{"data":{"id":"885334","string_id":"rdcqfa6H","status":"1","parent_id":"885333","slug":"rdcqfa6H","order":"0","name":"Beginner's Guide","type":"1","cover":"","cover_alt":"","desc":"","website":"","contact":"","seo_title":"","seo_keyword":"","seo_desc":"","author":"","published_time":"1785825074","add_time":"1785825074","update_time":"1785828228","corp_auth_id":"0","verify_code_auth_id":"0","editor_type":"3","tags":[],"parent_name":"Sensors Analysis","parent_type":"1","parent_slug":"zJeBZdZb","child":[{"id":"885335","string_id":"UKeR3Fvx","status":"1","parent_id":"885334","slug":"UKeR3Fvx","order":"0","name":"What is Sensors Analytics?","type":"2","cover":"","cover_alt":"","desc":"","website":"","contact":"","seo_title":"","seo_keyword":"","seo_desc":"","share_switch":"0","share_code":"","author":"","published_time":"1785825075","add_time":"1785825075","update_time":"1785828228","corp_auth_id":"0","verify_code_auth_id":"0","editor_type":"3","file_type":"","auth_id":"[\"885335\", \"885334\", \"885333\"]","content":{"content_id":"885335","content":"\n

Introduction to Shence Analysis<\/span><\/h2>
Sensors Analytics is a deep user behavior analysis product designed for enterprise clients. It supports private deployment and comprehensive data collection and modeling across client, server, business, and third-party platforms. This drives applications such as marketing channel effectiveness evaluation, refined user operations improvement, product feature and user experience optimization, management dashboards to aid decision-making, personalized product recommendations, and user tagging system construction. As a PaaS platform, it supports secondary development and can be integrated with internal IT systems such as BI, big data platforms, CRM, and ERP to build user data systems, unlocking the profound value of user behavior data.<\/p>
<\/span><\/p>
Sensors Analytics' Product Advantages<\/span><\/h2>
<\/span><\/p>
Private deployment is possible<\/span><\/h3>
Eliminating customers' concerns about data storage security \u2013 When deciding on Sensors Analytics technology, we considered customers' concerns about data security and privacy, as well as their need to accumulate their own user behavior data assets for in-depth data application and secondary function development. Therefore, we made private deployment the core design concept of Sensors Data's products.<\/p>
Basic data collection and modeling<\/span><\/h3>
Achieving seamless data integration for the same user across different sources\u2014As the internet becomes increasingly integrated, a user's behavioral data on the same product needs to be collected from multiple different sources. These sources include iOS, Android, Web, H5, mini-programs, business data, historical data, and third-party data. To address clients' needs for cross-platform data collection and the integration of data from different sources for the same user, Sensors Data provides methods such as code tracking, full tracking, and import tools to connect data to Sensors Analytics. Furthermore, to solve the problem of cross-screen integration for a single user, Sensors Data also offers solutions for user ID mapping.<\/p>
Real-time and flexible multidimensional analysis capabilities<\/span><\/h3>
Offering full-featured multidimensional analytics capabilities to dissect user behavior\u2014dimensions and metrics do not require predefinition; funnel analysis, retention analysis, and distribution analysis can all drill down to any dimension. Sensors Analytics not only stores the most granular details of user behavior data but also supports private deployment, provides API interfaces, and supports secondary development. Data analysis is a broad field, and Sensors Data primarily meets clients' needs in the specific area of user behavior analysis. Sensors Data promises not to touch client data, helping enterprises use their internal data for analysis, focusing on user behavior analysis relevant to their business data.<\/p>
Provides a PaaS platform for in-depth development.<\/span><\/h3>
We support secondary development using the underlying interfaces and functions\u2014privatization does not equal customization. Sensors Data provides standard code for standard requirements. If a customer has only one standard data analysis need, they don't need to make any decisions and can directly use Sensors Analytics. If a customer has personalized data analysis needs, they can use Sensors Analytics' underlying data interfaces or perform secondary development on the product functions.<\/p>
Sensors Analytics solves enterprise pain points<\/span><\/h2>
Business pain points<\/span><\/h3>
Marketing analytics gaps: High marketing costs and gaps in tracking user acquisition effectiveness make it difficult to monitor actual conversion rates across channels and accurately analyze ROI. Sensors Analytics offers channel tracking capabilities, allowing you to track the source of new users and intuitively evaluate channel acquisition capabilities with data. It also analyzes registration and paid conversions, measuring end-to-end channel acquisition quality and continuously adjusting channel influence strategies. Sensors Analytics also provides marketers with site-wide user behavior tracking, optimizing the site browsing experience and content interaction to improve registration lead conversion rates.<\/p><\/li>
Product iterations cannot be quantified: The lack of real-time user behavior analysis capabilities makes it impossible to quantify the effects of product feature redesigns. Core process optimizations rely more on guesswork, and bug identification is delayed, leading to long-term losses. Sensors Analytics provides funnel analysis to help businesses improve user conversion rates, monitor core conversion processes (such as payment, registration, and account opening), and identify churn issues. Simultaneously, Sensors Analytics offers retention analysis to help businesses improve user retention, validate the most popular product features with data, provide user behavior path analysis, rationally distribute in-site traffic, fine-tune product layout and interactions, and enhance user experience.<\/p><\/li>
