In the fast-paced world of music streaming, companies have to constantly innovate and make bets on new product initiatives in order to stay ahead. Simply relying on current revenue streams is not enough – you have to take risks by investing time and resources into projects that may or may not pan out. The key is balancing risk versus reward. That’s why Spotify adopted the DIBB framework to manage innovation through “bets boards.” DIBB stands for Data, Insights, Beliefs, and Bets. Here’s how it works:
The Need for a Framework
With so many new ideas and potential product directions floating around, Spotify needed a way to analyze, prioritize and keep track of key innovation investments. The bets board and DIBB process allow PMs to align on the biggest bets that have the best chance of driving impact based on available data and insights.
By providing a common framework, DIBB enables more structured conversations around where teams should be allocating their resources and effort for maximum company impact. Bets boards facilitate communication between teams and ensure proper execution on the highest priority bets.
Examining the Data
The first step in the DIBB approach is to examine all available data that could influence innovation priorities. For a music streaming company like Spotify, this data may include:
- User behavior data – what features are customers using most/least? What playlists or artists are trending?
- Market data on trends in music streaming and entertainment at large
- Competitive analysis – what are competitors like Apple Music doing?
- Internal data on operational metrics or app performance
- Customer feedback/sentiment – comments, reviews, support tickets
Product managers analyze these quantitative and qualitative data sets to paint a picture of what’s happening across the marketplace and Spotify’s position within it. Advanced analytics, modeling, and segmentation may be used to surface key user trends.
Translating Data into Insights
The next step is translating all this data into meaningful insights that could drive potential innovation. The insights derived help identify areas of opportunity, risk, or potential growth for Spotify based on what the data is saying.
For example, data may show that classical music listeners use the platform in a very different way than pop music listeners. An insight could be that there’s an opportunity to tailor certain features like recommendations and playlists more specifically to classical fan needs.
Other insights might highlight rising markets where Spotify has an opportunity to grow users, or identify countries where brand awareness is lagging competitors. The goal is to uncover the “so what?” behind the data that points to possible drivers of strategic bets.
Forming Beliefs to Support Bets
Beliefs are formed based on the insights derived from the available data. These beliefs represent hypotheses about changes that could be made or directions to explore with new products or features.
For example, the insights above could lead to beliefs that:
- Tailoring playlists and recommendations to classical music fans could increase engagement and retention among this segment
- Investing in growth in Brazil could tap into a large potential user base not capitalized on yet
- Ramping up marketing in Germany could increase brand awareness and attract users from competitor platforms.
These beliefs represent what Spotify thinks could happen, but they need to be tested and validated. This leads us to the final B – the Bets.
Placing Bets on the Highest Potential Ideas
A bet represents an investment of resources, time, and effort into a project or initiative that tests a given belief. Bets involve taking risks for the potential of sizable returns if they pay off. For each belief, PMs need to decide if it’s compelling enough to warrant a bet.
For example, Spotify may decide to make “Classical Fan Growth” a Company Bet by having cross-functional teams collaborate on building a special classical music experience personalized for this audience segment.
Smaller bets may be more specific features like a “Germany Marketing Campaign” bet to increase marketing spend in that country, or a bet on a new “Daily Brazilian Hits” playlist targeted to users in Brazil.
The key is to allocate resources to the bets with the highest potential while keeping them focused and measurable. Bets are not open-ended projects but have defined outcomes, success metrics, and timelines. Teams check in on the progress of bets frequently through the bets board. Successful bets can lead to doubling down on similar initiatives while failed bets indicate that a pivot may be needed.
Types of Bets
As mentioned, Spotify aligns bets across Company, Functional, and Marketing:
- Company Bets are huge, cross-functional projects that can last 6-12 months involving coordination across many teams. These are high risk, high reward. An example could be a bet on “Social Listening” – building out more viral social features.
- Functional Bets are confined to a specific function but still substantial in impact and scope, aligned to high priority Company Bets. For example the engineering team might have a functional bet on rebuilding the algorithms for personalized playlists.
- Marketing Bets are smaller and more tactical, like testing a new type of video content or targeting a campaign to a micro-audience. These can move faster.
Adopting a DIBB Framework
The DIBB framework helps companies like Spotify stimulate innovation through structured data analysis, insight development, forming beliefs, and placing well-informed bets. It provides a clear process for deciding where to allocate resources for maximum impact.
Other companies could adopt this framework by:
- Ensuring teams have access to the right data sources and are equipped for rigorous analysis
- Leveraging cross-functional expertise when developing insights and beliefs
- Defining a bets board process that aligns with company structure and culture
- Focusing bets on outcomes that tie to company goals and priorities
- Tracking bet progress, results, and learnings in a structured way
With the right organizational commitment and discipline around the process, the DIBB framework can help teams balance risk-taking with data-driven decision making in order to drive innovation, growth, and competitive advantage.
