In 2011, American Express purchased Loyalty Partner Solutions (LPS) to drive the expansion of Plenti, their coalition loyalty program. The strategic value of the Plenti program was the augmentation of AmEx’s card usage data with additional transaction data from cardholders’ home, life & automotive insurance (via Nationwide Insurance), home and mobile television viewing habits (via Hulu), all the to even household energy usage (via Direct Energy). These transactions represent customer data not normally associated with on-card spending for AmEx, nor any other card vendor for the most part. Putting aside the marketing challenges Plenti has faced as an “over the top” coalition program that required partners to subordinate their brands, and even in the case of Macy’s their own in-house loyalty program, there’s little question as to the value of this type of extended data in fleshing out a more comprehensive picture of a cardholder and their lifestyle. That value however was offset by a complicated and lengthy effort to build out the systems integration effort between partners, the business agreements, and the expensive launch of a new brand to promote consumer adoption.
Even with such types of richer transaction data, most companies today already have more data, from more sources, than their marketing teams and systems can reasonably consume, let alone derive effective insights from, or develop a competitive edge. Disappointingly very few of companies possess either the human resources or tools to manage and process these massive transactional datasets into any one all-encompassing analysis, were that even possible.
The integration of richer customer data needs to be simplified, and the only limitations reduced to ensuring data security, and customer-permissioned sharing of the data.
Now imagine that without such a strategic coalition program in place, you need to target a new marketing campaign. Your analytics team has spent weeks correlating non-card spending data purchased from a handful of third parties, to build the best segmentation and look-alike models possible for one or more segments of your consumers. The team invested substantial time in sourcing the most relevant data, probably having to negotiate a new agreement or two. Yet still, the best data available simply represents a snapshot in time, taken through a keyhole, to try and ascertain just who this target customer is, and what is going to motivate them to engage and increase your share of their wallet.
Now imagine that in near real-time, your Marketing systems could simply ask an individual consumer’s Wallet what types of transactions she has been participating in, even those not on your card; which loyalty programs did she earn points/miles in recently? Has she ever received recognition for completing a big project at her job? What kind of car does she drive, and how long is her daily commute? What type of gift cards is she spending loyalty program points/miles for, and what is her velocity of spending on those? The aggregate picture of that consumer painted by a wide variety of transactions can build a dataset equally if not more powerful as that of the Plenti program, without the corresponding overwhelming effort.
The manifest of additional data that can be used to augment the profile of any individual consumer is near endless, as these types of financial, and incentive transactions are written to private blockchains now and in the future. Moreover, all these transactions are being written to the blockchain by companies utilizing its power to track and manage their own customer engagement data, and thus data sharing between participants is effectively reduced to negotiating a business agreement detailing data access rights.
Loyyal’s platform was designed to enable companies to record and manage incentive transactions such as traditional loyalty program earning and redemption events, along with the myriad of additional customer engagement behaviors. These other engagement behaviors can include actions such as geo-check-in’s, social media posts, and more. This unique approach to storing the core transaction data on the Loyyal Distributed Ledger, with a secure, parallel Corresponding Transaction Ledger on a storing additional metadata related to the primary transaction itself, enables a secure, permissioned set of data exchange between partners.
The first reaction of most organizations is concern about the visibility of such transactions to the public, or competitors. It is important to note that identifying information about the transaction on the Loyyal Distributed Ledger is obfuscated with a Unique Universal Identifier (UUID) as a first order level of protection. The Corresponding Transaction Ledger provides an additional layer of security, with partner-by-partner access control being administered by each participating company, via controls embedded in Loyyal’s platform. Discrete sharing arrangements can be established on a partner-by-partner basis, controlling shared access to data right down to individual fields.
Where necessary in order to maintain compliance with data privacy legislation, the platform can support ledger-based authentication that an individual member has proactively given permission to link their account data between partners. This is most typically provided by consumers in response to some form of additional bonus incentive or benefit for having done so.
Via data-sharing agreements between partners, building a comprehensive picture of customer behaviors is simplified more so than ever before via Loyyal’s platform, without the costs, timelines, or complexity traditionally involved.