How product marketing management is organized and what the role of a PMM specialist in it really is
The PMM position is often surrounded by many misconceptions. Some people are sure that its duties are limited to marketing, while others believe that PMM is the same as Growth or Product Manager. Dmitry Tsapiy, who holds the position of Product Marketing Manager at Universe Group, debunks the popular myths associated with this role. He also reveals the nuances of metrics interpretation, approaches to funnel creation, product analysis and conversion rate estimation, writes HBJ blogger Kateryna Shevchenko.
Myth 1: PMM is only concerned with funnels and researching new segments
It is often believed that the main (and practically the only) task of a product marketing manager is to create a sales funnel, making it as efficient and attractive as possible. However, in reality, the responsibilities of a PMM (product marketing manager) are much more complex and encompass much more. In addition to working with the funnel, product marketing involves a wide range of tasks such as strategic product planning, market research, customer research, audience engagement, managing payment processes and analyzing new opportunities.
Another common misconception about PMM responsibilities is the notion that they are solely focused on launching new campaigns and finding promising niches. While launching is indeed a key part of their job, this task does not exhaust their role. A large part of their efforts is focused on analyzing, optimizing, and improving existing products, processes, and marketing activities.
Myth 2. PMM is a hybrid position between marketing and product
The PMM role is often perceived as an amalgamation of marketing and product functions, incorporating tasks from both areas. However, the PMM is an independent professional who not only coordinates efforts between the marketing and product teams, but also takes on additional tasks: researching new niches and prospective markets, creating and testing hypotheses, and initiating product changes. In addition, the PMM may play a key role in the development of new products or directions. Overall, this professional actively seeks opportunities to grow and scale the product.
The product marketer must be highly flexible and have the ability to find, generate, and test fresh hypotheses. For example, an important skill is the ability to validate the main feature or hypothesis of a new product in just a week, avoiding months of development before results are available. This can be realized by adding a simple question to the initial testing, such as “Are you interested in this feature in our product?” or “Have you encountered this problem?”. Collecting these responses is much faster than a full development and testing cycle, saving the business time and resources.
Another tool available for PMM is the Fake Door method, a feature simulation to analyze conversion rates. For example, after identifying a user problem to be solved, it is important to preliminarily estimate demand before investing in full-blown product creation (this helps in understanding its relevance to the audience). With the Fake Door method, time costs are significantly reduced: a new sales funnel is created, campaigns are launched with minimal budgets, and user behavior is studied. In the end, users see the message, “Sorry, *product name* is not available yet. Would you like to purchase it at a discount?”. This allows you to determine in a week whether it makes sense to develop this product.
Product marketing is such a diverse field that when you try to describe your activities within a specific function, people outside of that context are likely to respond, “That sounds like…”. For example, researching new niches and generating sales funnels may conjure up associations with growth marketing. Optimizing messages present in the funnel to improve conversions may be perceived as content marketing. Managing user interaction with both the product and the funnel may seem dissimilar to product management tasks. While PMM responsibilities overlap with other roles, their functionality has its own unique characteristics. A PMM’s primary job is to take a strategic approach to marketing the product, developing go-to-market strategies, defining product positioning, communicating value to users, and executing tasks and analyzing key metrics.
Myth 3. It is possible to look at each metric separately
All companies track customer acquisition cost (CAC) and customer lifetime value (LTV). However, neither of these metrics in isolation can provide meaningful insight into the health of a business or the effectiveness of its marketing efforts. If you’re launching a new project, how do you determine if a CAC of $300 is an excessive amount or perfectly acceptable? Or perhaps $30 is a lot or a little? The situation is similar with LTV. The relationship between these metrics is key, because it is what contributes to positive unit economics. Metrics considered outside the context of the business lose their importance. The only exception is ROMI, an indicator that clearly demonstrates the effectiveness of your marketing campaigns. But even here, additional context is needed to understand what level of ROMI is appropriate for your company. For one company, 1.05 may be satisfactory, while for another, even 1.5 may not be enough.
It’s equally important to understand which actions increase user retention and LTV, and which actions have the opposite effect. For example, there is a belief that the more actions users take within a product, the higher their loyalty and duration of engagement. However, this is not always true. Forcing users to perform too many actions can degrade their experience and reduce their desire to return to your product.
Here’s a case study: in one project, we actively sent users push notifications with promotions and special offers. The high open rate signaled a possible link between the number of messages, order volume, and retention rate. This was partly true. However, the excessive number of notifications began to irritate users over time. As a result, some turned off notifications, which discouraged further interaction. This negatively impacted retention and engagement levels with the app in the long run.
Myth 4: Focus on product or focus on marketing?
The community often discusses A/B test results, new approaches to funnel formation, and the implementation of payment solutions. When exploring new funnel models, product and marketing strategists (PMMs) typically fall into two groups: those who emphasize marketing and those who base their thinking on the features of the product itself.
The first methodology focuses on the universal product funnel, where users are attracted through experimentation with new ideas and scenarios. For example, if your product is in the area of productivity, you can initiate discussions on topics related to overcoming procrastination. Initially, this becomes part of the overall funnel. If testing of new concepts shows that the topic resonates highly with the audience, it is separated into a separate funnel specifically focused on overcoming procrastination. This approach allows you to create additional marketing channels or funnels that strengthen audience engagement around the product.
The second technique relies on a deep analysis of the functionality of the product itself to create new funnels. For example, the topic of productivity as a broad category can include a variety of aspects: improving emotional well-being, establishing an effective daily routine, or developing habits. In this case, the idea of a new funnel is formed not as a result of concept testing, but as an independent business model with clear economic calculations.
Both methodologies are effective, and the choice between them is determined by the specifics of business goals and strategic priorities.
Myth 5: An abundance of analytics slows down processes
How much resources should be allocated to analyzing a product? On the one hand, it is often argued that analytics cannot be redundant and that everything possible should be measured. On the other hand, too much data can complicate work and hamper team processes. It is crucial to find the optimal balance, as well as to understand how metrics interact with each other and what impact they have. It is recommended to focus on prioritized metrics to keep the team productive. Chasing velocity without a goal or analytics for the sake of analytics is meaningless. There needs to be a deliberate balance between agility and analytical approach.
The amount of data required for quality analytics varies greatly depending on a variety of conditions:
- The nature of the analysis;
- the level of complexity of the problem at hand;
- the quality of the data available.
Well-organized, high-quality data with the right characteristics are often more important than vast amounts of information filled with noise and irrelevant elements.
Statistical power plays a key role in correctly interpreting the results of A/B tests: in hypothesis testing, the necessary sample size to achieve sufficient statistical power is determined by the expected effect, the variability of the data, and the level of reliability required. Larger samples usually provide more accurate conclusions. Thus, before launching an A/B test, it is important to identify in advance the metrics (both primary and secondary) whose changes will help evaluate the success of a new feature. Testing new functionality often impacts multiple metrics, not just one. Therefore, we analyze the overall dynamics and realize that changes in a metric that has not reached a minimum sample size and lacks statistical power are insufficient for decision making. At the initial stage, we identify the metrics that may be affected by the test, calculate the minimum required sample size, ensure statistical significance and power, examine the main and additional metrics, and then draw conclusions.
General recommendations include the following:
- Small analysis: hundreds or a few thousand data points are sufficient.
- Suitable for a basic study or simple visualizations.
- Mid-level analysis: tens of thousands of data points are required for more detailed statistical analysis.
- Large-scale analysis: complex machine learning algorithms, such as in speech or image recognition, typically require hundreds of thousands or millions of data points.
Thus, the amount of data required is determined by the specifics of the analysis, its goals, and the methods chosen.