The Marketing Ops Guide to Product-Led Growth

In a product-led growth (PLG) company, Marketing ops is not just an operational role; it’s the strategic engine that powers growth by ensuring that every marketing effort is data-driven, efficient, and aligned with product goals. As PLG companies rely on their products to attract and retain customers, marketing operations or MOPs plays a crucial role in integrating the right tools, optimizing workflows, and managing data to enable scalable success. This function is vital in creating a seamless, efficient environment where marketing teams can thrive.

Why PLG Matters for Marketing Ops Professionals

With more B2B SaaS companies looking at PLG to run alongside their sales-led motion, Marketing Ops have to keep up with the new ways of doing business. Diving deeper into PLG and implementing PLG strategies matters to MOPs professionals because:

Scope of the job is changing. MOPs will have to work towards operationalizing massive volumes of product and user data to support the GTM strategy. This responsibility comes in addition to supporting marketing and sales functions.
When PLG initiatives are implemented MOPs will have to work with never before seen data volumes. Product and user data coming from different sources needs an adept MOPs team to utilize it effectively in driving the new GTM strategies.
A shift to PLG means working with new tools and updated tech stack. Integrating them seamlessly into the existing ops setup requires a deeper understanding of how product data is orchestrated.
The way GTM teams define ‘qualified lead’ is bound to change with PLG implementation. Lead scoring will have product data signals in the mix to qualify a lead’s intent. The existing MOPs setup for lead scoring and qualification has to be updated to align with PLG strategies.
When a business implements PLG motion, the metrics of success will also get a face lift. The updated MOPs set up should also be equipped to track and measure the right KPIs to align with the new strategy.

Marketing ops professionals play a pivotal role in the success of PLG initiatives and PLG can significantly alter the work landscape for Marketing Ops.

The Role of Marketing Ops in a PLG Company

Marketing ops in a PLG environment is more than just an operational function—it’s a strategic partner that empowers marketing teams to be data-driven, efficient, and aligned with product goals. Here’s how:

Data Management: Whether it’s user behavior data from product analytics or lead data from marketing campaigns, the ability to effectively manage and leverage data is crucial. A well-structured data pipeline enables precise targeting and personalized marketing efforts that resonate with users. MOPs ensures that data is not only collected but also stored and analyzed in a way that provides actionable insights for the GTM teams.
Process Optimization: A PLG strategy thrives on efficiency. Marketing ops plays a pivotal role in identifying bottlenecks in workflows and implementing process improvements. This might involve automating repetitive tasks, streamlining communication between teams, or refining campaign execution strategies. The goal is to create a seamless, efficient environment where GTM teams can focus on strategy rather than getting bogged down by operational issues.
Technology Integration: Ensuring that the GTM tech stack works seamlessly is no small feat. Marketing ops is responsible for overseeing the integration of all the GTM tools—whether it’s connecting a Customer Data Platform (CDP) with a CRM system or ensuring that product analytics tools feed into marketing automation platforms. This integration ensures that data flows freely across systems, enabling a unified view of the customer and more effective marketing efforts.
Performance Tracking: Marketing ops monitors key performance indicators (KPIs) and other metrics that reflect the impact of marketing on growth. This includes everything from user acquisition rates and customer lifetime value (CLTV) to the effectiveness of specific campaigns. By continuously measuring performance, Marketing ops provides the data needed to iterate and optimize PLG strategies.

MOPs creates a cohesive environment where marketing teams can thrive. But to execute these responsibilities, having the right tools is essential.

Differences in Marketing Ops Functionality: PLG vs. Sales-Led Businesses

While the core functions of Marketing Ops remain similar, their execution and focus differ significantly between PLG companies and traditional sales-led businesses:

Collaboration with product teams: Marketing ops in PLG companies collaborates closely with product teams to align marketing initiatives with product development and enhancements. This partnership ensures that marketing campaigns reflect product updates and user feedback. In contrast, sales-led organizations may focus more on collaboration with sales teams to align on lead generation and sales tactics, potentially creating silos between marketing and product functions. More on this later.
User-centric approach vs. Lead-centric approach: In a PLG environment, Marketing ops emphasizes understanding user behavior and engagement within the product. This approach focuses on enhancing user experience and driving product adoption, whereas sales-led businesses prioritize lead generation and nurturing prospects through traditional sales funnels. Marketing ops in PLG must analyze product usage data to inform marketing strategies, whereas sales-led firms may rely more on demographic and firmographic data.
Emphasis on metrics for product engagement: Marketing Ops in PLG focuses on metrics related to product usage, such as Daily Active Users (DAU) and engagement rates, which directly correlate with user retention and satisfaction. In sales-led businesses, the emphasis might be on conversion rates, lead qualification metrics, and sales pipeline health, aligning more with traditional sales processes.

