Customers today jump between their phones, tablets, and laptops constantly. They'll see your Instagram ad, check their email later, and maybe buy through your website the next day. This channel-hopping behavior keeps growing, and marketing teams are scrambling to keep up.
Most successful campaigns now use multiple channels, which makes sense. You need to meet customers wherever they are. But here's what I've learned after years of running campaigns: testing individual channels is still crucial, even when you're running multi-channel strategies.
Different channels work differently. What converts on email won't necessarily work on SMS. Your Instagram audience behaves differently than your YouTube viewers. That's why I always run channel-specific tests alongside my broader campaigns.
Know Where Each Channel Fits in Your Customer's Journey
Before you start testing any channel, you need to understand its role in your customer's path to purchase. Not every channel serves the same purpose.
Take digital ads, for example. They're often the gateway that brings people to your website or landing page. But they might not play a role later when someone's ready to buy. Understanding this timing helps you set realistic expectations and measure the right things.
I learned this the hard way when I was putting too much pressure on my Facebook ads to drive direct sales. Once I realized they were better at generating initial interest, I adjusted my expectations and started measuring them differently.
Choose the Right Metrics for Each Channel
This is where a lot of marketers mess up. They try to use the same metrics across all channels, but that doesn't make sense. Each platform has its own strengths and user behaviors.
Here's what I track for each of my main channels:
Email Marketing:
- Open rates (how many people actually see your message)
- Click-through rates (who's engaged enough to click)
- Conversion rates (who takes the action you want)
Text Messaging:
- Delivery rates (are your messages getting through?)
- Response rates (who's engaging with your texts)
- Opt-out rates (are you annoying people?)
Instagram:
- Engagement rates (likes and comments per follower)
- Reach (unique users seeing your posts)
- Story views (who's checking your behind-the-scenes content)
YouTube:
- View counts (total video views)
- Watch time (how long people actually watch)
- Engagement (likes, comments, shares)
Website Landing Pages:
- Traffic volume (how many visitors you're getting)
- Bounce rates (who leaves immediately)
- Conversion rates (who completes your desired action)
Notice how different these are? Instagram and YouTube share some similarities with engagement and views, but email and SMS are completely different animals.
Understand What Each Channel Actually Contributes
Here's something that took me years to figure out: most channels are just one piece of a larger puzzle. A customer rarely sees one ad and immediately buys. There's usually a whole journey happening.
Let's say you send an email promoting an upcoming webinar. The email itself might be performing great - high open rates, good click-through rates. But if nobody's actually signing up for the webinar once they reach your landing page, you've got a problem.
Maybe your email isn't giving enough context about the webinar topic. Or maybe it's overselling and the landing page feels disappointing by comparison. The email metrics look good, but the real performance is terrible because it's not driving the behavior you actually want.
This is why I always track the full funnel, not just the channel-level metrics.
Attribution Models: Giving Credit Where It's Due
When customers interact with multiple touchpoints before buying, how do you decide which channel gets credit for the sale? This is where attribution models come in. I've tested most of them, and each has its place.
First-Click Attribution: Crediting the Introduction
First-click attribution gives 100% credit to whatever channel first brought someone into your world. If they clicked a Facebook ad first, Facebook gets all the credit for any eventual purchase, even if they later engaged with your emails and retargeting ads.
I use this model when I'm focused on brand awareness or want to understand which channels are best at attracting new customers. It's perfect for product launches where that initial spark of interest is everything.
The downside? It completely ignores everything that happened after that first click. Just because someone discovered you through Facebook doesn't mean your email nurturing sequence didn't play a huge role in their final decision.
When I use first-click attribution:
- Brand awareness campaigns where the first touch matters most
- Testing which channels bring in new website visitors
- Understanding which channels start customer relationships
When I avoid it:
- Long sales cycles with many touchpoints
- When I need to see the complete customer journey
- Campaigns where multiple channels contribute significantly
Last-Click Attribution: Crediting the Closer
Last-click attribution does the opposite - it gives 100% credit to whatever channel someone interacted with right before buying. If they clicked a retargeting ad and bought immediately, that ad gets all the credit.
This model works well for performance marketing where you care most about what directly drives sales. E-commerce companies love it because it shows clearly which ads are converting browsers into buyers.
