Segment of One: Fact vs. Fiction

A wide gap between strategy and execution as they pertain to the concept of “segment of one” marketing allowed a fair amount of misunderstanding to materialize around the approach. As a strategy, segment of one loosely translates to the delivery of a personalized customer experience (CX). On the execution side, however, operational marketers hear “segment of one” and think they will never see their families again, tasked with having to create a different offer or different content for every customer.

Segment of one took off as a buzzword because the phrase succinctly captures the transition from mass marketing with very limited, manual segments to the objective of providing customers with a hyper-personalized CX. The issue, though, is the impracticality for an enterprise with millions – or hundreds of millions – of customers to segment to that level.

Perhaps it just boils down to semantics, but what the segment of one concept really means is having a deep, personal understanding of a customer’s behaviors, preferences, and intent within a customer journey. At the operational level, hyper-personalization at scale is the job of machine learning, not the day-to-day marketer. That’s really what an understanding of “segment of one” comes down to; a customer may be grouped in a segment with hundreds of other customers, but with a golden record of each customer, a brand will still be able to offer a personalized experience within a segment through machine learning.

Behind the Segment

As an example, perhaps a brand creates a segment of married women in the Pacific Northwest ages 25 to 34 who work full time, have two or more children, attend jazz festivals, grind their own coffee, wear floppy hats and are interested in taking out a mortgage to buy lakefront property. Granted, that’s a peculiar segment, but certainly well within the capabilities of technology. Jane Doe and Sue Smith may both be in the segment, but perhaps Jane is more actively researching mortgages than Sue. Within the segment, a real-time understanding of how one journey differs from another through a persistently updated golden record might then activate a rule; if Jane or Sue visit this landing page, this is the next-best action. A rules-based decision point triggers a hyper-personalized CX in the context of the customer journey.

The content may be identical, but perhaps it is delivered on a different channel or at a different time. Depending on the sequence with which Jane or Sue interacts with the brand, maybe a specific piece of content is not delivered at all.

A more accurate way to think about “segment of one” than just creating more granular segments is to think about it more in terms of the purpose of a segment – why it was created to begin with. This is where the construct of a rules-based marketing platform vs. a list-based system comes into play.

Rules vs. Lists

In a list-based system – think the mass marketing approach – a list of customers is created and extracted, and messages, offers, and content is generated based on the list. Jane Doe and Sue Smith are in the same segment, and thus once the list is extracted from the database, they will receive the same communication regardless of how their customer journeys differ from that point forward. A list-based, outbound list simply does not allow for any new condition that is not defined prior to extraction.

In a rules-based platform, by contrast, a rule is applied to a campaign in place of a list, and this set of logic that defines an audience is evaluated at each point in a campaign where a list would normally be used. This accounts for changing circumstances regardless of which channel that they occur, so whereas a list-based system might generate an email for both Jane and Sue, a rules-based platform re-evaluates at each decision point – up to the moment the email is sent, or even changing the content of an email until it is opened.

The Power of AML

Embedded advanced analytics in the Redpoint rgOne automated machine learning (AML) component is where the rules-based system comes to life. In-line, self-training machine learning models built on a golden record can recommend an algorithm-produced next-best action for an individual customer based on a specific journey – unbound by channels, and not locked into a list-based constraint. Here is where differentiating between one customer journey and another comes into play, the rendering of “segment of one” without having to take models offline to rebuild them any time a business objective or circumstances change.

With the fitness function in rgOne, code-free, self-training models run 24/7 continuously chasing whatever business metric an organization is trying to push. Primed to intelligently orchestrate a next-best action within the construct of an individual golden record, the resulting personalized experience is always in the cadence of a unique customer journey. Instead of “segment of one,” try “an always relevant, hyper-personalized experience in the cadence of a customer journey” on for size.

Related Content

Beyond the Hype: Put the Power of AI into the Hands of Marketers

3 Reasons Evolutionary Machine Learning Drives Digital Transformation

Scale New Heights with a Rules-Based Omnichannel CX Platform

Be in-the-know with all the latest customer engagement, data management, and Redpoint Global news by following us on LinkedInTwitter, and Facebook.

Get Started on Getting Ahead

Schedule a conversation and learn how Redpoint can put your goals within reach.

Get Started on Getting Ahead

Schedule a conversation and learn how Redpoint can put your goals within reach.

Email Your Molecule
Do Not Sell

Submit the form below to set a "Do Not Sell" preference for your user within our persistent customer records.

Meet with a Redpoint Partner
Hello there!

Please fill out the form below and we will reach out to you.

Open

Your Unique rgOne™ Solution

Click below to create a personalized Molecule to meet your specific goals.

Create Your Molecule