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We know you’re clued up, but just to recap so we are all on the same page. In marketing, segmentation means dividing your target market and customer base into actionable groups based on similar needs, characteristics, and behaviours. 

In ecommerce, segmentation is broadly grouped into market segmentation and customer segmentation. Market segmentation usually deals with pre-sale groupings, and customer segmentation deals with post-sale.

However, despite the technological revolution in marketing, segmentation strategies have fallen behind. As marketing technology (MarTech) has rapidly evolved, the strategies underpinning its usage have struggled to keep up; they’re no longer fit for modern consumers.

Segmentation: The practice of dividing your target market and customer base into actionable groups based on similar needs, characteristics and behaviours. 

When actioning segmentation, ecommerce businesses usually determine the segments to focus on in one of two ways:

1. Marketing Personas
2. Data-Driven Audience Groupings

Personas & Segmentation

Turning People into Numbers

As it says on the tin, marketing persona segmentation centres around a persona. 

We gather insights through market research (think focus groups and nationally representative sample surveys). Then, we weave those details into clustered audience groupings, character crafting to create vibrant representations of real people so they are more than just names on a page.

From here, audience groupings are clustered from the research, and a persona document is created.

The output is often:

“This is Sarah. Sarah is 35 years old, has a busy life, doesn’t have time to think about her purchases…”

But how do we turn Sarah's story into something tangible? We dive deep into her world, ascertaining what she'd love to see in our marketing. With that base work, our job is to find others who mirror Sarah's interests and bundle them into the 'Sarah' bucket.

Yet, bridging the gap between this research and real user behaviour data on our site is no small feat. (Even for the most adept marketer.)

Personas aren't just labels—they're rich portraits of our customers' attitudes and lifestyles. The real challenge is figuring out how to draw in more customers like Sarah, both in numbers and value.

Personas provide a more holistic view of the customer, shaping how we tackle problem-solving and connect with our audience. But they fail to translate into meaningful segments that provide actionable ���Բ�������ٲ�.��

Data-Driven Segmentation

Turning Numbers into People

The second kind of segmentation is a more data-centric approach, where we dig into our customers' digital footprints and online buying habits.

Here, your data or data science team employ clustering techniques to group numbers, looking at data like user journey touchpoints, keyword alignment, and specific online behaviours.

This might translate into "Nike Aficionado": someone who consistently purchases Nike products and demonstrates a strong brand affinity without requiring additional contextual information. They don’t need help progressing through the buyer journey; they are confident and take charge.

In cases where businesses have achieved a high level of data maturity, these data-driven segments may be enriched with detailed behavioural insights. Yet, achieving such sophistication requires significant investment in data tagging and meticulous tracking.

For many businesses, this can be out of reach. Even for those who attain it, challenges often lurk beneath the surface.

The State of Segmentation

People, Numbers and Problems

Both traditional forms of segmenting pose significant challenges for ecommerce. There are some major challenges at play:

Creating self-fulfilling prophecies

When embarking on the journey of segmenting specific groups, there's a curious phenomenon at play: you tend to see what you seek. For instance, if you say, "We're targeting the Sarah’s, not the Betty’s, because that's our brand vibe," you narrow your vision, missing out on valuable insights and untapped opportunities—often pigeonholing the answers you’re looking for.

But imagine flipping the script: prioritising volumes and value first, then sculpting strategies based on newfound insights. It's like shifting from tunnel vision to panoramic view.

Marketing persona segmentation presents a huge challenge, which is exacerbated by one of data-driven segmentation’s biggest challenges: hard bucketing.

Hard bucketing of customers

Hard bucking adds customers into neatly packed, rigid, overly defined segments. 

What is the trouble with this approach? Surely that’s just how segmentation works? Well, customers aren't static beings; their habits can change depending on where they are in their journey. Segments need fluidity to cope with changes in behaviour.

For example, just because I’m a “Nike Aficionado” doesn’t mean I can’t and won’t deviate from this when gift shopping for my Adidas-obsessed friend.

Additionally, hard buckets overlook the nuances of multi-person households. Imagine two people, one website, and completely different shopping behaviours. Treating them both the same is like trying to fit a square peg into a round hole—it just doesn't work.

While seemingly sensible, this overly rigid approach can sabotage your marketing efforts. You miss the nuances needed for true personalisation at scale. It goes hand in hand with our next challenge: unoptimised segmentation.

Unoptimised segments

Businesses often neglect to revisit their segments, even when the economic climate shifts, new products are introduced, or a fierce competitor enters the fold. The result? Stale segments that fail to capture the pulse of the present moment.

By sticking to the same old segments, businesses miss out on vital insights into how these groups evolve over time and the fluctuations in volume. 

Volume change is crucial for providing strong marketing insights and creating a blueprint for where your marketing efforts should focus. Just imagine the questions when discovering a 10% drop in the Sarah demographic—cue the detective hats.

Ultimately, it all boils down to one thing: inflexible segments that fail to adapt to changing behaviours, volumes, or market dynamics. This leaves businesses pulling insights and taking action based on partially flawed information.

Retroactive vs ���ϴ��¼� Segmentation

In the realm of segmentation woes—hard bucketing, optimisation, and self-fulfilling prophecies—another villain lurks: 

Traditional Segmentation, the time traveller stuck in the past.

These methods have a shared fatal flaw—they're retroactive, relying on data and events from the past.

This backwards-looking approach means serving customers' experiences based on past actions—imagine showing someone running shoes because they bought a pair last week. It's throwing money out the window and bombarding your audience with irrelevant ads.

So, how do we pivot from retrograde to progressive?

"Good" segmentation is actionable, insightful, responsive and adaptable.

With all fluff removed, we can boil segmentation back down to the core concept of making marketing more efficient and effective by dividing your audience.

Retroactive segmentation is a relic in the age of modern ecommerce. It struggles to be what good looks like.

This is where predictive segmentation steps up to the plate.

���ϴ��¼� segmentation involves creating segments based on the probability of future behaviours, events, or conditions.

But predictive segmentation alone lacks a crucial ingredient: context. As any good marketer knows, context is king.

Let’s take a breather for a moment. 

What have we learned?

We've learned from the limitations of outdated personas (RIP Sarah, gone too soon) and the constraints of rigid categorisation. Today, success hinges on data-driven insights that empower us to predict customer behaviours and personalise at scale.

Where do we go from here? 

Intent-Based Segmentation

Intent is the driving force behind every click, every scroll, every purchase. It relates to the behaviour that underpins a user’s actions and motivations regarding purchasing. 

By tracking intent signals, you can identify where a user is in their buying journey and how likely they are to seal the deal.

It's all about context—understanding the user's current needs, what they are trying to do and providing timely solutions. When you can read the signs, you're better equipped to respond to positive or negative behaviours with precision.

Combining intent with predictive segmentation allows you to bring a retail sales process to ecommerce. You can be adaptive and respond appropriately.

Unlike marketing personas or data-only segmentation, it’s less about holistic customer behaviours and attitudes and more about the here and now—right now.

But hold on. ���ϴ��¼� segmentation isn't just about real-time; it's also a guidebook for the entire customer journey.

Good intent-based segmentation has predefined variables for customer journey segments that customers can move between at the end of their session and/or journey. It's like having a GPS for customer satisfaction—always guiding, always relevant.

There are 25 intent-based segments you should be looking at, across 3 stages of intent based momentum. Check out our Intent-based predictive segmentation framework and bring your segmentation out of the dark ages.