How location data can spur entrepreneurs in smaller cities

Data solutions can provide better foresight and enable quicker market adjustment, while reducing overall risk and the cost of research.
Data solutions can provide better foresight and enable quicker market adjustment, while reducing overall risk and the cost of research.


  • Applications of real-world data along with AI capabilities can help startups in tier II and III cities.

While Mumbai, Bengaluru and the National Capital Region have become the most-talked hubs for startups in the country, new businesses are fast emerging in tier II and III cities. The major investment roadblocks and challenges for this segment are the absence of a mature ecosystem and a lack of access to reliable market knowledge. This can be tackled, however, by harnessing intelligence and market insights from real-world data that can aid in finding the right entry points to these markets.

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Startups in tier II and III cities have primarily focused on solving local problems through local solutions. Some of these can be replicated and scaled to work on global problems. Such patterns have fuelled and encouraged the startup ecosystem in these cities to survive, scale and thrive. According to recent data released by the Department for Promotion of Industry and Internal Trade (DIIPT), 555 districts in India have at least one new startup. These numbers are expected to grow and unlock the potential beyond metropolitan cities. Recognizing opportunities in such cities, industry leaders are now seeking effective entry points to these untapped domestic markets. But there are certain challenges that are hampering this.

Key challenges: Lack of awareness, infrastructure, incubators and accelerators for a startup ecosystem have been roadblocks for startups in tier II and III cities. With these challenges being resolved over time, lack of data, varying customer behaviour and immaturity of these markets in terms of viable entry point still remain a challenge.

The key barriers to entry to these markets are as follows:

One, a good understanding of customer behaviour is hard to obtain.

Two, metropolitan data, insights and trends are usually inapplicable to these new markets and organizations’ internally generated data for such markets is often void of real-world insights.

Three, data that is available is scarce, inaccurate, incomplete, non-conforming, duplicate, or of poor quality.

Four, the traditional methods to acquire data are cost-intensive; for example, field surveys.

Five, markets are new, untested and driven by gut-based decisions.

Overall, lack of data in tier II and III cities reduces investment prospects and tremendously increases entrepreneurial risk and cost.

There are a lot of questions and roadblocks that could be solved if businesses and investors have access to quality data and sophisticated analytics capabilities. Having access to data is not enough, drawing insights from it to solve business problems is crucial. Data solution providers are developing sophisticated artificial intelligence (AI) and machine learning (ML) capabilities to enable real-time analytics over petabytes of data.

Moreover, most real-world data is unstructured and so not consumable directly. This data needs to be collected from multiple sources, stored, processed and transformed into usable form. After all this, the data is ready to be fed to prediction models for insights.

Location data and intelligence could be a key investment accelerator: Access to reliable, effective and actionable market intelligence for all stakeholders holds the key to boosting entrepreneurial ambitions in tier II and III cities. To enable this data-led approach, location analytics and intelligence offer answers to complex problems and reliable predictions for informed and logic-based decisions.

Location data can help provide critical insights to entrepreneurs and investors alike. They could, for example, gain a better understanding of market size, presence of the target audience, and competition. Entrepreneurs could also seek actionable insights on customer behaviours, manufacturing possibilities and capabilities, existing businesses and how they are performing, and effective business models among others. Sophisticated AI/ML capabilities also enable prediction models to gauge risk and forecast revenue and profits, based on the attributes of a specific location.

With location information at granular levels, businesses can predict risks and opportunities involved with being present at that specific location. They can also identify critical user behaviours, patterns and affluence in these new areas. Businesses can also identify exact catchments where their target audience is present, rather than targeting their efforts at the city’s entire urban scape within a geographical boundary.

Today, India has data solution providers offering custom solutions equipped with AI capabilities for businesses across sectors. These data solutions can provide better foresight and enable quicker market adjustment, while reducing overall risk and the cost of research.

Location data analytics and intelligence could therefore be game changers in solving some of the pressing problems across industries and achieving prediction accuracy. Some of the big issues being resolved by location intelligence are credit risk and fraud prediction, site selection and retail expansion, lead prioritization, hyper-local digital and out-of-home targeting, and more. Applications of real-world data along with ML capabilities can help investors, entrepreneurs and other stakeholders accelerate business growth as they enter tier II and III markets.

Ankita Thakur is co-founder and CDO, GeoIQ.

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