Customer Retention Data: Best Customer Retention Datasets
Customer retention datasets are collections of data that provide insights into the behavior and characteristics of customers who continue to engage with a particular business or brand over a specified period of time. These datasets typically include information such as customer demographics, purchase history, engagement metrics, and customer satisfaction scores. By analyzing customer retention data, businesses can gain valuable insights into the factors that contribute to customer loyalty and develop strategies to improve customer retention rates.
Recommended Customer Retention Dataset
Retail Analytics Data | Certified European Retail Data Insights | Footfall, Retail Sales & Customer Retention
Consumer Edge Vision Retention Data | CPG, Grocery, Food Delivery Psychographic | US Transaction | 100M+ Cards, 12K+ Merchants, Retail & Ecommerce
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Bold Data | B2B Data Appending | Data Enrichment [high matchrates!]
Consumer Edge Vision Competitor Intelligence & Analysis | Retail & Ecommerce Product Data | US Commerce Transaction Data | 100M+ Cards, 12K+ Merchants
Digital Audience Data | VisitIQâ„¢: Advanced Audience Segmentation Platform for Digital Marketers.
What is a Customer Retention Dataset?
A customer retention dataset reports on a company’s retention vs. churn rate. It enables businesses to keep customers by tracking the reasons customers churn. Businesses can use customer retention datasets to build models which predict churn. This way, they’re able to identify unhappy customers and put strategies in place to nurture these users.
What are the attributes of a Customer Retention Dataset?
Most customer retention datasets include basic demographic customer information. For example, their gender, age group, and affluence status.
The dataset will also include company information taken from churn/retention cases. Usually this firmographic data includes industry vertical, annual turnover, and company size.
Also, a customer retention dataset will include qualitative information. Most obviously, the reasons customers churn. Customer satisfaction can also be expressed in quantitative terms using an NPS or CSAT score.
What is Customer Retention Data analysis?
There are several steps to customer retention data analysis. Usually, analysis follows these basic steps:
1. Acquisition
Acquiring the right customer retention dataset starts by visiting a data marketplace. From there, you can find the right data provider by comparing data samples. Once you’ve tested the customer retention data in your company’s systems, you can buy the data with confidence.
2. Remove outliers
Always check your customer retention dataset for outliers or anomalies. These will affect how accurate your understanding of customer behavior is.
3. Churn vs Existing customer variables
Separate churned customers from existing. Then compare the demographic and qualitative variables. This will help you identify why one group churned and another stayed loyal customers.
4. Model building
The final stage of customer retention dataset analysis is model building. This is where you take the raw data - including variable discrepancies - to make predictions. Predictions will relate to customer churn risk.
Who uses Customer Retention Datasets?
Subscription-based businesses buy customer retention dataset. The nature of the business model means it’s crucial that sales teams understand if customers remain loyal. Customer churn damages ARR. So businesses use customer retention datasets to foresee potential churn cases.