Where do you use Segment Hypothesis in GTM strategy?

Arise GTM specialises in helping B2B firms design and execute comprehensive go-to-market strategies. A vital strategy component is hypothesising which segments of your target market will give you revenue and by what proportion. By documenting this upfront and before executing your strategy, you have already started setting metrics and objectives to measure success.

This deliberate approach to goal setting helps you understand how much of your audience you understand and can sell to. By failing short or exceeding your hypothesis, you can build a case of how you shift your business strategy year after year.

Segment hypotheses are crucial in the early planning stages and throughout the execution process. In a go-to-market (GTM) strategy, a segment hypothesis is a testable assumption about how a market can be divided into distinct groups of customers with similar needs, characteristics, or behaviours.

Here are the key points about segment hypotheses:

1. Purpose: Segment hypotheses aim to identify meaningful ways to divide a total market into distinct, uniquely identifiable segments of prospects or customers.
   
   
2. Basis: They are typically based on various criteria such as:
   • Demographic factors (e.g. age, income, location)
   • Psychographic factors (e.g. lifestyle, values, personality)
   • Behavioural factors (e.g. usage patterns, purchase history)
   • Needs-based factors
     
     
3. Formulation: Hypotheses are developed by considering logical links between customer characteristics and their needs or buying behaviours.
   
   
4. Testing: These hypotheses must be tested and refined through analysis and primary research to validate their accuracy and usefulness.
   
   
5. Importance: Developing effective segment hypotheses is a critical first step in discovering key competitive opportunities in a market.
   
   
6. Example: A simple segment hypothesis could be "Customers with an annual income above $50,000 will be more likely to purchase premium products".
   
   
7. Benefits: Using segment hypotheses allows companies to:
   • Reduce uncertainty and risk
   • Increase agility and adaptability
   • Improve customer alignment and satisfaction
     
     
8. Application: Segment hypotheses are used in developing go-to-market strategies, helping companies target their marketing efforts more effectively and efficiently.

The goal is to identify segments where prospects in the same group have sufficiently homogeneous needs and buying behaviours while those in different segments have distinct needs or behaviours. This allows companies to tailor their products, marketing, and sales approaches to each segment more effectively.

Here's how segment hypotheses are typically used in GTM strategy.

Market Segmentation Phase

Segment hypotheses are primarily used during the market segmentation phase of developing a GTM strategy. This is typically one of the first steps in the process. It’s typically executed during the discovery phase of planning your GTM. It will be tweaked as you learn more about the buyers or users of your product through research, sales and marketing activities.

Identifying Potential Segments

When identifying which market segments to consider, business leaders work together to determine ideal segmentation options for their business. They develop hypotheses about potential segments based on various criteria:

• Macro-segmentation (e.g., geographic and firmographic criteria)
• Micro-segmentation (e.g., technographic, psychographic, or sociographic criteria)

Including multidisciplinarians in the segmentation process is essential to grasp how different business teams experience your prospective audience. It is not uncommon for revenue-focused teams to drive the conversation, but be mindful to keep your product teams included if user data is to form part of the assumption process.

Developing Comprehensive Ideas

The goal is to develop comprehensive ideas on how the total market can be broken into distinct, uniquely identifiable segments. These hypotheses suggest how prospects in the same group might have sufficiently homogeneous needs and buying behaviour, while those in different segments may have different needs or buying behaviour levels.

Segmentation can occur by segmenting user or buyer personas to avoid developing multiple personas for the same function or by creating an ideal customer profile (ICP), which typically considers businesses' identifiers, not people's identifiers, such as size, revenue, and location.

Hypothesis Testing

Once segment hypotheses are developed, they need to be tested and refined. To generate a specific hypothesis, consider key business questions about your GTM strategy's facets (distribution channels, product messaging and customer acquisition cost) and turn them into prediction statements or hypotheses.

Here's an example prediction statement for a segment hypothesis for a SaaS company selling to CMOs:

"We predict that CMOs at mid-sized B2B technology companies (100-500 employees) with annual marketing budgets over $500,000 will be 30% more likely to purchase our marketing analytics platform within 90 days if we offer a free 30-day trial with personalised onboarding."

This hypothesis statement includes several key elements:

1. Target segment: CMOs at mid-sized B2B technology companies
2. Specific criteria: 100-500 employees, annual marketing budgets over $500,000
3. Predicted outcome: 30% more likely to purchase
4. Time frame: within 90 days
5. Proposed action: offering a free 30-day trial with personalised onboarding

This hypothesis is testable and specific, allowing the SaaS company to design an experiment to validate or refute the prediction. It combines multiple segmentation criteria (firmographic and value-based) to create a focused target segment and proposes a concrete action to influence the desired outcome.

Designing Tests

When developing your statement, you must consider how to test it to prove or disprove your segment hypotheses. These could include providing beta versions to potential customers, conducting surveys, or analysing market data.

