Opinions can be subjective. What might seem like a good decision to one might not seem like a good decision to another person, so there is a lot of subjectivity. Without data, the opinion of the highest-paid person can impact product decisions - typically known as HIPPO (highest paid-person’s opinion). Data provides an objective measure that everyone can align on.

Data can serve as a source of truth for the voice of the users. Product Managers get an improved sense of what their users want, what their experience with the product is, and how they can improve the product for the users. A great way to understand users is by analyzing their journeys on the product. Here are examples of key questions that can be answered through data:

What features of the product do users most engage with?

At what point in the product do you notice the highest bounce rate?

What is the average time between key user actions?

What percentage of the users come back to use the product in X days? And many more…

Making product changes based subjectively can result in a lot of wasted effort. Data helps identify the highest-impact changes that can truly address user needs. Based on information about user habits and preferences, any new ideas can be classified as ‘must-haves’ vs ‘nice-to-haves’. This segregation can help prioritize ideas and align large teams towards a common goal. In addition, data-driven insights can avoid building features which users don’t need, thus avoiding feature overload.

d. Align teams

Data can be a powerful lever to influence stakeholders and align them towards a shared goal. Product data is critical to the success of the org and is used by cross-functional teams to drive operations and strategy. This can also help with resource allocation on high-value product launches. Often cross-functional products can involve working closely with Sales teams, Marketing Teams, or Research Teams. Each team might have different priorities. Data can serve as a common baseline for product managers to influence key stakeholders and drive decision-making.

Let’s look into some similarities between the two domains. Problem identification and solution ideation require a deep understanding of the product and revenue model. Without a strong product sense, data scientists can find it difficult to derive actionable insights from the data that really help the users. Similarly, without a strong product sense, product managers can have a hard time ideating and prioritizing the right features to build. A common goal for both teams should be to understand their users, so they can ask the right questions.

Ideating, building, and shipping products often involves a lot of different stakeholders. Design, Engineering, Marketing, etc may be involved at different stages of the feature launch. Being able to work in a cross-functional environment and influence people at different levels of the organization is a key skill. Both data scientists and product managers can highly benefit by engaging with stakeholders and getting them onboard with their ideas and plan. Together, they can create products that are data-driven and ensure every change rolled out is a positive step towards the larger business goals.

Customer obsession places a focus on creating better experiences for the users. It is a commitment to having a customer-first approach, and requires an obsession with data. It has rightly been said - ‘if you can’t measure it, you can’t improve it’. For product-led companies, it is extremely important to make customers' needs and expectations a priority across the business. Gather direct and indirect feedback from the users and understand what keeps them coming back to you.
A focus on the consumer builds brand loyalty and generates customer trust so they continue to seek out your business and recommend it to others. By putting the customer first, data scientists can use technical skills to understand what drives their behavior, and product managers can prioritize features that provide experiences that customers seek.

While Product managers are not expected to be hands-on with data, it is essential to have a high data literacy and a strong data sense. A data-driven product manager should know enough about the data to ask the right questions to the data team. They should also value data enough to make decisions based on data instead of their intuition. Additionally, being data-driven can help product managers quickly gather basic insights and share insightful stories with the larger audience.

A lot of product teams consider data science teams as their ‘experimentation partners’ and loop them in when they have to measure the impact of feature changes. But data teams can add a lot of value in identifying the high-value opportunities, finding patterns in customer feedback, and deriving insights from user journeys. Working closely with them at every step of the product life cycle can be a great value-add.

b. Using data only to validate product ideas

Data should be used from the start to guide product direction. A common mistake product managers make is to rely on data science teams only to pull some numbers and query the database to validate selected product ideas. A successful collaboration is when data teams are involved in product strategy and their insights are used to drive product and experimentation roadmap.

c. Failing to communicate

Keep the teams updated on key milestones, success criteria, and business goals. Communicate wins and failures with the larger audience. Track the right metrics, measure success, and document learnings from the failures.