Happy Customer Service Week!! …. Or is it?  Really ?!… Are your customers happy or are they merely tolerating your brand’s message of goodwill. How can marketers truly know how happy (or sad) their customers are, much less predict the likelihood of retaining today’s customer for tomorrow’s business.

Thanks to customer data, there are evidence-based strategies that can provide a business with a degree of certainty on if their customers are here to stay or merely holding out for the next best offer.  How you may ask? …here is a nutshell summary on how our data science teams, tasks and technology help transform your customer data into future forward insights.

1.Data collection: Start by gathering relevant data about your customers. This may include demographic information, transaction history, shopping cart items, customer feedback, or customer support interactions etc. This data can be collated into a distinct picture of shopper behaviors by different customer segments.

2.Data Processing: While most businesses may be proud about the size of their customer databases, the reality is customer databases require regular maintenance if they are to be used as a data asset for business. Frontier’s CORE service (Clean, Organize, re-populate and Enrich -add missing data fields that can strengthen customer outcomes to databases) can be deployed with any type of customer database from ERP based systems to those generated by customer sales and delivery teams. Client benefits from our CORE service will ensure that duplicates are removed, missing values are filled, and data inconsistencies corrected. Enhanced features can be built into customer databases. This includes metrics such as customer tenure, sales frequency, or average purchase (basket) value.

3.Exploratory Data Analysis: Customer data can now be analyzed and visualized to identify patterns and correlations that might be indicative of possible churn behavior. Different market research software come with machine learning and AI capabilities that help identify features that have strongest churn prediction among different consumer segments. This analysis process feeds into dashboards and reports that help clients clearly visualize what actions or steps need to be taken to arrest potential churn.

4.Predictive modelling: While software powers intelligent business data, there still needs to be human capacity instructing the software to know what specifics to interrogate. Our analysts go through the process of data splitting, training, testing and validating different models to evaluate best fit for client data and business outcomes. Common models used include logistic regression, decision trees, random forests and gradient boosting. Model evaluation metrics for churn prediction such as accuracy, precision or re-call are then used and techniques such as cross validation deployed to optimize data set performance.

5.Retention strategies: Once an optimized churn model has been customized for a client database, insights can be derived from analyzed data to design personalized retention strategies for customers at risk of churning. These customized solutions may include special offers, personalized gift items, or even a simple phone call from a relationship manager to find out how a customer can be better supported.