Scientific marketing methods that will grow your business

Scientific marketing methods that will grow your business

Stop using these goals as your marketing goals!

  • Increase traffic
  • Increase conversion rates
  • Increase number of customers

Hate to break it to you, but these aren't goals.

They're wishes. Hopes. Dreams. And they're worthless.

Why?

Because they tell you absolutely nothing about:

  • How much improvement you need
  • When you need it by
  • How you'll achieve it
  • Whether your actions are working

You might as well write "make more money" and call it a day.

Real marketing goals are specific, measurable, achievable, relevant and time bound (SMART goals), and backed by a scientific method to achieve them.


Data as foundation

Marketing poeple love to talk about being "data driven".

But there is an uncomfortable truth:

... having data, is not enough (anymore).

Organizations find themselves drowning in metrics while still struggling to make confident decisions.

"We have big data collection"

They also emphasize on the "big data" part.

But is it the right data?

And what do you do with that data?

So, after approximately 15 years in digital marketing industry I have seen trends and methods, come and go. However, one thing is still important, and that is data, but its not the same use of it as before.

The problem with being "just" data driven

Picture this:

Your latest campaign generated thousands of data points across multiple channels. You can see exactly what happened.

Click through rates, conversion rates, engagement metrics etc..

But you are still left wondering why it happened and more importantly, how to replicate or improve upon your success.

Too many times I have seen digital marketing campaigns being run, and when results are higher than before, we are satisfied with that. "Our campaign outperformed our last", which is is a good thing. But left with a positive result, many dont see the point of learning more about it, since it worked.

But we need to know why? What caused the performance? In either a positive way or a negative way.

This is the limitation of being merely data driven. You are looking in the rear view mirror, analyzing what happened without a systemic way to predict and influence what comes next. Often just analyzing what went wrong, and not what went good.

Enter growth marketing methodology.         

Growth marketing is not just the AAARRR model or doing CRO campaigns. It is way more than that. Its about processes and mindset.

Growth marketing takes data driven approaches to the next level (I know this sounds very AI like, but this is actually true), by incorporating the fundamental principles of scientific inquiry:

  • Systematic observation / data
  • Hypothesis formation
  • Controlled experimentation
  • Rigorous analysis

It is literally, the next level of Performance Marketing.

The difference is profound. Instead of just collecting data, about what is done, you are designing experiments to generate exactly the insights you need. Rather than reacting to metrics, you are proactively testing hypotheses about what drives customer behavior.

How to transform your marketing into scientific process

0. Start with business goals and tracking.

I set this to a 0, since this is essential, but not necessarily part of the process. We need to have clear goals to what we want to achieve. And you need the tracking in place to make sure you can track the progress towards these goals. It's the foundation that needs to be in place.

1. Structured hypothesis.

Every marketing initiative should begin with a clear testable hypothesis.

Here is the structure:

"Based on [observation/analysis], if we [implement specific change], then [measureable outcome] will occur because of [underlying theory]        

Example:

"Based on current customer feedback, if we add video testimonials to our product pages, then conversion rates will increase by 5%, because social proofing reduces purchase anxiety".

This structure forces you to:

  • Ground your ideas in current observations
  • Specify exactly what you are changing
  • Predict measurable outcomes
  • Articulate your reasoning

2. Design rigorous experiments

The key to growth marketing method, is controlling the variables.

For every test, consider:

  • Control group definition
  • Sample size requirements
  • Variable isolations
  • Timeline
  • Success metrics
  • Risk factors

Real world example:

Lets say you are testing email subject lines. A non scientific approach might simply try different versions and pick the winner. A scientific approach would be:

  • Created matched audience segments
  • Control for send time, day and frequency
  • Account for seasonal variations
  • Test multiple variations to establish patterns
  • Run tests long enough for statistical significance

3. Implement across marketing functions

This method is not just for AB tesitng landing pages or email campaigns. Here is how it applies to other marketing functions:

Content marketing

Instead of “create more blogs”, try:

“Based on our current analytics showing enterprise readers spend 3x longer on detailed posts, if we publish in-depth technical guides (3000+ words) instead of general overview posts (1000+ words), then our organic traffic from enterprise customers will increase by 50% within 6 months because detailed content better demonstrates expertise to sophisticated buyers”

Paid advertising

Replace “increase of ad spending” with:

“Based on our current customer lifetime value analysis showing that the top 20% of customers generate 80% of revenue, if we target lookalike audiences based on our highest LTV customers instead of all customers, then customer acquisition cost will decrease by 30% because we are reaching prospects with similar characteristics to our best customers.”

Social media:

Instead of “post more frequently”, test:

“Based on our engagement rate data showing a 40% drop in mid-day post performance and user behavior surveys indicating morning activity, if we shift our posting time to early morning (7-9am) instead of mid-day, then engagement rates will increase by 25% because our B2B audience checks social media before starting their workday.”

SEO

Instead of "rank better in top 3 searches", try:

"Based on our current keyword ranking data showing we rank on page 2-3 for high-intent product comparison terms, if we create dedicated comparison pages with structured product data tables and expert analysis, then we will achieve page 1 rankings for 60% of these terms within 4 months because we'll better match user search intent and provide superior answer relevance"

Inbound marketing

Instead of "create more content", try:

"Based on our heatmap data showing 70% drop-off on long-form content but high engagement with interactive elements, if we transform our static guides into interactive assessments, then average time on page will increase by 3 minutes and resource downloads will increase by 35% because users are more engaged when actively participating in content."

