Traditional analytics can easily make you feel like you have all the answers. Let’s say you open a report and see that 60% of your conversions came from email. Case closed, right? Not so fast. Omnichannel analytics can reveal a very different story.
By looking at all the data, you’ll likely uncover a winding buyer’s journey. Those email conversions weren’t impulsive clicks. For example, they could be customers who first discovered you on social media, browsed your website, checked out the product at physical stores, and then finally clicked on your email to buy.
Miss those moments, and you’re left optimizing for the last click instead of shaping the path to purchase. The good news? Your omnichannel data is full of these stories. You just need to know how to read them.
What is an omnichannel analytics strategy?
An omnichannel analytics strategy is your blueprint for turning fragmented omnichannel data into a clear map of the real customer journey. You see not just where the sale happened, but the path customers took to get there.
As the path comes into focus, you often learn:
- Where the buyer journey begins
- Which channels play supporting roles versus closing deals
- Where customers get stuck or drop off
- What sequence of touchpoints leads to purchases
- What your most profitable journeys have in common
With these actionable insights, you can tune each channel to work in harmony, creating a truly frictionless omnichannel customer journey. Each touchpoint reinforces the others, building momentum toward conversion.
Key benefits of omnichannel analytics
Why is omnichannel analytics important? Understanding how your channels work together is the key difference between guessing and knowing what drives sales. And when you finally connect those dots, here’s what happens:
- Better budget allocation: You now know which channels build awareness, nurture customers, and close the deal. The result? You can fund the entire journey, not just the final step.
- Higher conversion rates: Streamlining the customer journey makes each step feel effortless. And when buying is easy, more people follow through.
- Improved customer lifetime value: A smooth, well-connected journey encourages customers to buy again. Each great experience boosts customer satisfaction and strengthens their bond with your brand.
The best part? Having the complete picture helps your team make faster, more informed decisions, saving you time, increasing operational efficiency, and keeping every marketing dollar working harder.
Using multi-touch attribution to visualize the customer journey
Seeing the whole story behind every sale starts with multi-touch attribution. By tracking how customers interact across every touchpoint, these models reveal the roles each channel plays. So, you can optimize the entire journey, not just the final click.
Some of the most popular attribution models are:
- Linear: Split credit evenly between every touchpoint. Use this model when you see all interactions as equally important or want a simple way to value each channel.
- Time decay: Give more credit to the touchpoints closer to the sale. Use this model for longer sales cycles where recent actions have the biggest impact.
- U-shaped: Put the most weight on the first and last touchpoints, with the rest getting less credit. Use this when brand discovery and closing moments matter most to your results.
You can stick with a single model, but it’s often better to compare different models to get a clear view of the entire customer experience. For example, use a U-shaped model to find which channels start the journey and close the deal most often. Then, use a time-decay model to see which touchpoints have the greatest impact right before the sale.
Key metrics for measuring and improving the omnichannel journey
Attribution models map the real customer journey, but key performance indicators (KPIs) reveal what’s happening along the way. These numbers help you see which paths get the best results, where people get stuck, and how you could improve customer experiences.
The most important metrics to watch include:
- Journey completion rates: How many customers start the journey and end up buying something? A low rate often points to friction points, like a lengthy checkout process or technical glitches.
- Drop-off rates by touchpoint: Where do customers leave most often? If it’s right after viewing product pages, you might need better descriptions. If they drop off on the shipping options page, costs could be too high.
- Channel transition rates: How smoothly do customers move between channels? If they keep switching back and forth in the same browsing session, it could be due to slow-loading pages or tricky cross-device logins.
- Conversion path analysis: Which paths through your touchpoints lead to the most sales? If your high-value path goes from email to product page to live chat, but most visitors skip chat, you could be missing an opportunity to boost trust and close more deals.
- Touch frequency analysis: How many interactions do customers need before they buy? A high number could mean people want more information or reassurance through social proof before they commit.
It’s easy to get caught up in advanced analytics, but don’t forget the everyday numbers. Track things like cart abandonment rates, average order value, and repeat purchases to keep tabs on the health of your marketing plan. If any of those numbers start looking off, you can use the data analytics methods below to identify the cause and determine your next move.
Four types of omnichannel analytics for journey optimization
You know your most important metrics, but how do you turn them into action? Omnichannel analytics uses 4 lenses to help you gain a deeper understanding of what’s happening and what to do about it. Let’s explore them using a real-world example: A customer abandons their shopping cart in your mobile app.
Type #1: Descriptive analytics
Descriptive analytics shows you what happens in your marketing campaigns. It’s all about the hard numbers, such as traffic volumes, ad impressions, click-through rates, social engagement, and sales totals. These metrics give you a clean snapshot of the performance of every channel, so you know where you stand.
For example, your dashboard might show that 35% of mobile shopping carts were abandoned last month. It might even put a dollar figure on it, like $15,000 in potential lost revenue. Through this lens, descriptive analytics answers the what but leaves you asking the next logical question: Why?
Type #2: Diagnostic analytics
Diagnostic analytics is where you figure out the why behind your numbers. It goes beyond just tracking results and starts connecting different data points to uncover the real story. An omnichannel view is key here because each channel’s results might be influenced by another.
To figure out why 35% of mobile carts were abandoned, you’d look at the full customer journey. You might notice that many shoppers logged in on a desktop later, only to find their carts empty. So, they gave up and left. Now, you know that multiple customer pain points didn’t cause the problem, but a single cart-sync failure.
Type #3: Predictive analytics
Predictive analytics uses your past data and machine learning models to answer the big question: What’s likely to happen next? That way, you can anticipate user behavior and act before the problem escalates, not react after the fact.
