Is your CRM team paying enough attention to data quality?

About Katie Morgan

Katie is a graduate of Tarleton State University where she earned a degree in Marketing. She started her career in sports with the Texas Rangers as a part of the Inside Sales staff in 2008. Katie was promoted from Inside Sales to Season Ticket Coordinator within two month of beginning work with the Rangers. She was once again promoted less than six months later to the position of Ticket Sales & Database Manager. She worked for the American Airlines center as the Corporate Services & Database Manager before returning to the Rangers as the Assistant Director of Ticket Operations. In this role she is helps the ticket operations department and company with all things data analytics.

With insights from the Texas Rangers, Milwaukee Brewers, Oakland A’s, and San Diego Padres

Whether you work with Microsoft Dynamics, Sales Force, or any other system, the key to maintaining a quality database system lies in the validity of data.

Teams constantly struggle with data from different sources.  For example, at the Texas Rangers, we import data with a variety of formats and quality levels including:

  • a nightly ticketing feed,
  • secondary market buyers,
  • appended demographic information for purchasers, and
  • leads generated from a variety of sources.

What does that mean on a daily basis? We must:

  • verify the accuracy of personal information,
  • find and remove duplicated records,
  • find existing records and update or add additional information, and
  • verify revenues and other sales numbers.

In case you forgot…

Without the proper measures the data quality suffers and the organization misses revenue opportunities.

Diny Hurwitz, Data Analyst for the Milwaukee Brewers, works with Microsoft CRM throughout the year in Major League Baseball.  Diny explains the critical issues,

“The general purpose of CRM is to get a 360 degree view of your customers. If that view is not accurate, your reps will spend time selling products that are not geared towards your customers’ needs. By having accurate data (e.g. rolling up duplicate account information into a single contact), you will be able to target correct products using whatever purchase criteria your organization chooses. Plus, you will have better match rates appending demographic data.  By maintaining a clean database, you will also see cost savings if you do any direct mail.  You will avoid sending multiple mail pieces to the same household/business and you will again be targeting the correct product to your customers.”

As Diny points out, data quality is important for several aspects of your business processes.

  1. Use the database to target specific products. The more well-rounded view of the customer allows your sales representatives the ability to create personalized sales pitches for each potential buyer they contact.
  2. Increase ROI from your database.  You save money on direct mail campaigns if the addresses for potential buyers are up to date and accurate.
  3. Increase sales volume and efficiency.  Better quality leads distributed to sales representatives equals more sales.

Building confidence

Data quality builds confidence in the system. If we want our team to use the system we need buy-in.

Database systems can also be used to report revenues to ownership and management when needing quick and easy reporting methods.  If your data quality is up to par you won’t have any reservations reporting these numbers out of your respective database, and can rest assured you are reporting accurate numbers.

Mark Bashuk, Database Services Manager with the Oakland A’s, speaks more to this point,

“They key benefit of a successful data quality initiative is confidence. If the ticket history and other details on each account are correct, the sales reps and other system users will trust what you are telling them and use the system as designed. They won’t waste time double-checking the ticket history or previous activities on each account. When management and other departments (especially finance) are able to use and trust CRM-based reports – it reflects positively on the entire department.”

Quality vs. Quantity

One common mistake teams make is focusing more on data quantity than quality. Without quality data it doesn’t matter how much data you have in your system. You won’t be able to build a successful environment for your data, users, or organization.  

Ben Roller, Director of CRM & Ticket Analytics with the San Diego Padres, touches on this,“There is virtually no difference between 100 records or 10 million records if the quality of data is lacking.  Analysis of such data only provides mediocre and sometimes false information leading to poor business decisions.  Compiling a CRM database with quality information, not only about who your customers are but their behavioral tendencies as well, will provide more accurate forecasting to better predict sales, retention, and possible customer service issues ultimately resulting in a better fan experience and increased revenue.”

The bottom line: Make or break

When beginning to work with a database or implementing a new one, keep data quality at the forefront of your mind.  Data quality can make or break the success of your implementation and usage of the system if the proper measures are not taken to ensure high levels of correct information.

 


 

Cover photo courtesy of http://www.flickr.com/photos/sineimago/

 

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