Customer Satisfaction and Retention Data Acquisition
Have market data on rolling basis instead of long-period basis:
There are two elements of measuring customer satisfaction- one is the transaction details, like customer purchase value, frequency of purchase, customer complaints logged etc. The other element is customer feedback beyond these transactions. For example customer surveys, focused customer group responses, mystery shopping etc. These exercises should be done at a ongoing level to enable more early understanding of the efficacy of your actions. This is also suitable given the increasing level of business dynamics.
Take the feedback at the point of transaction:
Every-time a customer buys your product, makes an enquiry/service request/complaint, try to get the satisfaction data, and prepare the company representatives in terms of scripts and forms. We call it the ‘moment of truth’- Customer can provide the best insight of what is going on in her mind, when she is closest to the experience.
Capture all customer contact points:
One should provide all possible channels for customers to share their issues and feedback. This could be through email, single call-in number, feedback forms at the point of sale.
Broad-based sampling for Customer Surveys
When you conduct survey around the customer satisfaction, include following possible samples:
- Customers having high relationship value
- Customer having low relationship value
- Customers who have attrited with a long tenure as well as the short tenure.
- Potential customers who did not buy OR did not convert.
This kind of broad-based sampling provides you deeper insight on your improvement opportunities.
Customer Data Integration
Customer Relationship Single View for Customer Retention
The true analysis on customer value is possible, if you have an ability to integrate customer data, to get an understanding of an overall relationship. This will enable you to analyze the attrition of a customer at a product OR an overall relationship level. It will better allow you to project on the risk of customer attrition. It will also help you to devise more informed strategies (like offering more creative combinations in the areas where customer relationship has reduced).
Customer Touch-points single view
One needs to have the record of customer touch-point activities. This includes customer service calls, repair and maintenance calls, contract renewals, suggestion and feedback by the customer etc... This enables you to relate and interact better with the customer. This will also help in making a customer feel that she has an identity.
Customer Data Quality
Non-production and in-complete customer survey data
Customer retention related data is mostly part of your production systems, therefore there is lesser of a challenge as long as you are able to integrate this production data. Customer satisfaction analysis also needs production data (as it has a strong co-relation to the customer retention). However, there is a less structured part of customer satisfaction related data. This includes customer feedback data and customer surveys. Much of this data may not have the details on the customer who has given the feedback. Some of this data may not be complete (as customer may not answer all the questions). Unlike other customer data, you may not be able to augment it by using law of averages OR extrapolation. This kind of data needs to managed in a separate cube OR pivot.
Qualitative Customer Survey Data
Customer gives statements and descriptive inputs in his feedback. In-spite of all objective type and close ended questions, the holistic insight to customer’s mind needs open ended questions. This leads to a painful task of parsing, structuring, categorizing and tagging this qualitative information. This arduous method is the only way to make sense of sometimes millions of the customer survey records. You can also make it little easier by using automated tools, which search on the keywords and do first level of categorization.
Getting data for the customers who did not covert
Some businesses have a high proportion of the customers who did not convert. For example, customers who take demo of electronics OR consumer durable products, but don’t buy. Companies typically don’t have clean sample data on why customer did not buy. This data is generally in the mind of a sales person. The sales manager may enquire from the sales person on the reasons of customer non-conversion. Sales organizations do not have the incentive OR band-width to contact the customer at the ‘moment of truth’ to capture the reasons for non-conversion.