Now every business is an eCommerce: I need to analyse data, run campaigns, increase conversion, reduce cost per click, decrease churn rate… Which tool can do all at once? (CMO)
Type: Product
Self-service machine learning for customer journey: a predictive analysis tool that helps business specialists and marketing experts with intuitive and actionable insight.
- Visitor analysis: optimize campaigns & increase ROI.
- Predictive analysis: increase uplift & anticipate churn.
Service Mgmt. & Support
Hotline (English):
- Business hours (GMT +03:00, negotiable).
- Remote support provided via ticketing system.
- Email groups are available to post questions (counts as a ticket).
- SLA defined for critical/major/minor issues.
- No additional difference for bronze/silver/gold service levels.
Competitor Product
(Machine-learning side:)
(Marketing side:)
- SAS Enterprise Miner
- RapidMiner
- DataRobot
- Alteriycs
- Power BI ML
(Marketing side:)
- CleverTap
- Optimove
- Criteo
- Dynamic Yield
Use Cases & Pain Points Addressed
This tool solves the following pain points, or greatly reduces their impact:
Data analytics projects are expensive, and results are not known.
With this tool...:
Every business user can perform machine-learning analyses on a prepared sample of tabular data of any kind (e.g. about customer data, products, company, sensors...).
The complexity of algorithms and scores of Python and R codes have been translated into an easy-to-use application with comprehensive dashboards. No need for sophisticated theoretical background.
With a few clicks, and within a few minutes, data can be uploaded and processed to extract future predictions & newly found business parameter values.
Churn, Cross-sell, Fraud and many more data-driven predictions for relevant business questions can be addressed without training or IT-induced latency.
Purchase propensity, specifically for eCommerce businesses: predict your visitors & customers.
Examples of business scenarios and achievements (non-exhaustive):
Data analytics projects are expensive, and results are not known.
With this tool...:
- Quick trials are possible, at no cost.
- Results can be seen, before the project starts! (you get what you see)
- There is neither need for IT support to integrate the results, nor for a BI Tool to visualize the results. Output shows immediately to the user, with clear, easily understandable parameters and intuitive charts.
Every business user can perform machine-learning analyses on a prepared sample of tabular data of any kind (e.g. about customer data, products, company, sensors...).
The complexity of algorithms and scores of Python and R codes have been translated into an easy-to-use application with comprehensive dashboards. No need for sophisticated theoretical background.
With a few clicks, and within a few minutes, data can be uploaded and processed to extract future predictions & newly found business parameter values.
Churn, Cross-sell, Fraud and many more data-driven predictions for relevant business questions can be addressed without training or IT-induced latency.
Purchase propensity, specifically for eCommerce businesses: predict your visitors & customers.
- Improving marketing ROI (+30%): by predicting eCommerce visitors, therefore have a better targetting in campaigns.
- Expanding the target audience itself (+35%): better identification of the visitors that have higher conversion than “add-to-basket visitors”.
Examples of business scenarios and achievements (non-exhaustive):
- In a Fortune 150 Payment Services company, revenue loss from churn was reduced by over 70%. It clearly identified churn characteristics and allowed to take preventive action.
- A retail chain gets real-time (predictive) information about the next best product recommendation, at the cash-it time, through API integration.
- Government's effort to fight business fraud and smuggling was predicted with over ten-fold accuracy compared to previous methods.
- For an OTA, target groups for a specific e-mail campaign could be predicted & segmented, leading to click rates 17% higher vs. a control group...
Key Features & Differentiators
The following features and advantages should be noted:
An intuitive & non-technical user interface, helps focusing on business value instead of technical details: “Self-service analytics”, rather than relying on data experts or consultants… It unifies analysis and marketing in one platform.
Simplicity: the platform was designed by predictive modelers, so the modelling flow is smooth.
None of the competitors are as interactive and transparent:
Predictive analysis-related results are augmented with plug-ins to support marketing towards target audiences, by feeding the results directly into marketing tools.
Support of marketing & targetting:
No IT footprint: the platform can be used over the cloud and web, unless required by the policies of the enterprise.
Costs/Expenses: competitors are either more expensive and/or have fewer capabilities.
Performance benchmark:
An intuitive & non-technical user interface, helps focusing on business value instead of technical details: “Self-service analytics”, rather than relying on data experts or consultants… It unifies analysis and marketing in one platform.
Simplicity: the platform was designed by predictive modelers, so the modelling flow is smooth.
- It has a unique responsive app interface: one can log in and build models on a smart phone or tablet.
- After uploading data, a fully transparent machine-learning model can be built in 3-clicks.
- Advanced users can then move on to more advanced features and settings in case of need.
None of the competitors are as interactive and transparent:
- None support playing with parameters in what-if scenarios... literally while sitting in a CXO meeting.
- None provide trials or else do not open the platform to public for unsupervised use.
- More than "try & buy": the tool's web-platform is live, ready to sign-up and use. Only one competitor provides such a service (though via a download to local pc).
Predictive analysis-related results are augmented with plug-ins to support marketing towards target audiences, by feeding the results directly into marketing tools.
Support of marketing & targetting:
- Uses AI-based model.
- Generates automated customer segments, to be connected with marketing platforms (e.g. GoogleAdd, FacebookAdd, etc), whereas other tools don’t use AI-based models or select targets manually.
No IT footprint: the platform can be used over the cloud and web, unless required by the policies of the enterprise.
Costs/Expenses: competitors are either more expensive and/or have fewer capabilities.
Performance benchmark:
- In one large customer, prediction of customer churn has an error variance of only 1%.
- 10 minutes are enough to learn from 1 Mio records (data points).
- For smaller data sets with less than 100K records, it runs in seconds.