Marketing Ops creates a cohesive environment where marketing teams can thrive. But to execute these responsibilities, having the right tools is essential.

Essential Tools and Technologies for Marketing Ops in PLG

The success of MOPs hinges on leveraging the right tools and technologies. These tools are vital for the marketing ops function, enabling everything from data management to process optimization.

Here’s a closer look at the must-have technologies that empower marketing ops in a PLG company.

Customer Data Platforms (CDPs): Platforms like Segment or Amplitude are indispensable for centralizing and unifying customer data. CDPs provide a comprehensive view of the customer by aggregating data from various touch points, allowing for more personalized and effective marketing efforts.
Product Analytics Tools: Understanding how users interact with the product is crucial for a PLG strategy. Tools like Mixpanel or Pendo offer insights into user behavior, helping marketing teams to tailor their messaging and campaigns based on real-time data.
Marketing Automation Platforms: Automation is key to scaling marketing efforts in a PLG model. Inflection is a marketing automation platform made for the modern tech stack that enables teams to automate complex product-led campaigns at scale, and nurture leads with personalized content.
Customer Relationship Management (CRM) Systems: Systems like Salesforce are essential for tracking interactions, managing customer relationships, and organizing prospect information. In a PLG company, where the line between marketing and sales can blur, a robust CRM system ensures that all customer data is in one place, enabling better coordination and communication between teams.

Knowing about the right tech for your company’s strategy starts with evaluating your current product data set up.

Evaluating Your Current Setup

Best scenarios for your product data setup:

You have a CDP like Segment connected to your data source. This CDP integrates with your marketing systems.

A data warehouse is housing data from your primary data source. All the analytics platforms and marketing systems are connected to it.

Good scenario for your product data setup:

Your product data is stored in a database like Postgres and you do NOT have a CDP that integrates with your marketing systems or a data warehouse.

🎯 Action item: Ask for a data warehouse to be set up on top of the database or implement a CDP.

Either of the above three scenarios is good news for Marketing because they allow centralization of data with the data warehouse and seamless integration with a marketing system-friendly CDP to allow access and flow of data. Automating workflows such as product activity-triggered emails becomes smooth sailing with these setups.

Any other product data orchestration might not be the ideal scenario for you to execute product-led marketing campaigns suited for all use cases and customer life cycle stages.

Once you learn about the tools and technology in place, the next challenge lies in choosing, building, modifying, and maintaining a tech stack that supports the company’s growth. Marketing ops must carefully select and integrate technologies that not only meet current needs but can also scale as the company evolves.

Building a Tech Stack for PLG

Building an effective tech stack requires strategic planning and foresight. In a PLG scenario, the following points are particularly significant for building a tech stack.

Real-time data processing: In a PLG environment, the ability to act on near real-time data is crucial. Rapid response to user behavior can directly impact user experience, engagement, and conversion rates, making this a key consideration.
Data quality and hygiene: High data quality is essential for personalized and targeted marketing efforts, which are critical in a PLG model. Ensuring clean, accurate data helps deliver the right messages to the right users at the right time.
Seamless integrations: Robust APIs and connectivity options are vital in PLG companies, where custom integrations and seamless data flow between various tools (e.g., product analytics, CRMs, and marketing automation platforms) are necessary to support data-driven decisions.
Interoperability: In a PLG company, marketing, product, and customer success teams have to work closely together. Ensuring the tech stack is interoperable with tools used by all the GTM teams facilitates cross-functional collaboration.
Custom reporting and dashboards: The ability to create custom reports and dashboards tailored to specific PLG metrics (e.g., user activation, retention, and expansion) is vital for tracking and optimizing growth strategies.
Scalability: PLG companies often experience rapid growth, so tools must be scalable to handle increasing data volumes and user bases without disrupting operations or performance.
Automation capabilities: Automation is key to scaling GTM efforts efficiently. Tools with strong automation features help manage repetitive tasks, enabling teams to focus on growth initiatives.

