The Data Gravity Effect

Ben Bloom’s recent post on the Gartner Blog (“CDPs Don’t Eliminate Friction With Customer Data”)shined a light on a topic we rarely hear about in the CDP world – the cost and effort associated with building a first-party data asset, and the possibly diminishing returns of building a complex “Customer 360” view.

Amidst the hype surrounding the “cookieless future” (albeit warranted by brands’ general lack of preparedness for it), the answer seems to be “collect more first-party data with a CDP.” While not wrong, the singular focus on data collection to make up for the scale and accessibility of third-party data misses the point. Bloom correctly argues that the more you enrich the profiles in your data store, the more friction you create in the process. In a nutshell, while it may be relatively simple to go from A to B (moving from authentication to opting into first-party cookies, as an example), getting to C and D (setting preferences, opting into a loyalty relationship, et al) makes things more complicated.

This is fairly obvious, but in the CDP boom we are living in, many brands are caught up in the notion that more data is better, and not considering that better data (and less of it) may be more impactful. One of the lenses through which we can view this conundrum is the concept of data gravity. Put simply, “data gravity” is when you reach a certain point in your collection efforts in which the more data you have, the more you attract.

As an example, the more I buy my groceries from Instacart, the better they know me. After the third time shopping with Instacart, I can basically pre-select a list of core items I have ordered previously, and consistently end up taking 75% of their “you might also like” suggestions before I check out. They have me nailed. I keep giving them just enough shopping data, and they return an excellent experience. Instacart is amassing data gravity in the same way Amazon does – by appending my profile with the exact purchase and behavioral data needed to power next-better offer recommendations.

The basic model is a value exchange. I give Instacart both my weekly grocery business and the data related to it, and they save me countless hours shopping. Brands looking to provision personalized experiences across channels need to think about what customers actually find meaningful. What is the minimum level of value that needs to be exchanged to amass more data gravity – and the right data needed to power that experience?

For a delivery business like Instacart, knowing my address, stores I frequent, and items I tend to buy frequently are the core data at the heart of driving experience. Instacart doesn’t need to collect 500 more attributes about me to provision my experience – just the kind of data needed to deliver it. Not more data, just better data fit to its use.

Brands that want to go beyond the “more is better” methodology and start to amass meaningful data gravity must consider a few things before they decide on the value exchange equation, however.

  • Authentication: As discussed in my last post, no modern CDP strategy can begin without accounting for customer identity and authentication. The first value exchange with customers in the post-cookie world is giving them a secure and trusted way to provide their data. That means making CIAM part and parcel of a first-party data strategy. There is no first-party data strategy without secure customer authentication, period.
  • Consent and Preferences: To get to the next layer of value, you also must have a scaled way of capturing detailed consent and managing preferences. This is today’s version of “permission-based marketing.” Thanks to GDPR and continuing privacy legislation, this is no longer a marketing framework, but a real requirement. Enterprise Consent and Preference Management (ECPM) enables brands to start a meaningful value exchange. As a brand, if you are giving me an offer I never asked for, you have failed at the start.
  • Data Governance and Stewardship: With great amounts of data comes great responsibility. Even brands with strong technological capability to manage authentication and preferences need a strategy that defines exactly what types of data to collect, how they are used, and who gets to access them. This is non-trivial. It goes well beyond buying software and requires the enterprise to carefully define the benefits of creating a value exchange with the customer upfront.

Companies that go into their start with these three keys going into their digital transformation efforts will have the basis to discover the actual cost of data collection and determine at which point the friction introduced in the process erodes, rather than adds, value.

Is more data better? This question will increasingly be answered by customers themselves. In the new world of first- and “zero-party” data, the answer is that customers will give you as much data as they receive back in real value.

[A version of this post appeared on the Future of Customer Engagement]

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