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What do marketers and Big Data have in common? They both have an insatiable desire to know more about their targets.
Big Data is that buzzword that describes the increasing volume of data surrounding every aspect of human life. It refers to the idea that nearly every human action can be quantified and logged in a bank of data that is growing at an unprecedented rate. In fact, there are nearly as many pieces of digital information as there are stars in the universe.
Marketers look into this vat of data bug-eyed and frothing at the mouth.
Why? Because Big Data holds the potentially to describe target customers with an accuracy and level of detail unfathomable only a decade ago. While old-school marketing efforts were limited to things like tracking returns on direct mail campaigns, or number of subscribers to newsletters, modern marketers can have data on people’s exercise habits, digital clicking behavior, time spent on various sites, purchasing history, personal preferences based on social media postings, time awake, time spent in the car, caloric intake, and almost anything else you can imagine.
So it makes sense that marketers would be chomping at the Big Data bit. While they used to know their customers like this:
Big Data reveals a defined buyer in real, dynamic time — like this:
But how should marketers leverage this new quantified customer landscape in the best way? And how does Big Data translate to revenue?
Here are five ways to pull Big Data into your marketing strategy:
Google Trends is probably the most approachable method of utilizing Big Data. Google Trends showcases trending topics by quantifying how often a particular search-term is entered relative to the total search-volume. Global marketers can use Google Trends to assess the popularity of certain topics across countries, languages, or other constituencies they might be interested in, or, stay informed on what topics are cool, hip, top-of-mind or relevant to their buyers.
Use heaps of analytics to learn more about your target buyers than you’ve ever known before.
Whereas in years past, marketers would make educated guesses at the age, demographics, and work profile of their target buyer, modern marketers have vats of data intelligence to prove their intuitions, and shed light on a more granular level of detail, such as: which web sites a user frequents most often, which social media profiles they have and use, and even which buttons they click on a given website.
ICP (or Ideal Customer Profiles) can be extremely targeted, while also data-backed.
For instance, in an Avis Budget case study, Tim Doolittle, vice president of CRM and marketing science, said adding Big Data to understand their customer profile “...increased the effectiveness of our contact strategy, in many cases above 30% over control.”
Marketers need to send the right message at the right time. Timeliness and relevancy aren’t just qualities of the fourth estate; they’re also the foundation of successful marketing campaigns, email click-through rates, and consumer engagement with your brand.
Big Data gives marketers the most timely insights into who is interested or engaging with their product or content in real time. Tying buyer digital behavior into your CRM systems and marketing automation software allows you to track the topics that your buyers are most interested in and send them content that makes the most sense to develop those ideas or extrapolate on those topics.
On average, companies collect customer and prospect data from 3.4 channels. Most commonly, this include the company's website, followed by the sales team and then the call center.
How successful was a singular blog or social post at generating revenue? Before Big Data that was an unanswerable question. We executed on social media strategies and content creation because we had a feeling that it was working, but we had no way to back that claim.
Now, marketers can distill the effectiveness of a marketing push down the to tweet. Tools like content scoring illuminate which individual content assets were successful to a closed / won deal, and which were inefficient. The allows marketers to hone the strategies around the content topics or types that resonate with their buyers the most, and truly compel them to purchase.
Predictive analytics is one of the most progressive (and maybe aggressive) strategies marketers can employ with Big Data.
In particular, marketers are seeing high rates of success in predictive lead scoring, which uses a company’s base CRM data and other third party Internet data to generate a model that successfully predicts future lead behavior. It pools and analyzes historical data around successful leads (leads that became closed won), thereby giving marketers clear indications about which digital behaviors are hand-raising activities or should be weighed more heavily in lead scoring.
Already, companies excelling at traditional lead nurturing generate 50% more sales-ready leads at 33% lower cost. The possibility that predictive lead scoring dwarfs that, is likely.
Will Big Data suddenly transform your marketing losses into wins? No. Will it make drive 10x lead volume per blog post? No. Data can only suggest trends, validate claims, and reduce the amount of human error in decision making processes. But, let’s face it. It’s still the human decisions, and human strategy that turns the wheel.
Not to say, Big Data shouldn’t stir up a marketer’s appetite. If there’s anything new on the menu for marketers, it’s Big Data. Go ahead, feast.
Jean Spencer is a content marketing manager at Kapost. She loves data, numbers, analytics, and writing about how those numbers can make an impact on marketing. Follow her @Jeanwrites.
