In today’s digital ecosystem, where competition is rampant, testing is no longer only a basic instrument but is necessary to ensure data-driven decisions.
As good in theory, standard A/B testing is insufficient in today’s ever-evolving world, where multiple factors influence user behaviour.
More advanced testing strategies, including causal inference models and multi-armed bandit testing, allow product managers to better improve their ability to decide by giving them better accuracy and greater efficiency.
Jennifer Agbaza, a senior product manager experienced in experimentation frameworks, explores why advanced testing is critical for the next generation of digital products.
“A/B testing has served product teams well for decades, but in fast-moving environments, we need methodologies that adapt dynamically, account for external market fluctuations, and ensure we’re not just reacting to surface-level data but truly understanding impact.”
A/B testing is an omnipresent and accessible approach for evaluating product changes. A/B testing is based on a simple principle: split users into two groups, introduce a variable, and compare outcomes.
But this method, designed for controlled environments, struggles with real-world complexities.
It assumes user behaviour is independent, doesn’t adapt in real time, and fails in high-variance environments where static splits miss shifting trends.
To address such limitations, product managers have started using causal inference strategies. Unlike A/B testing, where correlations take priority, causal inference seeks to identify cause-and-effect through adjustment for confounding.
Difference-in-Differences (DiD) is one such approach used in such cases, where treated and untreated groups’ trends before and after treatment are compared, thus eliminating initial disparity-induced biases. Instrumental Variables (IV) is another technique, where causal effects in scenarios where randomisation is infeasible are sought by using external influences on treatment but not on the consequence.
These tools allow product teams to make better decisions in such challenging scenarios where direct randomisation is infeasible.
Beyond causality, real-time experimentation is transforming how product teams deploy changes. Multi-armed bandit (MAB) algorithms dynamically allocate traffic to the best-performing variation in real time, unlike A/B testing, which waits for a winner.
This approach is particularly powerful in fast-moving industries where delayed insights mean lost revenue. Netflix and Airbnb have employed multi-armed bandit algorithms to personalise interactions, hence optimising suggestions in a timely fashion and minimising opportunity cost.
In maintaining a tradeoff between exploration, where novel options are experimented on, and exploitation, where options’ maximisation is achieved, MAB testing allows product managers to produce better outputs while minimising wastage of resources.
Industry leaders have developed advanced testing systems inclusive of advanced practices. For example, Netflix has gone beyond standard A/B testing by using causal machine learning models and counterfactual thinking to optimise content playback and suggestions, adapting dynamically to user preferences rather than waiting for long-term A/B test results.
Airbnb, on the other hand, structures testing methodology in hierarchical models to include heterogeneity in users, thus enabling generalisability in various market places.
Digital advertising platforms continuously fine-tune ad placements, reducing wasted impressions by automatically adjusting campaign bids.
These companies recognise that standard A/B testing does not pick up on fine details in behaviour in large datasets and have made considerable investments in advanced methodology to maintain innovation.
It is crucial to realise that power is also coupled with considerable accountability. Ethical testing is crucial in maintaining trust between users and enabling fair judgement.
The limitations of advanced testing include p-hacking, where several trials take place until a wanted result is achieved, and this may have spurious effects. In order to overcome such limitations, product teams ought to define their hypotheses prior to testing, use multiple comparison corrections, and maintain reporting transparency.
In addition, companies have to consider moral testing ramifications, especially for sensitive features affecting their users’ welfare.
Striking the right balance between accountability and innovation is essential for long-term success and building user trust.
For senior product managers like Jennifer Agbaza, mastering advanced testing strategies goes beyond simply optimising metrics; it is accomplishing mastery in data-driven, thoughtful decisions whose impact is profound.
Jennifer Agbaza is a seasoned Senior Product Manager with more than five years of experience in product strategy, development, and deployment. She has built a strong reputation for spearheading cross-functional teams in the development of innovative, user-focused solutions that meet business needs. Her strong background in market research, stakeholder management, and agile frameworks allows her to excel at converting customer pain points into core product features. Additionally, her ability to guide complex product lifecycles, streamline processes, and foster data-driven decision-making has played a pivotal role in the success of many technology-led organizations in achieving their goals.
Using causal inference models and multi-arm bandit testing, product leaders have the ability to skilfully handle market intricacies, thus speeding up product innovations while keeping in check an optimal amount of risk.
In today’s data-driven world, where data-driven decisions are paramount, those who move beyond basic A/B testing and embrace advanced experimentation will set new standards for product leadership and industry excellence.
