Where Systems of Record Will Go from Here

There is so much hype around customer data platforms (CDPs), data management platforms (DMPs), and the decline or replacement of systems of record that I thought I’d provide some clarity on at least some of this by detailing where we stand with systems of record as the 21st century jets ahead.

To that end, I’m excerpting/adapting a passage from my latest (and last business) book, The Commonwealth of Self-Interest: Business Success through Customer Engagement, which came out a few months ago and is doing quite well, thank you [Ed. note: To adapt for our use, we’ve edited the passage for length and per CRM style]. Here’s where I am describing the state of the system of record and how it’s progressing.

A few years ago, an executive of a major software technology firm made a rather overenthusiastic and just plain wrong statement: “Systems of record will be replaced by systems of engagement.” That will never happen. They are mangoes and strawberries. They each have a raison d’être. They each can work independently, but they work better together—like strawberries and mangoes (each awesome unto themselves, but as a smoothie … yum). To start, let’s chat about what systems of record are.

DEFINITION: SYSTEM OF RECORD

When CRM started hitting its apex, it not only began to serve the needs of sales, marketing, and customer service departments in a functional way, but also, more importantly, took all the data that was gathered as a result of the interactions with individual customers in those departments and aggregated it to a single customer record. Initially, it stored core CRM-related data, such as what I bought; what upset me enough to call customer service, and the resolution of the problem; to which marketing campaigns I responded and when, etc. But as time went on, more and more frequently it became important to know which media I used to communicate, which videos I was watching online, what I was saying about the specific brands in conversations with my peers, which reviews I was writing and responding to, which content I downloaded, and a multiplicity of other data. This created a lot of problems because of the vastly different forms of data and the different schemas that had to be reconciled to make the data reportable.

For example, while the transactional data—what you bought—was structured for the traditional customer of record, the conversational data—the social media back and forth—was called “unstructured.” That meant, realistically, the data was structured according to the norms of the medium that recorded the conversation, which varied by platform. But ultimately, even that unstructured data had to be captured and organized in combination with the traditionally structured transactional data to allow analytics engines, algorithms, and human beings to make some sense of the data so that it could be used for insights and, thus, have a meaningful purpose.

Before roughly 2010, a typical customer record consisted of the following: account data; order data from stores, phone orders, or e-commerce; billing and credit card information; interactions related to transactions, including emails, phone calls, and online chats; service data, including tickets; marketing data, including campaign responses and promotions offered; and segmentation data, including standard demographics and household and geographical information.

But with the advent of high volumes of behavioral data and data from external media, and the need to personalize at scale, the data required for a successful ongoing engagement with individual customers changed. Thus, in addition to the traditional data, the customer record now needs the following:

• unstructured individual customer conversations found via social-media monitoring and text analysis;
• profile information gleaned from Facebook, LinkedIn, Twitter, etc.;
• records of the content created by the individual influencer or customer;
• third-party information associated with an account;
• the nature of the customer’s role as a decision maker within a business (in a B2B transaction), such as the influence they wield and how and whom they influence; and
• customer journey data.