Constructor Search promises to improve conversions and revenue from onsite and in-app search, using search science and artificial intelligence, Constructor's cloud-based search-as-a-service solution uses natural language processing, machine learning-enhanced results ranking, collaborative personalization, and merchant controls to power enterprise-grade onsite and in-app search. Whether search results are optimized for relevance, revenue, conversions, conversations — or all of the…
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Coveo Relevance Cloud
Score 8.0 out of 10
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Coveo is an enterprise search technology which can index data on disparate cloud systems making it easier to retrieve. It has integrated plug-ins for Salesforce.com, Sitecore CEP, and Microsoft Outlook and SharePoint.
$600
per month
Optimizely Web Experimentation
Score 8.7 out of 10
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Whether launching a first test or scaling a sophisticated experimentation program, Optimizely Web Experimentation aims to deliver the insights needed to craft high-performing digital experiences that drive engagement, increase conversions, and accelerate growth.
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Pricing
Constructor.io Search
Coveo Relevance Cloud
Optimizely Web Experimentation
Editions & Modules
No answers on this topic
Base
$600
per month
Pro
$1,320
per month
No answers on this topic
Offerings
Pricing Offerings
Constructor.io Search
Coveo Relevance Cloud
Optimizely Web Experimentation
Free Trial
No
Yes
Yes
Free/Freemium Version
No
No
No
Premium Consulting/Integration Services
No
Yes
Yes
Entry-level Setup Fee
No setup fee
Optional
Optional
Additional Details
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More Pricing Information
Community Pulse
Constructor.io Search
Coveo Relevance Cloud
Optimizely Web Experimentation
Considered Multiple Products
Constructor.io Search
No answer on this topic
Coveo Relevance Cloud
No answer on this topic
Optimizely Web Experimentation
Verified User
Director
Chose Optimizely Web Experimentation
Optimizely has a more robust toolset and is also more affordable.
Constructor.io Search takes all of the guesswork out of maintaining a search engine. As merchandisers or product teams, we have educated guesses at how search relevance should work but the customer is always king. We can't always predict the ways in which consumers will search or what their intent is. That's why the behavioral-driven approach that Constructor.io employs works so well. It means that merchandisers can focus on their sales and promotional responsibilities, instead of wasting time and bandwidth on base-level relevance questions.
Coveo Relevance Cloud is a great solution to implement into Salesforce to provide Knowledge-Centered Support, Enhancements to a Customer Community, to provide sales aids, or to complement your customized app in Salesforce.
I think it can serve the whole spectrum of experiences from people who are just getting used to web experimentation. It's really easy to pick up and use. If you're more experienced then it works well because it just gets out of the way and lets you really focus on the experimentation side of things. So yeah, strongly recommend. I think it is well suited both to small businesses and large enterprises as well. I think it's got a really low barrier to entry. It's very easy to integrate on your website and get results quickly. Likewise, if you are a big business, it's incrementally adoptable, so you can start out with one component of optimizing and you can build there and start to build in things like data CMS to augment experimentation as well. So it's got a really strong a pathway to grow your MarTech platform if you're a small company or a big company.
The Platform contains drag-and-drop editor options for creating variations, which ease the A/B tests process, as it does not require any coding or development resources.
Establishing it is so simple that even a non-technical person can do it perfectly.
It provides real-time results and analytics with robust dashboard access through which you can quickly analyze how different variations perform. With this, your team can easily make data-driven decisions Fastly.
It would be great if Coveo 6 allowed you to rebuild indexes from a certain subtree instead of needing to rebuild the entire tree to see changes. This functionality was added in Coveo 7 and is very useful.
In Coveo 6, integration with Sitecore is more difficult than one would expect. This integration is much improved in Coveo 7.
I have seen cases where an exception thrown when crawling a specific document will cause the indexing to stop completely. I believe this only happens in implementations using custom faceting but it could be handled more efficiently if the trouble document was skipped and the indexing could continue.
