By 2020, experts estimate that 1.7 megabytes of data will be created every second for every person on earth. With recent reports claiming that 80% of executives have seen returns on their big data investments, it’s time to turn our attention to how big data can positively influence our content marketing strategies.
But what exactly is big data? The term refers to large data sets that can be analysed to reveal trends, patterns and associations relating to human behaviour. When leveraged intelligently, marketers can handpick content diamonds out of the data coal heap to drive engagement and customer loyalty.
To sift through this “data explosion”, six million developers are currently working on big data and advanced analytics. Marketers collaborate with these data scientists and use this information to catalogue customers’ media preferences, interests, bounce rates, purchasing history and habits in the hopes of predicting their next transaction. However, for companies to gain full competitive advantage they need to ask not what will trigger the next purchase, but what value big data analysis can create for customers.
There is, sadly, no magic formula for creating perfect content, but big data analytics can arm content marketers with unique tools to discover insight into their customer base, answering questions that we never thought possible. How are companies integrating big data into their content marketing strategies? Let’s take a look at how five top companies are utilising big data to create outstanding customer experiences:
Coffeehouse giant Starbucks knows exactly how you like your coffee through raw data pulled from barista industry reports and home-consumption research.
Big data is quickly revolutionizing how companies achieve higher customer response rates and deeper customer insights. A recent study by Forrester claims that 44% of B2C marketers are using big data and analytics to improve responsiveness while 36% are actively using analytics and data mining that gain greater insights to plan more relationship-driven strategies. These statistics are set to increase over the next couple of years as many SMEs take the big data leap forward.
Starbucks has created over 87,000 unique drink combinations to cater to customers’ ultra-specific palates, but it doesn’t stop there. They also use big data analysis to tailor their email and app content too. According to Forbes, the Starbucks’ mobile app is booming, with reports that Starbucks Rewards has over 13 million active members, marking a 16% increase from last year’s enrolment. Starbucks CEO Kevin Johnson calls it their ‘digital flywheel’ when commenting on their banner quarter in Jan 2017.
Anticipating a further rise in users, Starbucks analyses social media data, customer experience data, purchase history, click-through rates and conversion data to produce customised content for discounts and rewards. Starbucks are even using big data to analyse the effect of weather on order patterns and customer footfall. Using this information, they can send tailored content to encourage loyal members to step in from the cold and warm up with a frothy cinnamon frosted cappuccino on a brisk winter’s day.
By 2020, the International Data Corporation (IDC) estimates that the number of B2B and B2C internet transactions will reach 450 billion per day. With this stat in mind, ecommerce king, Amazon is digging deep for those raw data diamonds to keep their brand top of mind.
Thanks to big data, Amazon have revolutionised online shopping for their international consumer audience, boosting revenue to nearly US$136 billion in 2016 – up from US$107 billion in 2015. According to DZone, the company tracks over 200 million customer accounts by hosting one billion GB of data on more than 1.4 million servers to increase sales through predictive analytics.
Notice how Amazon recommends products, "Related to Items You Have Viewed”, “Recommendations For You" and "Customers Who Bought This Item Also Bought"? These suggestions are based on big data analysis, aimed at creating an instant connection with potential customers — and it’s working. In fact, Amazon generates 10%-30% additional revenue as a direct response of their accurate recommendation engines.
The results have been so successful that the company has even acquired a patent for ‘Anticipatory Shipping’, allowing their site to ship products even before users have placed an order. Amazon’s content is quickly converting customers along the buying journey. But how? With an algorithm-based system second to none, they’re using powerful data analysis to track how long potential customers stay in a specific purchase stage, including what types of products they eventually purchase or abandon.
The Financial Times (FT) has also been a big supporter of big data. Applying business intelligence throughout their operations, the FT extracts useful customer insights from their data development teams, aiming to push circulation levels to new heights, while increasing competitiveness in an increasingly digital world.
The FT allows users to read up to 8 digital articles per month for free, and if they register their customer details they can receive access to different package deals. Using this raw customer data, FT delivers targeted email campaigns and advertising to appeal to their job titles, age and location. Their data analytic software can also track patterns in reading behaviour helping them to convert casual daily readers to full-time subscribers.