Inaccurate user operations: A one-size-fits-all approach to marketing to all users makes it difficult to control ROI, and this imprecise, broad-based method fails to truly improve the long-term activity of existing users. Sensors Analytics helps businesses identify user groups with similar characteristics and deliver targeted marketing content tailored to their preferences, increasing engagement among different user groups. For users who have been inactive for a long time and are on the verge of churn, timely messages and coupons are sent to re-engage them. Simultaneously, Sensors Analytics establishes a user tagging system and user profiles, cross-analyzing user behavior with operational data to gain insights into core user characteristics and create precise user profiles.<\/p><\/li>
Global operational metric monitoring is not real-time: While there is an operational BI system, operational metrics monitoring is not timely, and a core metric early warning mechanism is lacking, leading to delayed decision-making. Sensors Analytics provides managers with real-time, cross-departmental, and multi-business-line core metrics. Managers can receive automatically pushed subscribed reports to their email accounts on a regular schedule, and the system also offers customizable metric early warning capabilities. This helps various business departments monitor changes in core metrics in real time, quickly detect and analyze sudden anomalies, and truly achieve real-time data-driven business decision-making.<\/p><\/li><\/ul>
Technical pain points<\/span><\/h3>
Building your own platform is costly: Creating a new product data platform from scratch within an enterprise, with the requirement for rapid deployment, incurs extremely high costs. With the support of the Sensors Analytics service team, even without a dedicated data team, enterprises can quickly store user target behavior data, achieve full-platform data access to Sensors Analytics, and complete the construction of their enterprise data analytics platform.<\/p><\/li>
Low efficiency in daily collaboration: Business analysts need to submit data requests to data engineers for their specialized research, entering a cycle of requirement specification, requirement assessment, iteration scheduling, and requirement correction. Sensors Analytics helps business analysts quickly get started using the product, without needing to request support from data engineers. They can view important daily data indicators through Sensors Analytics' data overview; at the same time, Sensors Analytics' nine data analysis models enable users to view custom indicators, perform cross-analysis across any dimension, drill down and roll up data, locate abnormal data indicators, set outlier alerts, and send email alerts to designated email addresses for abnormal indicators.<\/p><\/li>
ETL work is complex: Data engineers need to handle various complex data in their daily ETL work. Enterprises don't need to worry about the complexity of various data and not knowing where to start. Sensors Data's ETL engineers will provide full assistance in handling the process. Even if an enterprise has massive amounts of historical data, importing it into Sensors Analytics is easy.<\/p><\/li>
Repetitive Development for Common Requirements: For common data collection requirements such as traffic and clicks, R&D engineers need to redevelop the data for each specific business, resulting in a large amount of repetitive development work. Enterprises can use Sensors Analytics' full-tracking feature to collect browsing or click behavior across all pages at once, eliminating the need for repetitive development by R&D engineers. Sensors Analytics' integration SDK supports one-click activation, automatically collecting behavioral events such as startup, exit, page browsing, and control clicks, easily obtaining data such as PV, UV, and new users.<\/p><\/li><\/ul><\/div><\/div><\/div><\/body><\/html>","pure_content":"Introduction to Shence Analysis Sensors Analytics is a deep user behavior analysis product designed for enterprise clients. It supports private deployment and comprehensive data collection and modeling across client, server, business, and third-party platforms. This drives applications such as marketing channel effectiveness evaluation, refined user operations improvement, product feature and user experience optimization, management dashboards to aid decision-making, personalized product recommendations, and user tagging system construction. As a PaaS platform, it supports secondary development and can be integrated with internal IT systems such as BI, big data platforms, CRM, and ERP to build user data systems, unlocking the profound value of user behavior data. Sensors Analytics' Product Advantages Private deployment is possible Eliminating customers' concerns about data storage security \u2013 When deciding on Sensors Analytics technology, we considered customers' concerns about data security and privacy, as well as their need to accumulate their own user behavior data assets for in-depth data application and secondary function development. Therefore, we made private deployment the core design concept of Sensors Data's products. Basic data collection and modeling Achieving seamless data integration for the same user across different sources\u2014As the internet becomes increasingly integrated, a user's behavioral data on the same product needs to be collected from multiple different sources. These sources include iOS, Android, Web, H5, mini-programs, business data, historical data, and third-party data. To address clients' needs for cross-platform data collection and the integration of data from different sources for the same user, Sensors Data provides methods such as code tracking, full tracking, and import tools to connect data to Sensors Analytics. Furthermore, to solve the problem of cross-screen integration for a single user, Sensors Data also offers solutions for user ID mapping. Real-time and flexible multidimensional analysis capabilities Offering full-featured multidimensional analytics capabilities to dissect user behavior\u2014dimensions and metrics do not require predefinition; funnel analysis, retention analysis, and distribution analysis can all drill down to any dimension. Sensors Analytics not only stores the most granular details of user behavior data but also supports private deployment, provides API interfaces, and supports secondary development. Data analysis is a broad field, and Sensors Data primarily meets