Netflix success and data analytics
Netflix is your modern day story of under-dog success.
Around 1998, at a time when DVD rentals were the rage, Blockbuster was the Almighty of DVD-rentals – Boasting of 6,000+ stores, over 87 million members and a over a 27% market share. About the same period, Netflix was a fledgling subscription mail-order DVD not even listed as a Block Buster competitor.
Netflix pioneered the distribution of shiny discs in prepaid mailing pouches and the rest is history. Blockbuster was cut at the knees by this innovative start-up, and ten years in, dissolved under bankruptcy. But Netflix was just getting started.
Today Netflix has over 137 million subscribers, with over a 100 million hours watched daily. In 2018 alone, it made about $15 billion.
Looking back at its lowly origins, one might point to their creative brilliance (in creating mail-order DVDs) as the catalyst for their greatness.
But Netflix’s move into DVD by mail wasn’t based on some random surge of gut feeling. Instead it was an informed product from their ‘obsession’ with data analytics. They developed clever algorithms that diverted consumer attention from the big, shiny blockbusters of the time to lesser known but abundant movie titles – sounds familiar?
This move was a smashing success. And now Netflix has metamorphosed from a mail-order DVD company, to a video streaming mammoth, and to a content creator. But one thing remains constant – Data analytics is central to their vision and strategy.
What does this mean for you and me?
I can’t emphasise this enough: your business success rises and falls on data analytics.
A lot of companies think they have bigger priorities than gleaning insights from their data. I personally think the high failure rate of business start-ups is tied to this very negligence.
A typical start-up needs to know how and where marketing is done, how customers are gained, and how to hire the skills it needs. How about its business model? It needs insights from as much aspects as possible from its business environment. This is all in a bid to have as much competitive advantage as possible.
No one might understand this need for data better than Formula 1 professionals. The uninitiated would think that Formula 1 races are won by robust engines, car aerodynamics or even superb racing skills. But the truth is that the chances of winning or losing a race is decided by, you guessed it, data analytics.
Racing cars are fitted with 120+ sensors that monitor key racing features including temperatures, tire and brake heat, timings, air flow and so on. The data from all these sensors help analysts develop all sorts of visualisations that help predict the likely outcome of cars performing in a race. As such, competitive advantage in every race is data-driven.
In the same way, your business generates a lot of crucial answers about your business which are hidden in your data. Your competitive advantage is hidden in your data.
A McKinsey Global Institute report suggests that businesses that use data analytics in decision making are 23 times more likely to succeed in acquiring customers, 6 times more likely to reduce customer churn, and are 19 times more likely to be profitable (emphasised for the sake of the headline).
Perhaps I’ve really got your attention by now and your next question is:
So how do I start working with data in my business?
- Set clear goals
Goal setting is the prerequisite to data analytics. What are you trying to find out or accomplish in your business? Perhaps which day of the week works for certain products? How many customers respond to your sales calls as opposed to emails? Once your goals are clarified you will find it easier to outline the questions and relevant data to achieve these goals.
2. Identify the right data to work with
When you know what you want to accomplish, you need to identify which data and metrics will be able to help you reach your goals. For example, if you are aiming to check the number of online enquiries that translated in physical store purchases, you may need both website and sales data to investigate.
3. Identify a suitable tool to analyse your data
Now it’s easier to decide whether to hire a data analyst or run your analysis by yourself. This is because there are several tools available which can help you run your own data analysis such as RapidMiner. Analysing your own data will be a great learning opportunity as well as a cost-effective strategy for your business.
4. Make the decision
Good decision making skills are still critical even though data analytics can help pinpoint where to make the best decisions. Being able to make a sound business decision is as important as your ability to interrogate your data for meaningful insights.
In the end, here’s what matters the most: there are so many posts out there singing the praises of data analytics. But it’s important to realise that a lack of clear goals, the right kind of data or data analytics tools, or poor decision skills will undermine the value of your data. Your competitive advantage lies in a perfect balance these four critical areas just mentioned, in your approach to data analytics.