Big data marketing can help even the smallest companies succeed
Most people think it’s them who decide which Netflix movie to watch or which Kindle e-book to buy. After all, they’ve selected the product and paid for it, right? But what they fail to grasp is how Netflix and Amazon are directing their shopping behaviour. Algorithms are a key part of this, as they predict customers’ needs and suggest products using data about people’s online activities, known as big data. In fact, 80 per cent of the content we watch on Netflix is influenced by its recommendation system. At the same time, this system increases Amazon’s revenue by up to 30 per cent. Big data is used by many other companies as well. Banks use it to spot financial fraud and hospitals to identify high-risk patients. And of course marketers use big data to sell more products.
Consumers might not be too enthusiastic about of this, though. They’re kind of fed up with the over five trillion ads served each year. But big data marketing, however, – analysing data to improve marketing activities – isn’t about more ads. It’s about serving the right ads to the right people at the right time. It’s about answering questions regarding what type of message resonates with customers, which landing page is the most efficient, or which social media platform should the company use to reach their target audience. Without big data, companies operate on unproven assumptions, which is never a good idea.
That’s why businesses use big data marketing to solve puzzling issues. And the good thing is that there’s no lack of data. According to estimates, “By 2020, 1.7 megabytes of data will be created every second, for every person on earth”. Businesses and startups that still ignore such vast quantities of data – and its power – would be wise to catch a wake up. Getting to know your customers has never been easier, and it’s doable even for cash-strapped startups.
Collecting and analysing data isn’t as difficult as it seems
The first step in that process is to gather marketing-relevant data from several sources. For example, always ask for customers’ e-mail, name, or address when you communicate with them. Once you have large quantities of this type of information, you can run email marketing campaigns and segment customers based on how they react. Or data might show that people from one area buy more products compared to people from other geographical locations. In the next phase, you can track down and analyse the causes.
The second source of marketing data is the company’s website. Free tools such as Google Analytics can analyse web traffic and discover opportunities and problems. For example, it can show what led customers to your website. Was it Facebook, Twitter, Google, or some other source? This can guide the decision on where to invest money to attract new visitors.
And in the case of new companies lacking their own data, marketers should analyse competitors and industry reports. These sources can provide a wealth of information to work with until the company gets its own customer data. Also, savvy marketers harness the power of social media as it provides almost limitless amounts of data. For example, fan pages on Facebook can show the demographics of followers, their age, income level, and so much more. But once you collect data, it’s important to analyse it properly and avoid common pitfalls.
Costly mistakes to avoid and the right questions to ask
Dealing with big data for the first time can be overwhelming. The solution is to go one step at a time and focus on clear objectives. Max Galka, the founder of Metrocosm, breaks down some of the basic questions that should guide big data analysis. “A marketing campaign is the product of countless strategic decisions. Which marketing channels should I use? Who should I target? What message should I communicate? How should I measure its effectiveness?” says Galka. And as marketers address these questions one by one, they’ll have to dig even deeper as new insights lead to new puzzles. Once the work is done, a set of data-based recommendations is formed and used to guide marketing campaigns. And judging by many examples, this approach can produce impressive results.
Creative big data campaigns
One of the most memorable data-driven marketing campaigns involved weather forecasts and hair-care products. While conducting customer research for Pantene, the Leo Burnett advertising agency discovered that women use different hair products during ‘bad hair days’. Soon after, a new marketing campaign was launched. Using the Weather Company app, the agency pushed ads to women in regions affected by extreme weather. Consumers were urged to protect their hair by buying Pantene hair products in Walgreens stores. As a result, the sales of Pantene products in these regions increased by 24 per cent.
Another example is Coca Cola. Justin De Graaf, the director of data strategy at the beverage giant, explains that his team gathers customer data from social media, phones, emails, and many other sources. They’re then able to “create more relevant content for different audiences. We want to focus on creating advertising content that speaks differently to different audiences,” says De Graaf.
The future of big data marketing
Every successful company in the world has one thing in common – it know its customers. And the easiest way to know them is by analysing data. Both huge corporations and small startups can benefit from this approach. There’s no need to operate in the dark and rely on assumptions when so much data is produced each second. Not sure who your customers are and who to advertise to? Analyse the demographics on your Facebook page and website. Instead of letting your gut feel determine the marketing strategy for your business, turn to big data for the answer. Unsure what type of content to advertise? Analyse existing traffic and find what topics your customers like. The opportunities are endless. And with the growing power of AI and machine learning, big data marketing will only grow stronger.
This article was originally published in 2019 on the Richard van Hooijdonk’s blog.