Two to Tango: Mutually Beneficial Data Partnerships

The mutually beneficial data partnership may sound like a unicorn, but it is actually quite common. Market forces drive these partnerships for one of three reasons: shared clients, operational teams, and marketing. This contrasts with innovator partnerships, which are driven by sales opportunities, product innovation and channel distribution.

Mutually beneficial partnerships tend to between partners of similar size and scope. For this reason, the contracts between these companies are more balanced, with similar rights, indemnities, and considerations for both parties. Many of these partnerships don’t require payments between the parties concerned, but they do create cost savings, improve retention, and lower marketing expenses. Only the largest platforms can actually generate additional revenue from mutually beneficial data partnerships by means of their semi-monopolistic control over a particular market or platform, but we’ll have to safe discussion of network effects and tipping for another day.

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Sorry, Kimmy.

Customer-Driven Data Partnerships

Every business wants to keep current customers happy. To this end, your product or service has to meet current needs and evolve to meet the future needs of your client base.

When it comes to data partnerships, over the last ten years there has been a significant increase in “customer-driven” partnerships, in which a client will ask a data provider, platform, or service to integrate its data with data from another platform or service. Clients of your data solution often use multiple platforms. For this reason, they may ask (or demand) that data companies create cross-platform integrations to ensure that they have simpler access to the combined data they need.

The challenge is that customers are beginning to expect this type of integration at little or no cost. Customers may not realize that a proper integration of two datasets can be extremely difficult and time consuming. Consider foot traffic and sales transactions at a given store location. A business may have foot traffic data from their in-store WiFi provider and they most certainly have their point-of-sale (POS) system data, which shows all transactions for a given location, but matching up these two datasets from different providers can be a nightmare. While the time of a transaction, or time of day for foot traffic, can be used to crosswalk from one dataset to another, integration is typically not simple, and a qualified analytics team will need to review the “insights” from making such a leap.

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“Wait, you’re not saying that data can be misinterpreted, are you?”

Knowing that this type of data is messy, who gets paid for integrating the two datasets? Who will provide the analytical oversight and review, to ensure that the data is being merged and analyzed properly? From your point of view, as a data partnership participant, you must carefully consider the goals, costs, and timelines of such an integration. These projects tend to spin out of control because the customer who originally requests the integration between two data platforms may not have a clear goal in mind. In short, these requests are often “fishing expeditions” to see if an initial theory is valid or not.

For customer driven data partnerships, demand a clear professional services pricing strategy. Without one, or without a clear mandate of replying “no” toward these types of requests, salespeople are often left too much latitude when deciding if an integration is acceptable or not, and how to price it. Because sales staff rarely have to fulfill what they sell, this leads to a natural bias toward agreement with the prospect or customer to expedite the sale, ignoring the costs and complexities.

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“Sure, we can add that on, no problem!”

You can include professional services in a contract, simply by describing the details of this type of additional data partnership engagement. By including a professional services fee and structure, your company is ensuring that any customer-driven partnerships will be viewed through the lens of “additional work,” which helps you begin negotiation from a discernible initial fee, as opposed to starting with a fee of zero dollars looking to move upward. Ideally, in a mutually beneficial data partnership, the customer should pay the professional services to connect the platforms but will not have exclusivity for the new connected capability. In this way, a data provider can receive payment to extend their platform integrations without taking a loss on the work.

For most companies working with data, mutually beneficial partnerships focus on non-competitive datasets. When a customer directs a data platform to partner with another provider or source of data, it is rarely for the same or similar data.

There is one other approach to customer driven data partnerships. When a smaller, innovative data company supplies data to a large customer, the data supplier should leverage the client’s power to press for data partnerships wherever possible. By asking a large client, one with influential reach, to make a request of another data or platform company, you can improve the utility of your data and the overall appeal of your platform or dataset. To your customer, so long as the request is reasonable, the value proposition clear, and their rights (including the right to object) are clear, there is almost no downside in asking. We have seen many smaller data platforms grow through this approach by finding a champion at their larger customers to help drive a mutually beneficial partnerships with platforms that would be normally considered way out of their league.

Operationally Driven Data Partnerships

Operationally driven data partnerships are all about efficiency. They arguably represent the fastest growing relationships between major platforms. Financial service platforms for trading and banking were some of the first to adopt highly efficient integrations with each other for the transaction, settlement, and funding of trades and accounts, but they have very quickly been surpassed by countless marketing and social platforms. As companies continue to evolve their own data strategies, there are enormous pressures to keep costs down and efficiency up.

While many of these operational efficiencies are requested by customers, just as with customer driven data partnerships, many data businesses can recognize the likely market outcome before a customer asks for it. Take, for example, the CardLinx Association. This platform was created to provide seamless loyalty and discount solutions across financial platforms from credit cards to payment processors, marketers, and retailers. MasterCard, TransUnion, First Data, Discover, Bank of America, and Sumitomo Cards, to name just a few of the members, all benefit from the universal need for operational efficiency of integrated systems. Before their alignment on how to track rebates, discounts, loyalty points, and transactions, most major retailers just had stickers or punch cards. Remember those days? Now we live in a world where your loyalty program connects to your credit card which connects to your discounts and purchase information. This efficiency creates a new opportunity for every partner in the network and each platform plays an important role. Giving customers more functionality, while preserving their privacy and respecting their data sharing choices, is an essential part of delivering what customers want.  And, as credit cards tied to rewards programs become more tailored to individual users, the opportunities for more tailored rewards  grows as well.