These points are particularly relevant in a PLG scenario because they directly impact the ability to leverage product data, scale efficiently, and deliver a seamless customer experience—all of which are critical to the success of a PLG strategy. In other words, your product data setup should be Marketing friendly.

Marketing-Friendly Product Data

Building a successful tech stack involves more than just choosing the right tools—it requires ensuring that all technologies are integrated seamlessly, scalable for growth, and serves the purpose with ease. Here are some tips to make sure you have marketing-friendly product data setup:

If you have a data warehouse, you need to make sure it’s useful for marketing. Here are the questions you need to ask:

How often is the data getting into the data warehouse?

Having data come in real-time is the best scenario for marketing. If the synchronization (sync) frequency is much longer than an hour or so, you need to ask the product or data team to give more real-time data. Delays longer than an hour could cause communications to be out of sync with user actions. For example, you could send an email asking a user to do something in the product as part of onboarding, but they could have already taken that action.

Reasons:

Data sync is how periodically aligned the data is between your sources and destinations. Typically, the data sync setup contains a few more steps. A process called ETL is in place to organize raw data and process it for further analysis and gathering business intelligence.

📝 Side note: ETL stands for Extract, Transform, Load, and it is a crucial process in data management:

Extract: This step involves collecting data from various sources, such as databases, APIs, etc. The goal is to gather all relevant data that needs to be processed.
Transform: In this phase, the extracted data is cleaned, formatted, and transformed into a structure suitable for analysis. This may include data normalization, aggregation, and enrichment.
Load: Finally, the transformed data is loaded into a data warehouse or database, where it can be accessed and analyzed.

Consider a nightly data sync frequency setup. This means a 24-48 hour window for the ETL process to ingest data depending on the data volume and how your ETL is set up (some perform data validation and business logic processes along with ingestion). On top of that, a lot of businesses add a data validation step to maintain the integrity and accuracy of data. This could take another 6-12 hours. So, Marketing would be able to use the data only after 30 - 60 hours.

This kind of data orchestration might work for business intelligence needs but not for Marketing. Because:

Huge delays in triggering emails from data signals. You are essentially working off of stale data.
Can’t run dynamic and responsive campaign journeys. Imagine a campaign aimed at converting free users to paid in a limited trial period. You’ll lose opportunities before realizing the user’s intent.
High chances of sending the right message at the wrong time. Like getting an email with special offers after the user bought a subscription at full price.

Solution consideration:

To address this, marketers and data teams need to collaborate to explore options like:

Incremental Updates: Instead of performing full data refreshes run incremental updates to focus only on the new or changed data since the last sync.

This saves a ton of processing time because only a subset of the data is processed. It’s less resource-intensive as well, leading to more efficient use of computational power and storage. Businesses processing huge data volumes must consider the incremental updates approach not only for Marketing purposes but also for business benefits.

Parallel Processing: Utilize parallel processing techniques to handle different parts of the ETL process simultaneously, rather than sequentially, speeding up overall sync times.

Processing multiple data chunks concurrently can significantly decrease the total ETL duration. This method can be scaled up by adding more processing units to handle increased data volumes.

Resource Allocation: Investing in additional computational resources or optimizing existing infrastructure to support more frequent data syncs.

More resources can handle larger datasets and more frequent syncs without overloading the system. Optimizing infrastructure can lead to overall better performance and timely data availability.

What can MOPs do about it?

Near real-time data sync would be the ideal scenario for a marketing team to confidently run more timely and accurate marketing campaigns powered by product activity data. If you can’t get that for any reason, look for a middle ground.

An acceptable data sync frequency can vary depending on the specific use case, but here are some general guidelines:

Critical vs. Non-Critical Data: Determine which data is critical for real-time decision-making or campaign execution and which data can be updated less frequently. For instance, updating demographic data (e.g.: address) might be less frequently updated than change in log in frequency.
Use Case Dependent: The frequency should align with the specific use case. For example, hourly updates might be necessary for high-frequency trading, while daily updates might suffice for less time-sensitive marketing activities like running a survey for new customers.

