Two Examples of Valuable Data Partnerships

We’ve spent a lot of time talking about data partnerships because we believe in their potential as vehicles for growth, success, and innovation.  But sometimes it’s valuable to lay out particular versions of those partnerships to give you an idea of what data you can deploy and how it can be valuable.  Use this discussion to help you craft your own approach to data partnerships — mixed, matched, and modified to fit your own circumstances.

Marketing Lists and Append Partnerships

There are countless data companies such as Acxiom, InfoGroup, Neustar, Dun & Bradstreet, Pitney Bowes, and others that build partnerships around consumer data. They aggregate raw information like new home purchases with thousands of “propensity” models that let you buy leads and lists of prospective customers that fit into the “likes golf” or “active lifestyle” category.

While it is common practice to buy this type of data or to pay to append it to your own consumer or customer list from your CRM, there are also significant data partnerships with these same firms available. Many of these same companies, through their aggregation agreements, will be open to you sharing some of your data back with them to improve the quality of their content or to expand their breadth and depth with new data points.

While legally, you must have the rights to the data you are sharing in this type of relationship, there are real advantages here. Many businesses are starving for data sources that can improve their data quality or add some level of new unique qualifier, and they may agree to share data with you in a barter-style framework.

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Cross-walking is the term we use to describe taking one dataset, like your CRM, and using identifiable keys in the data to then match them against other databases. In this way, you take the weather data tied to a particular zip code in a weather data file and crosswalk to your CRM file where your customer’s zip codes are all stored. This gives you a picture of what the weather was and is like in their neighborhood on any given day. As we explore data appends and the process of “cross-walking” from one dataset to another, always consider how data privacy regulations require very specific contracts and terms regarding consent and removal of data.

So while you should definitely explore these data partnership types, you must also think about how the data will be used. In a humanized approach, the act of improving your own data quality about your customers must be understood in relationship to those same customers’ rights to understand the data you have about them and to request changes to or deletion of the data.

In a dehumanized approach, the data about your customers can be highly valuable for you to leverage in a “cross-walk” to new audiences that may also be interested in your services. This approach doesn’t focus on these records as extensions of human beings, but more as raw data needing refinement and expansion. We’re not necessarily passing judgement on either approach, you should just understand how data appends and third-party processing partnerships differ in their approach.

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Free Scans and Reports

Questionnaires, forms, and “basic data” are staples of user engagement.  They crop up, routinely, online and in apps, and there is no shortage services that will do their best to answer them.  The nature of the data collected is that it provides insights and segmentation about users (and even potential users) by framing entry or access to a service or product through the prism of “let us get to know you.” If customers want to know something meaningful, it’ll take some of their data to find out.  What’s my credit score? What is my business worth? What is the price of my home? How’s my outfit look?

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The reality is that every online form, scan, report or recommendation platform leverages a form of data partnership with the user. By entering your information into the form, the user asking the question is providing the platform or report provider with the rights to the data submitted, and in many cases, this data partnership can be incredibly beneficial to both parties. Typically, the data transferred in these types of apps or scans are part of a dehumanized approach to data, because the rights granted to the company providing the free scan or free report tend to be very broad in terms of usage rights.

Take the credit score solutions as an example of this data partnership. For users seeking to access this data, they are significantly more likely to be contemplating a major life decision like marriage or, alternatively, a major purchase like a car or a home. When viewed through this lens, there are very few data points as personal and human as the timing around life decisions like these. To marketers, financial planners, insurance companies, and dozens of other business types, this signal is incredibly valuable, and when a consumer requests this data from the report provider, they are granting access to this life-changing intent.

The provider of the scan or report can sell or share that data to any number of interested third parties because of the dehumanized approach. However, if the provider of the scan or service does not share the data, but rather uses the data to specifically and only create a deeper relationship with the data subject, this could be the start of a truly humanized approach to servicing this customer. Once again, it is not necessarily the data set itself, but the business model and personal needs of the data company and individual data subjects that determines whether a humanized or dehumanized approach is chosen.

The central point here is not to describe data partnerships or products in complete particularity; in fact, the point is that there’s no way to describe a data partnership that will capture the unique facts of your business, your partner, and your product.  That’s why you have to approach every data partnership as both fitting within a general framework and still unique enough to demand attention.  That flexibility will allow you to manage any partnership while still recognizing the kind of benefits and risks to consider before you begin.

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