IBM Watson Studio enables users to build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio enables users can operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. The vendor states the solution simplifies AI lifecycle management and accelerates time to value with an open, flexible multicloud architecture.
Predictive Pipeline kept track of every change in the sales pipeline and predicts quota attainment boasting 80% accuracy, with Neuralytics, the XANT predictive engine. The product was based on C9 Predictive Sales, owned and supported by XANT (formerly InsideSales.com) since May 2015, and no longer available for sale.
It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.
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
C9 is a good and economical solution for what it does. If evaluating them today, I would want to clearly understand their product roadmap and their ability to execute against it.
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
It gives you the ability to see where your pipeline was in the past and compare to how it is now. You can set queries that will allow you to show at risk deals, committed deals, etc.
This gives much better visibility for management to coach.
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. TR verified that a representative sample of customers was invited. More Info
We had uptime issues, weird error messages, sluggish performance, and bad data. I hope that some of this can be attributed to growing pains as the company was relatively new when we signed our initial contract.
It was not dependable enough to trust the data and to count on accessing the app when we needed it.
It actually was unavailable at EOQ one time.
I also did not care for the lack of real-time pipeline information. If someone was doing a pipeline review, there was no instant gratification i.e. to see changes in pipeline occur as an AE made a change to an opportunity.
You couldn’t do any configuration at all. Even if you just wanted to change a field or add a filter, you had to go through their services team. It was a recognized challenge and I saw roadmap addressing it.
The query tool is a little hard to use and we have found that queries are very slow and lock up.
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
C9 is now part of the sales management culture here at IPC. There is no longer any guesswork about the funnel or the forecast. C9 does something that SFDC does not...it increases significantly the value of the information in SFDC by unlocking the meta data that we all need to run the business
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. TR verified that a representative sample of customers was invited. More Info
From a sales manager’s perspective it was fairly easy to use the base functionality (just viewing current pipeline) and much harder to look at analytics (pipeline changes over time). C9 made this easier by allowing sales ops to publish views to sales managers. The query tool was harder to use than it had to be. For example, there were no out of the box relationships set up between Salesforce.com tables (e.g. accounts to opportunities), so I had to create those relationships myself.
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
I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path
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
C9 cares about their customers and responds quickly. However, the ticketing system could be better and there is no easy way to track the status of your requests.
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
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
Very simple implementation. They basically set up the imports and then they configure the tool per customer requests.
I wish there had been more consultation during the implementation, but it wasn’t bad given the effort expended. We ended up re-implementing after about a year and a half.
The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.
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. TR verified that a representative sample of customers was invited. More Info
Zoho CRM is less up to speed and much more out of date. The support at InsideSales.com Predictive Pipeline have been very helpful during the initial roll out face. Overall I was very happy!
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
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. TR verified that a representative sample of customers was invited. More Info
The product provides a single source for pipeline and forecast data and has scaled well with our organization. We have grown from 10 sales reps to 100 reps and we really needed a tool to to manage data and do roll-ups etc.
It's also important to provide senior management visibility into the pipeline, and the tool works well for this.