💡 Tip: Aim for a balance between the ideal and what is practically achievable within existing constraints. An hour or two sync frequency strikes a balance between the demand for timely data and the technical limitations of the current infrastructure. It reduces the risk of system overload compared to more frequent updates.

By understanding and addressing the importance of data sync frequency, marketers can ensure they have the timely, accurate data needed to drive effective and responsive marketing strategies.

💡 Tip: If you’re setting up a data warehouse for the first time, make sure that product and engineering teams understand that this is a requirement for marketing. So, you don’t have to deal with it after the fact.

What kind of data is in your data warehouse?

There’s a chance that data gets synced to the data warehouse only after going through an analytics platform. You need to understand if you are getting raw data into the data warehouse or ONLY processed data. And, this should include customer data points required for marketing use cases - such as email addresses, account name, plan/tier, amount, etc., in addition to product activity data.

Reasons:

Lack of Comprehensive Insights: Access to raw and granular data allows marketers to perform in-depth analyses, uncovering detailed customer behavior patterns and preferences.

For instance, knowing the exact sequence of product interactions can help tailor personalized onboarding campaigns. The same data when processed and stored might just show the aggregate of product interactions that occurred in a timeframe.

Flexibility: Raw data offers the flexibility to use it without being constrained by predefined data transformations. This adaptability is essential for using data for marketing purposes.
Detailed Customer Profiles: Having access to raw customer data such as email addresses and account names enables the creation of detailed customer profiles. These profiles are foundational for personalized marketing efforts, ensuring that messages and offers are relevant to each user.

Having a complete set of raw data provides a holistic view of the customer journey, from initial interaction to ongoing engagement.

Accurate Targeting: Granular product activity data helps in segmenting users based on their interactions with the product, allowing for precise targeting in campaigns.

For example, targeting the buyer in the account for renewal with a combination of demographic and product activity data. This scenario is possible if you can inform your marketing automation system about both product and account information.

Solution consideration:

Ingest Raw Data: Ensure your data warehouse is set up to ingest raw data directly from various sources such as product usage logs. This direct ingestion helps maintain data fidelity and provides a richer dataset to integrate with your marketing automation solution.
Handle Hashed Data: If some sensitive data is hashed (a process of transforming readable data into an unreadable format for security purposes), understand what data is hashed and the implications. Marketers should:some text
- Work with the data engineering team to determine if and how hashed data can be de-hashed or matched with existing customer records.
- Explore the use of hashing techniques that allow for comparisons without exposing raw data, enabling segmentation and targeting while maintaining data privacy.
- Ensure that any process of using hashed data complies with data privacy regulations and best practices.

What can MOPs do about it?

Request Detailed Data: Specifically ask for customer data points required for marketing use cases, such as email addresses, account names, and product activity data, to be included in the raw data ingested into the data warehouse.
Verify Data Availability: Confirm that all necessary raw data is available and accessible in the data warehouse, without being overly processed or aggregated, to support marketing use cases.

How’s the “Event” table structured in the data warehouse?

When structuring the "Event" table in the data warehouse, there are significant considerations regarding the number of tables and how events are organized. The decision between having a few consolidated tables versus creating a separate table for each event can have major implications on data usability, especially for marketing teams.

A common approach is to have all events recorded in a single "Event" table rather than creating individual tables for each event type. This approach consolidates data and simplifies querying and analysis.

Reasons:

Ease of Access: When events are consolidated into a single table, it simplifies data retrieval processes. Marketers can run queries on a single table to extract all necessary event data without needing to join multiple tables, which can be complex and time-consuming.
Simplified Data Management: Having a single table reduces redundancy and the risk of data inconsistency. It ensures that all event data follows a uniform structure, making it easier to manage and update.
Performance Optimization: Querying a single, well-indexed table can be more efficient than joining multiple tables. This can enable faster inputs to the integrated marketing systems.

