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How to Build Your Post-Cookie Data Strategy

There are plenty of reasons to completely own one’s data strategy that don’t include the all-important reason of staying on the right side of the ever-evolving legal landscape. One of those reasons is that it’s not always your legal issues you need to worry about but that of your data suppliers or publishing partners or one of their vendors. 

You don’t want to be caught leaning too hard on any given dependency (like, say, cookies). The best way to ensure an Antifragile Marketing program is to make sure you own your most strategically essential data. 

Owning your data strategy doesn’t require a 2- to 5-year roadmap or massive digital transformation. The first step is understanding what data you want to collect on your most strategic activities.

The camera analogy of a data strategy

I like to use a camera analogy to illustrate what it means to think about how you want to measure things.

The camera needs to move to achieve the desired perspective.

If the goal is to gather strategic information rather than exhaustive documentation, you won’t need every camera angle. Instead, the photographer will choose the angles necessary to provide a complete enough story in the fewest shots, including only the required visual information.

Each camera angle is analogous to a metric.

Exhaustive documentation is slow, costly, unnecessary, and likely misleading. During the data strategy planning phase, you need to pick the best tools for the job

You don’t want to be the metaphorical blind men arguing about the elephant they’re all describing (Is it a snake (trunk)? Is it a wall? (torso)? Is it a tree? (leg)). But you also don’t want to obsess over trivial metrics as if all measurements are equally important in every given scenario. 

For an acquisition campaign, you’ll likely want to investigate LTV:CAC.

For a reactivation campaign, you’ll likely highlight conversion rate and maybe AOV or basket size.

Among others, the big things you’re trying to understand include:

Different businesses will need to measure things differently. Not every angle will be interesting, useful, or even necessarily relevant in every context.

First and foremost, consider customer experience. Think about the moment someone becomes aware of your brand, their first purchase, their first unboxing, the entire process, A to Z. Within that, you ask: Are there any feasible measurements here? Can any of them help to move the needle toward optimization?

Understanding the entire customer journey

The customer journey is not a report spun up by a black-box analytical console measuring user touchpoints. The customer journey is a real-world experience that your customers go through every single day.

Some experiences are more digital than others. Some are completely offline (or were, in 2019). Within those that are offline, some are simply impossible to measure. Even for digital-only customers, you don’t know much about their day-to-day product experience, and propensity models would benefit from a dose of impossible-to-reach offline data.

At the same time, it’s critical to understand where the touchpoints are and develop hypotheses and intuitions around what parts of the journey most affect the customer experience.

Digging for nuance

On the flip side of being picky about what gets incorporated as key metrics for a given objective is expanding your capacity to measure different things across the customer experience. This might take the form of more in-depth instrumentation to measure precisely how someone might be interacting through a mobile app. It could include surveys or social monitoring.

The point is to determine the strategically valuable metrics that you’re not yet capturing and to find a way to achieve that capability while owning the data. One way to do this is as simple as breaking down an interaction into different behaviors. One everyday example would be assessing overall email performance as some combination of open rates and click-through rates.

Laying out product & business complexities

From a high-level perspective, you want to be sure you have visibility across the entire customer journey. This requires pulling from multiple data sources; for some companies, it could be dozens of sources. Below are a few to consider.


As we dig deeper into the concept of owning your data strategy, we can think about the benefits of independence concerning not being strategically reliant on too many external data sources.

We can also think about data independence as each department within the organization having independent access to an easy-to-use interface for interacting with privacy-compliant customer data.

With an efficient segmentation process housed inside the marketing department, brands can develop their Optimization Muscles on a more nuanced level than by-channel.

At this point, your marketing program is sophisticated enough to worry less about broad channel metrics and more about optimizing performance on customer segments from acquisition and across the entire lifecycle.

Website & in-store

Customers interact with your content and products in various ways. You need to be able to understand the impact of each touchpoint and the import of each channel. With a strategy for capturing a weighted multitouch attribution, you can use insights gathered to move spend to the most efficient channels as you make informed decisions about what to downgrade.

Email interactions may signify its most essential touchpoints for one business, where another’s would be in-store, on-site, in-app, SMS, or social.

When you can combine attribution with segmentation, you can ramp up your decision-making sophistication.

For instance, you may pin an egregiously low-ROI channel or product for the chopping block next quarter, but first, you pause and dig a bit deeper. You discover that the channel or product that seems so unpopular is actually viral but only among higher-LTV segments.

Though still technically “low ROI” with no means of accurate attribution, you can easily make a case for the product or channel’s business value.

Customer service

For many brands, customer service is the only live-human touchpoint a given customer may ever encounter. That makes the humble call center the hub of inspiring loyalty or crushing any hopes thereof.

Quite often, interaction with customer support is the most critical in a data arsenal. Customer Support is where things can go very wrong, even for savvy businesses. For many brands, customer support is also a revenue-driving function that can turn suboptimal experiences into new opportunities.


Next steps

Take a step back to get a sense of the essential areas for measurement. Is there good coverage? Do you need more or fewer metrics? Who needs access to what data, and to what degree? What is the use case of operationalizing this or that dataset?  

And if you want to learn more about data strategy, click here to watch our on-demand webinar, It’s Time to Own Your First-Party Data Strategy, hosted by me, Jason Davis, Co-founder and CEO of Simon Data. 

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