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
TensorFlow
Score7.6 out of 10
N/A
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
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
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
TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
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
Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.
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
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.
Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.
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
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.
Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice
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.
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
