In an earlier post, we discussed how developing a deep, data-driven understanding of the customer life cycle can help you leverage data based on how your customers are actually using a product—rather than how they or the business think they want to use it—to ensure that you are making the best decisions in sales and development. In this post, we will look at driving product engagement with behavioral data, as well as building an integrated analytical environment.
Identifying areas of struggle and opportunities for enhanced user experience through behavioral data is also called data-driven product development. You must have the proper instrumentation and analytical capabilities to evaluate product and feature engagement (e.g. “this new feature will increase engagement for user group x,” or “this new feature will reduce likelihood of churn by n%”). Developing a leading capability includes:
- Fully integrating product analytics into your development and sales workflows
- Building the experimental frameworks needed for rapid testing and evaluation of product hypotheses and deployment of valuable new features
Developing product analytics
You probably offer a number of products across various platforms, each with different usage data being captured. There may be pockets of analysis occurring, but you probably have yet to apply product analytics using behavioral data holistically across your entire set of offerings. The value of this kind of product analytics can be most fully realized by integrating it deeply into an organization’s fabric. This involves:
- Instrumentation of products where there are gaps in visibility
- Integrating data across products and features to be able to “follow” users across platforms (client, web, mobile) and features
- Defining KPIs and metrics to identify common friction points or areas of user struggle
- Integrating testing frameworks to enable experimentation
- Creating feedback mechanisms for product managers and application designers to alleviate user pain points
Product analytics strategy and roadmap
Creating this holistic capability is best viewed as a long-term investment that can be implemented incrementally, but is ultimately quite transformational. If you develop a roadmap for delivering this capability, you can ensure that data-driven product development becomes core to your operations. This approach involves:
- Defining the long-term goals of your product analytics capabilities
- Understanding product development and sales workflows to inform how product analytics can be successfully integrated into your organization
- Identifying and prioritizing the capabilities needed for enabling product analytics at the product or feature level
- Assessing the data, analytics, and infrastructure required to deliver those capabilities
- Assessing the gaps in data collection across your current products
- Building a roadmap for addressing the above points to deliver value quickly, while also delivering the ultimate transformation
Such an investment would result in a clear path forward towards data-driven product development that will set you up for success. It takes into consideration the wide set of requirements of product development across your organization, while prioritizing the steps that will begin creating value quickly. We call this data strategy.
Experimentation is critical to data-driven product development. Without the ability to rigorously test new features and changes to the product experience, there is no way to know if product development actually leads to increased product engagement. A world-class experimentation capability involves a blend of engineering and data science work to develop both the instrumentation required for running A/B and multivariate tests and the analytical assets needed for analyzing test results. Building this experimentation capability would involve:
- Assessing your current product development processes and outlining the types of tests that could begin to take place to enhance development
- Identifying and implementing the instrumentation necessary to run A/B and multivariate tests
- Defining the set of metrics you want to use to drive product development and to assess the outcomes of A/B and multivariate tests
- Designing experimental frameworks to select test subjects that take into consideration the relationship between individual product users and their larger institutions
- Developing the code assets necessary to implement the experimental frameworks
- Working with product teams to incorporate testing and results analysis in their product development workflows
Enabling experimentation will begin to deliver value immediately, as each product decision will be informed by statistically significant evidence that it is moving you in the right direction. This capability will allow you to compete with new market entrants who are likely to have built their products from the ground up to enable experimentation, and are able to iterate quickly according to the results of rapid, regular testing.
Building an integrated analytical environment
Making a strategic decision to integrate your product infrastructure with your analytical infrastructure can make all of the analyses described above easier. By integrating data across product operations, support, and sales into a common platform, you can dramatically increase the throughput of product hypotheses evaluated, business questions asked, and sales opportunities captured.
Many of the analytical capabilities described above can be implemented with spot solutions or in ad hoc efforts. However, if your strategy for these capabilities is more holistic, you will ultimately want to invest in integrating product development, operations, and analytical environments into a common platform. Companies that do this are able to achieve really differentiated outcomes.
For example, with deeply integrated capabilities, when a support call is received or a ticket is opened, you can easily:
- Validate support entitlements
- Understand exactly what the user was doing in the product when the error occurred
- Know whether the user is experiencing a known issue or something novel
- Prescribe remedies for known issues far more quickly
- Alert account management of issues and summarize them ahead of customer interactions
This is just one example of the kind of customer experience a deeply integrated platform can deliver.
SVDS has designed and built advanced analytical environments for a spectrum of clients with highly demanding workloads in industries including retail, media and entertainment, financial services software, and healthcare. Our approach is designed to meet evolving business needs, as well as to include the architectural principles that SVDS considers foundational to large-scale data platform development:
- Supportability and maintainability
- Modularity & loosely-coupled components
- Security & compliance
If you’d like to talk with one of us about developing engagement analytics or building an integrated analytical environment at your organization, please get in touch.