A recent study by Forbes Insight found that big data expertise and department-specific analytics were sufficient to achieve positive customer engagement results. Mass cultural change was also another notable success after pilot programs delivered positive results. And this is exactly what the FT have accomplished after reaching a record 650,000 digital subscribers fuelled by data-rich, up to the second news on Brexit and the US Elections.
But the FT isn’t the only content-driven brand that is utilising the full potential of big data. Newspapers like The Guardian and the New York Times are already investing heavily in digital journalism to create better websites, better advertising campaigns and even better news articles through data-driven storytelling.
New York based company, Next Big Sound (NBS) creates an unlikely bridge between two completely different worlds: music and data. NBS promises ‘Intuition backed by intelligence’ by anticipating what bands from across the globe are about to break with their newest hit.
This platform provides a unique way for audiences to discover new music they love by tracking artists’ trajectory and other raw data. Previously, careers lay in the hands of mercurial music producers who hand picked talent. Now, the future of talented artists isn’t pre-determined by music moguls but instead by the quality of the music itself.
Alex White, Co-Founder and CEO of NBS, claims “There are plenty of artists with minimal online presences who have recorded incredible songs that haven't found their audience,” he comments, “A data-driven music industry is one where artists’ music can find its full audience and be monetized as efficiently as possible.”
By analysing key data from YouTube engagements and hits from Pandora plays to Facebook shares and Twitter followers, NBS is able to predict with a high degree of accuracy album sales and popularity that has never been possible. With 58% of Chief Marketing Officers (CMOs) claiming that SEO and marketing is where big data is having the largest impact, NBS are also looking towards catering for a new category of clients: brands. In particular NBS are helping brands like Pepsi, American Express and Absolut to steer the $1 billion yearly spend on music related marketing and sponsorships through their patented “likelihood of success” algorithm. Not only can they anticipate the next Stevie Wonder, but they can also arm marketers with raw data that will greatly impact every aspect of their content marketing strategy, in both digital and offline channels.
Walmart is the world’s largest retailer with over 20,000 stores located in 28 countries. Part of their success can be attributed to the power of big data analytics that drives Walmart sales and boosts customer engagement across a variety of different sectors.
VP of customer experience engineering at WalmartLabs Lauren Desegur claims, “We’re essentially creating a bridge where we are enhancing the shopping experience through machine learning. We want to make sure there is a seamless experience between what customers do online and what they do in our stores.”
Walmart has big ambitions for big data and are currently in the process of building the world’s biggest private cloud software that will be able to process 2.5 petabytes of data per hour. This will have a huge impact on how Walmart customises user experiences.
In its Bentonville, Arkansas headquarters the company has created a Data Café. This state-of-the-art data hub extracts information from 200 billion rows of transactional data and key insights from economic data, social media, Nielsen data and locational-based data to come up with real-time solutions in a matter of seconds.
Walmart tracks these patterns to find out what works and doesn’t work to drive customer experiences and enhance long-term customer loyalty. Research claims that Customer Value Analytics (CVA) based on big data makes it possible for marketers to deliver consistent omnichannel customer experiences, which ultimately enhance campaign success rates and increase ROI.
Relying on text analysis, synonym mining and machine learning, Walmart tracks user experiences to produce relevant search results for each unique customer. Walmart says adding semantic search to their content strategies has helped increase purchase completion online, resulting in a 10%-15% boost in overall revenue.
Today, big data is helping content marketers address fundamental questions to which answers have previously lain out of reach. However, we are only touching the surface of possibilities when it comes to using big data analysis to create unique customer experiences. Big data is no longer the sole realm of large corporations, as increasing accessibility of big data analysis tools is giving SMEs the power to disrupt industries through the use of mass analytics.
When it comes to marketing, it’s not only about what advanced technology you use to collect and analyse data, it’s also about how you interpret the results to ensure your content hits home with customers.
The key to successful application of big data analysis lies in using the findings to create unique customer value. Only this will allow companies to transform raw data into a sustainable competitive advantage that delights customers with unique shopping experiences carefully tailored to shoppers’ preferences.