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Why You Need to Keep Powering the Personalization Cycle

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Many cycles exist in retail marketing: seasonal sale cycles, product life cycles, B2B sales cycles... The list goes on. One of the common traits of these cycles is an endpoint. Seasonal sales and product life cycles end. A B2B sale gets closed. An end point is logical in most cycles, due to slowing consumer interest or pursuing new clients. In regards to customer engagement, however, the cycle should never end.

The cycle of personalization is a prime example. The main concept of this methodology is a continuous loop of "Build-Measure-Learn," which can be applied to many aspects of business. Employing software that can infinitely optimize and execute personalization on an individual basis for millions of customers is a retailer's dream.

This dream is also shared by consumers. A recent article about retailers and personalization shared a variety of statistics supporting the mutual demand for customized messaging:

  • A Marketo poll of over 2,200 consumers found 63% were annoyed by generic brand messages.
  • An Accenture study found 55% of consumers want a personalized experience on all engagement channels.

Now that we know everyone wants it, how do you power this endless cycle?

A loop doesn't loop without an energy source. Profile creation, campaign execution, optimization, and data synchronization all influence the process in their own distinctive way. One cannot work without the other, and when they all work together, the endless cycle is set in motion.


Customer Profiles

A unified ID is a customer profile that links temporary identifiers (e.g., cookies) to more permanent pieces of data like emails, phone numbers, and app IDs. You can also integrate demographic data, shopping behavior, and geographic location to build a more dynamic profile.

Let's say we created a unified ID for a customer named Ryan. Once the ID is established, the marketer only has to look in one place for all of Ryan's data. This single source of information can help a marketer personalize marketing messages to him on any channel, any time. For data-lovers, that is when the fun begins.

Campaign Execution and Data Synchronization

Ryan lived in Anytown, USA up to his mid-20s. During this time, his wardrobe remained unchanged: polos, graphic T-shirts, button-ups, jeans, and casual shoes. In addition to always shopping on his desktop, Ryan was predictable in just about everything he did. That is, until he packed up and moved to Chicago.

Ryan's new work dress code called for suits, ties, and dress shoes. However, his newfound love for house music required an entirely different wardrobe for hitting the scene. He shops neighborhood clothing stores but always browses his mobile or brand apps for an online deal first. Ryan's commute and on-the-go lifestyle keeps his phone in his hand and his desktop collecting dust.

Can your current marketing strategy keep up with Ryan's new lifestyle? Think of all the variables here: new geographic location, new browsing behavior, heavy mobile and app usage, and a much more diversified purchase history.

Sending Ryan an offer for the newest line of striped polos a waste of time. "Online Only" deals won't resonate, either. Your campaigns need to be optimized to the New Ryan.

However, even "failed" optimized attempts are not worthless. Now you know those items don't resonate with him anymore. The process of making sense of all of these interactions and data points is known as data synchronization. This tactic allows you customize Ryan's next campaign based on his actions on all devices.

Once you begin to fully understand the new Ryan, you can associate him with similar customer segments and predict purchase behavior.

The Payoffs

As each cycle passes, the payoffs start to emerge. Some happen quickly. Some take time. During the process, both the retailer and the consumer reap tangible benefits.

Here are four of the many payoffs of personalization:

  • Increased loyalty. Customers remain loyal if they feel singled out and appreciated. As the campaigns get smarter and more personal, loyalty will deepen.
  • Lower marketing spend and wasted impressions. Instead of deploying messaging campaigns at random, you will begin to learn the most effective time and channel for each customer. This modified approach will reduce wasted impressions and unnecessary spend, and prevent active disengagement.
  • Improved inventory management. Do you have pallets of sweaters you need to move before spring arrives? Create offers for recent sweater buyers and those who purchased out-of-season stock at a discount in the past.
  • Increased lifetime customer value. All long-term relationships must keep the fire alive to survive. Optimized and personalized content is designed to keep a consumer satisfied and engaged as long as the interest is mutual. Some things do get better with age and the cycle of personalization is one of them.

Build. Measure. Learn. Repeat.

The lean startup cycle may be simple in theory, but it helps create technology that is changing the world. The cycle of personalization follows the same principles and helps retailers change their approach to 1:1 marketing.

The fundamental difference is the cycle of personalization represents thousands of individual cycles zipping around in loops, not one giant wheel struggling to please everyone. Once retailers learn to create these infinite cycles of personalization, the marketing possibilities are endless.


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Matt Diehl is digital content writer for Persio, a multichannel marketing and decisioning platform for retailers. Persio helps marketers send data-driven and personalized campaigns across Web, email, SMS, mobile app, and other channels.

LinkedIn: Matt Diehl

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  • by Angela S. Thu Dec 3, 2015 via web

    I had a hard time getting through this article because I was stopped in my tracks by the supporting data. I know that surveys and polls can’t collect data from everyone and often a sample is surveyed to provide a decent amount of data to work with, but I wanted to know what type of sample size we are talking about here. So I got some U.S. census data and estimated there be about 194 million consumers in the United States (about three-quarters of people ages 18 to 80). 2,200 of them comes out to about 0.0011 percent. But I went a little further, did some digging, and found the articles that your reference article references. The Marketo poll of over 2,200 consumers was a worldwide poll (http://investors.marketo.com/releasedetail.cfm?ReleaseID=918797). My bad for making the assumption this was U.S. only data. I didn’t want to do the research on the worldwide estimates of consumers, but I am pretty sure the sample just got a whole lot smaller. Then I found the the Accenture study… well, actually, I found a footnote in an article they published in 2014 (https://www.accenture.com/t20150624T211456__w__/us-en/_acnmedia/Accenture/C...) which led me to the original global study which was done in 2013 (http://www.slideshare.net/jonquois/accenture-seamless-retailresearchfinding...). In it 6,000 people were surveyed in eight countries (United States, United Kingdom, France, Sweden, Japan, China, and Brazil). So, on average that is only 750 per country surveyed. 6,000 people is better than 2,200 people, but it is still a very small sample. I tried to muddle my way through the remainder of the article, but I could no longer take it at face value. I am sure your information about how to keep the personalization cycle going is very helpful to those who want to do so, but when your authority (by saying, “Now that we know everyone wants it” you gave your resource authority) is based on far, far, far less than “everyone” I can’t believe its relevance anymore. It saddens me, too, that sources like Marketo and Accenture put out content that has such a small sample size. I am usually one to just move along when I see this kind of misinformation, but it is this type of data misuse (over generalizing, not fact-checking) that is driving a rather crazy world right now.

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