I overheard the following lament from a B2C marketer the other day:
“We want to run more customized ads to our best customers, to make each message they see from us seem more relevant. But onboarding our customer data with multiple vendors is so time-consuming. Plus, the match rates aren’t great, and we don’t get much, if any, data back for ROI analysis.”
If this sounds like something you or someone in your marketing organization might say, you’ve already experienced some of the flaws in traditional identity resolution approaches — flaws that can derail your marketing efforts. Here are four fatal flaws and why they matter.
Flaw #1: Solving identity resolution one vendor at a time doesn’t solve the larger problem.
The traditional approach to customer identity focuses first and foremost on delivering a specific marketing campaign and solves identity for that purpose only. Problem is, it takes many different marketing channels and platforms to keep up with today’s customers. So, with a campaign-based approach, you have only “solved” identity on a supplier-by-supplier basis, and each supplier only has a fraction of your single customer view.
Impact on marketing:
- You have a fractional rather than unified customer view. You can’t maximize your view of the customer journey or evaluate the combined effect of multiple marketing activities at the customer level.
- You can’t use customer actions in one channel to market to them in another. Some brands try to re-combine data feeds from various vendors in a data warehouse, and then feed the unified data to other analytic and marketing activation systems. But that creates a troublesome time lag between customer intent signals and marketing response. You need to keep the data as close to customers as possible to address them in the moment of intent — not a day or three later.
Flaw #2: So-called cross-channel systems don’t cover all the channels you need.
With traditional identity resolution approaches, most cross-channel identifiers really cover only a few channels. For example, a marketing cloud solution may sync customer identifiers across your owned media, but not address the issue of identity across your paid media vendors. A data onboarding solution that has direct feeds to paid-media vendors is fine, but it still leaves you with a fragmented view of your paid-media results. You still don’t end up with a broad and unified customer view, which you really need when consumers are continuously moving across channels and making decisions so quickly.
Impact on marketing:
The ideal situation is that marketing can detect consumer signal X and respond quickly with helpful response Y, improving the customer experience and sales. But without a complete cross-channel approach, you can’t always detect when X happened, nor activate response Y in the desired channel, and do both in a timely manner. For example, when a customer makes an in-store purchase, you need to be able to change out the retargeting ads for that item to a complementary upsell ad, as well as update their loyalty program status and alert them that they’ve earned a new reward, or activate a communication from your post-purchase service team.
Flaw #3: Cookies are too fragile to be a dependable basis for identity resolution.
Traditional identity resolution relies heavily on syncing your customer list to a specific vendor’s cookies. While cookies still play an important role in the advertising ecosystem, they were only designed to deliver ads from a single marketing vendor in response to a recent anonymous online action (e.g., delivering an offer for team logo apparel if NFL content was browsed). The interest was what mattered, not the identity of the individual. And because the goal was to advertise to fresh interest, cookies didn’t need to have a long life, nor be synced across vendors.
Impact on marketing:
Matching your customer lists to an ad vendor’s cookies only solves for identity with that vendor, and only for about 30 days, as people clear cookies from their devices, change devices, etc. Once a link to a specific customer is lost, it can take days or weeks to find that customer again, leaving you with permanent gaps in your marketing communication stream with that customer and in your view of their online behavior. Without a complete view of the customer journey, you lose insight into — and the ability to market at — critical moments.
Flaw #4: Traditional offline identifiers are more durable than cookies, but too slow and disconnected from other channels.
Traditional offline identifiers like a customer’s loyalty card number, home address or phone number have a longer lifespan than cookies — usually at least a few years. But they can be difficult to connect to digital identities, typically taking at least a day or two to run a match, preventing you from hearing and reacting to customer signals in real time. Also, this type of matching typically is done only on a vendor-by-vendor and campaign-by-campaign basis. You’ll have to repeatedly re-onboard to reestablish a link to a customer.
Impact on marketing:
The lag time and effort for matching offline to online identifiers is too great. Also, there’s no customer identity resolution continuity across campaigns and vendors. Therefore, the marketer can’t continuously listen for signals from identified customers and respond in real time, nor maintain an ongoing digital dialogue that builds emotional engagement and brand loyalty over time. To do so, a brand must invest in owning their identity graph and growing it as a strategic asset for the long term.
The bottom line: The traditional approach of batch onboarding has too many flaws.
While batch onboarding may be sufficient for a low-penetration brand primarily focused on customer acquisition, brands with a large customer base and a focus on retention will quickly be disappointed in its slow speed and the gaping holes it leaves in the brand’s view of individual customer journeys. You really need an always-on identity solution that can listen continuously to customers and support real-time marketing response.
Originally published May 22, 2017