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Digital Personalization

By Timm Henderlight, Sophelle

With all the different stimuli, options, mediums and content that customers are receiving daily, companies are searching for better ways to connect and engage with their audience. By some estimates, individuals are now exposed to over 5,000 advertisements in just a single day. So how best to reach customers with rich, relevant experiences that cut through the clutter and engage with individuals on a more interpersonal level? The answer is personalization.

A hot topic in today’s retail environment, it has proven to be demonstratively effective, with 90% of customers stating that individualized experiences have an impact on their buying decision and 80% of retailers reporting that personalization has increased their revenue. Yet according to a recent Internet Retailer study of the top 100 retailers, only 39% of the retailers suggested products on their homepage based upon the shopper’s behavior interacting with the web site, and only 15.5% used the shopper’s preferences in search recommendations auto-populating options such as color, cut, size or brand.

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With companies such as Avon reporting a 96% increase in conversions through personalization, it’s time to make digital personalization a priority. Retailers already possess a tremendous amount of information, even if it is underutilized. From purchase history to adaptive experience and geo-targeted content, there is such a wealth of information and thus an even bigger opportunity to create the compelling experience that your customers are expecting. Below we will dive in on three of the most compelling tactics that are yet to be commonplace:

Predictive Intelligence: Ever wondered how as you start to type something into a search bar, the answers begin appearing even before you finish typing the entire word?  This is a basic use case of Predictive Intelligence (PI).  PI utilizes an algorithm to best guess a user’s intent, based upon cues like their profile, demographics, location, preferences and web site interactions.  PI then looks at other similar users who have had positive outcomes, such as a conversion or interaction, and then displays content specific to that user.  

As more information about the visitor is captured, the more PI can be used in creating a personalized experience.  For instance, if you know that the user is a single 25-year-old female who is visiting your web site from Florida and previously purchased size 6 dresses in pink and yellow, you can then start to augment her experience much more than you can with an anonymous user from whom you haven’t collected any information.

PI is being implemented today in a variety of digital marketing areas including onsite search, email, product recommendations and web site content.  Eventually, PI will be the catalyst to a web where every experience is singular to each unique visitor.

Behavioral Marketing: Let’s say you are on a web site and you are browsing for men’s clothing, men’s pants, men’s socks and men’s jackets.  Then you decide to perform a search for ‘shoes’.  Are you surprised that the web site renders all women’s high-heels?  Shouldn’t they know that based upon your behavior on the web site that you are most likely interested in men’s shoes. Obviously, this retailer is not utilizing behavioral marketing properly on its site.

Behavioral marketing uses a customer’s behavior and interactions with a web site to draw clues about their interests and preferences.  As a user interacts with a web site, more information is collected about the user and can then be utilized to personalize their experience.

A great example of behavioral marketing is the ubiquitous retargeting ads you see across the Internet.  How is it that the Dutch oven you just viewed on Amazon is now in a banner ad on another web site?  Because Amazon is employing behavioral marketing and knows that since you previously viewed this item on their web site, rendering that product again in a remarketing campaign might just lead to a purchase with an additional impression.

Machine Learning: Why attempt to compute all the data that your web site collects when automation can do it much cheaper, quicker and more effectively?  Computers today are actively ‘learning’ more about your customers and business as each new piece of data is entered.  They use statistical modeling and computational learning to spot trends and patterns in the data and then offer insights or predictions into what to do next. The more data that the computer analyzes, the more proficient it becomes in the learned behavior.

Machine learning is now being implemented across many different areas of business including health care, image and facial recognition, and even autonomous vehicles.

I often get asked the question, “Won’t personalization get it wrong sometimes?” This is a fair question and the answer is ‘Of course’ especially when you have limited information about the user. Today I’m shopping for my newborn niece, tomorrow for my nephew who just graduated college, and the next day for basic household consumables.

This type of customer shopping pattern can make it harder at the outset for personalization strategies and software to decipher intent, from a frequent purchase compared to once a year or once in a lifetime purchase. However, it really comes down to the 80/20 rule. If retailers can offer a more relevant experience 80% of the time and are only incorrect 20% of the time, the benefits of offering an individualized experience far outweigh not providing this type of experience.  

Taking the initial step may seem like a giant leap, and that’s why it can be better to take a phased approach. These are monumental changes that won’t take place overnight, so incremental improvement is the best way to go. At the end of the day, an incremental improvement in personalization strategy should look something like this — Segmentation giving away to Personalization leading to Individualization, in an attempt to achieve 1:1 Engagement.   

Remember, a personalization experience not only contributes to the top line for retailers and brands, but most customers are more satisfied, and prefer an experience that is tailored to them. 


Timm Henderlight is Vice-President of the Digital Commerce and Marketing Practice at Sophelle, a retail consultancy that helps retailers create engaging and compelling customer experiences in stores, online, and throughout the customer journey.

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