Here are some options for testing the statement example we provided above:

• A/B Test:

• Group A (Control): Offer standard sales process without a free trial
• Group B (Test): Offer a 30-day free trial with personalised onboarding
• Measure: Conversion rates and time to purchase for both groups

• Cohort Analysis:

• Track cohorts of CMOs who receive the free trial offer vs. those who don't
• Analyse purchase behaviour over 90 days for each cohort

• Multivariate Test:

• Test different trial lengths (15, 30, 45 days) and onboarding levels (basic, personalised, high-touch)
• Determine the optimal combination for conversion.

• User Journey Mapping:

• Use analytics tools to track user behaviour during the trial period
• Identify key actions that correlate with higher purchase likelihood

• Qualitative Feedback Loop:

• Conduct surveys and interviews with CMOs who complete the trial
• Gather insights on the value of personalised onboarding

• Feature Usage Analysis:

• Monitor which features are most used during the trial period
• Correlate feature usage with purchase decisions

• Time-to-Value Measurement:

• Track how quickly CMOs achieve their first "win" with the platform during the trial
• Correlate speed of value realisation with purchase rates

• Competitive Comparison Test:

• Offer a subset of prospects a comparison of your platform vs. competitors during the trial
• Measure if this increases the purchase likelihood

• ROI Calculator Experiment:

• Implement an ROI calculator for a subset of trial users
• Assess if demonstrating potential ROI increases conversion rates

• Personalised Content Drip:

• Design an email sequence with personalised content for trial users
• Test if this engagement strategy impacts purchase decisions

These experiments would provide comprehensive data to validate or refute the hypothesis, offering insights into the effectiveness of the free trial and personalised onboarding approach for the specified target segment.

Prioritising and Running Tests

Once you have designed your test, you need to prioritise which tests to run first, I would recommend using a structured approach that considers multiple factors. Here's a framework for prioritising your experiments:

• Impact Potential:

• Estimate the potential impact on key metrics (e.g., conversion rate, revenue)
• Consider the size of the audience affected

• Ease of Implementation:

• Assess the technical complexity and resources required
• Consider the time needed to set up and run the test

• Strategic Alignment:

• Evaluate how well the test aligns with current business priorities
• Consider how it fits into the overall product roadmap

• Risk Level:

• Assess potential negative impacts if the test fails
• Consider any regulatory or compliance issues

• Learning Value:

• Evaluate how much the test will teach you about your users or product
• Consider if the insights can be applied to future decisions

• Confidence Level:

• Assess the strength of the evidence supporting your hypothesis
• Consider previous data or similar tests that suggest potential success

Using these criteria, you can create a scoring system. For example:

• Score each factor on a scale of 1-5

• Assign weights to each factor based on their importance to your business

• Calculate a total score for each test

• Prioritise tests with the highest scores

Additionally, you might consider using frameworks such as:

• The ADVIS'R Prioritization Framework™️, which considers factors like audience size, user intent, and potential lift
• Bain's Experimentation at Scale approach, which uses advanced analytics to optimise multiple variables simultaneously

Remember to balance quick wins with more substantial, long-term tests. Sometimes, running a few smaller, easier tests can build momentum and buy-in for larger experiments.

Lastly, remain flexible. As you gather more data and insights, be prepared to adjust your priorities accordingly. The goal is to create a dynamic, data-driven experimentation process that continuously improves your product and user experience.

Refining the Strategy

Harvard Business School says that your segment hypotheses will become more specific as you learn from hypothesis testing, leading to detailed insights that inform your GTM plan. 

For those of you who are new to strategy and, indeed, the hypothesis approach to GTM, here’s a framework for refining it:

• Analyse Results Thoroughly:

• Review all data collected from your experiments
• Look for statistically significant findings
• Identify both successful outcomes and unexpected results

• Validate or Invalidate Hypotheses:

• Determine which hypotheses were confirmed and which were disproven
• Consider the strength of evidence for each conclusion

• Synthesise Insights:

• Combine quantitative data with qualitative feedback.
• Look for patterns and trends across multiple experiments
• Identify key learnings about your target segment, value proposition, and overall strategy

• Prioritise Changes:

• Rank potential strategy adjustments based on impact and feasibility
• Consider both quick wins and longer-term strategic shifts

• Update Your Strategy Document:

• Revise your GTM strategy to incorporate new insights
• Document changes and the rationale behind them

• Develop New Hypotheses:

• Based on your findings, create new hypotheses to test
• Focus on areas where you still have uncertainty or see potential for improvement

• Plan Next Round of Experiments:

• Design follow-up experiments to further validate your refined strategy
• Consider testing at a larger scale or with different segments

• Communicate Results:

• Share findings and strategy updates with relevant stakeholders
• Ensure alignment across teams on the refined approach

• Implement Changes:

• Roll out validated improvements to your GTM approach
• Monitor the impact of these changes closely

• Establish Ongoing Optimisation:

• Set up a process for continuous experimentation and refinement
• Create feedback loops to quickly identify and act on new insights

• Reassess Metrics:

• Review and adjust your key performance indicators (KPIs) if necessary
• Ensure your metrics align with your refined strategy

• Conduct Competitive Analysis:

• Re-evaluate your position in the market based on new insights
• Identify any shifts in the competitive landscape or customer needs

By following this approach, you can systematically refine your GTM strategy based on experimental results, ensuring that your approach evolves with data-driven insights and remains aligned with market realities and customer needs. 

