The fate of product success hinges on understanding the user, and we’ve seen the way in-depth user research impacts the way meaningful and user-focused products are developed. We’re going to take a look at these differences and consider some key methodologies and case studies with Swiggy to show user research in action.
Effective user research helps organizations identify and solve pressing user problems. Though startups usually adopt lean and rapid methods of conducting user research to quickly validate hypotheses, large companies could also make use of their extensive resources to conduct deeper, more structured studies.
Startups require a lean and agile approach
Speed and agility are critical for any startup, and that approach seems to spill over in the way they do user research. Few resources and a pressing need for validation mean most startups want quick, actionable insights, either through direct interviews with the customer or surveys, in order to gain attitude-based insights into the shaping of features of the product.
Some startups test core features with real users through the MVP instead and receive early feedback based on real-world input. Indeed, informal testing might just give them enough to find the pain points without necessarily having to resort to extensive formal testing. Lean user research enables course changes to keep up while still keeping development aligned with user needs.
Larger companies want their research structured and scalable
Larger companies have way more resources at their disposal when it comes to user research, with the aim to make products highly scalable and sustainable for bigger sets of audiences. With enterprise UX teams, research becomes truly wide to ensure that products can connect with a wide variety of users.
They also draw extensively on the power of data analytics and A/B testing, using large-scale quantitative data to conduct deep analyses that inform design and feature decisions. Generally, in larger organizations, stakeholder alignment requires more formalized cycles of research, perhaps slower but more rigorous and structured methods of embedding user insights into the roadmap.
On continuous discovery
No matter the size, continuous discovery has grown to be a prime exercise in product development. This approach integrates the ongoing user engagement into refining and adapting the product to actually meet real needs and behaviors of users. In startups, continuous discovery is baked in with fast-paced iterations; larger enterprises rely more on periodic discovery cycles to feed into the product roadmap.
For startups, continuous discovery means constant feedback with the users. This translates to agility in operation–those teams are capable of quickly pivoting off user insights, adjusting the roadmap. And because iteration is fast, startups keep the product aligned with market demands that are continuously changing.
Large organizations are indeed practicing continuous discovery, though mostly less agile because of the larger scale and complexity. Enterprise teams follow schedules that were set for research and validation; thus, supporting continuous improvement but delayed in integrating immediate feedback.
There are some exceptions to this rule, however. While quick and lean research methods are leveraged by startups, constraints such as budget or time can sometimes restrict the extent of user research they may carry out. They might launch a product before features have been fully validated in order to gain early entry into the market. On the flip side, large enterprises might instead behave more like startups and opt for faster and leaner processes of research upon releasing high-stakes products, or even to hit competitive markets.
Data needs to be obtained at scale
As we’ve mentioned, data collection scale is one of the differences between startups and larger enterprises. The most common type of data collected in a startup is usually qualitative insights collected directly from users through surveys and interviews. While much bigger datasets may not be collected, the insight from early users usually provides very rich and actionable feedback. Large organizations collectively gain quantitative data from a huge user base that enables advanced user segmentation, A/B testing, and analytics. That scale gives the enterprise an edge of heavy advantage in predicting user behaviors and making data-driven adjustments to the product.
Attitudinal vs. behavioral research
User research generally falls into one of two broad buckets: attitudinal and behavioral. Attitudinal research investigates what users say they like; behavioral research looks at what they actually do.
On the attitudinal side, startups commonly conduct customer interviews, rapid usability testing, and surveys to find out what users want and need. These are relatively inexpensive methods for acquiring high-value qualitative information with speed. Large enterprises, on the other hand, will use sophisticated methodologies, including sentiment analysis, focus groups, and large-scale surveys, to aggregate attitudinal data. These are then often segmented by user demographics to enable personal experiences at scale.
Behavioral research informs actual users about their behaviors and interactions. Startups look at analytics tools like Mixpanel or Hotjar, which let them track and interpret user behavior as a way for teams to make data-informed adjustments to the product.
Large enterprises, with more resources at their disposal, are able to implement advanced techniques such as multivariate testing, funnel analysis, and large-scale A/B testing. These sophisticated methods bring out the detailed patterns in user behavior, hence permitting nuanced optimizations and more strategic product enhancements.
As for the toolset, for a startup, there are the usual suspects like Google Forms, Typeform, Figa, Lookback, and Mixpanel, which are great for running surveys, prototyping, testing with users, and analytics in general. These kinds of tools get the job done to collect quick, iterative insights. Large enterprises would use Qualtrics, UserTesting, and Amplitude to capture large-scale insights or conduct sophisticated testing in support of more comprehensive research and analysis.
User needs prioritization frameworks
Based on research findings, two widely used frameworks for prioritizing user needs include the MoSCoW and ICE frameworks. The MoSCoW framework, being more focused on “Must-Have” features, is common in startups since its main purpose is to make MVP development leaner with the aim of saving money and accelerating testing. Large enterprises can develop, with greater resources, additional features in the “Should-Have” and “Could-Have” categories to create a better experience and differential advantage for users.
Another popular framework is ICE, wherein startups would focus on only features that have high impact with low effort to try and maximize outcomes within resource constraints while large enterprises may focus on higher-effort features if they align with the long-term strategic goals of the business.
User research in action at Swiggy
Swiggy, India’s largest food delivery platform, is a true case of user research in practice. First, to improve its retention of delivery partners, Swiggy moderated focus groups and field observations by accompanying new partners on delivery rounds. This study showed that the new delivery partners were struggling to find high-demand areas and understand the payment rules, and thus introduced an app feature called “Heatmaps” which show demand zones and allow for easier navigation.
The second case is about Swiggy’s user research to optimize a new delivery partner app. Focus groups, field observations, and video recordings were done to point out certain usability friction in loading maps or even tracking earnings within the application. Smoothening app navigation, making sure that maps are responsive, and optimizing performance for battery efficiency, Swiggy enhanced the delivery experience.
Know your user
Let’s quickly recap the key steps to running user research.
- Set goals and describe what insights will be needed to understand the needs, pain points, or validate new features of the users. Choosing the Right Methodologies: Attitudinal methods allow the researcher to understand why users make certain decisions, whereas behavioral methods look at what they do. The goals will determine which of these two best serves the research.
- Align stakeholders and product early and make sure the insights are going to have an influence on the product decisions.
- Establish personas using collected data. Startups can create lightweight personas, while enterprises can create high-fidelity, data-driven personas.
- Run usability tests to bring the ways customers use the product into light.
- Apply findings to iterate and push new releases, and refine in a constant cycle.
User research is one of the most powerful tools a product development company has when it comes to creating products that really speak with users. Startups and big enterprises approach research very differently, with each having compelling advantages. This is how companies keep finding users and take that insight into product design to work out not only enhanced user satisfaction but also something worthwhile.
For building user-centric products, a lean or structured approach towards user research is considered to be the silver bullet by tech teams in the competitive market of today.
