Customer data is a corporate asset that has a value and if managed correctly can provide an organization with a sizable return. The challenge often becomes on how to measure the value of this electronic asset and quantify its internal rate of return. Imagine for a moment if we could increase customer retention considerably, increase per-customer value, and also increase the number of customers that we have in total? While different for each organization, calculating this kind of return can be a fun exercise to do, and I firmly believe one way to achieve these gains is through the customer data asset.
Now, we all know a company's success can often be determined from how well it manages its customer relationships. The raw material for managing these customer relationships exist in the form of an organization's customer data. In other words, accurate, comprehensive, and complete customer data can help considerably in achieving better customer relationships. After all, how well can you communicate with your customer base if you have poor data to do it with?
As an example, ongoing customer communication such as a newsletter can strengthen ties and loyalty, as well as keep customers informed of positive company developments that make them want to continue to be a customer. However, without having accurate and up-to-date information for this communication (such as accurate physical addresses and current email addresses), this is not possible to do effectively.
Also, simple post-order product delivery to the wrong place due to address typos can create considerable customer service problems. We have all experienced the pain of items not shipped correctly to us from an online vendor, and the maddening follow-up process which usually occurs in order to straighten things out. Simple data verification at the point of sale can help reduce the incidence of this drastically.
With better data, call center representatives can respond faster and more effectively to customers when they don't have to spend time correcting customer information. This of course helps them provide better service.
Amazingly, organizations have invested millions of dollars into sophisticated CRM systems such as Salesforce, NetSuite, Oracle, and others, and yet the overwhelming majority of these instances are still plagued by inaccurate, incomplete, and otherwise poor data quality that grinds away at the ROI potential of these kinds of systems.
While the analysts have broad ranges of the cost of poor data quality to an organization, depending on what all is included and the characteristics of the organization, the one thing they agree on is that the cost is large.
The good news is that on the customer data front, the key to better data is to perform validation and data enhancement at the point of data collection, whether it be from a Web form, a call center, a mailing response, or any other way customer information is collected. These days with XML-based customer data verification services available via simple integration over the Web, this is quite easy to achieve.
Sure, good customer data can be augmented with back office processing by utilizing additional data sources, but this typically is only successful if the originally-captured data is complete and accurate.
So while the bottom-line impact of poor customer data is not always a straight-forward calculation, trusting your intuition that better data equals better customer relationships, and addressing the issue of customer data quality early in the data life cycle can pay significant dividends.
Now, we all know a company's success can often be determined from how well it manages its customer relationships. The raw material for managing these customer relationships exist in the form of an organization's customer data. In other words, accurate, comprehensive, and complete customer data can help considerably in achieving better customer relationships. After all, how well can you communicate with your customer base if you have poor data to do it with?
As an example, ongoing customer communication such as a newsletter can strengthen ties and loyalty, as well as keep customers informed of positive company developments that make them want to continue to be a customer. However, without having accurate and up-to-date information for this communication (such as accurate physical addresses and current email addresses), this is not possible to do effectively.
Also, simple post-order product delivery to the wrong place due to address typos can create considerable customer service problems. We have all experienced the pain of items not shipped correctly to us from an online vendor, and the maddening follow-up process which usually occurs in order to straighten things out. Simple data verification at the point of sale can help reduce the incidence of this drastically.
With better data, call center representatives can respond faster and more effectively to customers when they don't have to spend time correcting customer information. This of course helps them provide better service.
Amazingly, organizations have invested millions of dollars into sophisticated CRM systems such as Salesforce, NetSuite, Oracle, and others, and yet the overwhelming majority of these instances are still plagued by inaccurate, incomplete, and otherwise poor data quality that grinds away at the ROI potential of these kinds of systems.
While the analysts have broad ranges of the cost of poor data quality to an organization, depending on what all is included and the characteristics of the organization, the one thing they agree on is that the cost is large.
The good news is that on the customer data front, the key to better data is to perform validation and data enhancement at the point of data collection, whether it be from a Web form, a call center, a mailing response, or any other way customer information is collected. These days with XML-based customer data verification services available via simple integration over the Web, this is quite easy to achieve.
Sure, good customer data can be augmented with back office processing by utilizing additional data sources, but this typically is only successful if the originally-captured data is complete and accurate.
So while the bottom-line impact of poor customer data is not always a straight-forward calculation, trusting your intuition that better data equals better customer relationships, and addressing the issue of customer data quality early in the data life cycle can pay significant dividends.
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