How is Aampe different from AI segmentation tools?

The future of AI segmentation and 1:1 personalization

Introduction

What is AI segmentation? Ask your favorite search engine or LLM and I bet the answer is a mishmash of the words “algorithms”, “machine learning”, “artificial intelligence”, “automatic”, and “dynamic”. More importantly, you’re unlikely to find a simple explanation of how AI segmentation works and how it differs from traditional segmentation. 

This article will do just that—offer that missing explanation—while also covering Aampe’s approach to segmentation. 

What is AI segmentation? How does it work?

Unlike traditional segmentation, where folks like you and I would spend hours combining events and attributes to build segments based on our understanding of our respective users, AI segmentation simply  groups users with similar behavioral characteristics or traits (attributes); some examples below: 

• Users who are in a similar stage of their journey

• Users who spend more or less time in the app than the average user does

• Users who have bought similar products or tried specific features

• Users who spend more or less money than the average user does

• Users who buy more or less frequently than the average user

You get the idea. 

In essence, these are segments that we can create manually by specifying rules—the AI is simply making the process faster by automatically creating the most obvious segments and letting us further refine them. At the end of the day, even these AI-generated segments are rule-based. 

Is there value in this approach? Most definitely. 

Is this a game-changer? Not really. 

How is Aampe’s approach to segmentation different? Why?

Aampe has flipped the rule-based segmentation approach we all know—one we have a love-hate relationship with—on its head. What that means is that at Aampe, we believe that it is time for us humans to move past the drudgery of building, documenting, and maintaining a bajillion segments by hand.

Instead, Aampe leverages a method called Reinforcement Learning where it assigns an agent for every user that decides what to deliver, when to deliver, and most importantly, whether or not to deliver in the first place. 

How does it work?

Each agent learns the behaviors and preferences of its client—your user—and adjusts message delivery based on the feedback it receives from the user. You can think of an Aampe agent as an additional headcount for every user of yours. This process takes place continuously and at the same time, the agent tests its biases constantly so that wins are not continued in perpetuity. 

Aampe’s agents begin by generating a large set of features or characteristics that describe everything they know about a user, which enables the agents to group users based on those features. 

Imagine a spreadsheet with a row for each of your team members. Next, imagine a column for every possible way to describe each team member’s outfit; the columns might look like these:

• Wears Spectacles? (Yes or No)

• Spectacle Rims Shade (Dark, Light, Clear)

• Shirt Has Collar? (Yes or No)

• Shirt Pattern (Solid, Striped, Checked)

• Shoe Type (Formal, Sneakers, Sandals)

• Shoe Color (Dark or Light)

• And so on

Now, imagine the different ways you can group the rows based on the above columns; here’s a random selection of some of the groups:

• Wears Spectacles = Yes AND Shirt Has Collar = Yes

• Wears Spectacles = No AND Shirt Has Collar = Yes AND Shoe Type = Sandals

• Shirt Pattern = Solid AND Spectacle Rims Shade = Clear

• Shoe Color = Dark AND Show Type = Formal AND Shirt Pattern = Striped

You get the picture, don’t you? The number of permutations and combinations is in the thousands—maybe even hundreds of thousands considering that we haven’t considered all variables (pants, belt, etc).   

The process of creating all these groupings by hand is, well, exhausting and rather impractical.  Fortunately for us, AI agents don’t get exhausted and don’t care about the practicality of a given task—they just do it.

So when do you—Aampe’s user—build segments? 

Well, only when you need to send one or more messages to a predefined audience—agents can take care of the rest. 

Why does Aampe suggest this approach to segmentation?

Because it is humanly impossible to keep track of the changing habits and preferences of every user and a sheer waste of one’s capacity for creativity to decide who receives what message when. Instead, Aampe lets people like you and me focus on crafting lots and lots of message variants and tagging them accurately for Aampe’s agents to use them in the right context. 

This approach frees us from everyday grunt work and provides much-needed space to focus on higher-impact, meaningful tasks—tasks like creating a catalog of compelling messages for our diverse audiences. 

But that’s not it.

Aampe also helps us better understand what our users are interested in by surfacing hard-to-gain insights about the needs and preferences of every user, enabling us to improve the overall customer experience. 

The future is 1:1 personalization

This is an unbiased take based on my experience both as a personalization evangelist and a consumer: It’s no longer enough to anticipate a user’s needs and put them into a box based on one’s limited understanding of who the user is and what it is that they’re looking for. 

We’re all unique and as our circumstances change, our tastes, needs, and wants are prone to change. Therefore, it only makes sense to let an intelligent piece of technology take over the messy work of keeping up with our changing preferences while we do what we’re best at—unleashing our creativity to build better relationships with our customers.

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Originally posted on the Aampe blog