Without proper customer master data management, it can be difficult to determine the scale and type of client that you have or that you wish to acquire. One of the major problems with tracking customers is trying to determine how to improve poor data quality: without data that is clean and accurate, a company faces communications bottlenecks that can eventually lead to low ROI activities and possible damage to a brand. However, solving the issue of bad data can improve communications with existing customers and help you market to potential new clients.
Proper Customer Accounting Means Good Business
Getting the correct figures when it comes to customer scale is essential for a business to run properly. Accurate buyer profiles rely on a precise account of how many customers you have, as do certain revenue reports and marketing strategies. Poor customer accounting practices may damage revenues, and when you do not know your customer—their demographics, their preferences, their habits—you cannot properly fulfill their wants and needs. The effect of using a proper data management system is that the other parts of your business (accounting, human resources, marketing, sales) are better equipped to serve your employees and your customers.
Problems with Tracking and Scale
It is natural for a company to lose track of its customers on an individual basis as it scales past the local level. The digital nature of modern sales complicates the process even more if a company does not have the appropriate customer data management software. As a business expands beyond the capacity for a manual accounting of customers, its customers will demand more personalized service. Fortunately, there are solutions for the company that is looking to upgrade its customer accountability.
Your master data management strategy must include a software solution that accounts for your customers at every data point. If customers are properly organized at the beginning of the process, this is actually quite easy to accomplish, especially if a process has been set. The initial switch to a new data management system provides access to a long-term program that can integrate into preexisting infrastructure, replace and upgrade functionality, and provide usable reports and analytics.
Several types of “dirty data” that your company can avoid with precise customer accounting are:
- Records that are obviously incorrect. Typos and computer errors may list a customer as 200 years old, a completely useless statistic when creating buyer profiles or trying to personalize a birthday message.
- Records that are slightly inaccurate. Stored information may be partially true; however, there are one or two small elements that may completely change the meaning of a record, such as a missing digit in an address.
- Records that are inconsistent. Different departments may enter records in different ways, which may cause entity management problems.
- Records that are incomplete. Incomplete records can cause bottlenecks in automated campaigns or make data altogether useless.
While dirty data is going to be present in most systems, the good news is modern matching algorithms and data quality processes are more than adept at processing dirty data.
Gold in the Dirt
Many standard IT process disregard incorrect, inaccurate, and inconsistent data. Data profiling tools and analytics can be used to find out more information about the data itself. For example, “Entered Online” in the address attribute of 16k out of 500k records, might indicate that either an internal company process is appending online data to normal store records or standardized data entry procedures are not being enforced. Investigation into the data can provide insights into the processes used to collect and manage data, giving your company valuable knowledge about your internal data processes.
Rather than disregarding or disposing of dirty data, more advanced entity resolution processes not only bring all of the data together, but keep all RAW data so that broken and orphaned records are retained in case they can be used in future matching processes. Once data is linked across all data sources, specialized profiling algorithms and machine learning techniques process the data to provide ROI back to your company. Data profiling coupled with advanced entity resolution processes are cornerstones of any strong customer data management solution.
When you are ready for more precise knowledge of your customer base, get in touch with Black Oak Analytics. As soon as you know how many customers you really have, you will find your marketing, sales, and accounting functions running more smoothly than ever. With the proprietary entity resolution software solution HiPER that will integrate into your current system, improving your data management has never been so easy.