The key differentiator between two startups is pace. Things need to be done at a faster pace for startups to be competitive against large companies. And, in order to react to market conditions and changing consumer trends, startups today rely heavily on data analytics. The power of being able to gather, identify, understand and execute upon patterns of data is critical for long-term success of companies as well as for advancement of humanity.
Any organization can leverage the exponential data growth but size is on the side of smaller businesses that are perfectly suited to act on data-derived insights with speed and efficiency, unlike large organizations that are often less nimble and hindered by clunky, legacy IT infrastructure. All that’s required is somebody in the business that understands two key fundamentals: data analytics and data science.
For example, for a startup organization, product marketing act as a growth catalyst in establishing brand value in the market, which is very costly and usually eats up a huge part of the budget.
However, while a business can be built on a combination of inspiration and perspiration, being able to manage analyses and interpret data requires a very specific skill set that will actually enable innovation and drive it forward. From predicting and reducing churn to winning business from new and existing customers, the opportunities are endless.
Data Analytics can help startups in identifying and reaching out the right target market for launching product(s) and providing better return on the marketing investments. Moreover, it can also help in understanding the customer needs and leveraging their requirements for designing or updating offerings.
Advertising and marketing without data based insight are akin to trying to hit a target in an unfamiliar dark room with only 2 to 3 bullets in your gun. While Big Data science is evolving, and is not fully precise, it does tell you the direction in which to shoot, so that your probability of hitting the target is higher.
Whether you are looking for funding, thinking about the best way to deploy your latest round of investment or a scale up looking to fuel growth, here’s five quick ways analytics and data science can help you:
Evidence-based decision making: One of the rarest commodities when a business is in the growth stages is time. Decisions are taken in days, sometimes hours that in more established organizations would take months. Young businesses especially spend most of their early stage time probing the market and looking for the right product offering to execute upon. Unlike an established company, one mistake can cost its future so having a data scientist on board is the key to being able to gather and analyses data from multiple channels to mitigate risk and improve decision making.
Test your decisions: Making decisions and implementing change is only half of the battle; it’s vital to know how those changes affect the company. A data scientist can measure key metrics related to important changes and quantify their success (or lack thereof) so that learnings are made and substantiated when it comes to playing back results to investors and moving the business forward.
Perfecting the target audience: Everything from social media profiles to website visitor reports contains data which can help a startup pinpoint its target audience – and therefore target them more effectively. Even if it has gone as far as roughly identifying its demographics, a data scientist can identify key groups with laser precision through careful analysis of disparate data sources. This in-depth knowledge can help tailor products and services to key customer groups.
Making use of the information: Data has to be at the fingertips of every decision-maker, which are usually most people in the business at its early-stage. This is reflected in the data science and analytics space right now with predictive modelling and machine learning both attracting huge amounts of interest – a sentiment underlined by the recent acquisitions of DeepMind. It is not hard to see why when this particular type of data management enables real-time responsiveness when it comes to translating the raw data into insights, which can be transformed into actionable applications to propel business growth.