Marketing Automation EP 2

Crucial building blocks for your Marketing Automation


Last time, we debunked the myth that email nurture flows with Mailchimp equate to marketing automation.

This week, we're digging deeper into the foundations of a system that -when done right- doesn't just automate but transforms your approach to leads and customers.

It’s one thing to know how to set up some parts of an automation flow, but for it to have a lasting, positive impact, you need to understand the necessary preliminary steps.

But first, a quick question: Do you know what your music taste has to do with marketing automation?

I’ll let you know at the end of today’s course.

Before we get started, if you find these insights valuable, the best way to support our work is by inviting your friends to become Dot Connecters and learn more about Connecting The Dots between marketing activities and financial goals.

Topic Of Today

🎙 Episode Highlight: defining the funnel & data truths

  • 📚 Deep Dive: building blocks of true Marketing Automation

  • 📌 Actionable Advice

  • 📚 Homework

  • 💡 Pro Tip

📽️ Business Companion EP 2 📽️

  1. 🎙 An Episode Highlight: Defining the Funnel & Data Truths

    Thibaut Hennau (Head of Paid Social & Marketing Automation) draws attention to understanding the data model and the 'invisible' infrastructure that supports marketing automation.

    He walks us through the process of assessing the visible parts, such as customer interaction points on the website, and the invisible parts, like the backend tool stack.

    Ricardo Ghekiere (Founder of Saasmic) then reinforces those points by focusing on the importance of defining your sales funnel stages and understanding the data that fuels your marketing automation.

    Without a common language and clear metrics, your marketing automation efforts could be building on sand.

    Key Takeaways:

    Data Model Assessment: It's crucial to understand how data is interconnected from the moment someone fills out a form. This involves tracking where the data comes from and where it goes.

    Visible Interaction Points: Analyse all customer interaction points, including forms on the website, login procedures, and free trial starts. This helps in building a map of the customer journey and understanding the data captured at each point.

    Invisible Infrastructure: Delve into the backend to see how different tools, such as CRMs, are interconnected and how they handle data.

    Ecosystem Mapping: Use tools like Miro to visualise the entire customer journey ecosystem, capturing every data point from form submissions to email follow-ups, and identify any gaps in the process.

    Funnel Clarity: Each company's funnel is distinct. Without a universal definition of stages like SAL, SQL, and PQL, you risk misinterpreting your data and misaligning your strategies.

    Data Consensus: Establishing a single source of truth for your metrics is essential. It ensures that everyone -marketing, sales, customer success- is aligned and moving in the same direction.

    Data Dialogue: The discussions around what data is needed at each stage of the lead qualification process are as crucial as the implementation itself. This clarity allows for a marketing automation system that truly reflects your business's needs.

2. 📚 Deep Dive: Building blocks of true Marketing Automation

True Marketing Automation is not simply about automating a flow or capturing as much data as possible.

It’s about defining 1 true source that collects data from multiple points based on clear definitions and goals with said data. It’s an ecosystem that creates a clear overview of both your outbound & inbound efforts and possible friction points in your customer journey.

  1. Visible Pathways: Customer Journey Examination

    When setting up marketing automation, it's imperative to start with a thorough examination of the customer journey. This means meticulously tracing the customer's path from the first website visit through to the final post-purchase steps.

    By doing so, we gain a comprehensive view of every touchpoint a customer encounters.

    This includes:

    Website Navigation: How customers find and interact with the website, including page visits, clicks, and time spent on each page.

    Conversion Points: Identifying where and how customers are converted, whether it's through signing up for a newsletter, downloading a whitepaper, or starting a free trial.

    Form Analysis: Examining the forms filled out by customers, noting the information requested at each step, and understanding the purpose behind each data point collected.

    Login and User Experience: Observing the login process and any subsequent steps a user takes within the platform, which can reveal insights into user behaviour and preferences.

    Customer Feedback Loops: Looking at how customers provide feedback, whether through surveys, support interactions, or product reviews and how this feedback is captured and utilised.

    By visualising these pathways, typically on a platform like Miro, we can not only understand but also begin to optimise the customer experience.

    This visualisation helps us to identify bottlenecks, unnecessary steps, or opportunities for enhanced engagement.

