Conga CPQ empowers sales, partners, and customers to efficiently configure complex products and services offerings, and provide personalized prices and quotes, utilizing codified product and pricing information - to drive higher win rates and a more pleasurable buying experience. Conga CPQ also helps to maintain a single price book, discounting structure, and quoting structure across all channels. With an API-first approach, configuration, pricing, or quoting…
$35
per month per user
H2O.ai
Score6.4 out of 10
N/A
An open-source end-to-end GenAI platform for air-gapped, on-premises or cloud VPC deployments. Users can Query and summarize documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project. And the commercially available Enterprise h2oGPTe provides information retrieval on internal data, privately hosts LLMs, and secures data.
It is well suited to providing quick pricing recommendations, allowing those who are quoting to get our agreements out efficiently. Where I find there may be some limitations is around the details that it uses to establish recommendations and the overrides. For example it would be nice to have a way to set overrides for those criteria like length of agreement, etc. and have it apply across the board
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Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use. If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
The perceived power strength is that it is supposed to contain CPQ, Contract Management, Document generation and template manipulation, and cash/invoice process all in one wrapped package.
It was developed on the Force.com platform.
They provide multiple releases of their product per year.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Our number one complaint with Conga CPQ has been speed. In my experience, Conga CPQ is extremely slow, especially for large orders.
In my opinion, the configuration methods of Conga CPQ are outdated and error-prone. One literally puts configurations into string-based custom settings, including the API field names. This often leads to deployment issues and run-time configuration errors.
In my experience, Conga CPQ is everything but simple to develop. You need things like a 12-step pricing callback to support custom pricing.
In my experience, Conga CPQ support is not responsive.
When it comes time to lock in a renewal contract for Conga CPQ, in my experience, they delay engagement, so you are truly behind the 8 ball when it comes time to decide if you are going to continue with Conga CPQ.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
The rating is based on several things: 1) Ongoing support requirements being able to be addressed by cross training existing Salesforce administrators 2) Apttus superior corporate vision for the quote to cash space 3) Apttus execution of the corporate vision with automated agents (Max), and Artificial Intelligence/Machine Learning offerings to leverage the investment in Configure Price Quote 4) Apttus corporate health and investment in the product line
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Conga CPQ is a great tool but lacks good support and [a] very limited knowledge base which doesn't include day to day errors which users face, thus leading us to support and take more time in turn. Also cart performance can be improved drastically which will enhance the user experience as the user doesn't have to wait for the pricing.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Tier1/tier 2 support can only handle native functionality. Customizations have to be escalated to developers which aren’t included in the support program.
I go ahead and copy the people I directly worked with on implementation for assistance. I would rate them an 8 for support assistance.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Be iterative. Take the opportunity to build a catalog based on how Apttus works well. Learn the tool yourself or use an SI. Take the time to build a configuration / pricing migration tool with X-Author for Excel or roll your own. Stick with OOTB Apttus as any customization will cost you every time a new version is released
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
We selected Apttus CPQ over SteelBrick due to the simplicity of SteelBrick's out of the box pricing and ability to customize quoted products. As a global organization with selling a highly configurable products, we felt the ability of Apttus to handle our requirements as standard functionalty rather than a customization was a material difference between the platforms.
Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a competitor but rather a complementary tool. The use case is usually not only about the algorithms, but also about the data model and data logistics and accessibility. H2O is more accessible due to its UI. Also, both can be accessed from Python. The community around TensorFlow seems larger than that of H2O.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
The ability to generate engineered configurations that is right by construction has reduced the cycle time of the customer engagement. The fact that we are able to guide the process and end up with a validated bill of material reduces the iterations with the customers.
As long as the validations rules are correct the generated bill of material is accurate. We are now looking at using Apttus to perform quality checks in our product rules since the tool is able to test different configurations quickly and efficiently.
Configuration that use to take weeks and consumed valuable engineering resources has been transformed to become a customer facing application that is simple enough for customer to self-service.
Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction
Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors
Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info