7 Examples of data-driven marketing that inspires and fuels fast growth
Data offers a tremendous opportunity to solve challenges and gain a deeper understanding of ideal customers. The data on its own isn’t enough. However – we need the human element to interpret it and use it effectively.
These examples show how data can be used to learn more about the customer and support human assumptions and insights, ultimately creating compelling messaging.
Looking past the numbers
Data is an excellent tool for finding detailed information about customer likes, dislikes, behavioral patterns, purchasing factors, etc. But the data isn’t enough to fine-tune messaging to the customer.
Responses customers have towards marketing messaging go deeper than obvious factors. Even choosing between the mundane, everyday products takes a lot of thought – conscious and unconscious – and these factors are remarkably fluid. How the customer’s day went, how well they slept, their cash flow, the background music, and more can play a role in how they respond to a particular message.
Data can’t capture all of this. Numbers and algorithms work within a handful of variables. Those variables have a lot of weight in the overall decisions, but their context can get lost in the noise.
Customers may also be suspicious of algorithms. More and more, customers are aware of and fearful of their privacy and personal data, so there’s a line between marketing that’s intrusive and marketing that’s personalized.
Humans provide the context and ask the right questions to glean insights from the data. Instead of being data-driven, we can be data-informed, using evidence to support theories and assumptions – not the other way around.
Inspirational examples of data-driven marketing
1. Weather data to personalized offers
The majority of customers prefer ads that are tailored to them, even if the brand is unknown. They’re more likely to click on an ad if it feels personal, regardless of the brand. This not only engages customers but it delivers a more relevant marketing message.
Very.co.uk tested this with a homepage banner informed by weather data. The company used customer information to recommend products based on the local weather, such as a rainy or cold day, complete with the visitor’s first name.
Sports teams are also using this tactic to fill seats. The San Francisco Giants use weather data to optimize ticket sales, and the Philadelphia 76ers use weather data and machine learning to improve ticket sales and increase revenue from sponsorships.
2. Retargeting website visitors based on behavior
Hubspot uses visitor data to retarget customers with arguments to overcome major sales objections
3. Data to create new products
Instincts, opinions, and assumptions alone will likely result in a disaster when developing products and services. However, companies can use data-driven insights to guide them to make logical decisions and achieve higher success.
An innovative example of this is Netflix. The streaming platform used the power of customer data to run predictive analyses and learn what the customers would be interested in watching. This involved analyzing over 30 million “plays” daily and over 4 million subscriber ratings and searches.
This is how the company predicted the development of acclaimed series like “House of Cards” and “Arrested Development.” Human creativity was behind these projects, of course, but betting on their success relied on numbers to support intuition.
4. Data to serve the right ads to the right audience
Companies today invest a lot in digital ads. In fact, according to Rakuten, marketers spent about $283 billion on digital ads in 2018, but they estimated that around 26% of that budget was wasted on the wrong campaigns or channels.
With data and analytics, marketing teams can serve the right ads to the right audience to maximize ROI. One excellent example is Coca-Cola, the soft drink company. The brand has huge followings on Facebook and Instagram, offering a wealth of customer data to analyze.
Using these tools, the company targeted customers based on their shared photos. It revealed insights into how they consume products, where they’re from, and why they’re tagging the brand. Personalized ads based on this data had a four times greater click-through rate than other methods.
5. Optimizing products or services with customer data
If you’ve used an UBER ride service, you may imagine there are several drivers in the area ready to answer ride requests. This wouldn’t be a very cost-effective method of providing their service.
UBER felt the same, which is why the company used predictive analytics to assess historical data and key metrics involving the number of ride requests and trips fulfilled in different parts of the city and the time and day. These insights reveal areas with a supply crunch, allowing UBER to move drivers to areas where requests are expected to be in demand in advance.
Humans can inform these insights by tracking events, weather patterns, and other details that may increase the likelihood of a passenger requesting a ride. For example, ride requests may go up if it’s exceptionally cold or hot outside, if there’s an event going on, or if the area receives a lot of tourists at that particular time. Historical data can then back these assumptions.
6. Enhance both employee and customer experiences
Banks face a lot of competition. One of the premier banks in Singapore, DBS, is facing fierce competition among rising fintech competitors, and it needed a solution to bring in more customers.
With over 4.4 billion SGD invested in technology, DBS included artificial intelligence and data analytics to provide hyper-personalized insights to customers and make recommendations that improve their financial decisions.
Some of the features and capabilities include investment proposals for financial products. Stock recommendations based on portfolio information, notifications for unusual transactions, and notifications for favorable rates.
On top of that, the bank trained over 16,000 employees in big data and data analytics to transform it into a data-driven organization with human insights at the helm. Employees in the organization can use data to address challenges and identify opportunities for more intuitive customer experiences.
7. Demographic data to address market gaps and opportunities
Demographic data, such as a customer’s age, gender, income, job, and location, can provide much information about their interests and needs. These insights can drive a greater return from the campaign or, in some cases, may identify gaps that we haven’t considered.
Examples of data-driven marketing to learn should include DirectTV. The satellite television provider used various data points to identify a specific market to target – people who have recently moved to a new location. The company discovered that when people move to a new location, they’re more likely to try out a new service.
Using USPS data, DirectTV targeted homeowners who have recently applied for a change of address. Then, it created a personalized version of the company’s homepage tailored to those visitors. The personalized version vastly outperformed the standard version.
Transform your company with these examples of data-driven marketing
Making data-driven decisions doesn’t mean abandoning the humans at the heart of it. The shift to a data-driven organization should involve employees and a culture that focuses on asking the right questions and supporting opinions and assumptions with the right data.