Relevancy ranking editor is good but not as powerful as GSA. GSA offers a self-learning scorer which automatically analyzes user behavior and the specific links that users click on for specific queries to fine tune relevance and scoring.
We've ran into issues on multiple clients with Sitecore items being indexed multiple times in Sitecore 7 and Coveo 7. The fix Coveo suggested was to upgrade our Sitecore version and Coveo but unfortunately this didn't resolve our issue. After months of testing we were finally able to resolve this by implementing our own CoveoItemCrawler to get around the issue (based on https://developers.coveo.com/display/public/SC201404/Items+in+the+Same+Language+Gets+Indexed+Multiple+Times;jsessionid=3C1A2AE33540E0A0B8BB52BA3A64AF70).
Integration with RabbitMQ in Coveo 7 seems error prone. We often see the error "The AMQP operation was interrupted" and on occasion, need to restart the Coveo service to get this operating again. In some extreme cases, we have also had to restart the server because of issues when attempting to restart the Coveo service.
I rated this question because at this stage, Optimizely does most everything we need so I don't foresee a need to migrate to a new tool. We have the infrastructure already in place and it is a sizeable lift to pivot to another tool with no guarantee that it will work as good or even better than Optimizely
Optimizely Web Experimentation's visual editor is handy for non-technical or quick iterative testing. When it comes to content changes it's as easy as going into wordpress, clicking around, and then seeing your changes live--what you see is what you get. The preview and approval process for sharing built experiments is also handy for sharing experiments across teams for QA purposes or otherwise.
I would rate Optimizely Web Experimentation's availability as a 10 out of 10. The software is reliable and does not experience any application errors or unplanned outages. Additionally, the customer service and technical support teams are always available to help with any issues or questions.
I would rate Optimizely Web Experimentation's performance as a 9 out of 10. Pages load quickly, reports are complete in a reasonable time frame, and the software does not slow down any other software or systems that it integrates with. Additionally, the customer service and technical support teams are always available to help with any issues or questions.
They always are quick to respond, and are so friendly and helpful. They always answer the phone right away. And [they are] always willing to not only help you with your problem, but if you need ideas they have suggestions as well.
The tool itself is not very difficult to use so training was not very useful in my opinion. It did not also account for success events more complex than a click (which my company being ecommerce is looking to examine more than a mere click).
In retrospect: - I think I should have stressed more demo's / workshopping with the Optimizely team at the start. I felt too confident during demo stages, and when came time to actually start, I was a bit lost. (The answer is likely I should have had them on-hand for our first install.. they offered but I thought I was OK.) - Really getting an understanding / asking them prior to install of how to make it really work for checkout pages / one that uses dynamic content or user interaction to determine what the UI does. Could have saved some time by addressing this at the beginning, as some things we needed to create on our site for Optimizely to "use" as a trigger for the variation test. - Having a number of planned/hoped-for tests already in-hand before working with Optimizely team. Sharing those thoughts with them would likely have started conversations on additional things we needed to do to make them work (rather than figuring that out during the actual builds). Since I had development time available, I could have added more things to the baseline installation since my developers were already "looking under the hood" of the site.
The ability to do A/B testing in Optimizely along with the associated statistical modelling and audience segmentation means it is a much better solution than using something like Google Analytics were a lot more effort is required to identify and isolate the specific data you need to confidently make changes
We can use it flexibly across lines of business and have it in use across two departments. We have different use cases and slightly different outcomes, but can unify our results based on impact to the bottom line. Finally, we can generate value from anywhere in the org for any stakeholders as needed.
Quick to find things in a massive database when needed.
Results need to be more concise - sometimes we spend more time looking for the right file than if we were to just search amongst our own networks instead.
Coveo is not always the most useful but does its job when general information is needed.
We're able to share definitive annualized revenue projections with our team, showing what would happen if we put a test into Production
Showing the results of a test on a new page or feature prior to full implementation on a site saves developer time (if a test proves the new element doesn't deliver a significant improvement.
Making a change via the WYSIWYG interface allows us to see multiple changes without developer intervention.