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I mean, we can’t all be Lisa Turtle.

Two Perspectives on Operationally Driven Data Partnerships

You can view operationally driven data partnerships through the eyes of the data company or platform that is providing the service, or through the perspective of the user, who desires an efficient, non-captive experience.

The data platform’s perspective typically favors the captive data approach, in contrast to a more open data framework that shares what could be proprietary data with others. When it comes to operational efficiency as in the CardLinx Association, the data owners recognized that, although they may compete with each other in some aspects, the massive market opportunity of creating a comprehensive solution to this complex financial transaction problem was worth the potential loss of advantage. This same realization drove the New York Stock Exchange, Nasdaq, and other global markets to allow third parties to integrate with their feeds to access real-time stock prices. In both these cases, data originally was captive and then slowly opened up as the market opportunity was clear.

Sometimes data companies and platforms recognize these opportunities themselves, but sometimes market disruptors come along and figure out ingenious ways to access the data needed anyway. This is the concern that many companies now face with structured data easily scraped or ingested by massive data crawling platforms. The legality of taking what is visible to any visitor of a website and programmatically gathering and storing that data for use is not yet settled.

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Your legal department’s favorite four words.

On one hand, companies can use their terms and conditions to prevent or dissuade such activity, but on the other hand, they let Google and Apple crawl and store their data because of their reliance on those platforms for users to discover them. When it’s easy to pull companies’ data from their own data platforms, disruptors can push those same companies to adopt more operationally efficient approaches to sharing their information. From a data partnership perspective, if your role within your organization is to manage this tradeoff, you need to focus on what customers are asking for as well as what small, innovative firms are doing around your data.

The other perspective of operationally driven data partnerships is from the user’s or consumer’s point of view. Most people want to simplify their lives. Also, humans are lazy. Regardless of whether you view it as simplification or laziness, most consumers are willing to connect different data platforms to get a better or more efficient experience.

For example, millions of people find it a burden to keep track of their personal banking statements, 401(k) plans, college savings plans, expenditures, and stock portfolios. Unfortunately, since each of those platforms or tools typically become part of a user’s life at different times, the solutions tend to come from different companies. Naturally, each of those companies provides ways to login and access what a user needs, but this fails to give any consumer the “complete” picture. You don’t have the perspective to get insights into your 401(k) savings in light of your monthly expenditure rate, credit card debt, and rent payments.

This is where efficiency platforms step in. By providing the these platform with all of your logins and password credentials to your various financial institutions, they can bring together a broad, comparative view of your financial situation. This is a significant benefit to the user, and it may well be worth the cost of the platform.

However, there is a cost, and that cost is becoming more and more apparent in recent years. As consumers have allowed (and even begged) for their data to be more accessible so that they could have more operationally efficient lives, they have also handed over the keys to vast amounts of personal data and confidential information. In other words, the cost of having consumer apps and tools that connect all of your relevant financial, health, educational, social, and purchasing data is, in fact, all of your relevant financial, health, educational, social, and purchasing data. Your personal data is the currency with which you purchase this efficiency. And that cost may be too much.

Consider the cases of Facebook Connect and Facebook Pixel. The first is the developer capability to login to a website or app using your Facebook username and password. The second is the advertising pixel (a tiny piece of code on your webpage) that every developer or advertiser that wishes to leverage Facebook ads must deploy onto their site. With millions of sites, apps, and platforms using both of these, users can login easily and seamlessly across the web or on their phones. They can also like or share any piece of content they find on sites that show the Facebook thumbs-up icons, making their ability to browse and share mindlessly simple.

Unfortunately, and not so obviously, this massively efficient connectivity is also tracking you almost everywhere you go online. Facebook can even trace your offline purchases back to your online, because it knows almost everything you are looking at. By tracking your dwell time on the advertisement for those Cole Haan shoes on Facebook, then on a site that has the Facebook Pixel, Facebook can coordinate your ID with offline point-of-sale data to know that, you did, in fact, succumb to the desire and buy those great shoes. Add your geolocation data from the Facebook App on your mobile phone pinpointing you in the Cole Haan store, and boom, the circle is complete.

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He is the .Gif that keeps on giving.

The point here is not to tell you that these kinds of partnerships are best, or even that they make the most sense for your business.  The central idea is that you have to understand how these partnerships work before you enter into one unwittingly.  It’s never the carefully planned relationship that goes most awry, but the haphazard “Oh sure, we can do a deal about that data” kind of relationships.  As we have emphasized time and again, data partnerships require careful planning and open eyes.  Mutually beneficial partnerships around data can provide you with an engine for growth and the tools for improving your product.  Just make sure that the benefits of the relationship aren’t flowing in one direction alone, and that your customers’ rights aren’t lost in the shuffle.

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