Adobe Analytics vs. Heap vs. New Relic

Honestly, because Adobe Analytics is so customizable, I found that it is very well-suited for almost any type of web digital experience tracking of behavioral analytics. It has a very robust mech architecture for any type of e-commerce platform. But it is extensible and is easily adaptable to other circumstances. For example, in our university situation, we've been able to use it for student portal experience tracking, how well they are interacting, interfacing with our internal sites, and how well they are working with our task submission processes. But it does a great job of managing all aspects of the key journeys, especially from a marketing perspective. So while it might not be as out-of-the-box for some of those other alternative use cases outside of marketing, it's extensible and customizable enough that it's worked really well and met our needs.

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Scenarios when Heap was well suited: It is when a user claims that he encountered a bug without giving us the details of the error message. Scenarios where it is less appropriate: Its when we try to capture user interaction in our mobile app

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New Relic its an excellent tool for monitoring services used on the SAAS universe, like web servers, relational and nosql dbms, reverse proxies, text databases, etc. Its also a powerful tool to monitor resource usage on said servers. However, its not well fitted to monitor custom services - if you need to generate alerts based on logs or database information, for example

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• Within my role of advertising, I can come in, and I can see I'm paying for visitors, paying to drive people to the website. So I can see the differences in my different traffic sources, whether that's a Google search campaign or a Facebook social campaign. I can measure the quality of that traffic and see what they're doing, whether they're bouncing right away and leaving the website, or spending more or less time on the website. And whether they're taking the actions. My ad campaign is focused on filling out forms, and ultimately, that's it. Just measure and see if my campaigns are successful or not.

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• Realtime interactions which are captured automatically is a unique feature which no other product offers.
• Low code implementation is a big plus for marketing teams, as there is less dependency on engineering
• Detailed customer behaviours can be studied with captured replay sessions
• Due to all the above points - it helped to improve customer experience

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• It gives us insights of applications, infrastructure, clouds very well.
• The alerting system in New Relic helps us to easily identify the root cause which reduces the MTTR.
• It has new features related to AI which helps to troubleshoot any problem in a very short time it gives future predictions also.

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• I think the biggest room for improvement is performance. When I go in certain times of the day or for certain clients, it's slow and it won't load the reports that I need. And as a result, needing to answer a question where you normally have the expectation of it being a near real-time answer that you get when you have to wait for reports to load or you have to wait because the reports can't load at all. It's a really unfortunate thing. It's a big problem actually. So I'd say that's one area of improvement. It's just improving the performance of the reports so that they'll load consistently all the time quickly and effectively.

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• I think Heap can improve its onboarding; I would love to see some info that is helpful for someone who isn't in Heap every single day.
• Sharing dashboards can be tricky, it feels like a huge learning curve that I already have forgotten how to do.

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• And while powerful, building tailored dashboards with organ-specific metrics (such as energy load variance across regions) can be difficult to navigate. The UI isn't as drag-and-drop easy, and query-based widgets typically involve some trial and error for non-devs.
• Alerts may be hypersensitive or over general. I We often get a spam of non-critical alerts while doing load testing, all overhauling to me alone and making it difficult to identify actual issues especially in energy systems where spikes are very common.
• With our expanding fleet of Iot devices, the per-host pricing model is becoming expensive, quickly. More detailed billing based on microservices, or that works at sensor level, would make it more adaptable for energy platforms.

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We need it to discover threats long before they become a loophole in the security ecosystem. Also, it is very much compliant with customer standards and expectations. It provides marketing intelligence through in-depth analysis. Overall, a very good product to gain customer attention and thereby improve market

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It's a great platform. I'm glad that one of our product managers introduced it because it has allowed us to create all kinds of new functionality. We're not only able to create a better product experience from our communications because of Heap, but we're also able to generate all kinds of helpful analysis.

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The only issue that we have had with New Relic is that the price might be a little expensive for smaller companies. The amount of data you store in New Relic impacts the cost, and can get away from you if you don't work closely with the vendor. Overall though the application is top notch.

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It is necessary to have a minimum knowledge on tracking tools so you can use the tool on full performance. It is not an introduction tool, so please bear that in mind. Once you got the knowledge you just need a small training on how to create your custom reports, where to find the components you need and how to add them to your dashboard. Then you share your report or create a rule for periodic sharing and it's done. Finally, if you have a lot of data stored the tool might be a little slower but that's ok.

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On a scale from 1-10, I find Heap to be incredibly user-friendly and easy to use. I enjoyed the training videos available and was quickly able to pick up how to create events and reports to track user interactions on our product. I would recommend Heap for its usability first and foremost.

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I have given this much rating as I am used New Relic in different sectors and for different use cases like its K8s monitoring, infra monitoring, full stack monitoring as compare to other tools New Relic gives data in a formatted and connected way, and also it is giving us value for money. It also launches new features day by day which helps users to track the issue very quickly. It also supports OTel integrations which is the latest trend of observability tools. thats why I had given this much rating to New Relic.

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I do not ever recall a time when Adobe Analytics was unavailable to me to use in the 8 or so years I have been an end user of the product. My most-used day-to-day analytics tool Parse.ly however, generally has a multiple hours planned offline maintenance every two to four weeks, and sometimes has issues collecting realtime analytics that last anywhere between 15 minutes to an hour, and happen anywhere between 1 to 5 times a month.

