Customer data is the lifeline of any business, and more of it is available than ever before. Data integration issues...
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can undermine the value of the precious information that companies collect in their CRM systems, however.
In many customer data integration systems, the data becomes "dirty" -- inaccurate or just plain wrong. This challenge often goes unnoticed or unaddressed until it's time to migrate to a new system.
Another data integration challenge is incomplete or minimal data. Data enrichment is something that many organizations decide to undertake to facilitate marketing or a wider sales strategy. Let's break those two scenarios apart and look at the subject of data quality and data enrichment.
Ensure customer data quality
Data quality degrades for a number of reasons over time. Some CRM systems fail to enforce data entry standards or don't even have the capability of a structured data entry, perhaps limiting all data to a series of text fields.
Of course, the weakest link in the chain -- the human element -- seeks the easiest option. Some years ago, I worked on a project where an organization's 100,000-plus customers and prospect data were being migrated from one CRM system to another. As part of this migration, we checked data quality and found some issues.
As an example, the "country" field in the old system was a plain-text field. There was also no enforcement of data entry to make the field mandatory, so some fields were empty, and many were just wrong. This may seem like a minor issue, but for an organization that was represented on most continents, the "country" field was important. It was used for customer contacts, but it was also necessary for customer geographic segmentation and marketing, among other things.
In this example, United Kingdom had the following options: UK, U.K., U.K, United Kingdom and even United knidom. Just follow that issue through to the next stage of marketing or contacting customers in a specific geographic area, and you realize this data is used for postal addresses. Immediately, we can see the issue around data quality.
The choice we then have is: How do we fix the issue? Old system, pre-migration? Or new system, post-migration? Then, how do we stop this from happening?
The decision on when to fix data integration issues often depends on which system you are moving from and which one you are moving to. A CRM system that doesn't validate data will unlikely be the best place to fix it.
In the example above, the solution had two parts: First, do all customers or prospects need to be migrated? The initial reaction from most businesses is yes. But a quick query of the data set revealed that many records had not been touched since they were created. (Even most rudimentary CRM systems will have a created-on date and a last-modified date -- a quick query will reveal if they have been touched.) Losing that sort of data as part of a cleansing can be as important as cleansing the data you keep.
The second step was to fix the problem -- which can require some creative thinking. When you are trying to fix an error like the one above, and unify a customer data set, you first need to define all the wrong options, then fix them. We found all the wrong options for United Kingdom, then changed the data to what was required in the new CRM system. Some CRM tools allow you to query the data in suit, then bulk update. Other options are to complete this in another application (good old Excel), or perhaps create a staging database.
To prevent this from happening, we moved away from a free-text field. Alternatives include a drop down, lookup or option set. We used a lookup to another entity that held the proper names of countries. Now this might seem like a slightly over-the-top option for a single field, but sometimes you have to think about the future uses of data. On our newly created "Country" entity, we were also able to store the continent and any other country specific data, which could also be further used for marketing and other needs.
How to enrich your data
Data enrichment is the process of expanding or enhancing your data set with additional information over and above the current requirement. For many organizations, this would likely be to enhance data for marketing opportunities or to better understand customers. For marketing purposes, data enrichment will allow you to bring in additional information, allowing you to better target your marketing.
Data enrichment doesn't necessarily mean using external services or providers. Data enrichment is also about how you think about your data and how you want to use it. This can then be reflected in the way you collect, store and use your data.
In the example discussed earlier, the additional Entity would allow the expansion of information known about that particular client. For example, by adding the continent to the country, additional information on those continents can be used. The World Bank provides a full catalog of data by country that could easily be used -- even providing a handy API.
Experian also offers a number of data enrichment options. The company collects your data, enriches it and returns it as a managed software as a service platform. To do this, Experian overlays demographic, behavioral and transactional information along with Mosaic USA household lifestyle segmentation to provide a deeper understanding of consumers' characteristics and preferences -- a combination that can help companies identify prospects and land more sales.
Data is one of, if not the most important commodity for many businesses and organizations. Being aware of the quality and depth of that data can lead to a better user experience and, more importantly, potentially increase sales and improve customer relationships.
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