Build Loyal Customer Relationships using Data Analytics

Considering the valuable resources, time and efforts that companies invest in customer acquisition, it becomes highly important to retain those customers. This requires a system in place for customer approach, their behavior analysis and segmentation, customer evaluation, CRM, loyalty and engagement channels. Building loyal customer relationships is thus a strong base to start in this direction.

Loyalty Relationship is now not limited to traditional ‘Reward Points’ only. Companies now tend to build a strong following of loyal customers through data-driven strategies for a more sustainable growth. Not just economically, loyal customers prove beneficial to company in many ways:

  • Eased Retargeting
  • Converged, accurate analysis
  • Free Brand Endorsement
  • Word of Mouth
  • Increased Brand Equity

Data and Relationship Management

Of all the Loyalty programs, only those are seen to be successful which thoughtfully amalgamate Data with Relationship management. Unplanned expenses on customer relationship management has resulted heavy losses for the companies in the past. As data collection, analytics and CRM have become more and more sophisticated with advancing technology, more pin-pointed and accurate results are being expected from this process by managers (in terms of brand equity, loyalty and profitability) and also by customers (in terms of customer satisfaction).

Building Loyal Customer Relationships

The process of building loyal customer relationships can be implemented in following steps:

  • Observe: This basically includes keen observation and data collection. Knowing the consumer behavior, buying pattern and demographics builds a good aura for data analytics to be applied to sort out the loyal customers. This is then processed with data analytics models like:
    • Regression models to spot outliers
    • RFM Model (Recency, Frequency, Monetary Value): Time-wise sorted data of all the buyers is evaluated to individual level based on last purchase date, frequency of buying and also the total purchase value by that customer.
    • NPS (Net promoter Score): Customers evaluate brand on a scale of 1-10. NPS is then calculated by taking difference between percentage of Promoters (9,10) and Detractors(1-6)

These are just some of the many methods used by companies. These traditional methods along with latest technologies are used by companies to identify loyal customers and plan their customer loyalty and relationship strategy accordingly.

  • Act: After using Data Analytics to segment the loyal customers, companies act in two ways:
    • Retaining and Up-Selling to Loyal Customers (Through custom rewards, loyalty programs and offers)
    • Converting Non-loyal Customers to Loyal (Through Feedbacks, offers)
  • Repeat: Data Analytics and Feedback is also done post-sale, which is then used to build more optimized loyalty programs.

Thus we believe that this Observe-Act-Repeat technique using Data Analytics is beneficial to build loyal customer relationships for long term sustainable brand equity. After all, happy customer is your best marketer!

At Globcon, we bring data to life. With our expertise in Machine learning, big data, business intelligence and product strategy, we are in a position to create immense value for your organization. Click here to contact us and let us understand how we can help you!

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