1.2 – Knowing Your Customer

As an analyst, it’s easy to get caught up in product features, funnel metrics, or revenue charts. But behind every click, bounce, or subscription is a real person — with goals, frustrations, and expectations.

In this chapter, we’ll zoom in on the customer. Because if revenue is the outcome, customer value is the input.

It’s not just about what the business offers — it’s also about what the customer wants, who they are, and how ready they are to engage. Understanding these elements helps you ask smarter questions, interpret data more clearly, and drive insights that actually matter.

1. What Do Customers Actually Care About?

Let’s start with the basics: what does the customer actually want?

Most customers care about a few simple things:

• Solving a problem — quickly, clearly, and effectively
• Ease of use — no one wants to feel stupid or lost
• Good service — they want to feel supported and respected
• Price — it needs to feel worth what they’re paying

In the end, customers care about value — not in abstract terms, but in how your product makes their life easier, faster, or better.

2. B2B vs. B2C: Who You’re Selling To Changes Everything

One of the first questions you should ask when analyzing any product or service:

Are we selling to individuals or to businesses?

This is the difference between B2C (business-to-consumer) and B2B (business-to-business). And it shapes nearly every part of customer behavior — and your data.

B2B vs. B2C: Two Very Different Customers

B2B vs. B2C: Key Differences

Aspect	B2C (Business-to-Consumer)	B2B (Business-to-Business)
Audience	Individual consumers	Companies or teams
Decision Cycle	Fast, impulsive, “buy now, think later”	Slow, structured, may take weeks or months
Purchase Volume	High — many customers	Low — fewer customers, larger deal sizes
Motivation	Emotional, convenience, price sensitivity	Rational, value-driven, based on ROI
Stakeholders	One person (the buyer is the user)	Multiple decision-makers and users
Examples	Spotify, Amazon, Duolingo	Salesforce, HubSpot, Snowflake

How this affects your analysis:

• B2B data = fewer users, longer timelines, more emphasis on account-level insights
• B2C data = faster cycles, larger data sets, more focus on segmentation and funnels

Understanding this difference helps you make sense of everything — from engagement patterns to the risk of customers leaving (churn).

3. Not All Customers Are Equal

Here’s a hard truth: some customers matter more than others. That’s not cynical — it’s math.

Revenue isn’t evenly distributed:

• A small % of customers usually drive a large % of revenue
• Some users are free; others are high-value
• Some try the product once; others become power users

As an analyst, your job is to spot those differences. You need to understand:

• Who your most valuable customers are (by spend, retention, engagement)
• Where they came from — this is often referred to as the acquisition channel (we’ll dive deeper into that in this chapter)
• How their behavior differs from low-value users

This is where concepts like segmentation and LTV (lifetime value) become critical. Don’t rely on averages — they flatten everything. Averages can hide both your best customers and your biggest problems.

If this isn’t clear yet, don’t worry — we’ll spend more time on it later in the course.

4. The Customer Journey: Where Are They in the Process?

When you look at user behavior, always ask:

Where is this person in their journey with us?

• Are they just discovering the product?
• Are they comparing it to others?
• Are they deep into usage?
• Are they on their way out?

We call this the customer journey — and it shapes what behavior is expected vs. alarming.

A few key points in a customer journey can be:

• Conversion — When someone takes a desired action, like signing up, starting a trial, or making a purchase. This is often the first sign of interest turning into engagement.
• Activation — When the user experiences real value for the first time. For example, not just signing up for a tool like Trello, but creating their first board and adding tasks Activation shows they’ve started using the product, not just trying it.
• Retention — When a user keeps coming back. If someone uses the product again after a week or a month, it’s a good sign they’re finding ongoing value.

Each of these stages tells you something different. Together, they help you understand whether users are getting value, getting stuck, or getting ready to leave.

Example:

• A high bounce rate (which means users visit a page and leave without clicking, scrolling, or engaging) might look bad at first. But if those users came from a casual paid ad on Facebook, it could be completely expected.
• A sudden drop in usage from your top 10% of company customers — the ones generating 60% of your revenue — isn’t just a data blip. That’s a red flag that could signal churn, dissatisfaction, or a competitor entering the picture.

Without journey context, your analysis can easily mislead. You might try to "fix" something that isn’t broken — or miss a real problem entirely.

5. How Customers Find You (Acquisition Channels)

Where a customer comes from affects what they expect — and how they behave.

Common acquisition channels:

• Organic search (Google): typically means the user is actively looking for a solution and has a clear purpose (higher intent to buy).
• Paid ads (Facebook, Instagram): often attract users who weren’t actively looking — they’re more likely to be casually browsing (lower intent).
• Referrals: users who hear about your product from a friend, colleague, or trusted source are more likely to trust you and take action.
• Email campaigns: performance depends on who you're targeting, what you’re saying — and where users are in the customer journey.

See how it all connects?

Why This Matters for Analysts

You may notice users from paid ads churn faster or have lower activation rates.

Or maybe referral users convert faster and stick around longer.

Knowing the acquisition source helps you segment (group users into distinct categories) correctly and offer better insights. It also helps you avoid false comparisons between different user types who may have very different motivations, expectations, or levels of intent.

Key Terms Recap

• B2C / B2B – Business-to-consumer vs. business-to-business models
• Customer Journey – The stages a customer goes through, from discovery to activation, usage, and potentially churn
• Segmentation – Grouping users based on shared traits or behaviors (e.g., power users, casual users, paid vs. free)
• LTV (Lifetime Value) – The total revenue a customer is expected to generate over time
• Acquisition Channel – How a customer first discovered your product (e.g., search, ads, referrals)
• Conversion – When a user takes a key action, like signing up, starting a trial, or making a purchase
• Activation – When a user first experiences meaningful value from the product
• Retention – When a user continues to return and use the product over time
• Bounce Rate – The percentage of visitors who leave a page without interacting with it
• Intent – A user’s level of motivation or readiness to take action (e.g., buying, signing up)

Conclusion: Know the Person Behind the Number

Your analysis is only as good as your understanding of the customer behind the data.

Ask yourself:

• Are we serving individuals or businesses? (B2C vs. B2B)
• Where is this user in their journey — are they discovering the product, recently converted, newly activated, or already a regular user?
• How did they find us, and what does that say about their expectations?
• Are they showing signs of value, or at risk of dropping off?
• Do they belong to a segment that matters more for revenue or growth?

Analysis is rarely generic — if it were, there’d be no need for analysts.
Generic insights can come from self-serve dashboards or, increasingly, AI tools.
What makes analysis valuable is starting with the right perspective: the customer, the business, or ideally, both.
Clear thinking at the start leads to sharper questions and more useful answers.

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Up Next → Business Concepts That Power Your Analysis

In the next chapter, we’ll explore foundational concepts that shape how analysts think and prioritize:

• The 80/20 Rule (Pareto)
• Customer Segmentation
• User Journeys
• Customer Acquisition
• Customer Retention

These ideas will help you focus on what actually drives value — and ignore the noise.