shipments. The company defined their customers into three main segments which are Direct Customers, Relationship Customers, and Strategic Customers.
To be able to effectively manage such a diverse customer base, DHL has implemented a sophisticated customer segmentation and loyalty management system. The focus of this system is to assess the profitability from its customers, reduce customer churn, and increase share of shipments. The company defined their customers into three main segments which are Direct Customers, Relationship Customers, and Strategic Customers.
Based on the segment analysis, about 75% of their users are 'Direct Customers'. Interestingly, the biggest revenue and profit are not from this group but from the 'Relationship Customers' who use their service regularly with a lower volume.
The 'Strategic Customers' who ship large-quantity and often use the express-shipment are not much profitable to the company. This is probably due to the percentage of the customer is also very low. |
Further analysis is projected on the 'Relationship Customers'. The graph shown that almost half of the total numbers are decreased performers and lost customers. This is a major concern as decreased performers could have been prevented earlier before it happens.
Friction Point The friction point presumed to be the expensive switching cost that resulted to lost customers and decreased performers. Customers who use the DHL basic products find it easier to switch as switching costs are low and all key competitors offer similar products. In contrast, the switching costs are significantly higher for 'Relationship Customers' and 'Strategic Customers' groups. |
The possible solutions are to analyse the customers’ activities, predict who will churn using a machine learning technique, then conduct customer engagement such as offering a special deal, take marketing leads, or request a good review from the satisfied customers. The following phase of customer lifecycle explains the initial stage of customers friction point on DHL case.
Customer Segmentation
The main goal of segmentation is to assess further the geographic, demographic, behavioural, and psychographic of the Relationship Customers. Qualitative and quantitative data can become a powerful measurement to further define how the marketing strategy can be improved or reinvent new services/product and customer experience.
The main goal of segmentation is to assess further the geographic, demographic, behavioural, and psychographic of the Relationship Customers. Qualitative and quantitative data can become a powerful measurement to further define how the marketing strategy can be improved or reinvent new services/product and customer experience.
The key is to understand that churn could happen long time before customers decided to cancel without having a proper customer engagement from the company. As the data from the company is not available for public, below is just an example of the self-made randomize data which could help to understand the predictive analysis model on customers.
The next step is to perform customer segmentation based on similar background. In this case, the customers are segmented on their level of education, occupation, and how often they use the product/service. Segmentation helps to assess geographic, demographic, behavioural, and psychographic of the existing customers.
The graph revealed a pattern that customers who used the service less often are prone to churn as they do not get enough learning from the platform. It also shows that education level of the customers are not much effecting because those who did not go to college did not find it difficult to use the service.
Conclusion
Consumer analytics help to improve organization’s overall business development and get to know their customers' friction point, problems, and behaviour in detail through data analytics.
The positioning strategies allow us to set a product or services to the targeted audiences and ensure the business stands out and meet the customers’ needs. The Data Science Analytics method is simple to apply if all required information is collected.
Data Science can provide highest accuracy in defining customer needs, however, it does not eliminate the risk of inaccuracy prediction. The future plan for DHL company in this report is to prevent the lost and decreased performers by providing a program that suits the customer needs, regain the lost customers by making a promotion of products/services, and maintain the existing one by continuing becoming the lead in the market.
Consumer analytics help to improve organization’s overall business development and get to know their customers' friction point, problems, and behaviour in detail through data analytics.
The positioning strategies allow us to set a product or services to the targeted audiences and ensure the business stands out and meet the customers’ needs. The Data Science Analytics method is simple to apply if all required information is collected.
Data Science can provide highest accuracy in defining customer needs, however, it does not eliminate the risk of inaccuracy prediction. The future plan for DHL company in this report is to prevent the lost and decreased performers by providing a program that suits the customer needs, regain the lost customers by making a promotion of products/services, and maintain the existing one by continuing becoming the lead in the market.