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Customer Data Analytics : How important is to have complete and accurate data?


For marketers data is like air in the 'big data' era. Data is getting bigger and more complicated as ever. In a typical B2B scenario, your data-driven organization is in need of clean, relevant, and consistent data. As data is a key to build any successful customer relationship, managing error-free, clean and relevant data becomes all the more important. Customer data analytics not only helps in controlling the overall consumption, but also contributes in customer relationship building. 'Big data' provides better customer insight and operational efficiency.

It is important for you to provide your customer a memorable experience. With productive, error-free data you can prioritize customer relation and successfully sustain yourself in market competition.

Your customer is in need of strong, relevant and clear communication from you. A simple and consistent experience makes them remember you.

Key challenges

How your organization efficiently manages enormous data in the midst of a complex relation between your customer, accounts and the system?

These huge data can be redundant, irrelevant and inaccurate as well. The MDM (Master Data management -consists of a series of processes and tools) can be used, to consistently define and manage data.

In a customer-centric scenario it becomes extremely important for you to manage customer data. MDM not only manages data but also provides user-friendly, cost-effective solution for marketing, research, information technology and so on. This data helps your organization create online surveys, newsletter or reports.


Customer Data and cloud computing

Cloud computing offers an attractive choice for managing customer data due to its' cost effectiveness, speed, and greater accessibility. Your organization can decide, who can create, access, share or analyze customer data.


How is customer data collected? What data should be collected?

Collect the below data while building your repository

* Name and contact details

  • Allows to market directly
  • Helps you to make communication personalized
  • You can contact them if there is delay in delivery

* Type of customer and their behavior

  • These data helps you know more about your customer, their buying nature, how often they will buy, their ability and so on

* Transaction history

  • This data tells you their habit, pay-mode and terms

* Type of the transaction

  • Type of transaction helps you figure out, their capacity and if it is a credit purchase whether they are able to pay within the time limit and so on

* Demography details of the customer

* Profile: Based on the behavior, buying nature, transactions details, type you can profile your customers, which helps you maintain an effective, long-term relation with them. Following are the factors based on which this profiling is done. On

  • Age
  • Gender
  • Profession
  • Hobbies
  • Income

* Buying nature/habit:

How your customer buys product from you or avail the service provided by you?

This is obviously significant for cash-flow and it is worth adding to the list anyway if you're thinking about data collection.

* The various ways they buy your product can be of following types:

  • Impulse buys
  • Considered purchases
  • Comparing prices from different businesses
  • Whether they are always with you on a regular basis, and so on

The above factors help you to analyze your customer data effectively; based on these data you can build a strong bonding with your customer.

Poor quality customer data leads to significant cost, such as customer turnover, excessive expenses on customer communication process like mail-outs and overlooked sales prospects.

Cleaning data is as important as managing and maintaining these data. Irrelevant, Incorrect, inappropriate, outdated, redundant and missing data are few examples of unclean data. If this data is used for decision-making then it may lead to a critical error. Predictive, predefined model will become unpredictable and unreliable. The inconsistencies detected or removed may have been originally caused by user entry errors, by corruption in transmission or storage, or by different data dictionary classifications of similar entities in different data stores. Once this dirty data are detected, they should be cleaned and rectified as fast as possible.


Storing data

Initially you store your data using spreadsheet kind of software. Later on, as your data grows, you need specific database management software system to store this data efficiently.


Conclusion

The more detailed picture you have about your customer data, the more effective and focused your marketing efforts can be. If you understand your valuable customers better, you can go the extra mile for each and every customer you have in your organization, and persuade them in an effective manner that showcases your ability to solve their business problems and increase productivity.


Call our experts at 877-837-4884 or email us at info@spanglobalservices.com to know more.

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