Key Aspects of a Data-Driven Approach in Product Management
According to a survey conducted by Splunk, organizations that prioritize data and apply advanced strategies to increase business value experience significant financial gains.
Specifically, prior to the year when the research was made, the organizations witnessed an 83% increase in revenue and a 66% rise. Moreover, an impressive 93% of these organizations expressed confidence in their ability to make faster decisions compared to their competitors.
As pioneers shaping the tools of tomorrow, product development teams and product managers should lead by example, showcasing the transformative power of a data-driven approach in streamlining processes and enhancing overall efficiency.
In this article, we will explore the key aspects of a data-driven approach in product management and provide insights from our Delivery Manager, Christopher Van Horn, on the impacts of data for an organization.
How a data-driven approach works
The main idea behind a data-driven approach is to iterate and improve product development by fulfilling users’ needs and enhancing the impact of the product on their lives.
This is achieved by using various metrics, customer feedback, and actively seeking opportunities to optimize the product based on evolving user preferences. In the early stages of the product life cycle, the focus is on achieving product-market fit, while later stages prioritize enhancing the user experience and delivering increased value.
Proposed updates undergo a thorough evaluation, prioritization, development, and subsequent launch and rollout to ensure the product meets users’ expectations and business objectives.
Types of data used in data-driven product management
Essentially, product management relies on user data, product data, and market research. However, while there are many different metrics you can use, take into consideration your product strategy.
Are you focusing on customer retention, drumming up new logos, or something else? Make sure you use the most appropriate metrics to manage your business.
User or account data encompasses metrics such as NPS (Net Promoter Score), retention rate, churn rate, CAC (Customer Acquisition Cost), LTV (Lifetime Value), MRR (Monthly Recurring Revenue), Repurchase Rate (RR), Customer Conversion Rate (CCR), Customer Satisfaction Score (CSAT), Daily Active Users (DAU), Monthly Active Users (MAU), Net Retention Revenue (NRR), Adoption, Stickiness, Growth, Product Engagement Score (PES), Top Feature Requests, and Product Performance.
Product data provides valuable insights about the product, including pricing, sales trends, user flows, bounce rates, and heatmaps.
On the other hand, market research focuses on aspects like market viability, feature demand, positioning, pricing, and communication.
By leveraging these data sources, product managers can make well-informed decisions for successful product development.
Overcoming the challenges of a data-driven approach for product development teams
When implementing a data-driven approach, there are several key challenges that product development teams must address to ensure its success.
According to Christopher Van Horn, they can be summarized as follows:
Understand the objectives
Before diving into data analysis, it is essential to step back and identify the key objectives and goals. Without a clear understanding of what you are trying to accomplish, data alone may not provide meaningful insights. Consider the long-term strategy, the role of the work in achieving it, and the launch approach taken to reach your objectives.
Recognize the relevance of data
Timing is crucial when interpreting data. For instance, rolling out a new feature may require significant setup and configuration for customers. Measuring adoption within the first 4-5 months may not accurately reflect its success, as customers might still be in the process of implementing the feature. Context is vital in data analysis. In these cases, you should work with your Customer Success organization to ensure that they have the tools at their disposal to educate the customer and can accurately track setup in a UAT environment.
Utilize qualitative data alongside raw data
To gain a comprehensive understanding, raw data should be supplemented with customer sentiment and qualitative insights. You may find that a customer is not using a feature due to the fact that it is hard to set up or it is not solving the right problem. Balance the amount of qualitative data that you need to corroborate your findings. Additionally, in learning more about your customer, you establish a better relationship and may find new nuggets that will help drive your product forward.
Incorporate feature-related data from the start
While rolling out new features, it’s crucial to identify the essential questions you need to answer and ensure that data capture is properly instrumented. Neglecting to do so may result in blind spots, hindering the overall success of the feature rollout. It is always harder to pay down debt on missed standard operating procedures, so make sure that this is an integral part of the product.
Consider several metrics instead of relying on one
Relying solely on one metric can be risky as it may overlook important information and insights. Diversifying your data will give you a more optimal glimpse into the function and usability of your product.
Interpret data properly
Putting too much emphasis on a single piece of data or misinterpreting it can lead to inaccurate conclusions and decision-making.
Consider context in the interpretation of data
It is crucial to interpret each set of data in its proper context to extract meaningful insights and avoid drawing incorrect conclusions.
Avoid tunnel vision
By gaining a broader perspective through the lens of a customer, we can avoid getting stuck in narrow thinking and ensure that the product effectively addresses the genuine requirements of users.
Incorporate data throughout the entire product development life cycle
Data should be incorporated at all stages of product development to ensure informed decision-making and mitigate risks.
Planning for a successful launch with a data-driven approach: strategies and measurements for success
A meticulously executed launch plan holds immense importance, not only for achieving a successful launch but also for facilitating a data-driven approach. An effective launch strategy plays a vital role in generating momentum. Typically, this strategy comprises a series of fundamental steps.