But it has the same problem as first-click in reverse. You might have spent months nurturing someone through content and emails, but if they bought through a Google search, Google gets all the credit.
When I use last-click attribution:
- Performance campaigns focused on immediate conversions
- Short sales cycles with fewer touchpoints
- When the final push toward conversion is most important
When I avoid it:
- Long sales cycles where nurturing matters
- Brand awareness campaigns
- When I want to understand the complete journey
Linear Attribution: Sharing Credit Equally
Linear attribution splits credit equally among all touchpoints. If someone interacted with five different channels before buying, each channel gets 20% of the credit.
This gives you a balanced view of how all your channels work together. It's especially useful for complex multi-channel campaigns where every interaction plays a role in moving someone toward a purchase.
The problem is that not all touchpoints are actually equal. An early awareness ad might be less influential than a targeted email offer that directly led to a sale, but linear attribution treats them the same.
When I use linear attribution:
- Multi-channel campaigns where every touchpoint adds value
- Longer customer journeys with many interactions
- When I want to understand how channels work together
When I avoid it:
- When some touchpoints are clearly more influential
- Short sales cycles dominated by a few key interactions
- Performance campaigns focused on specific high-impact touchpoints
Time Decay Attribution: Weighting Recent Interactions
Time decay attribution gives more credit to touchpoints that happened closer to the conversion. The thinking is that recent interactions had more influence on the final decision.
This works well for time-sensitive campaigns like flash sales where the final touchpoints (reminder emails, retargeting ads) are crucial for getting people to act quickly.
The downside is that it can undervalue the early touchpoints that built awareness and interest in the first place. That initial blog post or social media ad might have started the whole journey, but it gets minimal credit.
When I use time decay attribution:
- Short sales cycles where final interactions matter most
- Time-sensitive campaigns (flash sales, limited offers)
- When I want to prioritize touchpoints near conversion
When I avoid it:
- Long sales cycles where early awareness is important
- When I need to measure all touchpoints fairly
- Campaigns where first-click interactions are critical
Position-Based Attribution: Crediting Both Ends
Position-based (or U-shaped) attribution gives 40% credit to the first touchpoint, 40% to the last touchpoint, and splits the remaining 20% among everything in between.
This model recognizes that both discovering your brand and the final conversion step are crucial, while still acknowledging that middle touchpoints play a supporting role.
It works well when you're running campaigns that focus on both awareness and conversion. But it might undervalue important middle-funnel activities like email nurturing or product demos.
When I use position-based attribution:
- Campaigns balancing awareness and conversion goals
- When first and last interactions are equally important
- Strategies using both top-funnel and bottom-funnel tactics
When I avoid it:
- When middle touchpoints are essential (like nurturing sequences)
- Very short or simple customer journeys
- When all touchpoints should get equal credit
Why Single-Channel Testing Still Matters
Even in our multi-channel world, I still run single-channel tests regularly. Here's why they're valuable:
Testing individual channels lets you focus on what makes each one unique. You can experiment with different messaging, timing, and creative approaches without worrying about how they interact with other channels.
I've discovered channel-specific insights that I never would have found in broader tests. For example, my email audience responds completely differently to subject lines than my SMS audience responds to message previews.
Single-channel tests also help you identify problems that might be hidden in multi-channel data. Maybe your Instagram ads are performing well overall, but when you test them in isolation, you realize they're only working because your retargeting campaigns are doing heavy lifting.
These focused tests give you insights you can then apply to your broader multi-channel strategy. Once you understand what works on each individual channel, you can orchestrate them more effectively together.
Putting It All Together
The key is using single-channel testing to understand each piece of your marketing puzzle, while using attribution models to understand how they fit together.
I typically start with single-channel tests to optimize each platform individually. Then I use different attribution models depending on what I'm trying to understand about the customer journey.
For awareness campaigns, I might use first-click attribution to see which channels are best at bringing in new people. For performance campaigns focused on sales, last-click helps me understand what's directly driving revenue. For complex nurturing campaigns, linear or position-based attribution shows me how different touchpoints work together.
The goal isn't to pick one approach and stick with it. It's to use the right measurement strategy for each situation, so you can make smarter decisions about where to invest your time and budget.
Remember, your customers don't care about your attribution models or testing strategies. They just want a smooth experience that helps them solve their problems. Use these measurement approaches to give them exactly that.