  2. Data Mapping:

    Data mapping is a collaborative effort that involves working closely with backend developers and tech teams to ensure that every piece of customer data is captured accurately and efficiently.

    This process includes:

    Data Collection Review: Assessing current data collection methods to confirm that they are capturing the necessary information without redundancy.

    Data Cleaning: Removing duplicates and outdated information to maintain a clean database, which is essential for accurate automation and analysis.

    Naming Conventions: Establishing clear naming conventions for data fields to ensure consistency across different systems and platforms.

    Data Flow Mapping: Creating a visual representation of where data comes from, where it's stored, and how it moves through various systems.

    Future Data Collection Practices: Defining how data will be collected in the future to improve efficiency and ensure that the business is prepared for scaling and evolving customer needs.

    Single Customer Record: Confirm that there is a single, accurate record for each customer that can be relied upon for personalised marketing efforts and customer service interactions.

    Through data mapping, we create a blueprint that not only guides current marketing automation efforts but also lays the foundation for future growth and optimization. It's a strategic approach that aligns marketing efforts with business objectives and customer expectations.

  1. Lead Definitions: We scrutinise how leads are currently defined, understanding that a misdefined lead can skew data and misguide strategies. We then establish a clear structure for MQLs, PQLs, SQLs, and the like.

  2. Beyond Automation: This process goes beyond automation. It's about fixing loose ends in data collection, storage methods, and lead definitions, resulting in a more streamlined business operation.

  3. Investment in Clarity: While setting up a comprehensive marketing automation ecosystem requires time and resources, the return is exponential. It's about investing in clarity to avoid the pitfalls of operating on assumptions.

3. 📌 Actionable Advice

Foster cross-departmental communication:

Encourage regular meetings between marketing, sales, customer success and product dev to discuss data needs, lead quality, and customer feedback, helping alignment on objectives.

Embrace incremental changes:

Rather than overhauling your entire system at once, implement changes in stages. This allows for testing and refinement without overwhelming your team or your customers.

Leverage customer feedback:

Actively use customer feedback to refine your automation strategy. This ensures that your efforts are customer-centric and not just data-driven.

4. 📚 Homework:

1. Customer Journey Mapping:

Create a visual map of your customer journey, including every touchpoint. Use tools like Miro or a simple whiteboard to get started. Identify areas for improvement & potential automation.

2. Data Collection Inventory:

List all the data points you currently collect at each stage of the customer journey. Identify any gaps or redundancies.

3. Lead Definition Workshop:

Schedule a workshop with key stakeholders to review and refine your lead definitions. Document the agreed-upon criteria.

4. Form Field Analysis:

Examine the fields in your forms to determine their necessity and effectiveness. Consider eliminating any that don't serve a clear purpose.

5. Tool Stack Review:

Assess the tools you are currently using for marketing automation. Determine if they meet your needs or if there are better options available.

5. 💡 Pro Tip:

Remember, the goal of marketing automation is to simplify complexity and make the simple compelling. As you refine your system, always aim to enhance the customer experience. The best automation is invisible to the customer but invaluable to the business.

Fun fact answer:  Spotify's "Discover Weekly" feature is a powerful example of marketing automation that leverages user data to enhance the customer experience. Each week, Spotify users receive a personalized playlist generated through a complex algorithm that analyses their listening habits, as well as the habits of others with similar tastes.

It’s not only a cool feature but has been a huge hit, with millions of users tuning in weekly.

What makes this so special? Spotify's algorithm doesn't just look at what you've listened to; it also considers how you listened to it, the time of day, and what other users who like similar music are enjoying.

This level of detail in data analysis allows Spotify to create highly personalised experiences that feel curated by a close friend who knows your music taste intimately.

Isn't that just fun?

Keep learning and growing! 🚀 ❤️

New episodes of Connecting The Dots Business Companion are coming out regularly, so stay tuned.

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LinkedIn posts worth reading this week:

1. Why you shouldn’t use ChatGPT to write

While I personally do think AI can help in some less creative parts of writing (e.g. preparation, outline) I do have to agree with Tim in that: “Every time you write, you get better at writing.”

Is not what you should be worried about.

Or is it just a great product?

The 1 best tool of the week:

Are you thinking about starting a podcast or looking to create short episodes of your current ones?

Try this tool:

1. Riverside

Record in studio quality without the studio

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