Customer Segments

How do I build Customer Segments and how can I apply them to my reports.

Valentine Strunz-Happe avatar
Written by Valentine Strunz-Happe
Updated over a week ago

tl;dr

  1. You can build Customer Segments by creating filters from over 100 different dimension.

  2. One segments can contain multiple conditions to clearly identify a subset of customers.

  3. Customer Segments can be applied on your reports allowing you to drill down into your data.

What is the Customer Segment builder?

Most reports in Klar already have filtering options built in. But filters are limited in their flexibility and the numbers of dimensions that can be filtered.

That's where Customer Segments come in. You can use the Customer Segments builder to create complex filter options using over a hundred different dimensions.

Any type of segment you could think of can easily be created in the segment builder.

These segments can then be applied across reports in Klar, allowing you to drill down into you data and uncover what is working and what isn't.

Let's look at the different options together so that you can build you segments with confidence and be sure that the filters you set work exactly as you intended.

General Layout

To keep things easy to navigate, we grouped dimension together.

  • Customers Who Ordered Product

  • Customers By Most Bought Product

  • Customers With Revenue Of Product

  • Customers By Marketing Source

  • Customers By Activity State

  • Customers By Value Metric

Each of these groups contains two classifiers through which you can define the filter.

Similar to the marketing channel builder, you can build different conditions and groups, linking them with AND/Or logic, to create any kind of filter rule you might want.

Let's look at the different groups.

The Different Dimension Groups Explained

Customers Who Ordered Product

Here you can filter on customers that have ordered (or not ordered) a certain product in the past.

You can use the first classifier to define the product dimension (title, type, SKU etc.) you want to base the filter on and then select the product. When selecting multiple products within a single condition, a customer only needs to have bought one of them, not all of them to be included in the segment. If you want customers that have bought both of two products, you need to create a second condition and link both with an AND.

In the second classifier, you can define in which order you want to the customer to have bought the product. Here you can select from various options:

Specific Order Count - this could be his first, second, third etc. order

Any Order - in any order he has placed

Last Order - he bought the product in the latest order he placed, irrespective of order count

The Operator gives you even more flexibility with the following options:

Equals - customer placed an order with the product in the value (see options above) you select

Not Equals - customer didn't placed an order with the product in the value (see options above) you select

Less than - filters on customers that placed an order with the product in an order count lower than what you select. Only relevant when using the specific order count.

Greater than - filters on customers that placed an order with the product in an order count lower than what you select. Only relevant when using the specific order count.

Customers By Most Bought Product

With his dimension you can select customers based on the product they have bought the most.

The first questions popping into your head probably is now: How do you define most?

Good question. For that we have the first classifier. Here we can select the value metric that we want to use to define "most bought".

  • Net Revenue

  • Contribution Margin 1

  • Contribution Margin 2

  • Order Count - Orders that contain the product at least once.

  • Unit Count

The second classifier can then be used to define the product dimension that we want to base it on. We have the same options here as in the previous dimension:

  • Product Type

  • Product Title

  • Product Variant

  • Product SKU

  • Product Brand

Customers With Revenue Of Product

Another product-based dimension filter. With this one you can select customers that have spend a certain amount of money on a product.

The first classifier can be used to define the product dimension level.

The second classifier can be used to define the monetary value that you want to use. You can pick from the following options:

  • Gross Revenue

  • Net Revenue

  • Contribution Margin 1

  • Contribution Margin 2

Customers By Marketing Source

With this dimension group you can filter customers based on the marketing source that lead to an order.

With the first classifier you can select the marketing source you want to filter on. Here you have access to 17 different dimension:

  • Sales Channel

  • Marketing Channel Name, Group & Category based on the Channels you built in Klar

  • UTM Parameters - Source, Medium, Campaign, Term, Content

  • Landing Page

  • Device Category

  • Discount Code & Type

The marketing source is referring to the visit they placed the order in.

The second classifier can then be used to define to order count that you want to filter on.

You again have the option to select a specific order count, any order or the latest order.

Customers By Activity State

This dimension group can used to filter on the activity state of a customers.

With the first classifier you can select between the two different type of activity state:

  • Customer Frequency State - One-time Buyers, Repeat Customers, Loyal Customers, Evangelists

  • Customer Recency State - Active, At-risk, Defected, Reactivated

A detailed definition of the activity states can be found here --> Definition of Activity States

With the second classifier, you can select the point-in time you are referring to:

  • Any - was the customer ever in this state

  • Current - is the customer currently in this state

  • Previous - was he in this state immediately before moving into his current state

Customers By Value Metric

With this dimension group you can filter out customers based on their overall spending and purchasing behaviour.

The first classifier defines the metric you want to define their purchasing behaviour by. You can select from the following options:

  • Net Items

  • Gross items

  • Net Revenue - First Order, 90 Days, Lifetime

  • CM2/Lifetime Value - First Order, 90 Days, Lifetime

  • 90 Days Net Revenue Extension - How much did the net revenue increase in the first 90 days after his first purchase

  • 90 Days CM2/Lifetime Extension - How much did the CM2 increase in the first 90 days after his first purchase

  • Average Order Value - What was the Net AOV across all his orders

  • Average Discount Rate - What is the average Discount Rate across all his orders

  • Average Voucher Rate - What is the average Voucher Rate across all his orders

  • Average Return Rate - What is the average Return Rate across all his orders

The second classifier can be used to define the filter for this metric.

You can set the filter one of two ways:

  • Absolute - create the filter based on actual value

  • Percentile - create the filter based on the percentile of the value

So let's say you want to customers that have spend more than €200 over their lifetime. In this case you would select:

  • Classifier 1: Lifetime Net Revenue

  • Classifier 2: Absolute, greater than, €200

But if you would want the top 10% of your customers based on net revenue, you would select

  • Classifier 1: Lifetime Net Revenue

  • Classifier 2: Percentile, in, 90th Percentile + 95th

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