G2M Platform vs. IBM SPSS Modeler

Creating impact analysis when there are multiple levels of categories for comparison, i.e., firmographics, geo, etc.

Fast NLP analytics are very easy in SPSS Modeler because there is a built-in interface for classifying concepts and themes and several pre-built models to match the incoming text source. The visualizations all match and help present NLP information without substantial coding, typically required for word clouds and such. SPSS Modeler is good at attaining results faster in general, and the visual nature of the code makes a good tool to have in the data science team's repository. For younger data scientists, and those just interested, it is a good tool to allow for exploring data science techniques.

Incentivized

• Data Insights
• Machine Learning
• Drive Growth

• Combine text and data
• Provide facilities for all phases of the data mining process.
• Use a node and stream paradigm to easily and quickly create models.

Incentivized

• More Visuals
• Better Data Hygiene Features

• Has very old style graphs, with lots of limitations.
• Some advanced statistical functions cannot be done through the menu.
• The data connectivity is not that extensive.
• It's an expensive tool.

Incentivized

The ability to do predictive modeling, text analytics for both structured & unstructured data, decision management, optimization, and support for various data sources

Incentivized

The online support board is helpful and the free add ons are incredibly appreciated.

Incentivized

Analyzr gives more transparency than the others tools we have used, allowing us to see the actual model and data insights instead of a black box approach. The tool is also more intuitive then others, allowing members with limited Python, R coding to create models.

When it comes to investigation and descriptive we have found SPSS Statistics to be the tool of choice, but when it comes to projects with large and several datasets SPSS Modeler has been picked from our customers.

Incentivized

• Identified a 30% lift in certain Marketing Efforts, justifying the continuing spend here.
• Clustering allows us to target specific customers for upgrade, resulting in a top 10% to focus sales and marketing resources on.

• Positive - Ease of decision making and reduction in product life cycle time.
• Positive - Gives entirely new perspective with the help of right team. Helps expanding the portfolio.
• Negative - Needs to have good understanding about mathematical modelling, of which talent is rare and expensive. Hence, increase the costs for R&D and manpower.

Incentivized