Despite being a young, 4-year old car-sharing startup, Udrive leverages trip and location data to inform its Customer Acquisition strategies. In this ‘data is the new oil’ age, collecting and analyzing data has become an unspoken mandate for all companies, big or small. And the benefits of doing so are large and significant.
A common problem early stage startups face is the shortage of [useful] data to conduct even basic analytics. Unlike huge organizations, startups have limited users and hence it becomes difficult to get statistically significant insights when running A/B tests or measuring engagement.
Does this mean there is no room for analytics in a startup?
The answer is a big NO. Startups that leverage analytics and data science right from their early stage are more likely to script success stories. The hack is developing a data-centric mindset from the beginning; which eventually helps startups rapidly scale and succeed, gain a competitive edge over other startups as well as not-so-tech-friendly large businesses.
Now, the bigger question is; how does one measure and iterate quickly without enough data?
Here are a few practices to consider:
Track the right KPIs:
Start with the limited data you do have. Instead of using every metric you can get your hands on, focus on 3 that help drive your short and long-term goals. For instance, total number of website hits is a vanity metric that does not help early-stage startups. Instead, target ‘Conversions per day/ week’ to optimize sales and marketing efforts.
Every business is different and should use a unique set of KPIs to drive analytics. Choose growth-focused qualitative and quantitative KPIs. Some metrics startups can track are acquisitions, customer churn, new site visitors, repeat visitors, daily/ monthly active users, number of SKUs sold per day/week/month etc.
This is possibly most important, yet most neglected by early stage startups. Individual Analysis is the process of monitoring user activity at a granular, individualized level.
Compiling customer tickets, tracking transaction data and user profile helps derive a holistic picture per user. This reveals patterns that can provide deep insights about product usage. Below are a few examples:
- Users could be spending ample time on the product/ app, but not on features that will actually benefit them. An immediate action point for the business is — highlight key features and make it easier for users to discover/ access them.
- A user cancels his/her account. You can track their activity prior to cancellation. Maybe they got frustrated trying to figure out a certain feature or never went through the full setup process. An immediate action point here would be — improve the on-boarding process.
Thus, Individual user analysis is more than just a low-fi version of customer feedback. Instead, it helps achieve a 360-degree view of the user, to conduct deep-dives into individual user’s behavior and discover pain-points.
Group customers with similar expressed or implied characteristics and preferences eg. job titles, geography, actions taken. Such dynamic micro-segments can then serve as building blocks of your hyper-personalized campaigns to customers.
It is beneficial to capture activity at each stage of your customer journey or funnel i.e. awareness, activation, retention, revenue, referral. Any piece of user data that companies track can be used to segment, as long as that data is meaningful and can be tied back to business goals like sales volume/ revenue. For instance, an ad-driven online magazine startup might make money from views and be quite happy tracking viewer traffic, whereas a CRM platform startup might care more about ARR (annual recurring revenue).
Customer segmentation allows businesses to develop cohorts, listing the characteristics, behaviors, and interests of each segment which in turn allow startups to develop or improve products with a solid understanding of user needs.
Build the right tech stack:
For companies having a strong understanding of relevant KPIs, finding the right analytics tools (whether free or paid) is a matter of preference and accessibility. The simplest of tools, when applied effectively, can deliver outcomes you expect from your Data Science/Analytics function.
Always choose tech that works for you, rather than ‘most popular’ tech. When evaluating tools, do apply the ‘Occam’s razor’ principle : “The simplest solution is usually the one that requires making the least assumptions”. Less tech is better than irrelevant tech.
Once startups sense the potential of data analytics, it is quite common for them to resort to trial runs of tools like Google Analytics, Firebase, Segment — all of which are great options to get a feel in terms of their usability and KPI-tracking capabilities. However, good things don’t come free, as don’t effective tools. All free versions come with restricted time and feature access. It is extremely important for startups to choose tools wisely, so when the need to upgrade to the paid version arises, the cost is in sync with benefits derived.
Bolt by ThinkBumblebee, a data science engagement for early stage startups can help you kickstart Analytics in just 2 weeks and at a phenomenal savings of upto 90% in costs.
Planning to leverage analytics/data science for your startup’s growth? Get in touch with us here.