The new era of customer analytics: How brands can benefit
Have you ever had the experience of learning a new word then hearing it three more times that week? Or have you ever bought a new car, and although you’d never noticed one like it before, you suddenly see it everywhere? There’s a term for that: the Baader-Meinhof phenomenon. What feels like a coincidence is really just your brain noticing something it previously overlooked — like the thousands of people who were already driving the car you just bought.
But that’s only in the real world. Online, there’s another word for this phenomenon: customer analytics.
When you view an item on a retailer’s website, you immediately start seeing ads for it everywhere — in your inbox, on Facebook and YouTube, in banner ads at the top of every other website you visit. That’s not a coincidence, nor is it your brain playing tricks on you. It’s digital marketing, fueled by behavioral data, analytics and sophisticated re-targeting algorithms.
How has this changed consumer behavior and expectations? What are the latest trends? And which strategies help companies excel in the new era of customer analytics?
High demands and short attention spans
Not long ago, it seemed like magic when Netflix accurately predicted which movies you’d like, when Amazon emailed you about best-selling products you would actually use or when the same jacket you viewed on the Target website showed up in your Facebook feed. Today it doesn’t seem magical at all.
Not only do consumers want seamless, personalized interactions with brands, but they also expect to have those experiences. And they don’t make it easy. They bounce from one channel to another and from one device to another and expect brands to recognize them regardless of where they are. They block out ads, literally and figuratively. They abandon mediocre apps — and the brands that develop them — without a second thought. After all, there are plenty of other options, and they’re seeing ads for competing products everywhere too. What’s a company to do?
Connecting the customer data dots
In the digital age, all the markets are buyers’ markets, but brands also have a digital advantage: analytics.
Companies can collect data that shows who customers are, where they are in the buying journey, what they want and which marketing strategies will most effectively reach them. Historically, the trouble has been connecting data from all the different channels and devices, which is often gathered by different technology and different departments. When customer data is fragmented, so too are customer profiles.
Modern customer analytics solutions enable companies to bring all this data together into one big-picture view of the customer journey, individually or aggregately. These insights enable brands to personalize content and marketing strategies, and they help leaders across the company to improve the customer experience, develop more engaging sites and apps and ultimately boost the bottom line.
Research shows this approach pays off in terms of competitive advantage and customer engagement. Fifty-nine percent of managers say their companies use analytics to gain competitive advantage, according to MIT Sloan Management Review. The study also found that analytically mature organizations are twice as likely to report strong customer engagement as their peers who are less savvy with analytics.
Better customer analytics, thanks to machine learning
Machine learning has been a hot topic for several years, but to successfully leverage AI algorithms for customer insights, companies need a lot of data — and not just any data. They need clean, structured data from a variety of sources. That includes internal databases and apps as well as social media, research firms and other third parties.
Advanced analytics solutions are making it easier than ever for companies to access and categorize unstructured and third-party data. Machine learning algorithms can then analyze all that data and provide businesses with real-time contextual insights.
This enables companies to personalize customer communication and deliver messages at the right time via the right channels. It also helps improve campaigns over time by identifying tactics that aren’t working — and better yet, why they aren’t working. Why aren’t people responding to SMS offers? Why aren’t they finding the app on the App Store? Why are they using some features of the app but not others?
As machine learning becomes more advanced, predictive analytics become an even more powerful tool for businesses. For example, algorithms can predict customer churn, giving companies a chance to win back their loyalty before it’s too late. Analytics help companies understand when, where and why customers are leaving the brand and how to better optimize marketing strategies going forward. Analytics also help predict changing customer preferences both on an individual level and in terms of larger market trends.
Simply put, AI-based analytics solutions enable companies to understand their customers better than ever before and to deliver the experiences they want, regardless of device or channel. Yes, it’s a buyer’s market, but analytics-driven organizations know how to excel in that market.