Since becoming a signature catchphrase in the 80s referring to US foreign policy, “trust, but verify” has since been highjacked to refer to efforts to provide online security and cut down on identity theft, as anyone who provides two-factor authentication can attest. The phrase is so common it is sometimes mistakenly used interchangeably with trust as a standalone, but there is a distinction. Trust, but verify stipulates that outcomes are more important than the underlying relationship.
The phrase offers some nice parallels for how marketers work with customer data. If the outcomes in question are business outcomes, the imperative to deliver a personalized customer experience explains why “trust, but verify” should now be the standard operating procedure for marketers. Consider a new Harris Poll survey commissioned by Redpoint, where 39 percent of consumers surveyed said they will not do business with any company that does not deliver a personalized customer experience that reflects a deep understanding.
Marketers, then, really have no choice but to verify that the data they’re working with are accurate, complete and timely, that the data represent a contextual view of an individual customer, household or entity that persists over time. The analogy isn’t perfect – unlike a personal relationship, data cannot have hurt feelings – but traditionally marketers would have no recourse to verify the quality of customer data. There was – and often still is – an inherent mistrust or doubt about data, but marketers lacked a way to prove to themselves that it’s correct, leading to conservative strategic decisions. Moving forward despite mistrust was acceptable when outcomes mattered less and when customers themselves were more tolerant of a mass marketing approach, not expecting a personalized, omnichannel customer experience.
Now that outcomes are paramount, it is vital that marketers have full confidence that customer data has been put through its paces. With absolute, unshakeable proof in the accuracy and completeness of customer data, confidence will extend to the outcomes being delivered. Marketers will be certain that they are delivering hyper-personalized, omnichannel interactions.
How is this trust generated? It’s one thing to trust the underlying technology. It’s quite another to have real-time visibility and transparency into the veracity of a customer profile – a virtual all-systems-go, flashing green light notification that data is 100 percent accurate and complete.
This is the marketing version of “trust, but verify” – having unfettered access to why customer data has been deemed pristine. It’s the difference between marketers having the confidence to make bold, ambitious marketing decisions vs. taking conservative positions that savvy customers will see through as half-hearted, surface-level efforts.
Bold decisions are only possible when a marketer knows, with certainty, that they have all the information they need to make an accurate, informed decision not just for a single point of engagement but across all aspects of an omnichannel engagement experience.
Data Quality is the Foundation for a Personalized CX
Purchase history provides a straightforward, instructional example. If marketers possess real-time insight into a customer’s purchase history and trust that the information is correct because they can see how it’s being calculated and that it’s updated in real time, the marketer can then make a better, more personalized offer or next-best action on the next point of engagement. This could even include updating emails in real time – up to the moment an email is opened – where an offer is changed based on real-time transaction history flowing through the system. The converse – the more conservative outreach that comes with a calculated risk – might entail displaying a promotion or sending an email offer for a product a customer recently bought.
That deep-seated level of trust, however, also carries over to anything that purchase history touches, such as customer lifetime value, loyalty tiers, retention strategy. And it’s not just for the one customer, rather how those metrics impact marketing strategy overall. A definition of a “high-value customer” will determine audience clustering and segmentation rules, and a higher degree of trust will flow into confidence in the analytics, leading to a better understanding of the customer base and an uninterrupted cycle of better personalization, better outcomes, and ultimately even higher trust in customer data.
The holy grail for marketers is personalization at scale – individualized, hyper-personalized experiences at a high volume and rapid cadence, laser-focused on being relevant for an individual or household the precise moment a next-best action is rendered. Basing decisions on inaccurate data, data that may be accurate but is not complete or even latent data are all major roadblocks for a marketer’s ability to demonstrate a deep, personal understanding of a customer that meet or exceed customer expectations.
Data quality is the foundation of everything that ambitious marketers are trying to accomplish in customer engagement. With clear visibility into data quality, marketers can direct their attention on creating and executing innovative experiences at scale without having to concern themselves with the trustworthiness of customer data.
Data Quality for the Everyday Marketer
In this context, visibility does not mean a technical expertise for how data records are matched, or a data scientist level of understanding for how data is pulled together or how to set confidence levels. Rather, a basic understanding of the parameters put on data, where it’s coming from, how or why a data stream is broken, access to the underlying algorithms to see how a confidence level was generated, or how segments and clusters are built – these and other basic tools give marketers the ability and means to see for themselves what data quality measures are being taken, and how those measures yield a certain trust level.
With an ability to verify why data is trustworthy, marketers can ultimately hold data to account. Marketers often stake their reputations and careers on the effectiveness of a campaign or a marketing strategy. The data they use to build out a strategy should be held to the same standard.
You can read more about perfecting data and resolving identities in real time, while keeping your company’s data within your own security perimeter here. Redpoint In Situ™, a cloud-native data quality as-a-service (DQaaS), provides business users with an unprecedented level of trust and confidence in data, empowering them to drive positive change and revenue impact for their organizations.