Alexa Comprehensive Checklist for Auditing Customer Database in CRM

A Comprehensive Checklist for Auditing Customer Database in CRM

Small Biz Resources | by Patricia Jones
Customer database in CRM

In this modern world of big data and ever-increasing data availability with respect to its volume and variety the challenge to understand, which customer database in CRM is valuable for your organization’s growth is the key for starting a revenue generating marketing solution.

When we talk about customer database in CRM, the most often cited response is that “all data in the CRM database is important”. Although this may be true, but to help you in deciding which data elements are most important in the initial phases of building your customer relationship management software solution, can be illustrated as a method to evaluate and identify the value of each type of data for your businesses.

Bad data sources result in 30% of all leads failing- Cyber Sphere Click To Tweet

After several implementations of online lead management software solutions, we have seen many types of data including “favorite color”; “pet names” and even “number of car doors” all have potential value to different markets for buyers of best small and medium business CRM software.

While “pet names” can be an important data for pet supply retailers using easy to use CRM, “favorite color” can be an essential data field for retails in the clothing industry and “number of car doors” for users of best small business CRM in the motor insurance industry.

Therefore, when you first consider the importance of each data element of the customer database in CRM, the ability to classify the data can help in determining how valuable the data is or in which phase of a solution it should be delivered, if at all.

This following list provides examples of the data elements that can quickly identify the critical pieces of information, which are needed to accomplish your business goals from an array of different data sources.

The priority order of this data is as follows:

#01. Identity Data in CRM Database

In the heart of customer database in CRM lies the individual, so knowing the customer or the individual and to view and maintain that information in a single customer view provides the primary data type which is known as identity data.

Identity data can be any information about an individual that enables the individual to be uniquely identified, which include:

Name Information: Title, First Name (Forename), Last Name (Surname), Designation, and others.

Postal Address Information: Building Name, Building Number, Town, County, Zip/Postal Code, Country, and others.

Person Information: Date of Birth, Gender, and others.

Telephone Information: Home Telephone, Work Telephone, Mobile, and others.

Emailing Information: Personal Email, Work Email, and others.

Social Network Information: Twitter Address, Facebook Identifier, LinkedIn Identifier, and others.

Account Information: Details of your customer’s account ID or User ID.

Professional Information: Company Name, Department Name, Job Title, and others.

Permission and Suppression Data: Although this not essentially an identity element of data, but it is important for finding information concerning permission to communicate and the reason for not communicating (suppression).

#02. Quantitative Data in CRM Database

Now once you recognize who these individuals are in your customer database in CRM, the next element is to find focus on the measurable operational data that enables you to find how the customer behaved,  reacted or has transacted with your business. This data includes any info, which describes the activities completed between the customer and your business.

Transactional Information (Offline and Online): Quantity of products purchased, Subscription/Order Value, Renewal/Order dates, Abandonment rate (abandoned shopping), Product Returns, and others.

Online Activity: Product views, Website visits, Online registrations, and others.

Social Networking Activity: Twitter interactions, Facebook likes, and others.

Customer Support Information: Customer complaint details, Customer query details, and others.

#03. Descriptive Data in CRM Database

Knowing your customers and the type of activities they have completed with your brand provides a good starting point for any marketing activity for up-selling and cross-selling your products. Therefore, to find a fuller perspective of your customer’s additional information, beyond the identity and quantitative details, you must include any info, which may include:

Family Details: Marital status, number of children, age of children, and others.

Lifestyle Details: Car type, Property type, Pet ownership, and others.

Career Details: School name, College/University name, Education level, and others.

#04. Qualitative Data in CRM Database

The concluding type of data, (which you will come across apart from the one, already mentioned) will provide additional description of your customers and their potential behaviors. It is the data acquired by questionnaire type of information captured, where your customers provide data on motivation, attitude, and options that include:

Attitudinal information: How do you value our product, How do you rate our customer service, How likely you are to purchase our products and services again, and others.

Opinion based information: What is your favorite holiday destination, What is your favorite color, and others.

Motivational information: Why was the product purchased (personal use, a gift for someone), What was the key reason for purchasing our product (quality, price, locality), and others.

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Takeaway

Using this simple CRM data classification process and relating them with your business goals, will not only enable you a quick identification of which data is vital for the success of your business, but it can also help you to understand the value achieved from each of this data element.
Therefore, combining the most advanced customer modeling, micro-segmentation, and analytics technologies with an easy to use CRM platform you can really grow your revenue by  using data classification process and associating them with your business goals.

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