Release the Kraken
2025 DTC competition is expected to be fierce. Customer expectations are relentlessly evolving. New options - and distractions - are popping up every day. Despite these challenges, brand marketers who build strong customer journeys will weather the storms and be more likely to come out on top.
In this chapter, you’ll learn:
The core components of the DTC Journey
Foundational metrics to calculate and track
Expensive disconnections in the DTC Journey
Focus on generating a powerful first impression. Clear and memorable to instantly signal your brand's unique value.
Demonstrate concrete solutions. Present clear case studies and comparisons to showcase your unique advantages over competitors in solving specific problems.
Provide detailed value evidence. Clear ROI calculations and implementation guides to validate your solution's fit with prospect needs.
Remove purchase barriers. Focus on clear pricing, simple processes, and risk-free trials to convert interested prospects into confident customers.
Focus on exceeding expectations. Provide clear onboarding support and success milestones to generate authentic advocacy and testimonials.
Focus on deepening relationships. Clear upgrade paths and exclusive benefits to strengthen customer bonds and encourage broader adoption.
Make sure your DTC journey is addressing ALL six of these components.
Journeys are rarely formulaic. Consumers may step into - and out of - the journey at odd junctures. And the combination, pacing, and number of touchpoints can vary wildly.
Take a holistic view instead.
Spotting Disconnects in the Journey
Look for these signals of a disconnected DTC journey
Rise in negative feedback
High website bounce rate
Slipping conversion rate
Now that you have the foundational elements of the DTC Journey, it’s time to build one.
You can use anything from a whiteboard, to Canva, to Microsoft Clarity.
Don’t fear the octopus!
When you start exploring all the paths to purchase while mapping your journey, it will grow large, messy, and scary.
Stick with it.
The process will uncover disconnects - and optimization opportunities - early
Once mapping is done, generate customer personas.
Simulate how each persona would travel through your mapped out journey.
Your personas should include, at a minimum:
- Demographics: Age, gender, income, education, occupation
- Psychographics: Lifestyle, interests, values, beliefs
- Behaviors: Purchase habits, online behavior, communication preferences
- Goals and motivations: What are their goals and what drives their purchasing decisions?
Now go over your journey yet again, but this time view it through these emotional lenses.
Fear and anxiety: Customers may experience fear or anxiety when making a purchase, especially if the product or service is new or expensive. They also may fear missing out on limited-edition items
Excitement and anticipation: Customers may feel excitement and anticipation when they find a product or service that specifically meets their needs, motivating them to make a purchase
Disappointment and frustration: If a customer has a negative experience, they may feel disappointed or frustrated, which could stop them dead in their tracks
IMPLEMENT Cursory Learnings
Here’s an example of a change we made to one of the pathways of a customer journey after going through this three-phase process:
Watch this rapid roundup of tools and tactics for building a DTC Journey on a budget →
Use free and low-cost tools like Microsoft Clarity and Canva to map out customer journeys and analyze behavior.
Visualize your website, landing pages, and paid media touch-points on a whiteboard to see how they all connect.
Leverage the Heat Mapping and Click Data functionality in Microsoft Clarity to capture a complete view of user interactions.
Use social media and surveys to gather direct customer feedback and complement your digital monitoring.
You’ll get way more mileage out of your DTC Journey Maps, by upgrading your analytics capabilities to track more advanced data.
In essence, examining the data in this way increases the resolution of your holistic view of the journey, making your finding more accurate.
Micro Conversions
These are smaller actions that customers take before making a purchase.
Newsletter signups
Freebie downloads
Wishlist product adds
Watching a video
Commenting on a campaign post
Churn Analysis
Track churn rate through the lens of customer lifetime value (CLTV), customer acquisition cost(CAC), and customer satisfaction.
Attribution Models
Last click attributionFirst click attributionLinear attribution (each click is given the same weighted value)U-Shaped attribution (first and last clicks are given higher weighted values)Time decay attribution (more weight given to clicks nearest the time of conversion)Experiment with the different models to find which delivery the most valuable insights.
Predictive Analytics
EXAMPLE: a DTC subscription box service might use time series analysis to predict sales fluctuations throughout the year based on historical data, or to see when seasonal events pegged to holidays like Halloween or Valentine’s Day are most effective.
The Power of Advanced Analytics: Mastering the DTC Customer Journey Livestream Excerpt →
Use media mix modeling and predictive analytics to help identify what’s really driving results across channels.
Go beyond ad performance by integrations your analytics with your CRM
Build multiple attribution models (first touch, last touch, and so on.)
Tailor creative and messaging based on less common audience segment test groups, like prospecting vs. retargeting.
It’s critical to remain grounded in reality when working with DTC Journeys. DTC Journey optimization demands data-backed decision making. When you identify optimization opportunities, transform them into strategic A/B testing initiatives.
Test Scale Examples:
Micro-Optimization: Fine-tune your post-purchase flow by testing current email sequence against an enhanced 4-touch nurture campaign.
UI/UX Enhancement: Transform conversion rates by split testing conversion-optimized landing page architectures.
AI-Powered Innovation: Maximize CLV through comparative analysis of next-gen product recommendation engines.
Key Success Factor:
Design statistically significant experiments to validate your optimization hypotheses.
Most enterprise marketing platforms offer built-in statistical analysis tools to streamline this process.
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