Customer Segmentations: Bloomreach vs. Braze and Klaviyo
Explore the key differences in customer segmentation between Bloomreach Engagement and leading alternatives like Braze and Klaviyo.
About the Author:
Co-founder of Datacop, a digital agency that fulfils marketing operation roles in large eCommerce companies such as OluKai, Melin, Roark, Visual Comfort and Company, Dedoles and others.
In the world of e-commerce marketing, Email and CRM managers spend a lot of time thinking about their customers and how to split them up into meaningful audiences, based on the information available about those customers. The identification & use of these audiences in marketing channels is the basis for succesful targetting and personalisation - as it enables the tailoring of messaging and content. It is no surprise then that most digital marketing automation offer no-code interfaces to target specific audiences, based on the data captured by their respective softwares.
At first glance, the Segmentations features of Klaviyo, Braze, Attentive and Bloomreach seem similar. The feature offers users to build dynamic logical rules that define users’ membership into a particular “list of users” or a segment. These logical rules are recalculated as the user database of updates with data such as user behaviour events and user customer attributes. (note that the recalculation speed between platforms may vary)*
Common features of Segments features across all platforms include
an easy to use “customer filter logic builder”
the use of logical combinations “Logic A AND / OR Logic B”
the ability to use negation logic “user has not done A”
the use of parenthesis “(A OR B) AND (C OR D)”
calculations of the size of segment membership based on current rules
the ability to use the generated Segment of customers in marketing campaigns
It is not easy to be able to distinguish the differences between the platforms at first sight. The platforms also often look alike, just consider these screenshots from the documentations of Klaviyo / Braze and Attentive on how the interface of their respective Segments features look like.
At Datacop, over the years we have worked with all 4 of the platforms and we have found there is one key difference between the Bloomreach Engagement Segmentations and the more generic Segmentations offered by platforms like Klaviyo / Braze / Attentive, which is that…
Klaviyo / Braze / Attentive segments can only “hold one segment”
In Klaviyo / Braze / Attentive, the segmentations features only support Single-lists! That means that to maintain an Email Health Segmentation that has 10x segments, would require the building and maintain of 10 separate “Segmentations”. If these segments have a risk of overlap between them, these risks need to be mapped and included as exclusion rules into each separate segment.
For e-commerce companies that have scaled beyond basic segmentations of such tools these limitations present a lot of added extra effort to implement a campaign, increased risk of error and finally prevents more advanced use-cases and analytics.
Bloomreach Segmentations are More Sophisticated and Flexible
Unlike the typical marketing automation players such as Klaviyo / Braze / Attentive / etc. ; Bloomreach Engagement allows businesses to build mutually-exclusive multi-segment audiences. Mutual exclusivity means that a single user within a Segmentation can only belong to one of segments. That means that a database of say a 1M subscribers can be divided up or “segmented” into groups that are both mutually exclusive and cumulatively exhaustive. For bigger e-commerce brands that have more types of customers and more data to segment them with; having access to multi-segment audiences can make the building of more complex segmentations easier.
The functionality to host multiple segments in a single segmentation means that:
Building audiences with overlapping customer membership is far easier to execute for marketers than using “single-list” segmentations.
Building mutually exclusive and cumulatively exhaustive segmentations enables building important user analytics such as CLV and A/B test Reports directly in the platform.
In the rest of this article we will dive deep into the above and illustrate why we prefer to work with Bloomreach Segmentations in Datacop over the typical “single-segment” Segmentations available on the mark-tech e-commerce market.
What are the advantages of having multi-segment Segmentations?
1. Reduced Effort & Mutual Exclusivity
Audiences can often has multiple overlapping outcomes for a customer, or there are multiple outcomes for a customer. Consider the below examples:
Email Health Segmentation (e.g. New, Active, Passive, Lapsing, Lapsed, Inactive)
Gender-affinity Segmentation (e.g. Male / Female / Kids)
Category-affinity Segmentation (e.g. Shoes / Tops / Hats )
When building such segmentations, having the ability for a single Segmentation to host multiple segments means that, while normally for each possible outcome it would be necessary to build a separate segment object in the platform, in Bloomreach Engagement there will be one object to maintain “housing” all of the possible outcomes of an audience in one Segmentation. This saves time and reduces the scope of error when changing and maintaining their Segmentations and their application in campaigns.