It’s essential to emphasize that a launch is fundamentally a business decision, rather than a purely technical one. While technical readiness is undoubtedly necessary, it’s equally important to address a multitude of business-related aspects that deserve careful consideration.
Step 1. Choose a launch strategy
Based on the product or business goals, there can be different launch strategies to consider. Setting clear launch goals serves to align the team’s focus correctly, establish a sense of urgency, and identify crucial tracking points. In the process of goal setting, it’s essential to outline specific metrics, quantities, and timeframes. For instance, this could involve aiming for a new revenue of 10 million within 6 months, acquiring 6 reference customers in the upcoming 3 months, or reducing onboarding time to 5 days within 3 months.
To execute your launch effectively, you have the option to employ one or multiple strategies from the list below.
Retention launch: Retain existing customers and secure renewals.
Share of wallet launch: Increase customer spending by promoting new products or add-ons.
Migration launch: Manage customer retention during significant product changes.
Wedge launch: Target competitor’s vulnerable customers when they are not defending their market.
Displacement launch: Take market share from competitors when they are vulnerable.
Mindshare launch: Capitalize on known buying activity to increase market share.
Breakthrough launch: Persuade potential customers to make a purchase, provided that their product concerns have been addressed.
Additionally, it is vital to ensure that the organization is fully prepared to support the launch and have solved issues such as lack of awareness, technical infrastructure, lack of experience/knowledge/competition, inability to execute, and resistance to change, says Christopher.
Step 2. Select the launch team
The composition of the launch team can vary between different organizations or even from one launch to another. Typically, this team consists of members from various functions, including support, legal, marketing, customer success, finance, and other key contributors.
For every launch team, several essential roles need to be filled. These include a Launch Owner, who is the decision maker, an Executive Sponsor, who champions the launch and may also serve as the Launch Owner, representatives from different functions, and a Project or Program Manager responsible for driving the program’s progress. During the launch team meetings, it’s beneficial to treat each session akin to a standup meeting. In this format, functional resources provide updates on their accomplishments, commitments, obstacles, and any assistance they require.
As organizations expand, there might be a requirement for multiple groups. One group would handle day-to-day functions, while another, at the executive level, would focus on resolving more significant issues that may arise.
Step 3. Measure success
Examine both leading activity-based indicators and lagging results-based indicators to gauge the progress of your launch process and its aftermath. Additionally, take into account the following factors:
- Identify how the category stage will affect your launch strategies
- Analyze historical data related to your indicators
- Make comparisons to past performance
- Prioritize your indicators effectively
Step 4. Plan
Formulating the launch plan guarantees alignment with the initial business objectives. This plan will be reviewed and approved by the team and ultimately the decision maker. It also includes the identification of key buyers and outlines the sales approach to be employed. Moreover, it ensures the organization’s readiness to execute the plan and secures the necessary budget approvals. This budget might cover areas like market spend and the allocation of new resources to bolster product support, among other factors. Importantly, the plan also highlights and addresses potential risks and challenges.
Step 5. Enable the organization
Enablement is critical to ensuring launch success. We need to ensure that all parts of the organization are enabled to support the launch. This ranges from the sales team, to the customer success team, support team, and if applicable any partners.
When considering enablement, focus on empowering the respective teams to achieve several goals: gaining mindshare, instilling confidence, assisting the teams in self-improvement, and overcoming any resistance. To achieve this, it’s crucial to equip your teams with targeted content tailored to the specific persona.
Additionally, craft presentations and talk tracks that emphasize problem-solving rather than just highlighting features. Furthermore, ensure that the demos effectively showcase solutions to real-life issues by illustrating day-in-the-life scenarios. This approach enhances the readiness of the teams and promotes the overall success of the launch.
5 key aspects of data-driven product management
Below are five key aspects of data-driven product management that can empower product teams to effectively utilize data and achieve their goals.
1: Failure is an opportunity for growth
Data-driven product management thrives on an experimental culture where failure is embraced as an opportunity for learning and growth. Product managers should approach their work as a series of experiments, iterating quickly through build-measure-learn cycles. This approach necessitates adopting a hypothesis-driven mindset, avoiding biases, and utilizing automation to measure actual outcomes.
Christopher Van Horn, Kanda’s Delivery Manager says:
Efforts should be made to identify failures as soon as possible with minimal cost. For example, you can use a mock-up review with your Product Advisory Council approach, etc., without writing a line of code. This represents significant cost and opportunity savings.
2: Democratized data makes for better decision-making
Data democratization is crucial for fostering an experimental culture. Every member of the organization should have access to the data they need to make informed decisions. Effective data governance, data quality, and tools for self-service analytics play a vital role in democratizing data and enabling better decision-making across departments.
