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The world of business runs on buzzwords: “Always be testing.” “Content is king.” “Growth hacking.” “Fail fast.” And the always popular: “Blank (e.g., social, email, seo) is dead.”
However, the buzziest of buzzword is “data," especially “big data.”
As Brian Kardon pointed out, “The era of big data is alive and well. It is leading to better and faster decisions in a diverse set of industries — from disease detection and insurance, to stock trading, crime prevention and election forecasting.”
Naturally, big data's popularity is not without merit. The volume, velocity and variety of big data offers enticing opportunities for businesses of all kinds to increase their return-on-investment (ROI) by analyzing and learning from the past actions of real people.
But is "big data" as magical as it sounds?
Related: 4 Ways a 'Data-Driven' Approach Anticipates Buyer Behavior
Yes … and no.
Despite its surging popularity, "big data" comes with a unique set of challenges, in particula the tendency to confuse correlation with causation, a lack of standard scientific protocols, generally unqualified practitioners, and a host of ethical concerns.
However, the primary disadvantage of big data is that it relies on past behavior to predict future actions. The truth is a customer's past is not failsafe indicator of their future, especially since consumer tastes and industry trends evolve rapidly.
This principle is especially true when it comes to personalization. In the aptly title “Personalized Marketing Is No Longer a Luxury,” Eric Siu explains, “The expectation of personalization has become ubiquitous and no business can afford to ignore this trend. The problem is, most businesses don’t have the kind of engineers and data volumes needed to implement such powerful, automated resources.”
With that principle in mind, here are the two most important factors that historical big data can’t help you with and exactly how to fix them.
Where your customers are
Don’t underestimate the simplicity of this first insight. As the old saying goes, three things are absolutely essential to the success of any business: “Location. Location. Location.”
However, the new wrinkle online business adds is that location isn’t about where you are, it’s about where your customers are.
Trends, needs and buying habits are all profoundly regional. And marketing is waking up to this fact. As Kissmetrics reported, a full “90 percent of U.S. marketing agencies have had clients request geographically targeted online ad campaigns.” Why? Because advertising professionals also report that “geo-targeted ads deliver a stronger 60 percent ROI than other buys.”
This connection becomes even stronger when it ties together mobile marketing and brick-and-mortar retail. According to data aggregated by MDG Advertising, “72 percent of consumers respond to calls to action in marketing messages they received within sight of the retailer.”
Naturally, big data solutions like Google Analytics can easily tell you where your visitors are coming from. But this only applies to spotting historical trends and does not enable you to dynamically personalize your marketing based on location.
The answer lies in geo-precise personalization tools.
Related: You Can Become a Data Scientist. Yes, You.
Geo-based (or geo-targeted) personalization works by automatically adapting your website’s copy, images, and even its offers to match the priorities of local markets. For example, in a CloudEngage case study, Runnings Retailer was able to raise their offers' overall click-through rates to a full 30 percent by coordinating “store-specific promotions on their corporate site, each with its own geo-fence.” This meant that customers automatically receive a personalized experience based on their specific location simply by visiting Runnings single website.
And real-time data about your visitors location doesn’t just apply to personalizing your website. As Search Engine Land discovered, geo-targeted mobile display ads outperform the standard CTR benchmarks in literally every industry.
What your customers want
Even the most astute predictive-analytics software will still not be as revealing as understanding a visitor’s present state. Again, when you depend on past behavioral patterns to personalize your messages, you are essentially ignoring your consumer's immediate actions.
Online personalization experts call these immediate actions “digital body language.”
Digital body language, similar to traditional body language, works by monitoring and automatically adapting content to distinct characteristics like referral source, number of visits, pageviews, cart additions, price range, item types, brand preferences, purchase history, exit intent and more.
How does it work?
Take the Dutch clothing brand Coolcat. With over 130 traditional stores and more than 1,500 employees, Coolcat’s goal was simple: increase customers and increase revenue.
To do that, Coolcat partnered with Fanplayr and developed a three-pronged approach based not on historical data but behavioral personalization. One, they engaged first-time visitors with a one-time welcome message and initial offer. Two, they followed that welcome message up with real-time offers based directly on the value and products in their shopping cart. And three, they used “mouse movement tracking to detect visitors about to leave the site” and create a final, custom message as an exit-intent offer.
All told, these efforts increased their conversion rates by 58 perdent and their total net revenue by 71.5 percent.
Thankfully, creating personalized offered doesn’t have to be complicated. The simplest way to leverage digital body language is by building entry and exit popups triggered by referral source. For instance, Gleam reports seeing social-channel specific opt-ins like the one below “convert up to 70 percent of users.”
Big data and personalization.
Historical big data has its advantages, but real-time personalization is not one of them.
The key to implementing real-time analytics and personalization into your business comes down to two key areas: localized marketing and custom responses to user behavior.
Related: When It Comes to Analytics, Ignorance Is Not Bliss
In other words, be proactive, not just predictive.
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