Marketers tend to think in journeys.
Whether it’s shopping behavior through a site, interactions through a campaign, or the customer relationship as a whole, it’s your job to take people from awareness to conversion and prospect to evangelist.
But the most effective marketers also think about smaller points of engagement—near real-time signals, decisions, and opportunities that, if handled in the right way, can make a big difference to things like:
- Understanding what customer behavior actually indicates (rather than guessing)
- Getting a unified, omnichannel view of complex, nonlinear customer journeys
- Making your customer experience and activation opportunities more people-based
This visibility into the customer journey requires near real-time measurement, which unleashes a host of use cases that your media, measurement, and e-commerce teams dream of.
Fair warning—this undertaking is also a journey. The steps below take you through a journey of understanding your customers better across the open web, providing insights you may not have otherwise come across.
Enable near real-time measurement
There are a lot of moving parts to supporting near real-time measurement, but broadly there are three main components.
1) A well-integrated tech stack that breaks down silos and unifies all the data, systems, and channels you use into one seamless function.
2) A source of resolved customer identities to tie specific interactions and moments across the open web to the consumers within your CRM.
3) The right data partnerships that provide you with the near real-time data you need to optimize campaigns on the fly.
(And with identity-led tech stack integration, you can move towards #1 and #2 at once.)
With those three assets in place, there are now numerous tangible opportunities for marketers to understand the nuances of customer shopping behavior—and make better decisions based on it in a privacy-centric or privacy-conscious manner. Let’s take a look at a few examples.
What near real-time measurement looks like
Say a cosmetics brand could see that a tokenized customer added a few items to their cart, but then abandoned it before checkout. Without tech-stack integration, the media team would probably keep serving them ads and/or emails to encourage them to come back and complete the purchase.
But with a resolved identity, an integrated tech stack, and the right data partnerships, the analytics side of the house would be able to see that, for instance, they subsequently purchased the items in-store or at an affiliate site instead.
This is an insight marketers often struggle to land on because they don’t have the right data to understand purchase paths across the open web. With near real-time measurement, you can finally see if a disproportionate number of customers browse on your site and convert elsewhere. That’s a great opportunity.
With that understanding, you could stop serving them ads and impressions encouraging them to convert on an item they’ve already bought. You waste less ad spend and facilitate better customer experiences. But what if you wanted to go beyond damage control and into making things better?
Your e-commerce teams could send that customer a personalized offer the next time they were browsing similar items on your site—preventing cart abandonment and hopefully leading to a conversion.
This kind of near real-time measurement and activation isn’t easy. Tying online and offline purchase data back to on-site browsing habits requires close integration among your ecommerce, analytics, and media teams, plus any affiliate partners, at a minimum.
But all this relationship building and leg work is worth it because near real-time measurement opens the door to insights into the customer journey you wouldn’t be able to access.
How near real-time measurement scales
Near real-time measurement is all about near-near real-time insight that supports marketers to continually drive better performance through optimization—both for individual campaigns and for the wider customer journey.
At the campaign level, it’s possible to tie transaction data to exposure data, so marketers can measure effectiveness down to individual transactions almost instantaneously, provided you have fast, on-demand data inputs. That kind of insight equips marketers in fast-moving sectors to make multiple daily adjustments as needed—to ad spend, media strategy, promotions, creative, and so on.
But zooming out, (as with the personalization example for the cosmetics brand above) near real-time measurement gets really powerful when it’s applied continually and at scale—across the whole customer journey.
It would be the difference between that cosmetics brand measuring on-site performance of a single campaign for a single product versus a near real-time, interconnected understanding of performance across the entire beauty range—wherever customers shop.
You’d clarify opaque and disparate customer journeys (and discover new ones altogether) to reveal what’s going on at a single-consumer level—for every product on-site, in-store, and through retail partners.
The result is a people-based, privacy-first understanding of the touch points that make up every customer journey, so you have the best shot at promoting the right items, fine-tuning your performance, and building better relationships, customer by customer.
If that sounds ambitious, it is—but with identity resolution and tech stack integration, it’s possible.
Ready to get started? Dive into tech stack integration.