Christopher shares his experience on the above:
If you follow a Product-Led Growth strategy, then every aspect of the company is invested in the product. This means that every department—support, service, product, branding, etc.—considers the customer’s needs. The product is at the center of the business, and success requires every part of the organization to focus on the product.
One of the benefits of this approach is increased communication across the organization, anchoring around a common view of success. This means that each part of the organization has specific metrics to which they provide input. For example, you may find a fall-off during onboarding and will work with the Customer Success department to optimize the process, the product or both. After making these changes, continuously monitor to identify if the desired results are being achieved.
3: Responsible data collection makes for enhanced security
Responsible data collection involves ensuring a fair value exchange for privacy and adhering to guidelines to protect customer data. Product managers should establish clarity on privacy commitments, anonymize data whenever possible, and provide clear justifications for data collection. Understanding security measures, embedding user controls, and leveraging anonymization tools are crucial in today’s regulated environment.
4: Accuracy of conclusions is vital
While data-driven decision-making is critical, product managers must exercise caution in differentiating between correlation and causation. Careful analysis of data is required to determine whether a relationship is based on causation or mere correlation. Testing hypotheses and ensuring statistical significance are important in drawing accurate conclusions and making informed decisions.
5: Intelligent tools can boost data analysis
Product managers of the future need to leverage artificial intelligence (AI) in conjunction with data. This allows them to harness structured and unstructured data from various sources, combined with multiple machine learning (ML) algorithms. Sharing AI and ML models across teams and cultivating an efficient operating model and governance are crucial for maximizing the potential of a data-driven approach.
Leveraging data for enhanced product development: Real-life use cases from Christopher Van Horn
In this section, we will delve into several use cases where Christopher Van Horn, along with his team, effectively harnessed data to significantly impact the product development cycle and improve user efficiency.
Use case 1. Feature deprecation — streamlining the code base
Under Christopher’s guidance, the team utilized data to deprecate features, developing a clear action plan for a seamless transition. This strategic approach streamlined the code base, freeing up resources from testing, analyzing, and managing unnecessary features. Additionally and equally important, they also worked with the customers to ensure that they were aware of these changes and could plan for and orchestrate a transition. The Customer Success team was instrumental in this process as they guided the customer through the transition to new and improved features, suitable alternatives, or changes to their process.
By prioritizing the roadmap and enabling a well-planned transition, Christopher and his team minimized risks, reduced complexity, and preserved significant revenue and customer loyalty in a strategic managed approach.
Use case 2. Conversion plan optimization — maximizing revenue and customer retention
Christopher’s team analyzed feature usage, revenue, renewal data, and customer sentiment to build a comprehensive conversion plan.
The actions above allowed the product team to optimize the conversion process, ensuring better customer retention and higher revenue generation. The communication strategy established around the plan further enhanced its effectiveness.
Use case 3. Enhanced customer onboarding — streamlining the process
Christopher’s team meticulously analyzed data and process flows during customer onboarding, identifying pain points and areas where customers were dropping off.
Collaborating with customer support and customer success teams, they devised an optimal set of enhancements and streamlined the onboarding process.
The team efforts led to a remarkable 80% reduction in drop-offs and error conditions during onboarding, leading to improved customer satisfaction and loyalty.
Use case 4. Simplified document generation administration — empowering admins and accelerating processes
Under Christopher’s leadership, the team successfully developed a complex document generation platform. This platform empowered administrators to standardize document formats, such as NDAs, SOWs, and MSAs, and automate the extraction of data from various systems using predefined rules. However, this process was intricate, time-consuming, and required substantial expertise and training. As the business shifted its focus towards expediting onboarding processes to facilitate expansion into a broader market segment, they took the opportunity to reevaluate crucial aspects of the overall solution.
By leveraging both usage data and qualitative insights, they pinpointed common patterns, methods for creating rules, and tools for data selection. These elements could be readily accessible to customer business analysts or subject matter experts. The team developed templates that could be effortlessly combined, empowering customers to model their workflows within a matter of hours, a significant improvement compared to the previous time frame of days or even weeks.
The introduction of this lightweight version not only accelerated time to value but also significantly improved CSAT scores.
In this article, we explored the key aspects of a data-driven approach in product management and provided insights and use cases from our Delivery Manager, Christopher Van Horn, on the impacts of data on an organization.
The use cases above showcase how leveraging data can greatly impact product development cycles and enhance user efficiency. By analyzing and interpreting data strategically, through the efforts of the product manager, organizations can achieve remarkable results, enabling them to make informed decisions, drive innovation, and deliver unique customer experiences.
Kanda helps businesses in various industries achieve exceptional results by harnessing the power of data for informed decision-making and product development. With a customer-centric approach and advanced analytics, Kanda drives innovation and competitive advantage for its clients.
Ready to kickstart your data-driven journey? Contact us today!