September 30, 2023 Ultimate News Hub

Smarter Startup Moves: Data-Driven Ways to Drive Innovation and Growth

Large industry incumbents frequently have an advantage when it comes to innovation through data-driven business strategies. Due to their size and experience, they have access to vast data sets produced by both internal and external consumer interactions. Building a large enough client base to produce relevant data that can spur product and service innovation is frequently the chicken and egg challenge for startups.

However, a number of creative small businesses are figuring out methods to get around this and generating commercial value by reusing already existing data that may be obtained in many places. These examples show how a creative approach to locating and fusing frequently dissimilar sources of data combined with the zeal and zealousness prevalent in successful businesses may be a winning combination.

  1. Data for urban planning

Based in San Francisco A new entrant in the smart city market, Populus combines data from various ride-hailing, car- and bike-sharing, as well as e-scooter companies, to assist local government officials in better strategizing their traffic and parking plans. By combining various data sources and presenting them in formats that city planners can use readily, it offers value. Populus is able to supply insights to both sides of this market as it expands its client base of local authorities and data providers. These insights come from the analysis of larger and richer data sets.

  1. Making use of the social web

Large FMCG companies in London are starting to take notice of Black Swan’s growing reputation for providing up-to-date insights on changing consumer behavior trends. It accomplishes this by gathering data from social media, product review websites, online forums, and other online areas, then utilizing its AI software to identify trends. The company receives payment from Danone, PepsiCo, and McDonalds for these insights, which frequently offer more precise and timely data than depending on more conventional techniques like customer panels and polls. The important thing other businesses can learn from Black Swan is that their unique software delivers value even though most of the data they utilize comes from outside sources. Massive amounts of data are now more accessible than ever at no or minimal expense.

  1. Fitting the data

By giving retailers and makers of shoes and clothing access to size information, True Fit from Boston has discovered an intriguing niche in the online fashion industry. More accurate fit and size information is required in the market as clothes retail shifts online and consumer access to try on shoes and garments in-store decreases. True Fit offers a variety of data services to the industry to enhance the customer experience and lower the number of returned items to ecommerce operators by gathering data from designers, its registered users, and consumer surveys.

Similar to Populus, the business serves as an aggregator of data that it gathers from various industry stakeholders and provides value through analysis and presentation. The company has raised $97 million in capital since 2010, and it is forging a strongly defendable position in a quickly expanding sector.

  1. WiFi marketing insights

British-based Purple has figured out a way to make money off the information customers give when they connect to public WiFi networks in hotels, stores, and other commercial establishments. Dates of birth and personal hobbies, as well as frequent visits and transit between places, may all be included in the information they collect via their login site.

Similar to Populus, the business serves as an aggregator of data that it gathers from various industry stakeholders and provides value through analysis and presentation. The company has raised $97 million in capital since 2010, and it is forging a strongly defendable position in a quickly expanding sector.

  1. WiFi marketing insights

British-based Purple has figured out a way to make money off the information customers give when they connect to public WiFi networks in hotels, stores, and other commercial establishments. Dates of birth and personal hobbies, as well as frequent visits and transit between places, may all be included in the information they collect via their login site.

Their value proposition to the location operators is on providing more individualized social media marketing campaigns and, in their own words, “transforming your guest WiFi network into a revenue-generating tool for your business”. Pizza Express in the UK, one of their clients, has more than 1024 WiFi access points deployed throughout its 470 locations and uses the information gathered by Purple to encourage downloads of its Smartphone app.

  1. Statistics for everyone else

Last but not least, anyone looking for statistical data online is likely to be familiar with the German company Statista. More than 1.5 million users have enrolled since 2007, and the company now receives up to 12 million visitors per month. Statista has developed a successful subscription-based business model for customers who desire low cost access to data across a wide range of sectors and technologies by working with data suppliers and gathering open information from the web. They show the value of aggregation and presentation by repackaging primarily freely available material into user- and search engine-friendly bits.

Make your proofs of idea plain and sturdy, not complicated and fragile. Analytics has many more promising ideas than are actually useful. Often, the difference is not readily apparent until businesses attempt to bring proofs of concept into reality. An attractive enhancement of a social media marketing was the winner of an internal hackathon organized by a sizable insurance, but the insurer later abandoned the concept since it appeared to need expensive adjustments to the underlying infrastructure. This kind of stifling of outstanding ideas can be depressing for organizations.

A better strategy is to design proofs of concept whose viability in production is a key component. Building something that is industrial quality yet trivially basic at first and then raising the level is an excellent strategy. For instance, a data products company started by implementing an incredibly simple process that worked end-to-end: a small dataset flowed correctly from source systems, through a simple model, and was then transmitted to end users. This was done in order to implement new risk models on a large, distributed computing system. Once that was established, the company was able to enhance each element on its own, resulting in larger data volumes, more complex models, and faster runtime.

Conclusion

Bigger data sets do not automatically equate to market success, which is one of the most important lessons to be gained from these businesses. Building a viable business model from which spin-off products and services may be introduced can be accomplished much more successfully by coming up with innovative methods to reuse niche data and present it in ways that satisfy market needs.

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