Opinion: Current evolution of SaaS Product Management in a Product-led Organisation and challenges in aligning Customer Needs and Business Strategy

Abstract

Over the last decade, the tech industry has experienced a rapid growth of SaaS tools. According to a McKinsey report (citation 1) in 2022 the global SaaS market was worth about $3 trillion, and their estimates indicate it could surge to $10 trillion by 2030. We have all witnessed the surge in demand for AI-driven automation or productivity SaaS tools to boost efficiency in our professional lives. This high demand has obviously led to intense competition among SaaS tools' developers. To outperform their rivals, SaaS executives in the beginning relied on tactical instruments like competitive pricing or irrational marketing spending to acquire new clients, but soon most of them realised that it only resulted in higher churn (citation 2). This had led to the shift in focus towards customer retention driven by customer success via product experience in the late 2010s. And in the current macroeconomic context, with mounting pressure to drive profitability by cost optimisation and investors’ emphasis on reducing churn to maximise customer lifetime value, modern executives are further shifting their focus and investments towards enhancing the product experience and fostering a loyal community.

This transition from sales-led to product-led approach (citation 3) has become a success recipe to build a long term sustainable business, however it isn’t easy to achieve. It requires competency to understand customer needs and feedback, filter out the meaningful insights, combine with business strategy and deliver an engaging experience. And despite the existence of hundreds of solutions that claim to support every step of the product development process and deliver a flawless customer experience, there are some inherent shortcomings with the current solutions and practices that make the product management process highly inefficient, non-collaborative and less transparent.

Today, Product-led organisations are making informed decisions about what to build next and prioritize new features by considering several factors:

1. Customer needs and feedback: i.e. gathering information from customer surveys, focus groups, and other sources to understand what features are most important to their target audience. This is commonly known as 'Customer discovery and validation'.

2. Business goals and objectives or OKRs: i.e. alignning new features with the organisation's overall strategy and goals, and consider their potential impact on revenue and profitability.

3. Market and competition: i.e. analyzing the competition to see what features they offer and how they can differentiate their product.

4. Technical feasibility: i.e. work with the development team to assess the feasibility and cost/time of implementing new features.

5. Resource constraints: i.e. considering the availability of time and personnel needed to develop and launch new features.

6. Intuition and Experience i.e. based on similarity with the past experiences of founders or product managers and gaining experience on what worked or not. 

The greatest opportunity for Product Teams today: understanding customer needs.

Every year, billions of dollars are wasted on developing incorrect or unwanted product features. Product Teams can avoid such wasted development efforts (cost) by prioritising features based on customer needs, which can increase user retention (revenue or CLV). Despite this opportunity, it remains a complex problem to solve. Product teams gather customer feedback from two sources: quantitative usage behavior data or qualitative feedback. However, both set of information remain in siloes and this leads to a gap in understanding the full picture of what customer really needs.

Examples of quantitative techniques (or Product Usage Analytics) are:

• Understanding user behavior: using data from product usage to gain insights into how customers use the product, heatmaps, session recording etc.

• Measuring feature success: using metrics such as adoption rates based on clicks and session duration, session flow, and customer satisfaction. 

• A/B testing: using controlled experiments to compare different versions of the product.

• Predictive modeling: using data modeling algorithms to predict how different attributes.

Examples of qualitative techniques (Customer feedback) are:

• Customer Survey.

• Customer Support data (complaints, feedback etc.).

• Sales team's inputs.

• User research inputs.

The fusion of product usage analytics and customer feedback marks the next evolutionary stage in comprehending customer needs. Combining these two sources of information offers an unparalleled level of insight into user behavior, preferences, and pain points, which can revolutionize product development. 

Why Product Managers still struggle in prioritising customer needs?

The key reason for the struggle of product managers in prioritising customer needs is the lack of alignment with business goals. This happens due to following challenges:

1. Managing conflicting stakeholder demands: PMs often receive requests for new features from multiple stakeholders, including customers, sales teams, and executives, and must prioritize these demands based on their impact on the business. However the lack of consistent KPIs across marketing-business-support or their alignment across these department remains a key frustration. 

2. Dealing with limited resources or its visibility: PMs often make decisions within the constraints of time, money, and personnel, and may need to make trade-offs between competing priorities. However they are not involved in the hiring budget making processes or the strategic changes based on quarterly/financial results/board meetings. While one cannot control the business performance, but showing trust and swift communication by the Executives is often a quick fix.  

3. Balancing short-term and long-term goals: Product managers must balance the need to deliver new features quickly with the need to ensure that the product remains aligned with the organisation's long and short-term goals. However in most organisations the current workflow either leads to an long inefficient decision making process to adjust the roadmap even slightly, or a chaotic prioritisation system (probably due to executives with a short-term mindset) that keeps throwing long term feature requirements off-track. 

