Qubit, now from Coveo (acquired October 2021) uses visitor history data to understand different user segments and serve personalized messages to segments using JavaScript. It is available as either a managed or self-service model. Data is collected using Qubit's own Universal Variable data model, or by integrating the user's existing model via our API. It combines quantitative data with qualitative visitor feedback to give Qubit users the ability to detect areas for optimization.
Using…
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Elasticsearch
Score 8.5 out of 10
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Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
Coveo Qubit is a very helpful platform mainly for organizations that need to provide a solid business model before carrying out any implementation or new functionality. In addition, it is a very good tool to generate changes and show different content to different types of clients, with their personalization and segmentation criteria.It is ideal for simultaneous testing and customization, only one of these activities individually is not recommended.
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.
Qubit platform uses solid testing algorithms and delivers reliable reporting and analytics of testing campaigns. The dashboard section is easy to use and provides a good high level overview of the core campaign metric performances.
On-boarding and implementation of Qubit technology was painless and well handed throughout the entire process even with more complicated platforms.
Turn-around time for development and testing of campaigns is extremely fast. This enables the business to have a much higher throughput of tests and allows for quick validation of ideas rather than having to wait for months before a test is ready to go live on production site.
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.
While they do work very closely with us, especially our success manager, more involvement on a technical level or a dedicated engineer, would be a great advantage. We develop a lot of experiences in house and, like all companies that dev in house, we have our own way of working. A dedicated engineer that got to know us better as well and understood how we work could only help. Sometimes, a few errors can get through QA due to this.
Qubit is currently providing resources and support we do not have internally. Our relationship managers are exceptional and I feel well informed and well supported by their team. The tool is nice, but our contacts from the company are the real reason to maintain our relationship. They work hard for clarity and continue to help us push for additional opportunities.
Very simple user interface, built on top of advanced functionality, makes the platform easy to use. The team at Qubit are also very open to feedback and introduce new and useful features fairly often. On the reporting side, the inbuilt dashboard reports are good for a top-level view of test results, and Qubit have made a lot of their output data available should you wish to run your own analysis
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.
I would say that the Qubit account managers are always available for any request. We have a lot of different promotions that could always do with last minute optimizing or changes and Qubit can be relied on to get this changes up and running in an impressive amount of time, so that we don't need to patch live or wait for the next IT sprint. Invaluable to our business.
Technology is good for A/B testing and personalisation - allowing any team with a dedicated developer to create test relatively easily and to report/analyse them in a fair amount of details. Some advanced features, especially on the set up of test cells, are dearly missing. Unfortunately, new features are often not free of bugs... Also, support is sub-par, which means new features are realised without proper documentation, example or training (but of our Qubit counterparts and internally).
Qubit are supportive and flexible in providing support. They are happy working out of usual hours, even on weekends and if I have any doubts about the set up of an experience they’re quick to respond and willing to check my work. On particularly big revenue days they monitor our account and they’re quick to identify and problem solve any issues.
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.
The training was great, but would be great to have a script or PDF with some explanations of the qubit JS layer. Without the script you can just try to learn on your own, so the training is not as powerfull as it could be. On the other hand - would be great to have training related to reading statistics or personalisation.
Implementation couldn't be easier. All we needed to do was insert the tag. (easy) and set up the data layer. (dev required) This was pretty smooth in comparison to some of the other tools we use on our site, and was done in less than a day. Note : Data needs to be collected for a set period of time before you can accurately rely on the data that you are receiving. This is normal though with everyone else that we have used
At the time of taking the product, we found no comparable alternatives. Since then, the product has only grown from strength to strength, so it still does not have any comparable competitors that offer both the technical product and business knowledge that Qubit can. Google appear to offering a competing product which may be one to watch in future, and something for Qubit to keep an eye on
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.
Our requirements change throughout the year like most E Commerce retailers. At Christmas and Peak we're dealing with around ten times the usual traffic on the site. Qubit had no problems with this at all, tests continued to fire, and stats were still reported accurately. I don't think that it is the most server intensive .js anyway, but we have seen no issues at all.
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.