For instance, your data might show that multiple device owners who run into a cart-syncing error on mobile apps are 60% less likely to buy in the next 30 days. Predictive analytics tools can flag these shoppers in real time, so you can target them with the proper follow-up before they disappear for good.
Type #4: Prescriptive analytics
Prescriptive analytics takes the predictions you’ve made and answers the question: What should we do about it? It’s usually powered by a customer data platform (CDP). The CDP uses AI to recommend the best possible action and then tells your marketing automation platform to carry it out.
For cart-sync errors, the CDP might see that the best way to save the sale is to send a quick email with a link to restore the shopper’s cart on their current device. It could also suggest adding a small perk, like 10% off, to turn a frustrating moment into a win.
Five steps to creating your omnichannel analytics strategy
Ready to build your omnichannel analytics approach? Here’s your step-by-step roadmap for setting up a strategy to turn raw data into clear customer insights.
Step #1: Connect your data sources
Integrate data from all your different channels, from website and social media analytics to email marketing platforms and point-of-sale (POS) systems. You can use a CDP or another omnichannel analytics solution to pull everything together.
The goal is to get a comprehensive view of every customer touchpoint, so nothing slips through the cracks. But be sure to never compromise on data quality or security while implementing omnichannel analytics.
Step #2: Choose your attribution model
After integrating data, decide how you’ll give credit to your different marketing channels. Will you split it evenly (linear), give more weight to the first and last interactions (U-shaped), or favor the most recent actions before the sale (time decay)?
Don’t overthink this choice. Set up your chosen model in your analytics platform and commit to using it for 3-6 months. You need consistent data over time to spot meaningful patterns. Once you’re comfortable with a single model, you can always run additional models for comparison.
Step #3: Define your key metrics
Don’t try to track everything at once. Begin by selecting 2-3 advanced metrics, such as journey completion rates or conversion path analysis, based on your goals. For example, if you’re worried about losing customers mid-journey, focus on drop-off rates by touchpoint. Then, add 3-5 simpler metrics to gauge your overall omnichannel marketing performance.
Step #4: Set your analysis schedule
Omnichannel analytics is an ongoing process, not a one-time project. So, it’s important to set up a shared dashboard allowing your team to see the data generated across all your channels in real time. Encourage them to check in on your marketing metrics at least once a week on their own.
Then, schedule quick team check-ins (about 30 minutes every other week) to analyze the data together and decide how to improve the customer journey. Also, hold longer quarterly reviews to track progress toward bigger goals and potentially adjust your analysis approach.
Step #5: Optimize, test, and iterate
Using what you learn from your monthly reviews, pick 1-2 areas to improve first. It could be improving customer engagement strategies, streamlining the transition between channels, or boosting a high-performance path.
Test different approaches, track the results, and keep what works. Then, revisit the entire process in your next review. Over time, these small, steady changes add up to a smoother journey and better results across every channel.
Top challenges in omnichannel analytics
Even with the right strategy, omnichannel analytics comes with hurdles. Here are the biggest obstacles you might face and how to overcome them.
Customer data silos
Most businesses have way more data sources than they realize. Sure, there’s your website analytics and customer relationship management (CRM) systems, but also social media interactions, Contact Center chat logs, product reviews on third-party platforms, and customer loyalty program data.
If these systems don’t all talk to each other, you end up with data silos, making a unified customer view impossible. Fortunately, it’s easy to fix. You need to audit customer interactions across every touchpoint regularly. Then, pull your data into a single place, like a CDP, instantly revealing customer behavior patterns you couldn’t see before.
Inconsistent tracking
Many marketing platforms measure success differently, and some don’t track certain actions at all. Your email tool might count a conversion as any click to your website, while your e-commerce platform only counts actual purchases. So, you’re unable to understand which channels truly drive results.
You can overcome this issue by creating standard definitions for key metrics across all your tools. Decide how you’ll measure customer engagement, conversions, and other important events. Then, configure each platform to match those definitions as closely as possible.
Resource and skill gaps
Many Marketing teams lack the right omnichannel analytics solutions and skills to connect data across channels. Your team might excel at running email and ad campaigns, but analyzing data across multiple channels requires a different skill set. Plus, the software you’d need for this kind of tracking can be expensive and hard to learn.
You can solve this by using free business solutions like Google Analytics. Also, train your existing team with basic online analytics courses through platforms like Skillshop. Then, plan to invest in ongoing training for your team and upgrade your tools as their skills evolve.
Privacy compliance
Privacy regulations can make cross-channel tracking feel like walking through a maze. Laws like the General Data Protection Regulation (GDPR) limit how you can collect, store, and use customer data. And of course, new guidelines keep getting published regularly.
The easiest way to stay compliant is to build your omnichannel strategy around first-party and zero-party data. First-party data is the information customers share with you through their actions, like site visits, app usage, or purchases. Zero-party data is given to you intentionally, whether it’s customer preferences, survey answers, or form fills.
Want to start gathering data from other sources? Work with your Legal department to confirm what’s allowed. They can also help you set up consent processes that meet regulations while still letting you gather the omnichannel insights you need.
Key takeaways
- Get the full story: Omnichannel analytics shows how every touchpoint works together, so that you can optimize the entire customer journey from start to finish.
- Make attribution work for you: Attribution models can reveal which channels are the best at building brand awareness, nurturing relationships, and closing the sale.
- Let key metrics be your guide: Focus on a small set of journey-focused and everyday metrics so you can spot problems and opportunities faster.
- Use all 4 analytics lenses: Descriptive, diagnostic, predictive, and prescriptive analytics all work together to help you learn what happened and how to resolve the issue.
- Build your strategy today: You’re only 5 easy steps away from building an omnichannel analytics strategy your team can start using right away.