Additionally, adopting a continuous improvement mindset makes you more likely to interject with the current strategy faster to reorient the ship, mitigating the risk of losses. But what other benefits are there to this approach?

Benefits of Using Segment Hypotheses

As with anything we invest our time into, there’s doing it, and then there’s doing it. Anything worth doing is worth doing well in my mind, and so incorporating segment hypotheses into your GTM strategy offers several advantages:

• Reduces uncertainty and risk by testing assumptions before significant investment

• Increases agility and adaptability by allowing for pivots based on experiment results

• Improves customer alignment and satisfaction by focusing on delivering value to specific segments

By using segment hypotheses throughout your GTM strategy development and execution, you can create a more targeted, effective, and adaptable approach to reaching your market.

How does micro-segmentation differ from macro-segmentation in GTM strategies?

Earlier in this post, I mentioned that micro-segmentation and macro-segmentation are two distinct approaches used in go-to-market (GTM) strategies, each with its own level of granularity and focus. I got caught up with expanding on a lot of why I would like you to adopt more hypothesising when developing your GTM, and I drifted away from it. Let’s dive back into that now and discuss how they differ:

Macro-segmentation

Macro-segmentation is a broader, high-level approach to dividing the market:

• Scope: It focuses on larger, more general categories of customers or businesses.
  
  
• Criteria: Typically based on company-wide characteristics such as industry, business size, location, or other broad organisational attributes.
  
  
• Segment Size: Results in larger segments with more customer heterogeneity.
  
  
• Application: Often used as an initial segmentation strategy, especially for companies in early stages or with limited resources.
  
  
• Advantages: It provides a big-picture view of the customer base and is generally faster and easier to implement

Micro-segmentation

Micro-segmentation is a more detailed and precise approach:

• Scope: Focuses on smaller, more specific groups of customers or users.
  
  
• Criteria: Based on customer-specific data such as behaviour, preferences, purchasing patterns, and product usage.
  
  
• Segment Size: Results in smaller, more homogeneous segments.
  
  
• Application: Often used by more mature companies or those with more sophisticated marketing strategies.
  
  
• Advantages: Allows for highly personalised marketing, more accurate predictions, and better-targeted campaigns.

When forming your hypotheses, it’s essential to understand the differences so that you can plan and test accordingly. The two approaches can used fluidly depending on what you are trying to test with each approach, and below are ways you can understand how to apply them.

Key Differences in GTM Strategy Application

1. Personalisation Level:

• Macro-segmentation: Allows for broad, generalised marketing approaches.
• Micro-segmentation: Enables hyper-personalized experiences and targeted marketing.

2. Data Requirements:

• Macro-segmentation: Relies on high-level, easily accessible data.
• Micro-segmentation: Requires more detailed, customer-specific data, often including behavioural and transactional information.

3. Marketing Effectiveness:

• Macro-segmentation: Less precise may lead to less effective marketing campaigns.
• Micro-segmentation: More likely to result in higher conversion rates and customer engagement.

4. Resource Intensity:

• Macro-segmentation: Generally less resource-intensive to implement.
• Micro-segmentation: Requires more time, effort, and sophisticated data analysis.

5. Customer Understanding:

• Macro-segmentation: Provides a general understanding of customer groups.
• Micro-segmentation: Offers deep insights into individual customer needs and behaviours.

6. Adaptability:

• Macro-segmentation: Less adaptable to changing customer behaviours.
• Micro-segmentation: Allows for dynamic segmentation that can evolve with changes in customer behaviour.

7. Revenue Impact:

• Macro-segmentation: Can drive broad market strategies.
• Micro-segmentation: Often leads to higher customer lifetime value and more targeted revenue growth opportunities.

In GTM strategies, companies often start with macro-segmentation to get a broad understanding of their market. As they mature and gather additional customer data, they transition to micro-segmentation for more precise targeting and personalised marketing efforts. The choice between the two approaches depends on the company's resources, data availability, and strategic goals.

Summary

I hope that you enjoyed reading this article today. I am a huge fan of deliberate behaviours in strategic planning, and I believe a strategy without a segment hypothesis isn’t well thought out at all. With that in mind, I want to back that up by saying those who have come before and not applied this approach weren’t wrong. You can now address that by focusing on segmenting your next strategic planning session.

For help with your strategic B2B go-to-market planning, please contact the team, which will be happy to discuss your thoughts.