Without the Bloomreach Engagement’s feature of mutual exclusivity in multiple segments, when building audiences where overlap is possible - it is needed to add additional conditions to resolve possible conflicts in overlap. Consider the example of the Email Health Segmenation, which divides subscribers based on their recency of their subscription and user engagement across channels. To build a “Passive” segment for instance would not only include filtering on users that haven’t engaged in the last 90 days, but also include a rule that they are not “New” - meaning they haven’t subscribed in the last 30 days. In Bloomreach you would simply make sure the “New” segment is placed ahead of the “Passive” segment within the same Segmentation.
In Bloomreach Engagement, Segmentations automatically have an “conflict resolution” feature - if a user satisfies the conditions of more than one Segment in the whole Segmentation, they will “fall” into the one most recent, i.e. the one most on the left. This saves time and makes it easier to work with more complex audiences. Without this feature, in Klaviyo / Braze / Attentive it is needed to maintain far more segments, logic rules and run a much higher risk of a customer falling into 2 “mutually-exclusive” audiences at once such as New and Passive.
2. User Analytics
Segmentations in Bloomreach Engagement are not only very useful for marketing campaigns. That is because the Bloomreach automatically stores the segment membership of users within a customer attribute in the customer profile of each user.
That has great utility. It can be used for checking if the segmentation worked as intended and for debugging purposes. The most common use of this feature is however in building User based Analytics & Reporting.
In Bloomreach Reports - essentially an advanced PIVOT table that allows you to quickly build a wide range of reports based on Bloomreach data - Segmentations are available to be selected as a customer attribute. As a result it is possible to filter the whole report by a specific segment of users or exclude a specific segment of users from the report. An example of this would be to exclude Employee and Tester profiles from any user analytics or campaign reporting.
Additionally, as can be seen in the screenshot above it is possible to split the calculation of metrics in a Report per Segments in a Segmentation. In combination with the multi-segment feature of Segmentations, this enables valuable reports; where the mutual exclusivity can be guaranteed and each segment represents one “user cohort”. This is very useful when dealing with A/B test results for example, where situations where a user has seen both versions of the Variant are classified into the “mixed” segment so they do not “pollute” the final A/B test results.
Additional examples of relevant user analytics that take advantage of this feature are:
CLV (Customer Lifetime Value) Customer Report
Comparing B2B vs B2C users’ KPIs
Comparing users’ KPIs based on their home-market (UK vs US vs EU)
Comparing users’ KPIs based on the category of their 1st Purchase
3. Usage in Jinja
Furthermore, Bloomreach enables the use of Segmentations as a jinja object directly when building personalisation logic in Scenarios, in Emails, the Website, in SMS or Push campaigns.
The example of the below snippet builds the back-bone of a product recommendation logic based on collection preference of the customer. In combination with the recommendation object, the segmentation can be used to dynamically filter products at the “category_names” setting of the recommendation. As a result the products provided by the recommendation will be filtered by one of the six customer’s possibilities of the segmentation. If you only had single-segment lists available, to replicate this functionality you would required longer and more manual code.
{# example of a jinja snippet using the segmentations object #}
{% set preferred_collection = segmentations['667a12cbffbbcd991416fd88'] %}
{% set products = recommendations('669a40479382cf6a199631a9', fillwithrandom = false, size = 4, category_names = preferred_collection) %}Consider the example of a collection-affinity email block product recommendation. The marketing team had 6 different types of collections of products and would like to surface products from one of them to the customer. If the customer has expressed interest into one of the collections, based on on-site behaviour and transactional data - the products from that collection would be surfaced for that customer. If the customer expressed interest in more than one of the collections the team wanted to make sure there is a “pecking order” to them due to their varying quality and price points - so they can select which collections are “more important” in a conflict thatn others.
Thanks to Bloomreach Engagement Segmentation’s out of the box conflict resolution and multi-segment capabilities, more complex segmentations for website or email campaigns are easier to build. Furthermore, thanks to the flexible jinja abilities, it is possible to build easier and lighter code to implement the logic in email campaigns, banners on the website, etc.
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