Solution consideration:

Unified Event Table: Explore along with the product or data team the possibility of one comprehensive "Event" table. This table should include columns for event type, timestamp, user identifiers, and other relevant attributes.
Standardized Schema: Define a standardized schema for the "Event" table to ensure consistency. This schema should be flexible enough to accommodate various event types while maintaining a uniform structure.

📝 Side note: A schema is a blueprint or structure that defines how data is organized within a database. It specifies the tables, fields, relationships, constraints, and other elements that comprise the database.

Indexing and Partitioning: Implement indexing and partitioning strategies to optimize the performance of the "Event" table. This will facilitate efficient querying and analysis, especially as the volume of data grows.

What can MOPs do about it?

Define Requirements: Identify and clearly define the types of event data needed for marketing use cases. Communicate these requirements to the data engineering and product teams to ensure they are captured in the "Event" table.
Request Comprehensive Data: Ensure that the "Event" table includes all necessary attributes, such as user identifiers, event types, timestamps, and any additional context needed for analysis.

What happens in your CDP?

If you have a CDP connected to your marketing systems, spend some time understanding how product actions are tracked. Meet with your product and/or data team to understand what events are tracked, how, and where they end up. This is an exercise we did ourselves at Inflection.io and it's totally worth it!

This exercise helps you identify gaps in understanding and tracking the right product events that are useful for marketing campaigns.
Compile a data dictionary - a resource with descriptions of product events. You can be on the same page about how an event is defined.

For example, at Inflection.io, we have an event called ‘campaign_published’ in our Segment instance. It wasn’t explicitly defined if editing a campaign and then publishing it; or publishing a campaign as inactive (when not ready to take a campaign live) also qualifies as ‘campaign_published’. Meeting up with the product team helped us construct definitions of events to help all the teams have a common understanding.

Spend some time going through the Academy of your CDP. Understand the basics. For our marketing team at Inflection.io learning these basics of product data tracking made a lot of difference.

Understand Track and Identify Calls:

Understand the importance of "track" and "identify" calls and ensure they are properly implemented. These calls should include key user actions and identifiers like email addresses to facilitate personalized marketing efforts.

Track calls are used to capture specific user actions or events as they interact with a product or service. These actions can include activities such as:

Signed up for the product
Used X feature
Free to paid conversion
Visited Checkout page

A track call typically includes:

Event Name: A descriptive name for the event (e.g., "Signed Up", "Page Viewed").
Properties: Additional metadata about the event (e.g., button name, page URL, form fields).

Identify calls are used to link user activity data to a user profile. This is particularly important for creating comprehensive user profiles and enabling personalized marketing efforts. Almost all marketing tools that integrate with a CDP require an email address. Be sure your team is including email addresses in identify calls.

An identify call typically includes:

User Identifier: A unique identifier for the user (e.g., email address, user ID).
Traits: Additional information about the user (e.g., name, email, account type).

Track calls capture detailed user interactions, allowing marketers to understand how users engage with a product. This data can inform the design of your campaigns. Identify calls link product actions to specific users and accounts enabling the creation of enriched user profiles. These profiles can be used for effective targeting while ensuring that messages are relevant and personalized.

By combining track and identify calls, you can gain a complete view of the customer journey. This holistic perspective is why marketers need to understand the basics of how the CDP works.

Auditing the Events with Product and Data Teams

Once you find out where the data you need is stored and tracked, you need to check if the right events (for marketing use cases) are tracked. If not, you need to figure out and request tracking of some high-impact events.

Identify the key stakeholders in the product or data engineering team and explain to them your objectives. Explain how having the right product activity data can help you run more effective marketing campaigns. Some common goals for both teams can be to ensure data accuracy, completeness, and relevance.

Detailed steps for auditing:

Understand your product data orchestration: Find out how product events are tracked, stored, and analyzed. Check if data is directly ingested into the warehouse or passes through an analytics platform first. Direct ingestion helps maintain data fidelity.
Identify key events: Find out the events that are tracked and compare them against the list of events that are critical for marketing goals and the specific campaigns you want to run. You will understand which events are crucial but are not tracked. This can include user sign-ups, feature usage, purchase completions, etc.

Review a sample of events to ensure they contain all required data points. For example, when a user upgrades their subscription, including email address, previous subscription level, new subscription level, timestamp, and payment details.
These are some key product events that can help you get started on your way to a more comprehensive understanding of product data in marketing.

