An integrated data analytics platform - Accelerate digital transformation with data analytics
Prebuilt analytics models from best practices proven in the fields allow you to secure data-based insights faster than anyone else, because they can be instantly applied to your business.
Brightics Machine Learning can quickly process large amounts of data and present the results in a chart. It supports rendering of data with up to 100 million rows.
Even expert analysts can benefit from the recommendations. Optimal algorithms for the analysis environment and data guarantee easy modeling for the best analysis results.
The detailed instructions explaining the analysis process in each stage allows anyone to analyze data with excellent results.
By predicting demands and sales of products, sales can be optimized. Through more accurate prediction of sales, the promotiion effects can also be analyzed.
[Values for business]
- Optimize sales and preemptive market response through demands/sales prediction
- Anlyze promotion effects through accurate sales prediction
- Calculate product life cycle and adjust launching period for new products based on the sales trend in the past & plan promotion
It is possible to quickly find out about the market response by analyzing positive/negative feedback from customers based on buzz words arisen from SNS.
[Values for business]
- Find out about causes of customers' negative opinions regarding brands, product categories based on buzz words arisen from SNS / Manage product quality with prompt response
- Analyze buzz words and influencers from SNS, news and tech forums
※ Buzz words: customers' opinions about products, which can be found online including SNS, news and tech forums
The customer satisfaction can be improved by establishing efficient workforce plans through prediction of the number of visitors to the store, optimizing the workforce allocation based on the customer visit patterns and analyzing customer behavior data.
[Values for business]
- Predict the total sales amount by studying the prediction results of the number of visitors, labor cost and the conversion rate (CVR) / Reduce 11% of topportunity/loss cost of sales
- Improve operation effiviency by optimizing workforce allocation based on the customer visit patterns / Improve customer satisfaction through data analytics on customer behavior
Analyze marketing effects
It is possible to not only measure the degree of the sales influence depending on marketing activities but establish marketing plans depending on the investment size by promotion by establishing data-analytics-based marketing decision making schemes.
[Values for business]
- Measure modeling-based investment performance
- Predict the degree of sales increment influence and ROI depending on marketing activities
- Measure performance of promotions by product/transaction channel
- Establish data-analytics-based marketing decision making schemes
- Investment size/mix depending on marketing activities and promotion purposes
Customized recommendation
It is possible to provide customized services by predicting the preference of products/ad by customer. The customer satisfaction will be improved and the marketing targeted based on customers will be available.
[Values for business]
- Developing engines that recommend customization by predicting consumers' preference and suggesting highly preferred items
- By realizing algorithms that provides recommendations based on customization, utilizing the analytics results for customized recommendation businesses (e.g. products, ad) by combining with IoT in the future
- Expect the improved satisfaction by recommending based on customization through Prescriptive Analytics
Without complicated coding, the drag & drop function visualizes the workflow. This also enables to have a glimpse of input/output data and applied algorithms, which helps on-site/industry experts easily analyze data.
By providing the most optimized algorithm for data and functions that suggest algorithms to suggest proper parameters, on-site experts can easily do modelling. This can minimize trial and error in the course of finding the most optimized model and learning factors.
By providing the service of Guided Analytics that explains the analytics courses in each stage in detail, users can easily analyze difficult data while following the guideline stage by stage.
It is possible to immediately edit and serve analytics results as a report. By registering the scheduler, automatic updates are supported. Users can quickly check out the data and report that they want whenever.
Recommended specifications
x86 server with 3 or more nodes
-CPU: 8 cores/Node
-Memory: 16GB/Node
-Disk: 100GB/Node
-Operating system: Linux 64bits
- Browser : Chrome (Version 50.0 or higher)
- Screen resolution: 1280 x 900 (recommended)
RDB, File, HDFS
Scala, SQL, R, Python
REST API
SDS Cloud, Amazon Web Service, MS Azure, Google Cloud, etc.
This is the chart option guideline for Brightics Machine Learning.
This is the guideline for developers of Brightics Machine Learning.