Power of Analytics and Experimentation for an effective Product Strategy
Written by Rahul Mirchandani, Chief Product Officer for Dream Sports
“No one can whistle a symphony. It takes a whole orchestra. Similarly a product is an amalgamation of factors that help it succeed.”
[ – H. E. Luccock]
To build and launch innovative products, it is important to identify the problems they will solve for users. An important part of this process is having a strategy that integrates different aspects of product development to determine a strong product-market fit. This calls for three fundamentals for an effective product roadmap —
Data at the heart of everything
Whether it’s to personalise a user experience or launch products to increase sales — it has to be backed by data which is available with every technology company. In fantasy sports, data is essential to offer a personalised, engaging and safe experience for fans to test their skills and knowledge of their favourite sports. Similar to how ‘Specially For You’ pages changed the way social media users engage on their preferred platforms, fantasy sports companies are making user engagement more customised and intimate through data-driven interventions and applications. Real-time data can prove beneficial to keep artificial intelligence (AI) and machine learning (ML) systems updated and abreast of changing user needs and behaviours to support product customizations. By analysing data on user engagement, retention, and spending patterns, developers can identify areas for improvement, optimise game mechanics, and design new features like rewards and loyalty programs. Behind the most successful products today are data-obsessed teams – and product managers that democratise data across teams, use it to foster seamless collaboration, improve productivity and innovation, and deliver on customer-centric solutions. that help them stay ahead of the curve.
Analytics can make or break your product
Without analytics, product development is reduced to the basics of developing a product idea, implementing it based on the final outcome and disregarding if it doesn’t meet or exceed user expectations. For products catering to a large user base like in the case of fantasy sports, one of the common behavioural analytics requirements is that of funnel analytics. Funnel analysis is an effective way to calculate conversion rates on specific user behaviours. This can be in the form of a sale, registration, or other intended action from users. For this, the analytical strategy needs to be mapped within the product strategy down to the individual features and prioritised in the right order across the lifecycle of product development to maximise results. In fantasy sports, a simple statistical data of what a user likes and dislikes is broken down and analysed to convert into intelligent customised pages that will allow users to personalise their experience. Factors like pre-selected matches, team marketplace, contest bundles, missions, rewards, social gaming and other features that are relevant for users will be easily accessible and in the forefront for a user enabling more participation.
Continuous experimentation is key
Building a new product and scaling it, both involve substantial levels of experimental and trial and error. From personal experience in working across the product development life cycle, product managers, especially, should not have to come up with a perfect idea on the first try. Instead, they should be encouraged to treat everything as an experiment— and be ready to fail and learn from failure. Experimentation takes the guesswork out and helps develop products that are feasible through active management. As the product goes through parallel experiments, it allows developers to ensure new features and product customisations have the desired impact. Testing out innumerable hypotheses is a part of the process for a product to scale. In addition, there arises a need to conduct these tests on an isolated set of similar users so multiple experiments don’t bias each other’s results and accurately attribute the change in a data metric for every product increment.
Ultimately, the key to effectively using data, analytics, and experimentation is to view them as ongoing processes that require investment and attention. By continuously collecting and analysing data, experimenting with new ideas and features, and refining your product strategy based on your findings, you can gain a competitive advantage while delivering products and services that your users truly want and need.
The author of this article is the Chief Product Officer for Dream Sports