4. Build a testing infrastructure

To support this approach,you need to prepare the business and people for it:

Systems:

  • Hypothesis tracking database
  • Test documentation templates
  • Results repository
  • Learning management system

You can use anything from specific tools for this, project management tools or even google spreadsheets for this. The important thing, is that you have some sort of system that you use to support this.

Processes:

  • Hypothesis review and prioritization
  • Test design approval
  • Implementation protocols
  • Analysis frameworks
  • Knowledge sharing mechanisms

These pointers are not a must have, but a suggestion. You would need to figure out what you need to get up and running, but incorporating "hypothesis review and prioritization" in weekly meetings (30 min max), where team members bring one data backed hypothesis each. Just a suggestion.

Use simple scoring matrix. Like 1-5 for each hypothesis.

  • Ease of implemenation
  • Potential impact
  • Resource requirements
  • Risk level

Keep a running backlog in a shared spread sheet. Ideation in meetings add up to this listing.

Daily/weekly meetings for test design approval, Maybe set for the monday mornings.

Use a checklist in the beginning as your guidance, or specific protocols.

5. Create a growth culture

The hardest part is changing how you think. Encourage:

  • Questioning assumptions
  • Embrace failure as learning (as long as you do)
  • Rigorous documentation (ref the learning part)
  • Continuous learning

Getting started. Your first 30 days

Week 1: Audit current decision making processes

  • How do you currently decide what to test/run?
  • What documentation exists? (Update it, and make it available)
  • How are results shared?

Week 2: Set up basic infrastructure

  • Create hypothesis documentation templates
  • Establish test design protocols
  • Define success metrics

Week 3: Training

  • Train on hypothesis formation
  • Review experimental design principles
  • Practice documentation

Week 4: Run your first scientific test

  • Choose a small, low risk area
  • Follow the full process. The process is here actually more important than the results.
  • Document and share the learnings.

Common pitfalls to avoid

1. Trying to test everything at once.

The temptation:

When teams first adopt a scientific approach, they often get excited and want to test every idea across all channels simultaneously.

This problem leads to:

  • Diluted resources and attention
  • Difficulty isolating variables
  • Overwhelmed team members
  • Rushed analysis
  • Muddled results

All of these could result in abandoning the scientific process entirely, because it is too hard or you dont see the results you wanted.

Solution:

Start with ONE channel and ONE clear hypothesis. Master the process there first. For example, begin with just email subject line testing or one specific landing page. Only expand once you have a solid grip on the basic process.

2. Overcomplicating the documentation

The temptation:

Teams often create elaborate spreadsheets, multiple approval stages, and complex tracking systems in an attempt to be thorough. The thought is nice, but keep it simple.

This problem could lead to:

  • Team resistance to using the system
  • More time documenting than doing
  • Analysis paralysis
  • Delayed implementation
  • Reduced test velocity

Solution:

Keep it simple stupid. Start with one simple one-page template covering:

  • Hypothesis statement
  • Test parameters
  • Success metrics
  • Results
  • Key learnings

That's it. You can always add more detail later. Do not overcomplicate things in the beginning. Make it simple.

3. Waiting for the perfect data

The temptation:

Teams often delay testing because "we need more data" or "the tracking isn't perfect yet.

This problem leads to:

  • Perpetual delays
  • Missed opportunities
  • Analysis paralysis
  • Loss of momentum
  • Never actually starting

Solution:

Start testing with the data you have. Perfect tracking is a myth. As long as you can measure the basic metrics related to your hypothesis, begin. You can improve tracking systems while running initial tests.

Forgetting to share the learnings

The temptation:

Teams get busy with new tests and forget to properly document and share what they learned from previous ones

This problem leads to:

  • Repeated mistakes
  • Lost insights
  • Siloed knowledge
  • Wasted resources
  • Duplicated efforts

Solution:

Create a simple ritual:

  • 15-minute weekly sharing session
  • One-page test summary template
  • Shared folder for all test results
  • Monthly review of key learnings
  • Quick Slack/Teams updates for significant findings

The key to avoiding all these pitfalls is remembering that this is an iterative process. You're not aiming for perfection from day one. Start simple, learn from what works and doesn't, and gradually refine your approach.

Summary

Remember when we started talking about the uncomfortable truth about being "data-driven"? The journey from simply collecting data to actually using it scientifically isn't just about new processes or templates. It's about fundamentally shifting how we think about marketing performance.

This methodology isn't about becoming perfect data scientists or completely transforming your marketing overnight. It's about adding structure to what you already do, making your decisions more scientific, and building a culture of continuous improvement.

The real power lies not in having the data, but in knowing exactly what to do with it. By following this approach, you're not just collecting metrics, but you are building a systematic way to understand and improve your marketing performance.

Start today. Start small. But most importantly, just start.

?? Follow me for more marketing / analytics / growth / seo / cro tips.

?? Let's connect: https://www.dhirubhai.net/in/krister-ross/

#marketingscience #digitalmarketing #marketingengineering #marketingoperations #digitalmaturity #marketingmaturity #marketingstrategy #testing #growthmarketing #marketingexperimentation

要查看或添加评论,请登录

Krister Ross的更多文章

社区洞察

其他会员也浏览了