4. Understanding technical feasibility: Some Product Managers lack good understanding of what is technically feasible and the resources required to implement new features, in order to make informed decisions about what to build next. Hiring a Technical Product Manager(TPM) or involving the Development leader during the customer discovey phase can help bridge the gap. 

5. Navigating organisation politics: Product managers may face resistance from different departments or stakeholders who have conflicting priorities, and must navigate these internal politics to reach consensus on what features to prioritize. Founders and executives should foresee and address this ahead of time, for example with an organisation wide roadmap communicated transparently, that could be drilled down by Epics/Teams. 

6. Undocumented or outdated knowledge: On one hand the intuition of experienced executives could be an advantage for the organisation to prioritise development features faster based on what worked in their previous experience, however on the other hand this undocumented knowledge could often be outdated and must be validated before accepting blindly, with the help of Feedback Validation tools.

How does the current workflow for SaaS product management looks like?

1. Customer feedback tools: Such tools help product managers gather and analyze customer feedback, allowing them to prioritize features based on customer needs and preferences. For example: UserVoice, Canny, and Intercom.

2. Customer onboarding tools: A subset of feedback, such tools help businesses improve their user onboarding while collecting insights regarding the product adoption. For example: Userlane, Userflow, Userguiding.

3. Analytics and Insights: Such tools help with product usage analytics to give a comprehensive view of how customers are using the product and what they need, allowing them to make informed decisions about which features to prioritize and improve the customer experience. 

4. Prioritisation: This step of the workflow is split between multiple tools and teams currently, resulting in a fragmented overview. This is often the key reason for a clash between prioritisation by business goals vs customer needs. 

5. Roadmap planning tools: Such tools help product managers visualize and prioritize their product roadmap, making it easier to balance short-term and long-term goals. Example: Aha!, ProductPlan, and ProdPad

6. Collaboration and communication tools: After the first draft of the desired list of features is completed, such tools help product managers collaborate with other teams and stakeholders, allowing them to reach consensus on what features to prioritize and making it easier to manage conflicting demands. Example: Notion, Asana, Jira, and Trello.

Limitations of current workflow:

1. Balancing customer needs with business goals: While customer satisfaction is critical for the success of any SaaS product, it's equally important to align customer needs with the company's overall business goals. This can be challenging as it may require trade-offs and prioritization of features. Currently most of the management inputs on the business goals are documented outside of the roadmap planning tools, often resulting in clash between customer needs vs business goals based prioritisation. 

2. Evolving customer needs and market dynamics: Fast-changing market conditions can require adapting the product roadmap. Businesses must be agile and adaptable to stay competitive. Proactive monitoring and adjustment of the product roadmap is necessary to meet the evolving needs of customers, however the current workflow in most of the organisations isn't agile enough to adapt fast.

3. Fragmented information: There are a number of isolated tools to measure product usage, to run surveys, to collect feature requests, to track Net Promoter Score (NPS) and customer satisfaction (CSAT); the tricky part, though, is ensuring stakeholders across the organization have access to this data at one place and effectively use it to improve the customer experience at every phase. This requires quite a bit of manual work, since data is scattered in multiple places and things like slide decks and spreadsheets needed to be created or updated each month.

4. API Integration issues: Even though most of these tools provide an API integration to merge data with other existing systems, such as project management or data warehousing, the integration process can be difficult and quite time-consuming.

5. Data quality and accuracy: The quality and accuracy of data generated by these tools after third party API integration is in most cases affected by several factors, such as data collection method, inconsistent tagging, data cleaning and pre-processing. This reduces the readiness or reliability of insights derived from this merged data, especially in the context of AI powered insight tools.

6. Training: Implementing new tools also means conducting training for each stakeholder and getting used to different workflows and technical setup, that could be time or resource consuming

7. Difficulty in interpreting data for actionable insights: Even if there is a lot of data available and the right metrics have been identified, it can still be difficult to extract meaningful insights from the data.

8. Lack of data: One of the biggest challenges is simply not having enough data to work with for data analytics. This can occur if the product is new and there is not yet a large user base, or if there are technical limitations that prevent the collection of certain types of data. In such cases, the qualitative feedback from the power users could be a good alternative to understand customer needs, if available. 

9. Cost: Some of these tools can be expensive, especially for small teams, and require a significant investment to get the most out of them.

10. Privacy and security: The storage and sharing of sensitive customer data can raise privacy and security concerns, requiring product management teams to be diligent in managing and securing this data, For example, during manually exporting and analysing the data from data analytics to derive insights and uploading the trend reports in project management tool.

