台灣碩軟軟體有限公司 SoftwareONE Taiwan Ltd. - Smart Member Label Dashboard

This solution uses the Azure Machine Learning service to discover VIP, premium, classic, potential, new and first-time members from within company’s database of membership and transactions, then uses Power BI to show the difference in the transaction amount, transaction number and transaction time of each label. You can export data for various members directly from the Power BI website, and let the marketing unit get the latest list of member labels.

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The company has a large amount of member information and transaction data stored in the database, storage or text files, but they do not know where to start the analysis. SoftwareONE’s solution: “Agile Big Data and Member Labels” can help customers quickly attain their business goals.  


By transferring member information and transaction data to Azure platform through Data Factory, then using Azure Machine Learning to calculate the difference between members and export member clusters; finally using Power BI to integrate member labels with transaction data, we can help customers understand the differences in behavior between labels from the perspective of various members.


Overview presents the analysis of the six member labels in terms of the number of items selected, total consumption, total transactions, reward rate, the percentage of each label, the area ratio, and so on.

Time View can perform six member labels and patterns of trading time.

Merchandise View can perform the purchase preferences for each member's label and product. For example, the M200 Camera is the VIP’s favorite, but is third for classic members, so we can consider recommending it to classic members.

Label Compare can perform the results of Machine Learning calculations with an easy-to-understand quadrant chart, and can express the difference in the recent consumption dates, the frequency of consumption, the consumption amount, and the number of member labels. In the valuable quadrant, Between VIP members and premium members, they have similar frequency and recent consumption dates, but the difference is that VIP members show larger consumption. Both new members and first-time members have low frequency and long recent consumption dates.  Potential members have  considerable consumption amounts and numbers of members.  In this case, we will recommend developing marketing strategy to drive this label member group into the valuable quadrant.

This solution is suitable for any enterprise and industry with existing member information and transaction data.  SoftwareONE can help you achieve your goal of managing big data through “Agile Big Data and Smart Member Labels”.


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