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3 Powerful Ways Big Data is Helping Developers at Start-Ups Disrupt Markets

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3 Powerful Ways Big Data is Helping Developers at Start-Ups Disrupt Markets

How sophisticated monitoring, machine learning, and information gathering are helping startups get on a more equal playing field with established businesses.

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Start-ups have a ridiculously high mortality rate. Some studies indicate that as few as 48.8% of new companies survive their first five years. Bold entrepreneurship deserves, and in many cases, requires the absolute best information on their market, customer engagement and competitor performance.

Big Data has played a significant role in how startups are becoming leaner and more effective at creating cost-effective solutions that can completely disrupt an industry. Uber, Netflix and Amazon are just a few examples of companies that launched with the sole-focus of changing how the industry works, instead of just becoming another competitor.

Monitoring Brand Mentions and Public Reception of New Initiatives

In the early stages of starting up, it’s virtually impossible to understand how the broader market will react to your new idea or concept. Why? It’s completely new! This makes monitoring the initial reaction of the market to your product or service incredibly important.

During the roll-out stage, cast as big of a net as possible to try and find out how potential customers are reacting. Amazon is infamous for this. Every change to their site or platform involves a team of data experts analyzing how their user interactions are changing and evolving in response.

For start-ups looking to revolutionize a space, it’s unlikely that they’ll enjoy the same budget, or technical experience that Amazon has. To get around this, many companies take advantage of “Big Data as a Service” providers to collect macro market data.

Internally, tools like Google Analytics and MOZ can help cloud-based software start-ups to analyze the customer journey on their platform. The metaphor of watching a mouse make its way through a maze, dissecting its reaction at every twist and turn, is apt.

Some of these big data solutions are too sophisticated for brands to develop in-house. This has created a robust market for software outsourcing. According to Brandon Gaille, U.S companies spent nearly $350 billion on IT and software outsourcing in 2013. 

Real-time insights, based on both online discussion (forums, customer review sites, social media, etc.) and internal customer interaction is key to improving the odds of start-up success. Of course, the insights from this data is only as effective as the decision-making process of the entrepreneurs that receive it.

Machine Learning to Fine-Tune Software Customizations

Big Data, combined with the incredible power of machine learning has been a recipe for success in many start-ups. Look at Pandora and Netflix. These massive disruptors in their space accomplished market success by creating a user experience that was unique to the individual.

Instead of relying solely on voluntary customer data (i.e. surveys and manual software configuration by the user), they dug deeper to get involuntary customer data. Every mouse click, tap and query was a new data point that could be fed into the technical beast.

The up-front investment in experts to create algorithms based on software analytics is expensive. But, the long-term cost savings and improve customer experience can be game changing. The user feels like they have someone personalizing the service to their needs (many of which they didn’t even realize they had). The start-up sits back and optimizes the machine, which leverages a single technical investment to create millions of amazing experiences.

Understanding Human Logistics to Better Create Systems to Meet the Needs of the Customer, Without Increasing Overhead

Online interactions are just one side of the coin. Uber, for example, has to deal with the human logistics of transporting people from one end of town to another. Big Data on human movement around populated areas might be available, but it certainly wouldn’t be up-to-the-minute data.

Uber solved their need for human data by pushing a mobile app to their customers. The way that their customers interacted with them became the tool. Virtually every mobile device with the Uber app installed sends real-time location data to Uber’s big data analysts. And yes, this is occurring even when the app is “closed”.

Real-time location and travel information gave Uber an incredible edge over traditional public transit and taxi services. Start-ups are finding increasingly ingenious ways to gather real-time information on offline human behavior.

This translates to smaller firms completely unraveling the monopoly that larger companies hold on an industry. Big data is key to leveling the playing field, but it is only as effective as the methods used to gather and analyze the information. And the decisions made based on those insights are where the competitive gains are made.

Bias comes in a variety of forms, all of them potentially damaging to the efficacy of your ML algorithm. Our Chief Data Scientist discusses the source of most headlines about AI failures here.

big data ,start-ups ,machine learning ,communications

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