How to leverage data to drive startup's product marketing strategy

THE INTRODUCTION

Hello 👋 Marketing Context Fam!! Welcome to this week’s edition.

In todays edition of startup marketing deep dive we unpack a critical startup marketing process of ”leveraging data to drive PMM strategy”. We will cover:

• Why a data matters for startup marketer

• Deep dive the core concepts

• Unpack Spotify’s data driven marketing strategy

• Resources

43 times.

Yes, this is the number of times I got asked about data in product marketing in the past year. I know because I take notes.

Data is important. It's even more so now. We have tools to analyze numbers. We also operate in an age of endless demand for personalization.

As a startup PMM, you act as the bridge between product development and customer needs, shaping strategy and driving growth. Without data, you're lost.

So, as marketers, how do we use data? Buckle up now as we are going to unpack how we to use data as a startup product marketer.

THE CONTEXT

Why it matter?

Let's ask ourselves a question. Do PMMs need to be good with data? The simple and straightforward answer is yes, indeed.

For a long time in my career, I was living under a rock. I assumed only analysts and data scientists worked with data. I thought I could use their findings to drive my strategy. The thing is, I could get away with this in most big organisation, but in startups this wont be the case.

In a startup, PMMs often wear many hats. They must make quick, high-impact decisions. This makes embracing a data-driven approach necessary.

1. Deep user understanding: Data shows your ideal customer's pain points and gaps.

2. Refined messaging and positioning: User data shapes your marketing. It helps you craft resonating messages.

3. Optimized content strategy: Data insights help you make better content. This content engages your audience and increases conversions.

4. Efficient resource allocation: With limited resources at a startup, data shows what marketing efforts work best. 

5. Measurable impact: Data reveals the impact on key metrics.

For example, in its early days, Slack used data from user interviews and product usage. It found that reducing friction in team communication was a key pain point. This insight shaped their message: "Where work happens." It resonated with their target audience and drove rapid growth.

So here we go, validation from Slack themselves. Lets now dive into the step by step process of leveraging data as a marketer  

THE DEEP DIVE

Classifying your data

Data types are broadly classified as qualitative or quantitative. Think of this as organizing your marketing toolbox:

Quantitative Data: The numbers game. This is measurable data that can be analysed statistically. Examples:

• Feature usage rates

• Content engagement metrics

• Conversion rates

Qualitative Data: The story behind the numbers. This provides insights into user behaviour and preferences. Examples:

• User interview responses

• Open-ended survey feedback

• Sales call notes

Identify & tag data to relevant data source, type and method

Tagging your data is extremely critical. It is like our DNA identity. Every data set must tag its source, assign a type, and map the process of obtaining it.

Why is this necessary? Can we not take the data dump, analyse it and use it wherever required? The answer is a big no. Finding a relevant data source and how to get it is critical for informed decisions.

I led marketing at an startup in 2017. We ran campaigns on many platforms, targeting different users. Our goal was to get sign-ups, and it worked—our waitlist grew fast. But then, we hit a wall. Our data was all in one database, untagged.

We lacked source data for each entry. Our SEO and paid search campaigns overlapped, causing confusion.

In B2B, where personalization matters, this mistake can hurt your campaigns. It derail your persona mapping and strategies.

Imagine, you have product usage data, but no source. It could be from freemium or premium users. Without knowing the origin, creating strategies is hard.

Below is one of the tagging format ( template) I use these days to map my data identity:

In the above table, I tag my data with relevant metadata ( providing information on more aspects of your data) and link it to specific stages of the customer journey. This will make it much easier to use later and also help qualify the context behind the data.

How do apply the data knowledge in your job? Lets understand this part now

Aligning Data with Product Marketing Objectives

Time for us now to roll up our sleeves and apply the data knowledge to our marketing objective. Remember, not all data is created equal and you should know what to use where.

I use the below approach

• Map objectives: Align your marketing objectives to the business objective.

• Identify Relevant Metrics: For each objective, determine which metrics will help you measure success.

• Match Data to Metrics: Connect your available data sources to these metrics.

• Assess Data Quality: Evaluate the reliability and completeness of the data for each metric.

• Prioritize Data Sources: Focus on the highest quality, most relevant data for each area of your strategy.

Once you have defined the above it is now time to align data to your PMM objective

Applying data in marketing strategy

After you have established the clarity on how to use the data. It is now time to focus on application

For a PMM, data is applicable in three key areas.

• Prioritisation:

• Drive experience and engagement

• Create growth levers

Here's where the rubber meets the road. To make your data truly valuable, align it with your core PMM objective or use case.

I believe every PMM effort should be outcome driven. It should cause a reaction in users. I like to look at this whole process as “use cases” for product marketing. For example, messaging efficacy or resonance. Relevance of ICP or pain point. Impact of value proposition etc.

It lets me measure and analyze the above as a use case. I can then improve them as an overall strategy.

Here's a handy framework to measure and map product marketing effort to use cases 

Best practices in a startup environment

We have seen how to use data to map it to your goals. Then, we generated use cases to apply the insights. This will make your product marketing effective.

But since I focus mainly on startups, the life of a startup marketer is not always what it seems. With experience and few bumps, I have learned to follow few guidelines to ensure I make the best use of the data at hand:  

• Prioritize data collection: As a startup, you may not have access to all these data types immediately. Prioritize based on your current stage and most pressing marketing objectives.

• Start small, scale up: * Start with available data, like product usage and customer feedback. As your startup grows, expand your data collection.

• Integrate data sources: Look for ways to combine different data types for more comprehensive insights. For example, combining product usage data with customer feedback can provide a fuller picture of user experience.

• Adapt to your context: The relevance of each data type may vary based on your product, market, and business model. Adjust your focus accordingly.

• Implement data-driven processes: Use these data types to guide your key PMM tasks. They include refining your ICP, optimizing messaging, and improving your go-to-market strategy.

• Collaborate cross-functionally: Different teams will own or collect many data types. So, build strong ties with product, sales, and customer success teams. This ensures access to crucial data.

• Invest in analytics: As your data needs grow, consider investing in analytics tools. They can help you collect, analyze, and visualize these data types better.

• Maintain data privacy: Always ensure your data practices comply with privacy laws. They must also maintain user trust.

Startup PMMs can build a data-driven marketing strategy by focusing on key data types, even with limited resources. This approach lays the groundwork for a product marketing plan that grows with the startup.  

STRATEGIES IN ACTION

Unpacking how Spotify uses data to drive engagement and feature launch

About the product

Spotify is a music streaming giant that leverages data extensively for product marketing.

Objective behind data usage:

They use data to create personalised experiences, drive engagement, inform content strategy, and optimize pricing.

The approach

The Impact

Spotify's data use has led to highly personalized campaigns. The most notable is the annual "Wrapped" feature. It drove over 60 million user interactions in 2019. Features like Discover Weekly spark viral engagement. They deepen connections and boost user activity.

Strengthened brand loyalty and perception

Spotify's custom user experiences and data-driven artist insights have made it an innovative brand. Its cultural phenomena have boosted loyalty among its 456 million active users.

Competitive advantage and revenue growth

Spotify's use of data-driven ad targeting and pricing has boosted revenue. Ad revenues grew by 30%, giving Spotify a competitive edge in music streaming.

RESOURCES ON SPOTLIGHT

Book:Data-Driven Marketing: The 15 Metrics Everyone in Marketing Should Know" by Mark Jeffery (Link)

Blog: Using date to power your product marketing effort ( link)

Resource:

Startup product marketing use cases aligned to data insights ( link)