Bloomreach is a Commerce Experience Cloud, that aims to enable brands to deliver customer journeys so personalized, they feel like magic. It offers a suite of products that aim to drive true personalization and digital commerce growth, including: Discovery, offering AI-driven search and merchandising; Content, offering a headless CMS; and Engagement, offering a CDP and marketing automation solutions. Together, these solutions combine unified customer and product data with…
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Elasticsearch
Score 8.1 out of 10
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Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
Bloomreach Commerce Experience Cloud is a great platform for building, managing, and optimizing end-to-end user journeys. It's also very scalable, and provides multiple channels for users, with good data and consent management features for those wary about compliance issues. Aside from the core functionality around user journeys, I feel that other channels and features could be built out more (and I have no doubt this will happen in the future). This is the main thing stopping this from being a 10/10 offering, which the core functionality is.
Elasticsearch is a really scalable solution that can fit a lot of needs, but the bigger and/or those needs become, the more understanding & infrastructure you will need for your instance to be running correctly. Elasticsearch is not problem-free - you can get yourself in a lot of trouble if you are not following good practices and/or if are not managing the cluster correctly. Licensing is a big decision point here as Elasticsearch is a middleware component - be sure to read the licensing agreement of the version you want to try before you commit to it. Same goes for long-term support - be sure to keep yourself in the know for this aspect you may end up stuck with an unpatched version for years.
All in one. We have experience working with other CDP/Marketing suites and it is really difficult to connect their different modules. Bloomreach Engagement has been built to be a single product which makes everything easy to use
Fast and real-time. Other platforms struggle with getting real-time volumes or segmenting based on real-time events, that is not the case with Bloomreach Engagement
GCP partnership. Being deployed over GCP and having BigQuery as raw data export makes really easy to expand and connect with existing stacks
As I mentioned before, Elasticsearch's flexible data model is unparalleled. You can nest fields as deeply as you want, have as many fields as you want, but whatever you want in those fields (as long as it stays the same type), and all of it will be searchable and you don't need to even declare a schema beforehand!
Elastic, the company behind Elasticsearch, is super strong financially and they have a great team of devs and product managers working on Elasticsearch. When I first started using ES 3 years ago, I was 90% impressed and knew it would be a good fit. 3 years later, I am 200% impressed and blown away by how far it has come and gotten even better. If there are features that are missing or you don't think it's fast enough right now, I bet it'll be suitable next year because the team behind it is so dang fast!
Elasticsearch is really, really stable. It takes a lot to bring down a cluster. It's self-balancing algorithms, leader-election system, self-healing properties are state of the art. We've never seen network failures or hard-drive corruption or CPU bugs bring down an ES cluster.
Simplifying how data is presented, the dashboard can quite often be confusing. Having a more intuitive interface and the ability for different reporting would be beneficial.
I'd like more of an overview dashboard that I could build and access on the home screen, to look at top-level metrics.
Easier to use segmentation, and quicker to build features within.
So far, Hippo is our ideal tool given its use of open standards which helps us to have a clear overview of what's coming next in the future Hippo releases. We are very confident in the future-readiness of the product
In my time working with Bloomreach Commerce Experience Cloud, I always liked to work with it. It is crucial that you get support from experts from the beginning to show you how to work with the vast amount of options and activities to choose from. The learning curve is also well-rounded because of its user-friendly interface and highly skilled customer support.
To get started with Elasticsearch, you don't have to get very involved in configuring what really is an incredibly complex system under the hood. You simply install the package, run the service, and you're immediately able to begin using it. You don't need to learn any sort of query language to add data to Elasticsearch or perform some basic searching. If you're used to any sort of RESTful API, getting started with Elasticsearch is a breeze. If you've never interacted with a RESTful API directly, the journey may be a little more bumpy. Overall, though, it's incredibly simple to use for what it's doing under the covers.
In this section I must say, the tech support for Bloomreach Engagement is doing an amazing job, not just with the basic stuff like getting around the interface, but more complexed like jinja code for some advanced personalisation use cases.
We've only used it as an opensource tooling. We did not purchase any additional support to roll out the elasticsearch software. When rolling out the application on our platform we've used the documentation which was available online. During our test phases we did not experience any bugs or issues so we did not rely on support at all.
It's way more comprehensive than any other mail provider I've used and has a lot more functionality. A huge step change for our business that has brought us to the forefront of personalized marketing. Mailchimp is lightyears behind this approach in particular. The other two applications are actually part of Bloom.
As far as we are concerned, Elasticsearch is the gold standard and we have barely evaluated any alternatives. You could consider it an alternative to a relational or NoSQL database, so in cases where those suffice, you don't need Elasticsearch. But if you want powerful text-based search capabilities across large data sets, Elasticsearch is the way to go.
We have had great luck with implementing Elasticsearch for our search and analytics use cases.
While the operational burden is not minimal, operating a cluster of servers, using a custom query language, writing Elasticsearch-specific bulk insert code, the performance and the relative operational ease of Elasticsearch are unparalleled.
We've easily saved hundreds of thousands of dollars implementing Elasticsearch vs. RDBMS vs. other no-SQL solutions for our specific set of problems.