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I've never run into any issues with Heap's availability, Heap is always there when I need it. I haven't run into any issues like application errors or unplanned outages during my 2+ years of using Heap. Each and every time I log in to Heap I have a completely functional experience

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Never observed an outage

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Again, no issues here. Performance within the day updates hourly. other reports are updated overnight and available to access by the next morning. Pages load quickly, the site navigates easily and the UX is quite straightforward to get command over. On this front, I give Adobe kudos for building a great experience to work within

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Heap doesn't affect page load times considerably nor has a large impact [on] our overall score, as far as page loading times inside of the tool its pretty reliable to retrieve data as much as "instant" that it can be the delay seems to be on data getting tracked into the servers to be read but it's not significant.

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there are times where browser cache will cause issues that require you to clear your browser before continuing.

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Support for Adobe Analytics is ok, it used to be worse years ago. Now, the technology team at Adobe is way more knowledgeable on the product itself as well as the implementation. They also study your custom implementation and have good knowledge of where your company stands. Dedicated support is something worth considering.

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Heap support has allowed us to troubleshoot and test a lot of different items. Their support team is always helpful and friendly, even when we come to them with the most complicated questions. I think this greatly improves the value proposition of the product because their support team is knowledgable and friendly.

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The support team has been really helpful and resolved most of the issues on time. However, for a couple of issues, several follow-ups were needed to elicit a reasonable response. The issue was deeply technical and could have been investigated only by their Architects, and bringing them into the ticket took longer than needed

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It was a one-day training several years ago that cost the organization several thousand dollars. There were only about 10 people in the training class. Adobe tried to cram so much information into that one-day class that none of our users felt like they really learned anything helpful from the experience. Follow-up training is too expensive

The online training for Adobe SiteCatalyst consists of short product videos. These are ok, but only go so far. For a while Adobe charged a fee for this, but recently made these available for free. There are many great blog posts that help users learn how to apply the product as well.

One of the benefits and obstacles to successfully using Adobe Analytics is a great / more accurate implementation, make sure your analytics group is intimate with the details of the implementation and that the requirements are driven by the business.

The implementation was smooth and easy. The Heap team helped us with implementation and it went great! Within a few weeks, we were fully up and running and utilizing the platform to its full capability. This is an additional thing that has made this platform so great and we couldn't recommend it enough.

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It's better to start by implementing New Relic in one project and test everything. Try to follow best recommended practices and read all the official documentation. Everything seems well tested. Then, start by installing agents to the rest of your projects and keep a close look to all logs and metrics New Relic gives you.

We evaluated and we currently use Mixpanel and we have Google Analytics on a couple of our properties. And honestly, once you get the hang of the Adobe Analytics workspace, the other products really don't stack up against it because the segmentation and the ability to create reports pretty rapidly are invaluable.

Heap offers a ton of functionality on a single platform.It also has an smart data science layer to offers suggestions for next steps in the analysis, allowing us to explore alternative paths we may not think to take. The low-code option for updating data is appealing, and there is a lot of automation with minimal engineering effort.

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Data Dog has solutions that look more attractive, but not at their price point. We have also tried to build a solution straight from the Cloud, where our business is built, but some things are too hard to replicate. This shows that New Relic is useful and helps our efficiency.

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Adobe Analytics is relatively affordable compared to other tools, given it provides a range of flexible variables to use that I have not found in any other tools so far. It is worth investing in if your company is medium or large-sized and brings a steady flow of revenue. For small companies, it can be overpriced.

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My organization uses Adobe Analytics across a multitude of brand portfolios. Each brand has multiple websites, mobile apps and some even have connected TV apps/channels on Roku and similar devices. Adobe can handle the multitude of properties that have simple, small(ish) websites and the larger brand properties that include web, mobile and connected TVs/OTT devices.

Each of those larger brands has multiple categories and channels to keep track of. We can see the data by channel/device or aggregate all the data together. This gives our executive teams the full picture and the departmental teams the view they need to see their own performance.

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The most challenging part of using Heap in a growing organization is the naming and structure in which reports and dashboards are organized. I work within the marketing department and our Heap leader internally works within the IT/Product department, which makes it challenging because we often don't speak the same language, so the learning curve has been steep without any specific use-case examples to leverage online.

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Agent deployment is easily integrated into our workflow. Adding the agent to new servers is quick and painless

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The professional services team is one of the best teams for complex adobe analytics implementations, especially for clients having multiple website and mobile applications. However, the cost of professional services is a bit high which makes few clients opt out of it, but for large scale implementations they are very helpful

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• Adobe Analytics impacts nearly every aspect of a billion plus dollar revenue eCommerce business. From measuring the impact of new build features to marketing campaigns.
• We are saving substantial money and resource effort by consolidating all of our properties to Adobe Analytics from alternative solutions, at which point we will finally be able to report on Total Digital, rather than disparate reports.
• We support experimentation on every platform and the performance is only known through Adobe Analytics tagging.

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• It has helped us make decisions on what products to scope for our MVP based on the usage data.
• It's allowed us to measure success with our beta product.
• It's helped us view our conversion rates in an easy and usable manner.

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• We were able to quickly identify our most time consuming APIs. In some cases we were able to bring down times for some apis from 4s to 200ms.
• We were able to identify our slowest database queries and optimize them for quicker response times.

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