Aligning interaction data to monitor customer spends

Customer Transaction Cards is a customer profitability application designed to create statement of transactions of each customer to effectively manage customer spends.

The application is integrated with the CUI string module, to align interaction data of a single customer, in date sequence, so as to understand the staging process of the customer and the relevant costs involved at each touch-point

Each customer’s interaction history is designed like a bank statement with credits and debits added to each interaction, where revenue earned is considered as credits and every spend on invitations or, product discount or periodic interests, is considered as debits

The following statement example shows how customer interaction data is aligned by interactions along with associated credits, debits and balance.
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Customer Data Unification

Customer data can be aggregated and unified, using web services offered by marketing tools housing customer data. But the challenge is to aggregate, unify, derive insights and applying it to actions in real-time. To address this challenge, Plumb5 CI platform, tags and scores data in real-time and extends it to personalization and messaging campaigns.

The graphic illustrates the aggregation approach, comparing traditional solutions with Plumb5

Data aggregation for unified profiling

To know more about our integrated customer tagging, click here to understand the system and method for customer identification using a real time unique string

Plumb into Advanced Customer Views

Here is a screenshot of our new release which will be available from Jan 5th 2013. The sample screen shows the preview of advanced customer reporting, where the scenario is such that 5 data sources are unified. The report unifies web analytics data, online loyalty data, sentiment data, CRM data and Offline POS Data, with the unifier being the email address of the customer/visitor

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Since the customer interaction events are sequenced, it becomes easier for the marketer to attribute or assign custom weights to each interaction. Assigning scores independently to each interaction will help arrive at near accurate custom attribution models.

You can also assign score to the sentiments of the customers and use this value to calculate retention rate , rather than just calculate based on trend. Gathering information through opinions and feedback should help arrive at predicting the retention rate of an individual customer

Please note that some part data on the screenshot has been either changed or blocked to make sure we stay aligned to the conditions set by the client. So the email address and some of the URLs are changed to suit the request

Cohort Analysis

Cohort analysis helps measure users who share common characteristics over a period of time. Cohort Analysis can be used to measure subscription attrition, user engagement or to estimate lifetime value. For example, when we do a cohort anlaysis for understanding traction in user engagement, we group people based on their joining date. The people who joined our service in January make up the January cohort, the people who joined in February make up the February cohort, and so on which is further analyzed to understand how each cohort stays engaged over time.
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