User Sign-Up: Tracks when a user creates an account.
Conversions: Monitors the number of users who convert from a free trial or freemium plan to a paid subscription.
Product Onboarding Completion: Tracks when a user completes onboarding.
Feature Adoption: This can be for any feature that makes sense for your business, like creating a dashboard, interacting with the chatbot, etc. Identify your key features and what adoption means to you before requesting to monitor them.
User Login: Monitor the frequency and recency of user logins.
Gated Feature Access Attempt: Tracks when a user tries to access a premium or restricted feature that requires an upgrade or additional permissions.
Purchase/Subscription: Track purchase or subscription completions.
Cart Abandonment: Similar to an e-commerce scenario of adding to the cart and not completing a purchase, this event in B2B could be an unsuccessful purchase after clicking on CTAs like Purchase, Upgrade, and Renew.
Page Views: Monitor visits to key product or landing pages like the Checkout page.

Define data requirements: Specify what data points are needed for each event so that they are meaningful for marketing use cases. This can include user identifiers (email, user ID), timestamps, event-specific details (product ID, feature used), and any additional metadata.
Cross-check event logging: Compare the defined data requirements with the actual data being logged in the data warehouse. Note any discrepancies or missing data points. Verify that similar events are logged consistently across different user sessions and devices.
Review data flow and transformation: Understand how data flows from the product to the data warehouse. Check if there are any transformations or processing steps that might affect data fidelity.
Hashed data: Understand if and why certain data is hashed (encrypted for privacy and security reasons). While hashing is crucial for data security, marketers need to collaborate with data teams to ensure they can still leverage this data effectively. Explore options along with the product/data team to de-hash or match with existing customer records.
Validate data quality: Use queries and data analysis tools to validate the quality of the event data. Look for inconsistencies, duplicates, or anomalies.
Document findings and actions: Create a detailed report of your findings, including any issues identified and recommended actions to address them.

Product event auditing can take a while but it will be worth the effort. Be prepared to dedicate resources for a few months even for a thorough audit. Schedule regular meetings with the product and data teams to discuss findings, address issues, and plan for future data needs. Also, understand that many engineering or data teams take some time to implement the change requests made during the audit.

Common Objections and How To Address Them

When you go to your Engineering, Product, or Data and ask for access to product activity events, two things can happen.

You get all that you need and get started right away on building a product-led marketing motion.
This is a more likely scenario. You put forward your case about using product data for marketing more effectively but, you’ll be met with some objections.

Here are some of the most common sticking points between Product and Marketing and how you can handle them with some help from your own team.

Objection #1: Data Security Concerns

Product and data teams are worried about the potential for data leaks when providing other teams with access to sensitive product data. These concerns are valid because the data warehouse has user data, financial records, or proprietary business information.

Here’s how you can address this:

Develop a Data Access Proposal: Work with the product and data teams to create a clear and detailed proposal outlining the specific data needed, what tables marketing is connecting to, and the intended use. This can help reassure teams that data will be used responsibly. Oftentimes security teams will assume that “data warehouse access” is for marketing to have full fidelity of everything in the data warehouse and can become at ease when understanding marketing is just accessing a portion of the data.
You can also discuss pushing data to a separate, isolated data warehouse instance that Marketing has access to.
Some data warehouses like Snowflake have come with data-sharing capabilities to create a secure data-sharing environment. Snowflake allows you to share data securely without duplicating it. This ensures that marketing can access up-to-date data without compromising the security of the main data warehouse.

💡Tip: The product or data team may offer access to only the tables containing anonymized user interaction data, hashed email addresses, and campaign performance metrics for easier execution without compromising security. But, this will defeat the purpose of the whole exercise. Reiterate the intended access to raw and granular data.

Objection #2: Handling Huge Volumes of Data or High Sync Frequency (Costs, basically)

Increasing the frequency of data syncs to accommodate marketing requirements can strain existing infrastructure. Frequent data syncs can lead to performance issues, increased server loads, and increased costs. This technical challenge can deter engineering teams from supporting more frequent data access for marketing.

