How to Use Opt-Ins to Personalize Your Marketing Campaigns

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This blog is part 3 of a series. Click here for part 1 and here for part 2.  

So you’ve snagged the opt-in. Now what? Data collection means nothing unless you know what to do with it. Once data transparency has been communicated and opt-ins have been encouraged, a new marketing monster takes over. Its name is one-to-one marketing.

This promotional tactic targets an individual based on their very specific habits. Although the concept of one-to-one has been around for awhile, the infinite amount of digital consumer data now available to marketers has changed the game entirely. Now, promotions can be personalized to speak to each customer, offering something they truly need in a specific time or place. Since 72% of consumers are turned off by generic marketing, one-to-one couldn’t be more important in the current buyer’s climate [1].

Out of all the data collected from consumers, two types are used most often in one-to-one marketing. The first is demographic data: the who and what of each customer. This includes name, gender, job, location, income – the works. With this information, brands can apply simple personalization strategies, like calling the user by their first name in an email newsletter.

The second type is engagement data. This dataset measures what the consumer is clicking on, from the websites and apps they visit to the digital forms they submit. Have you ever been followed by digital ads for the same shoes you were checking out online three weeks prior? It’s no coincidence. Your online shopping engagement is being closely monitored by the company and, no surprise, they don’t want you to forget about their new line of sneakers.

Engagement is everything in social media especially, where marketers measure what is being liked, followed, and re-posted the most. For example, a marketing manager at a record label will target Twitter users who follow their artist or neighbouring artists in the genre, producing content that has attracted the most user engagement in the past.  

Almost every company collects demographic and engagement data to learn more about their customers. But what happens when consumers start to catch on? Target – the second largest discount store in America – learned the answer the hard way [2].

After years of collecting buyer data, based on purchases and consumer opt-ins, Target became skilled at promoting to and capitalizing on the shopping habits of new parents. But some keen marketers wanted to strike sooner than that – specifically during a mom-to-be’s second trimester.

They asked Target’s data analyst to find a prediction method. Sure enough, patterns in data showed that they could know a female customer was pregnant based on her purchases and engagement. Soon, the discount giant was sending promotions for diapers and formula to women who had yet to start showing, let alone give birth.

The strategy was a success, until a Minneapolis father marched into his nearest Target with a handful of coupons for baby products that had been sent to his teenage daughter. He was angry: all these advertisements, he thought, might entice his baby girl to have a baby of her own. When the manager called back to apologize the next day, it was the father who said sorry. Turns out his daughter was already pregnant and due the following summer.

What does this case study tell us? First off, personalization can be invasive if not used tactically. No customer wants to feel like they’re being spied on. But most importantly, Target’s experiment in data analysis proved that one-to-one marketing can go further than simply calling a customer by their first name. If Target could predict a teenager was pregnant before her own father took notice, the possibilities for data analysis are surely endless.

With so many streams of data for brands to tap into, coupled with an honest and transparent method of retrieving it, companies now have the ability to offer their customers exactly what they want, exactly when they want it. But this level of one-to-one marketing comes with a challenge most businesses are struggling to overcome. How can you combine countless sets of complicated data from multiple sources to recognize patterns in a single customer’s behaviour?

96% of marketers agree that unifying disparate sets of data is incredibly difficult and 80% can’t do it at all [3]. This roadblock stands in the way of personalized marketing and makes valuable opt-in methods redundant. Fortunately, there are resources that can help. Flybits, an integrated software that combines disparate data, makes it possible to add context to consumer information. With this tool, businesses can make big data work for them. And in a world defined by data, knowing how to get it and what to do with it is a company’s greatest asset.

Click here for part 1 and here for part 2 of this 3 part series.  

Sources Used

  1. Marion, Guy. “3 Things Marketers Need to Know About Succeeding with Personalization.” Autopilot. Retrieved June 20, 2017 from http://www2.autopilothq.com/succeeding-with-personalization
  2. Duhigg, Charles. “How Companies Learn Your Secrets.” The New York Times Magazine. Retrieved June 25, 2017 from http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html
  3. Rogers, Stewart. “Despite the need to, 80% of marketers can’t personalize their marketing (study).” VentureBeat. Retrieved June 25, 2017 from https://venturebeat.com/2015/07/01/despite-the-need-to-80-of-marketers-cant-personalize-their-marketing-study/

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