5 data-driven decision-making examples

You would not be seeking investors or chasing revenue if someone paid you $1 every time someone said, ‘Data is the new oil.’ In fact, this phrase is often misunderstood, leading to questions like — How do I use data and find relevant data-driven decision-making examples? 

No matter how over exploited the term — data-driven decision-making becomes, you still need data for every business decision. Using data improves your odds of success. According to Mckinsey, data-driven organizations are: 

  • 23 times more likely to acquire customers
  • 6 times more likely to retain those customers and; 
  • 19 times more likely to be profitable. 

One must recognize the benefits of data-driven decision-making after looking at these numbers. But what does it mean to make data-driven decisions? In this article, let us look at a few examples of how businesses are using data to their benefit. 

What is data-driven decision-making (DDDM)? 

Never let this “jargony” term confuse you. Making data-driven decisions is about making business decisions based on real insights instead of hypotheses or ‘gut feeling.’ 

If you are a fan of market research and customer insights, you know how easy it is to make important marketing, product, positioning, and pricing decisions based on data.

Data-driven decision-making involves collecting, analyzing, and interpreting data that assists in making a choice while running a business.

Benefits of data-driven decision-making.

As illustrated above, McKinsey’s study on data-driven decision-making gives a good idea of the advantages of using data for business. According to common business knowledge, here are some evident advantages of data-driven decision-making: 

  • Register a Higher ROI: Using relevant data insights helps increase sales revenue and cut costs. According to a report, businesses using data boost their profitability by at least 8%
  • Turn Proactive: Success in any business is all about agility and the ability to adapt. Become proactive in your approach. Data helps gather and derive insights to help you identify patterns, trends, and market opportunities to help you make decisions faster and better than your competitors. You get to make early moves, leading to success.  
  • Enhance Customer Experience: Listening to the voice of customer help create delightful customer experiences. This helps you improve your odds of achieving PMF, build a happy community, and prevent chances of dissatisfaction. 
  • Stay Ahead of Competitors:  Many reports have revealed that data helps leverage opportunities early on. 62% of retailers share that using data, information, and analytics helped them achieve a competitive edge. Closely monitor your competitors’ online reviews, campaigns, tactics, and actions. Then analyze all the information to build counter strategies to help you stay ahead. 

Data-driven decision-making examples.

Man and woman discuss data-driven decision-making at a whiteboard with graphs on it.

Let’s get into some examples of companies using data to make business decisions.

Red Roof Inn — Using data to optimize marketing & boost revenues.   

Red Roof Inn is a hotel chain with properties near busy airports. The chain of hotels realized that they could use flight cancellation data to increase bookings and grow their revenues. They discovered the average flight cancellation rates were around 3% (based on publically available data) and 90,000 passengers got stranded daily.

So, they utilized this data to seize the opportunity to optimize their marketing campaigns. They combined weather reports and flight cancellation data to identify and grab marketing opportunities. Their marketing team targeted mobile users in the vicinity whenever there was a report of bad weather (leading to flight cancellation). Their check-ins increased by 10%, showcasing the value of using data for decision-making.  

Netflix – Using data to create viral content & web series. 

Netflix is famous for leveraging data to enhance the user experience with a preference-centric recommendation engine or user interface. It has also used data and predictive analytics to drive logical decisions about fresh content. 

Netflix successfully harnessed usage data on its platform to make winning bets on hits like House of Cards, Arrested Development, and more. They analyzed over 30 million plays, 4 million subscriber ratings, and 3 million searches before deciding to produce these successful series. 

They analyzed customer behavior and interactions like watch time, location, interests, etc., to increase time on the platform by showing similar recommendations over time. 

Google – Utilizing people analytics for a better workplace.  

Thanks to its search dominance and monopoly, Google has all sorts of data. It makes the best data-driven decisions. One such data-driven decision-making example is how they improved their internal hierarchies. 

Google’s top management wondered if managerial positions mattered in their ‘grand scheme of things.’ So, Google’s data scientists started analyzing qualitative data like performance reviews and surveys about the managers within an organization and plotted the data on a graph. As a data-driven decision-making exercise, this process turned out to be quite efficient.

They analyzed the dataset by running regressions further to unearth an insight — there was a massive difference in terms of productivity, employee happiness, and employee turnover between the best and worst managers. Good managers were helping Google make a better place to work, and they used the data to define ‘what makes a good manager at Google.’ 

The exercise helped them discover the top 8 behaviors that make a great manager at Google (as well as three qualities that don’t). Further, they used data to improve the training material, enhancing their Great Manager Award metrics and implemented a feedback survey to be conducted twice a year. 

Coca-Cola – Using customer data to achieve marketing efficiency. 

Coca-Cola spends heavily on marketing, ads, and social media to target Coke lovers around the world. They make data-driven decisions regarding who to target online using big data analytics, image recognition, and AI. 

They analyze their audience based on the photos they share on social media to find who is mentioning them online or sharing their drink’s picture on social. Then they analyzed the most common locations of the posts and the customer sentiment behind them. 

The info was used to create personalized and hyper-targeted advertisements, which led to a 4x increase in clickthrough rates. 

Uber – Providing faster rides using data. 

Uber is known for its ride-hailing algorithm that promises real-time information about the drivers in the vicinity. They have been using data to bridge the demand-supply gap using predictive analytics and big data. 

The company stores and continuously analyzes historical data about the most popular locations, number of ride requests, trips taken by a driver, as well as the time and day of the different trips. 

Uber’s approach to data-driven decision-making has helped them cater to customer demands in real-time, solve the supply crunch and command a premium in the form of surge pricing at most popular times and locations. 

Such kind of automated data-driven decision-making has helped companies unearth opportunities that they have otherwise ignored without data. 

Unlock growth opportunities using data with GapScout. 

Decisions based on relevant insights and data help businesses grow smarter, save costs, become more efficient, and turn profitable faster. It is critical that you make use of data to drive your decision-making in everyday affairs as well as strategic pivots. Use GapScout to gather insights based on real-world customer feedback data and unlock growth opportunities today. 

GapScout scans online reviews across all platforms and derives insights to help you save time, stay informed, and drive decision-making across every aspect of your business affairs. 

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Anmol Sachdeva
Anmol Sachdeva
Anmol is an independent content marketing consultant who loves to write actionable pieces on small business growth, marketing automation, market research, and growth as a solopreneur. Reach out on Twitter.

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