Our previous two articles discussed The Benefits of Business Intelligence and How to Become a Data-Driven Business: Dell Data Maturity Model. We decided to continue this theme for another week because we had a few people reach out to us and ask us the same question regarding becoming a data-driven business: Is the investment in time, equipment, and training really worth it?
It turns out that it is. According to a 2014 report by McKinsey, “Intensive users of customer analytics are 23 times more likely to clearly outperform their competitors in terms of new customer acquisition than non-intensive users, and nine times more likely to surpass them in customer loyalty. Our survey results also show that the likelihood of achieving above-average profitability is almost 19 times higher for customer-analytics champions as for laggards”.
Just in case that study isn’t enough to convince you, we’ve put together four inspiring success stories of companies who have made intelligent use of data in order to gain a competitive advantage.
You’ve probably read numerous articles about Netflix’s clever use of data from user habits for their movie recommendation system. However, Netflix goes far beyond that in terms of harnessing the power of data.
Netflix executives have publicly stated that the success of their 2013 hit “House of Cards”, was a result of a series of critical decisions based on data analytics. Netflix algorithms showed that the series was likely to be popular with Netflix subscribers based on: the subject matter, the success of the original British television series among a similar demographic, and the appeal of Spacey and Fincher. According to New York Times, company executives offered actor Kevin Spacey and director David Fincher contracts without even seeing a pilot.
To this day, Netflix continues to make use of data analytics when making programming decisions. This puts them at a tremendous advantage over competitors who relied on “gut feel”. Consequently, they are able to command the highest price and the largest market share in the streaming business.
Uber has made creative use of data analytics in solving perhaps their biggest problem– matching supply with demand. Uber has become largely successful because it simultaneously offers consumers the ability to quickly get an inexpensive ride while offering drivers the opportunity to make decent money and be their own boss. But this system will quickly break down if the app can’t efficiently match buyer and seller.
For example, imagine a city that is divided into two parts and connected by a bridge. Then imagine that one day at 5 pm, there are 10 Uber users on the east side of the bridge who need a ride, and there are 10 Uber drivers who happen to be on the west side of the bridge. The bridge is packed with traffic so the drivers are unable to get to the riders. This would be a terrible experience. The drivers would quit driving because they can’t make money, and the riders would uninstall the app because they can’t get a ride.
To address this issue, Uber has established an automated analytics system. This system collects data on how many requests are coming from each geographic location at a given time, and then creates a “temperature map” that drivers can use to find where the riders are likely to be. This will keep them from being on the wrong side of the bridge in the aforementioned example.
Additionally, Uber uses data to “secretly” keep tabs on their drivers. They can tell, for example, if their drivers are speeding or working for a competitor. This raises the question of whether data can and should be used for controversial purposes, however that is a discussion for another time.
Despite its potential for somewhat nefarious purposes, it’s safe to say that Uber has made effective use of data that enables them to provide their drivers and riders with a positive experience.
Most people think that Coca-Cola is the world’s best selling soft drink because of their secret formula. But the reality is that the Coca-Cola company has become so successful because of several smart business practices, one of which is that they make very effective use of data.
They use data for a variety of decisions, including new product creation, supply chain management, and most importantly, marketing analytics.
Business leaders have always struggled with deciding where to spend their advertising dollars. Most famously, U.S. department store magnate John Wanamaker is alleged to have said “Half the money I spend on advertising is wasted, and the trouble is I don’t know which half.” This is quite scary for Coke, who spends around $4 Billion a year on advertising.
In order to avoid wasting a large percentage of such a big spend, Coca-Cola invests in a complex AI-based system that analyzes consumer trends and behaviors across the 200+ countries that they serve. According to a recent Harvard article, they track every time someone either mentions or uploads a picture of one of their products. They then serve the consumer specific ads based on what they posted, which makes the consumer far more likely to click. Coca-Cola claims that ads targeted in this manner have a four times better likelihood of being clicked than non-targeted ads.
Furthermore, Coca-Cola has over 108 million Facebook fans, over 3.4 million Twitter followers, and over 2.8 million Instagram followers. They mine the data posted from all these “fans” in order to better understand consumer trends and tastes, which they use in both advertising and product development.
Everyone knows that Google is perhaps the best example of successful data navigation in recent history. They are able to scour through 50 billion web pages in order to find you exactly the subject that you are looking for within a fraction of a second. But they also apply a data-driven approach to other areas of their business, even Human Resources.
With data collection and analysis, Google can understand their workforce, manage people more effectively, and retain productive employees. For example, Google’s human analytics team recently dug into performance reviews, feedback surveys and other reports in order to assess their employees’ feelings on Google’s what makes a good boss. The results were counterintuitive – for example, they discovered that technical expertise was the least important skill for engineering managers.
They also have applied data science to recruiting, performance reviews, and even employee well-being. The decision to extend paid maternity leave from 12 weeks to 18 weeks, for example, reduced their employees’ postpartum leave rates by 50%.
These four specific examples of how Netflix, Uber, Coca-Cola, and Google have used their data to build a competitive edge should hopefully inspire you to revolutionize your own company.