The 3 Levels of Personalization: How Your Brand Can Offer a Custom Experience

by Michael Finn

Improving your customer experience gives your organization an advantage over your competition, and knowing your customers is key to delivering the most engaging customer experience. Every organization aspires to use the omni-channel approach to serve their visitors a seamless experience. This is certainly the right idea, but what if you could take it one step further?

What if you could predict and recommend the behaviors that are most relevant to each individual customer? This is exactly what digital titans like Amazon and Netflix do to provide an exceptional customer experience. However, creating a personalized experience for customers is no longer reserved for some of the biggest companies in the world anymore. The combination of behavioral data, enterprise data and machine learning models empowers your company to recommend actions tailored to each customer on a one-to-one basis.

Creating the best personalized experiences begins with a combination of behavioral data with your enterprise data in a virtual private cloud (VPC) of your choice. Popular VPCs like Google Cloud Platform (GCP), Amazon Web Services (AWS), and Azure are secure and remain reliable options for companies that seek a competitive edge. Once the data is processed, begin building custom machine learning models to predict and recommend products or services that your visitors desire.   

This may sound nice, but not all personalized experiences are equal — there are different levels of maturity. Too many companies are stuck in stage one and may not realize they have options to cater to their audiences on a deeper level.

Level 1: Use tags to predict and personalize   

Placing a specialized tag on your website is the simplest form of personalization. Tags on your website track outbound links and downloads, track form submissions, understand if a visitor liked and shared content via Facebook or Twitter, and identify when a visitor views your website. Whenever someone visits a website, these tags show users content that is meant to be personalized for them, measures their on-site behaviors, and identifies unique information about that individual.

At this stage, you will typically build segmentation rules using manually created business rules, and build audiences using manually created segmentation rules. This sounds useful, so what’s the problem? 

The problem with business rules is that they don’t perform as well as machine learning models. Since data changes faster than you can update these rules, it’s possible you could lose track of the changes and exceptions you need to account for when writing rules. Plus, rules-based systems become burdensome over time as more and more exceptions and rules are added.

Using tags on your website also builds another data silo by sending your behavioral data to another SaaS platform. Fragmented data is an issue that a lot of organizations are trying to rectify, and creating another silo is a step in the wrong direction. If you can’t have your data all in one place, you’re forced to combine this data by yourself from various locations. This is a long and tedious task because the volume and complexity of the data from multiple sources is a struggle to process for most companies.

Level 2: Putting a black box solution in charge  

Level two is a bit more advanced. This stage typically consists of combining enterprise data, profile data, and some transaction data, but lacks customized models. This is a positive step since combining data from different sources helps you better understand the complete picture of your visitors.

You may also be using a black box solution to construct models to predict behavior, but there’s still a problem. Black box solutions are not able to be tailored to your specific company’s situation. These programs tend to be rigid and limit any customization made to suit your preferences, as the retailer. Since these solutions aren’t customizable, you’re left using the same black box models your competitors are using. If you want to gain a competitive advantage for your company, you need to build machine learning models that are customized with your customer’s data.

An additional problem at this stage involves the types of audiences you’re able to build. The audiences are not global, but rather created locally within each channel. This means that recommendations that visitors receive from your website, email, and advertisements remain different from each other. Each channel is independent and the offers your visitors receive will not match across all of your channels.

Level 3: Taking the reins of personalization

This stage is the most important because it puts you in charge. It doesn’t rely on a marketing cloud or personalization vendor. You remain in control of the entire process instead. The ability to use your behavioral data, enterprise data, and combine it with custom machine learning models to utilize personalization on your website is now possible. This may seem complicated, but this doesn’t have to be a strictly technical pursuit. The doors are beginning to open up for digital analysts and marketers to drive these projects themselves. So, what kind of tools will make you successful?

The combination of behavioral data, enterprise data and machine learning models empowers your company to recommend actions tailored to each customer on a one-to-one basis.

You need a new kind of technology that ingests behavioral data from analytics tools like Adobe and Google on an ongoing basis. You also need an Identity Graph that stitches together your visitors’ impressions, clicks, sessions, product views, events, purchases, subscriptions, and cancelations. And most importantly, you will have a global audience to produce globalized recommendations to the same person across all of your channels as well.  

Companies like Dixons Carphone now take their first-party data to transform it into personalized recommendations to their own customers. They now own their behavioral data pipeline to process and deploy their models to offer a personalized experience. Dixons is so successful with their recommendations that they saw a 300% uplift in attachment rate to shopping baskets after they implemented personalized, AI-driven product recommendations.

There are real results associated with a true investment in personalizing your brand. The good news is that there are resources available to your organization to become independent from vendors and create a customized experience for your customers. If you’re unsure or have questions about what your organization may look like as a personalized brand, then check out our webinar, On a Scale From 0 – Amazon, How Personalized Is Your Brand? to learn more. 

Share this:

Michael Finn

VP of Product Marketing at Syntasa

Mike is VP of Product Marketing at Syntasa (a Marketing AI Platform loved by Marketers, Data Scientists, and Data Engineers for its ability to unlock real value from their enterprise and clickstream data).

×