11. User adoption: Getting all stakeholders to use these tools consistently and correctly can be a challenge, as some team members may resist using new tools or may not understand how to use them effectively.

12. Difficulty in defining cross-function agreeable metrics: It can be challenging to identify the right metrics to track in order to measure the success of the product. This requires a deep understanding of user behavior and business goals.

13. Cross-platform visibility: In some cases, it may be difficult to get a complete picture of user behavior, especially if the product is used across multiple platforms or devices.

How should the ideal workflow look like? An integrated solution for feedback, insights and business goals, with an iterative feedback loop.

The natural evolution of product management tools over the next decade appears to be in the direction of complete native integration. With the dawn of AI tools' adoption, the users (Product Managers) will be increasingly inclined to automate the mundane manual tasks, eliminate inefficient processes that involve juggling between multiple tools, and guarantee the highest data quality to derive meaningful insights fueled by AI. Even if a fully integrated solution is not immediately feasible, a shift in the mindset of Product Managers is necessary, where they view this vast array of user information as a unified and interconnected piece of information. 

Shortcomings of the solutions that have attempted to integrate the end-to-end workflow so far:

1. Missing strategy integration in roadmap planning: Many product management tools focus on specific stages of the product development process, such as ideation, planning, or execution. However they lack management's strategic inputs and this information is usually split over emails, powerpoints and offline discussions.

2. Unfulfilled promises: Some of the tools failed to deliver on the promise to generate meaningful insights based on analytics and help/automate the process of feature prioritisation. This seems to be either due to lack of understanding of client’s practical challenges or irrelevant insights that result in the clients using it for limited purpose only.

3. Missing pieces: There are some crucial steps of the product management workflow that are still missing in these tools, for example, bug and error monitoring, UI/UX beta testing or user forums.

4. Weak data analysis capabilities: Data Analytics and Science is like solving a mystery. While Product managers love the idea of deriving automated insights and recommendations, they also love to dive deeper and slice the data granularly. The current solutions in the market provide much shallow level of data analytics, For example browsing data only without the context of customer's purchase history. This results in Product Managers ending up again using separate data analytics tool for deeper investigation. 

5. Fake claims to replace human insights by Machine Learning solutions:  Though it's really tempting to test out such solutions, most of them end up disappointing a product manager, as the tools claim much more than they could deliver today. Even the 'AI Assistants' functionalities appear to be overhyped.

6. Cookie-blocker: Most of the solutions depend on the cookie based tracking for browser-based activity that leads to loss of information of 25-50% users, especially for non-logged in users. Only a few solutions are focused on combining the client-side, server-side and unique device information for identity resolution and giving the  true picture of cross-device usage. The cost of storing massive data of server side logs and processing for analytics is also a concern.   

7. Integration issues: lack of seamless integration with other tools eg. E-commerce or subscription shop information.

8. User Adoption: Such tools success relies on users actively using the tool to generate data, and getting users to adopt it can be a challenge.

9. Data Privacy and Security: Product managers need to ensure that the data collected and stored by tools is secure and protected from unauthorized access or breaches.

10. Cost can be a barrier for some product teams, especially for smaller businesses, as some of the most desired features are only available on the higher priced tiers.

11. Customization: Some product managers may find that it doesn't meet all their needs out of the box, and customization may be required to get the most value out of the tool.

Conclusion

As SaaS product management continues to evolve in product-led organizations, there are two key opportunities for success. 

The first is to combine quantitative usage behavior data with qualitative feedback from customers to gain a better understanding of user behavior, preferences, and pain points. By leveraging these insights, product managers can revolutionize product development and create customer-centric solutions.

The second opportunity is to align customer needs with business goals and objectives. While prioritizing customer needs is essential, it must be balanced with the company's overall strategy and constraints. By doing so, product managers can ensure that product development is not only focused on customer needs but also supports the company's growth and profitability.

Current SaaS product management workflows involve siloed tools such as customer feedback, analytics and insights, and roadmap planning, but the fragmented overview of prioritization across teams and hierarchy levels can lead to a clash between prioritization by business goals and prioritization by customer needs. Addressing these challenges can help product managers prioritise customer needs better and align them with business goals and objectives to build better products.

Some References

1. The SaaS factor: Six ways to drive growth by building new SaaS businesses, July 19, 2022, https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-saas-factor-six-ways-to-drive-growth-by-building-new-saas-businesses

2. Introducing customer success 2.0: The new growth engine, January, 25 2018, https://www.mckinsey.de/industries/technology-media-and-telecommunications/our-insights/introducing-customer-success-2-0-the-new-growth-engine 

3. How SaaS companies can protect profits with product-led approach, Feb 13, 2023, https://www.ey.com/en_us/strategy-transactions/how-saas-companies-can-protect-profits-with-product-led-approach 

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