IBM Digital Analytics
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What is IBM Digital Analytics?
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What is IBM Digital Analytics?
IBM acquired Coremetrics in 2010, and has since re-branded the platform to the IBM Digital Marketing Optimization Solution. This cloud-based solution includes IBM Digital Analytics, the core analytical product, as well as several add-on modules, including Benchmark, Product Recommendations, Lifecycle, Multisite, and more. IBM has integrated the Digital Marketing Optimization platform with other solutions, like WebSphere Commerce and Portal for ecommerce, IBM Campaign and Interact (acquired from Unica in 2010) for cross-channel campaign management, IBM Tealeaf (acquired in 2012) for customer experience management, and much more. IBM continues to integrate the Digital Marketing Optimization platform into many other solutions as well, from business intelligence tools, to social media analytics, and more.
IBM Digital Marketing Optimization primarily competes with other enterprise-level digital analytics solutions like Adobe Omniture, Google Analytics, WebTrends, and more.
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- Click pathways provided a robust way to analyze visitor pathways
- TruePath Funnels were essential to learn day over day changes through key lead capture paths
- Ability to accept and table a variety of user & registration IDs allowed us to perform off-UI multichannel analysis
- The JavaScript library sitting at the back-end of IBM Digital Analytics is extremely cumbersome for non-developer, marketing users. While the JS library does provides endless customization, the complexity and ramp-up time to understand the basics can be a barrier when trouble-shooting basic re-configurations.
- IBM Digital Analytics is HEAVILY geared towards online retailers, acting more as a website's cash register as opposed to a tried-and-true web diagnostic and analytics tool
- The support for IBM Digital Analytics was lackluster, we found ourselves often tangled in the bureaucracy that is IBM, when REAL problems occurred.
- Certainly gave us a an accurate, last click view of channels contributing to lead conversion
- Tracking ROI on marketing spend
- Highlight areas for UX improvement
- Benchmark historical performance
- Provide a blended view of the channels involved in a user's conversion cycle
- Creating an data-base centered around IBM Digital Analytic's registrationID, used to track off-site lead throughput and identify a full view on the user's marketing lifecycle
- Automation of email based on data from IBM Digital Analytics
- Vendor implemented
Reliable, Marketing oriented & efficient Digital Analytics Solution
In terms of business consultancy, we use IBM DA to produce regular dashboards and deep dive analysis for our E-commerce clients.
- Marketing attribution models - capable to deliver an holistic view of the marketing channels performance
- Multisite capabilities : allows to have a Cie view as well as individual site's view
- Unique customer view and Live Profile: we are able to go to the visitor level and therefore use the behavioural data for marketing actions
- Reliability of the data: we have full confidence in the data collected and the rules are clear for us
- Support: available support 24h/7.
- Limit in Explore Credits that are too costly
- Segmentation on the fly (like GA) in DA - segmentation in IBM DA are poor and too restricted
- Benchmark module that is a real strengh but under exploited
- DDX : not performant enough - better to use 3rd party solution such as Tealium
- R&D : no major next feature or project over the past year
- Increase conversion - onsite search analysis, promotional analysis etc allowed to prioritise the investments and the on-going efforts for our clients
- Better ROI - using the solution is allowing us to educate the client into measuring ROI on every action undertaken on the site
- Better decision : we do no longer take decisions without looking at the data first
- Google Analytics,Adobe Business Catalyst,Adobe Analytics
Compared to Google: the main asset of IBM compared to GA is the fact that you can go down to the visitor level in Analytics and run much more precise analysis. Also, in Google the data calculation and attribution models can be unclear and therefore difficult to trust.
E-commerce is real strong point.
- E-commerce is Coremetrics' strong point. The solution is very good at reporting for ecomerce sites. For example, it gives excellent data on products purchased and abandoned cart value and other E-commerce statistics. Omniture is perhaps better for more general content browsing statistics, but Coremetrics is the clear leader for E-commerce sites.
- Easy and well documented implementation
- Fast, easy and customizable reporting UI
- Data mining or AdHoc visit-based analysis: All packages provide a way to do a deep dive on data. Usually you will need a custom report to cover something that is not covered out-of-the-box. For example, you might want to know whether more purchases are coming from people using one browser over another. Coremetrics does provide access to the database to build relationships between objects to construct custom reports. though it is possible to build these ad-hoc reports in Coremetrics, it's not that easy. Once you stray outside the standard templated reports, it's not the best solution for creating custom reports.
- Since it's a flat, table-based analytics solution, there are potential table limits which can be reached on large datasets
- Close monitoring of the different marketing online initiatives. Making sure money is well spent.
- Coremetrics enables marketing stakeholders to easily follow and measure the success of their marketing initiatives.
- Track conversion in sales and enables you to test different marketing offers and see the impacts in real $
- Strong and very reliable solution for businesses taking critical marketing decisions based on analytics
Coremetrics is renowed for it's great support and easy implementation which - along with cost - was a deciding factor in many situations.
- Professional services company
- Online training
- Self-taught
Solid web analysis product
- Unlike Google Analytics, which provides generic/multi-purpose tags for any kind of implementation you can imagine, Coremetrics provides a set of single-purpose tags along with an implementation strategy to help businesses get the right data in the right format. For example, there are tags specifically for adding an item of merchandise to a cart. Since this is a fundamental action for most e-commerce sites, the straightforwardness of this tagging (and the subsequent e-commerce reports it populates) is very convenient (and in some cases, where customization is required, very limiting).
- As mentioned above, the use case-specific tags populate very specific reports, such as cart abandonment, checkout conversion for e-commerce, etc. These out-of-the-box reports can be very useful if you have a generic implementation that matches Coremetric's expected usage.
- Explore reports are powerful because they allow you to create custom reports on practically any data point, enabling you to answer very specific questions.
- The Coremetrics implementation guide is a thorough document that explains every aspect of how Coremetrics works and the technology should or could be implemented on your site. For an engineer responsible for implementation, it is immensely helpful.
- Compared to Google Analytics, the reporting interface is slower and less user-friendly.
- There is no way to easily manage different sites in a single enterprise (though I think this may have been addressed in a newer version).
- The Impression Attribution add-on module is quite rudimentary and, compared to an ad-serving platform, very costly per impression. (But if you need impression data populated within Explore and not some other third-party, then it's for you.)
- We sought to analyze visitor/traffic/content data in order to A) tailor the user experience to increase engagement and B) obtain the data points against which to sell media/sponsorship.
- We used Coremetrics Web Analytics to measure everything related to online marketing campaigns and customer behavior: visitors, traffic sources, content pathing, ROI for paid ads, checkout conversion rates, email campaign success, and visitor segmentation.
- We utilized Explore and Impression Attribution to dig into answers to very specific questions such as, "Which members who were exposed to brand X via step Y of web asset Z in the past 6 months?" These figures were valuable to sell and optimize branded campaigns to partners.
- Implemented in-house
- Online training
- Self-taught