Conga Advantage CPQ vs. DataRobot

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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DataRobot can be used for risk assessment, such as predicting the likelihood of loan default. It can handle both classification and regression tasks effectively. It relies on historical data for model training. If you have limited historical data or the data quality is poor, it may not be the best choice as it requires a sufficient amount of high-quality data for accurate model building.

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• 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.

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• DataRobot helps, with algorithms, to analyze and decipher numerous machine-learning techniques in order to provide models to assist in company-wide decision making.
• Our DataRobot program puts on an "even playing field" the strength of auto-machine learning and allows us to make decisions in an extremely timely manner. The speed is consistent without being offset by errors or false-negatives.
• It encompasses many desired techniques that help companies in general, to reconfigure in to artificial intelligence driven firms, with little to no inconvenience.

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• 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• In my opinion, the configuration methods of Conga CPQ are outdated and error-prone.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.

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• The platform itself is very complicated. It probably can't function well without being complicated, but there is a big training curve to get over before you can effectively use it. Even I'm not sure if I'm effectively using it now.
• The suggested model DataRobot deploys often not the best model for our purposes. We've had to do a lot of testing to make sure what model is the best. For regressive models, DataRobot does give you a MASE score but, for some reason, often doesn't suggest the best MASE score model.
• The software will give you errors if output files are not entered correctly but will not exactly tell you how to fix them. Perhaps that is complicated, but being able to download a template with your data for an output file in the correct format would be nice.

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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

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DataRobot presents a machine-learning platform designed by data scientists from an array of backgrounds, to construct and develop precise predictive modeling in a fraction of the time previously taken. The tech invloved addresses the critical shortage of data scientists by changing the speed and economics of predictive analytics. DataRobot utilizes parallel processing to evaluate models in R, Python, Spark MLlib, H2O and other open source databases. It searches for possible permutations and algorithms, features, transformation, processes, steps and tuning to yield the best models for the dataset and predictive goal.

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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.

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I have not heard of any issue

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.

As I am writing this report I am participating with Datarobot Engineers in an complex environment and we have their whole support. We are in Mexico and is not common to have this commitment from companies without expensive contract services. Installing is on premise and the client does not want us to take control and they, the client, is also limited because of internal IT regulations ,,, soo we are just doing magic and everybody is committed.

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We attended 5 hours worth of webinars early on.

We had to understand how objects worked to figure out how to structure our data.

I would have liked more in-depth training but it would have been an additional cost

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

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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.

I've done machine learning through python before, however having to code and test each model individually was very time consuming and required a lot of expertise. The data Robot approach, is an excellent way of getting to a well placed starting point. You can then pick up the model from there and fine tune further if you need.

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• 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.

• We have been able to cut costs by not buying leads that we will not be able to sell on
• We have been able to deploy loan eligibility reporting which brought in new business
• We have been able to improve the performance of our credit providers and our partners which has helped to retain business

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