When you think of marketing teams that use big data to generate big results, who do you think of?
Spotify and the wild success of their personalized playlists?
Or perhaps Netflix—the video streaming service known for its spot-on video recommendations?
Whoever comes to mind first, there’s no denying that there are some compelling case studies out there for big data. And marketers are paying attention. In fact, one 2019 study found that 89% of corporate marketers said they already use data to make strategic decisions. And 66% are increasing their data budgets.
So, should you join them? And how exactly should you use big data to drive real ROI? Read on for five use cases…
Wait…what exactly is big data anyway?
Before we get into the use cases, though, let’s get on the same page. What do we mean when we say big data?
The dictionary definition is “extremely large data setsthat may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.”
In more concrete terms, we think about these large data sets of customer data as a window into customer needs. A way, for example, to understand that Customer A wants to read more articles about retiring in Panama and Customer B is going to skip over Panama and go straight for the eBook on how to retire on a budget in the South of France.
5 big data use cases that drive real business results
So, big data helps us understand customers…but what does that mean in tactical terms? How does it impact our day-to-day as marketers? What use cases, exactly, does data support? Here are five we’ve seen generate big client results:
The number one use case for customer data is the one that companies like Netflix and Spotify are knocking out of the park: 1:1 personalization.
86% of customers say personalization plays a key role in purchase decisions. 45% are more likely to shop on a site that offers personalized recommendations. And 56% say they’re more likely to return to personalized sites.
Those are pretty compelling stats—and every single one of them is only possible because of big data.
If Netflix doesn’t know what you watched last week, they can’t recommend similar things to you this week. If Spotify doesn’t have a handle on your musical preferences, no more personalized playlists. If Amazon doesn’t have the data to know that customers who love tarot cards also tend to buy x brand of candles, they can’t recommend them.
To personalize at an industry-leading level, marketing teams use big data to generate insights about customers and personalize for them not just as groups or segments, but as individuals.
Spend your budget more effectively
When it comes to marketing, the less you know, the more you spend. And the better you know your customers, the better you can target niche groups with the exact offers and messages likely to resonate with them.
What does this mean in practical terms? Instead of spending $10 per prospect on a list of 200,000 people who may or may not be interested in your products, good customer data and insights let you narrow the list to those most likely to buy. So maybe you’re targeting 20,000 instead of 200,000, but you’re converting 50% instead of .5%. Which means much lower acquisition costs.
Lytics’ client The Economist is a great example of this strategy. When they used machine-learning predictions to target people highly likely to subscribe, acquisition costs dropped by 80%. Not to mention that digital subscriptions grew by 300%.
With a 1% price hike leading to an average of 8.7% more operating profits, using big data to analyze pricing is becoming more popular and more valuable by the day. According to McKinsey, 30% of companies are getting pricing wrong—and leaving significant revenue on the table. Also according to McKinsey, the answer is automated analysis of big data that tells you what your target customers are willing to spend.
Improve omni-channel customer experiences
These days, customers use an average of six different touch points before making a purchase. The more channels they engage with, the more likely they are to be a high-value customer. And 90% of those consumers say they expect consistency across those channels.
Those stats make it pretty clear that omni-channel marketing is making or breaking marketing success these days. And omni-channel marketing is only possible with big data.
Without it, you can make your messaging consistent. You can use the same capitalization rules on your headlines. You can make sure your brand colors are right. But you can’t know that Customer X already visited Channels Y and Z and purchased Product A. You can’t know that Customer X has clicked on Campaign B on three different channels (and thus you might want to serve up Campaign B on channel four). You can’t know that Customer X has an affinity for your new satchel line and would respond best to ads and messages about said satchels.
And without that information, you’re not really omni-channel. Customers expect that your customer service team already knows they bought a new computer on your website and pinged your team on Twitter. They expect that you understand that they’re a PC user, not a Mac. When they visit your website after checking out that satchel on Facebook, they’re going to be delighted if the satchel is the first thing they see.
They expect, in other words, that you are thoughtfully collecting their customer data and seamlessly integrating the understanding it gives you across channels.
Build your messaging around real customer needs
There are a lot of failed products, apps, and campaigns out there. And one thing almost all of them have in common? They didn’t check in with what customers wanted.
Tinder for dogs? An app that tells you when your friends take a pee break? A “perfect body” ad that just shows tall, thin, mostly white women? Customers said no, no, and no again.
Every single one of these faux pas could have been avoided with data. Quantitative data on customer behaviors and demographics. Qualitative data on customer opinions. Data on what products and features already do and don’t resonate with customers. Those ideas weren’t just a failure of creative. They were a failure to consult the data.
Getting your marketing team started with big data
Better omni-channel marketing, more spot-on products, optimal pricing, lower acquisition costs, and 1:1 personalization are pretty clear wins for any marketing team. The question now is where do we start? If your team isn’t already harnessing the power of data, how do you go from zero to hero?