Test enterprise data management at what level?

In your work process, you are not also full of such doubts, such as my company's data management at what stage? What type of data management do we belong to? Is my current data management method in place and correct and effective? Below a small test to help you understand their own enterprise data management, want to know the answer, then quickly start testing it!

1. A typical user database may double every year in data volumes. How do you decide when to add a contact to your dataset?

A. We have grown and updated the database based on the following factors. How many users are removed after cleaning, how much users are required based on the response rate, and how many users are required to overwrite each thin grouping.

B. Before a large event, such as webinar or new product launches, we tend to get a new user list from an external channel.

C. When our sales do not meet our expectations, we will find some new users from the supplier to add to the database to try to make up for the missing revenue.

2. What is the cycle of your ability to evaluate the e-mail addresses of your customers and prospects?

A. We http://www.aliyun.com/zixun/aggregation/6939.html "> proactive, and at least once a quarter, to ensure that e-mail sent at a continuing level, before we send mail, remove the hard bullet email address."

B. We actively respond and rely on the hard bullet report after email activity to remove incorrect contact information.

C. We basically do not evaluate the ability to send.

3. How to describe the accuracy of the telephone information in your database?

A. Reliable. Each quarter (or so), we use internal resources to assess the connectivity of the handset, or external suppliers to keep the phone information updated in a timely manner.

B. General. Sometimes salespeople complain that they are unable to contact a potential customer via their mobile phone, and must track these customers through a toll-free phone number or phone record, and eventually replace and update these obsolete numbers.

C. No concept. Some phone contacts are being lost and we have no way of knowing the accuracy of the existing numbers.

4. Please describe your overall record integrity in data management.

A. is our priority. When sales lead information is incomplete, such as a job or industry, we already have partners and processes to quickly add information.

B. We manually fill in the missing fields or automatically add data to the database.

C. is not our priority. The sales leads for missing fields are normal and we don't have enough resources to keep and populate them.

5. It is understood that the Janrain data shows that 88% of the buyers lie on the registration form. What role does quality play in the form collection data that you organize?

A. We use stepwise analysis, or joint suppliers to automatically identify and remove false information, and add background information such as industry and company size to the form.

B. We manually identify false information from forms every once in a while and block that information from flowing into activities or sales follow-up processes.

C. We've got a lot of false information from the registration form, but we've learned to put up with it because it's too common and unavoidable.

6. When it comes to large data, how do you describe your relationship to sales?

A. We have a protocol and incentive mechanism to determine data entry rules such as adding a new sales lead with complete information and automatically reporting expired contacts through our CRM system.

B. A little disconnected. We are asking for sales to stop entering blank fields into the CRM system, but there is no formal agreement and no rules.

C. Sales have no say in the quality of the data. They complain about the quality of the sales lead data, usually with incorrect numbers or incorrect titles, but they only focus on selling.

7. Does your organization provide investment/budget for data management?

A. We have established business cases for data management and can directly demonstrate the results of efforts to improve.

B. When we are blacklisted or in serious trouble, we can get a budget for data cleansing, but it does not belong to a line item we are actively planning.

C. Not at all. This is not considered a priority by our management team.

8. When you consider working with suppliers to increase your database, how do you decide who to work with?

A. Reliable suppliers are looking for a quality process, refund of security, control of existing contacts and other capabilities, and hope to have a high degree of precision positioning capabilities.

B. Based on price and quantity decisions: Who can help us get the most contacts at the lowest price?

C. Most of us do not use, but when necessary, we seek from reputable suppliers.

After you have finished the selection question, let's take a look at your data management level.

A) If you select a up to:

Oh, yes! You are the champion of data management. You strive to keep the database growing, to implement data quality rules throughout your organization, to increase and increase your database as needed, and to maintain data quality to have a positive impact on the entire activity. You are a model of data management and continue to perform well!

b If you choose b up to:

You're trying! While currently mediocre data managers, you may want to raise it to a new time to optimize your activities. Perhaps you are more focused on content and design drivers than data drivers. Occasional data cleansing can help you achieve a general effect. But it's time to take the quality of data seriously to avoid your organization being negatively affected by incomplete databases and unhealthy data. If you want to get more out of your marketing campaign, start putting more effort into the data.

c If you choose C up to:

Oh。 Looks like your data management strategy basically equals nonexistent. When a hard bullet causes a credit issue to be sent, you may need to re-evaluate your efforts, your sales team, or your activity income target is falling. Don't be discouraged: according to a study by Netprospex, more than half of US companies already have completely unreliable data. It's a good start to get to know what you're doing right now. Evaluate your current database status based on quality, integrity, and coverage and make informed decisions. Another suggestion is to make data quality your market KPI. This will help you save your database and help your career.