Here’s how you can address these objections:

Explain the Business Impact: Make the product or data team understand the business impact of choosing delayed data sync. Explain that when product data drives the company’s GTM, real-time triggers to marketing automation make a huge difference. For example, Sales has higher chances of closing deals when alerted timely on a user’s activity rather than when they have to work off of stale data.

Delayed data sync might save some bucks but the benefits of real-time data sync outweigh these costs.

Prioritize Data Needs and Batching Syncs: Identify the most critical data points and events needed for marketing campaigns and prioritize those.

Work with data teams to find a balance between real-time and batch data processing. Marketing use cases can still work with periodic but more frequent data syncs that don't strain the system.

Suggest grouping similar data points together and syncing them in batches to improve efficiency and reduce the frequency of individual data syncs. This approach can help manage server load more effectively.

For example: Real-time data for critical user actions (like account creation or purchase completions) can be prioritized, while other engagement metrics can be synced in regular batches.

Incremental Updates: Explore together the option of incremental updates to sync only new or changed data, rather than syncing the entire dataset each time. This can significantly reduce the volume of data being processed.

💡Tip: While real-time sync of all product event data for Marketing is a highly unlikely scenario, you can always negotiate for a balance between real-time and a few days old data. Suggest a sync frequency that meets marketing needs while being feasible for the data infrastructure.

We’ve interviewed a lot of companies that settled for a 6-hour sync frequency to strike a balance between timely data and system capacity.

Objection #3: Complexities in Integrating with Marketing Systems

Engineering teams might highlight the complexity of integrating product event data with existing marketing systems. Here’s how you can address it:

Modern Marketing Automation Tools: Discuss the capabilities of modern APIs and integration tools that simplify data sharing and reduce complexity. Highlight any existing integrations or tools that can facilitate the process. For example, Inflection.io is a marketing automation platform with native data warehouse integration to all the popular ones (Snowflake, BigQuery, Redshift) and CDPs (like Segment).

Utilize vendor or consultant support if needed to assist with integration complexities or provide expertise in data management and integration best practices.

Incremental Progress: Propose a phased approach to integration rather than a big-bang approach. Start with smaller, manageable projects or proofs of concept to demonstrate feasibility and benefits.
Resource Allocation: Justify your request for allocating valuable engineering resources for integration and other tasks by educating all stakeholders on the value of sharing product event data for marketing insights.

Providing access to product event data for marketing purposes can actually benefit product development indirectly. For instance, insights gained from marketing analyses can inform product decisions and user experience enhancements. Foster open communication to address concerns.

Objection #4: Ego

Yes, it’s a real thing!

The Product is running some of the marketing comms triggered off of product activity and maybe is not too happy with marketing wandering their turf.

Here are some tips on how to work with that.

Sit down with the product team and educate them about what you're trying to do. Understand their objections and share that you are looking to achieve common objectives.
Make a list and like what communications are going to be owned by Product vs Marketing.
Make a list of goals that Product and Marketing own separately and together. For example, product engagement metrics can be the product team's, and completing onboarding can be a Marketing goal.
Request and get access to the data required for your goals.

Educate the Product, Data, and Engineering about what you’re trying to do and listen to their objections. Propose possible solutions that everyone can come to terms with. Many of the objections can be discussed and negotiated to reach an understanding that’s beneficial for the product and business goals.

💡Tip: If you still see resistance to change, you can even propose to start small. Suggest implementing a pilot program of sorts to test out the impact. This can provide data and engineering teams with concrete insights into what is feasible without compromising performance. And, in the absolute worst case where you’re completely shut out, you might want to go to a higher-up to escalate the case.

Wrapping Up

Marketing ops is not just a supporting role in a PLG company; it’s the backbone that ensures all the moving parts—data, processes, technology—work together to drive growth. By effectively managing data, optimizing processes, integrating technologies, and tracking performance, MOPs enables marketing teams to be agile, data-driven, and aligned with the overall product strategy. In the world of PLG, where efficiency and precision are key, Marketing ops is the unsung hero driving growth.

If you are building a PLG tech stack, you need to check out Inflection for your marketing automation needs. Inflection enables you to activate your product data and streamline your operations, driving pipeline generation, product adoption, and revenue expansion—all from a single platform. To know more about Inflection, request a demo.