Rethinking Data Quality
There are of course venerated companies that are worth looking at, some of which have in recent years appeared in CRM magazine's Market Awards issues. These include veterans such as Experian, Informatica, SAS Institute, and Trillium. Pitney Bowes provides services to help companies identify the most accurate customer locations so that they might figure out better strategies for marketing and selling to them.
These vendors have long served as trusted resources and continue to hold much influence, but there are also lesser-known vendors that cater to specific niches. For instance, some are better suited for helping companies based in particular geographies. The German vendor Uniserv, for instance, might be a good choice for companies looking to improve their geocoding and identity resolution capabilities in European countries.
Melissa Data, likewise, stands out for its recent efforts to solve the issue of global inconsistencies in data entry, a problem that Wu notes is a pesky one, especially for companies that are looking to expand their services overseas.
And different industries can benefit from expertise that is particular to their verticals. Those companies in the financial services sector would benefit from looking into Innovative Systems, which specializes in services that cater to their needs.
DATA ENRICHMENT TOOLS
As customers come to expect stellar experiences from brands, their willingness to spend time giving information about themselves is dwindling. While it might have been excusable to ask a customer for his address multiple times 10 years ago, that same customer might have less patience now.
Furthermore, there are certain bits of data a company might want that it will not be able to discover on its own, or is simply difficult to gather. For instance, a jewelry retailer might wish to target a very specific subset of customers during a seasonal sales event. Let's say that demographic is men in their 50s who make more than $80,000 a year. That data might not be enough, though. It might wish to know if these men are married, if they have children, and, if so, the age and gender of their children. To get this information is no easy task. There isn't a graceful way to get a customer to volunteer this information without giving him something significant or costly in return.
Third-party data sources can assist with these kinds of scenarios for both B2C and B2B companies. For the B2B world, Dun & Bradstreet collects company data on over 200 million companies, and updates those profiles over 5 million times a day. "Many of our customers use our data to help build models to help them to prioritize customers, prospects, and accounts and a number of other use cases as well," Dun & Bradstreet's Dave says.
Having access to updated information could help B2B outfits avoid snafus. For instance, a company might be trying to sell software to another company. It's often the case that companies have different divisions that are all connected to the same parent company. A company might have the same name associated with different addresses or divisions. Knowing that AOL and Verizon merged would help understand why it wouldn't be advisable to reach out to both.
CLEANSING BIG DATA—THE NEXT FRONTIER
While getting customers to give away information is an ongoing challenge, much of the time customers are providing relevant information about themselves online across various social media outlets. Sites such as Facebook and LinkedIn give people the option of sharing their birthdays, marital status, interests, gender, alma mater, languages spoken, and hometown, for instance. Not everyone is willing to share these details, of course, but a great number of people do share the kind of information that companies can leverage to connect with them. For this reason, companies are interested in having clean access to this data, Wu says.
A growing concern for companies is collecting this unstructured data in a way that helps them make sense of it and match it to their existing profiles. For instance, companies want to keep track of those who are making noise, matching them to the profiles they have on file and understanding ultimately their relationship to the company. Are they loyalty members? Do they have a great impact on what others are doing?
While the tools for making sense of unstructured data are constantly improving, the use cases are are also becoming more sophisticated, Wu points out. "Appetites for fine grain data and analytics are insatiable," Wu says. One trend he notices is an increasing need for metadata—or "data about the data." As visual social media sites gain traction, for instance, companies are going to require information about the time an image was posted, where it was posted, and what device it was posted from.
Imagine, for instance, that a man has posted a photo of himself alongside his bride on a picture-sharing site. If the customer has opted to connect that profile to a customer record, the company could act on the data (and metadata) contained within the post to update the customer's information pertaining to marital status. Ideally, such insight would trigger an alert to action, signaling the need to check on and refresh the data in a graceful way, through an avenue the company deems most appropriate.
We are still in the early stages of such development, Hayler says, but headway is being made to better understand and organize these free-form materials.
Associate Editor Oren Smilansky can be reached at firstname.lastname@example.org.
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