clients' needs in the specific area of user behavior analysis. Sensors Data promises not to touch client data, helping enterprises use their internal data for analysis, focusing on user behavior analysis relevant to their business data. Provides a PaaS platform for in-depth development. We support secondary development using the underlying interfaces and functions\u2014privatization does not equal customization. Sensors Data provides standard code for standard requirements. If a customer has only one standard data analysis need, they don't need to make any decisions and can directly use Sensors Analytics. If a customer has personalized data analysis needs, they can use Sensors Analytics' underlying data interfaces or perform secondary development on the product functions. Sensors Analytics solves enterprise pain points Business pain points Marketing analytics gaps: High marketing costs and gaps in tracking user acquisition effectiveness make it difficult to monitor actual conversion rates across channels and accurately analyze ROI. Sensors Analytics offers channel tracking capabilities, allowing you to track the source of new users and intuitively evaluate channel acquisition capabilities with data. It also analyzes registration and paid conversions, measuring end-to-end channel acquisition quality and continuously adjusting channel influence strategies. Sensors Analytics also provides marketers with site-wide user behavior tracking, optimizing the site browsing experience and content interaction to improve registration lead conversion rates. Product iterations cannot be quantified: The lack of real-time user behavior analysis capabilities makes it impossible to quantify the effects of product feature redesigns. Core process optimizations rely more on guesswork, and bug identification is delayed, leading to long-term losses. Sensors Analytics provides funnel analysis to help businesses improve user conversion rates, monitor core conversion processes (such as payment, registration, and account opening), and identify churn issues. Simultaneously, Sensors Analytics offers retention analysis to help businesses improve user retention, validate the most popular product features with data, provide user behavior path analysis, rationally distribute in-site traffic, fine-tune product layout and interactions, and enhance user experience. Inaccurate user operations: A one-size-fits-all approach to marketing to all users makes it difficult to control ROI, and this imprecise, broad-based method fails to truly improve the long-term activity of existing users. Sensors Analytics helps businesses identify user groups with similar characteristics and deliver targeted marketing content tailored to their preferences, increasing engagement among different user groups. For users who have been inactive for a long time and are on the verge of churn, timely messages and coupons are sent to re-engage them. Simultaneously, Sensors Analytics establishes a user tagging system and user profiles, cross-analyzing user behavior with operational data to gain insights into core user characteristics and create precise user profiles. Global operational metric monitoring is not real-time: While there is an operational BI system, operational metrics monitoring is not timely, and a core metric early warning mechanism is lacking, leading to delayed decision-making. Sensors Analytics provides managers with real-time, cross-departmental, and multi-business-line core metrics. Managers can receive automatically pushed subscribed reports to their email accounts on a regular schedule, and the system also offers customizable metric early warning capabilities. This helps various business departments monitor changes in core metrics in real time, quickly detect and analyze sudden anomalies, and truly achieve real-time data-driven business decision-making. Technical pain points Building your own platform is costly: Creating a new product data platform from scratch within an enterprise, with the requirement for rapid deployment, incurs extremely high costs. With the support of the Sensors Analytics service team, even without a dedicated data team, enterprises can quickly store user target behavior data, achieve full-platform data access to Sensors Analytics, and complete the construction of their enterprise data analytics platform. Low efficiency in daily collaboration: Business analysts need to submit data requests to data engineers for their specialized research, entering a cycle of requirement specification, requirement assessment, iteration scheduling, and requirement correction. Sensors Analytics helps business analysts quickly get started using the product, without needing to request support from data engineers. They can view important daily data indicators through Sensors Analytics' data overview; at the same time, Sensors Analytics' nine data analysis models enable users to view custom indicators, perform cross-analysis across any dimension, drill down and roll up data, locate abnormal data indicators, set outlier alerts, and send email alerts to designated email addresses for abnormal indicators. ETL work is complex: Data engineers need to handle various complex data in their daily ETL work. Enterprises don't need to worry about the complexity of various data and not knowing where to start. Sensors Data's ETL engineers will provide full assistance in handling the process. Even if an enterprise has massive amounts of historical data, importing it into Sensors Analytics is easy. Repetitive Development for Common Requirements: For common data collection requirements such as traffic and clicks, R&D engineers need to redevelop the data for each specific business, resulting in a large amount of repetitive development work. Enterprises can use Sensors Analytics' full-tracking feature to collect browsing or click behavior across all pages at once, eliminating the need for repetitive development by R&D engineers. Sensors Analytics' integration SDK supports one-click activation, automatically collecting behavioral events such as startup, exit, page browsing, and control clicks, easily obtaining data such as PV, UV, and new users.","is_draft":"0","editor_type":"3","file_path":"","add_time":"1785825077","update_time":"1785828228"},"pure_content":"Introduction to Shence Analysis Sensors Analytics is a deep user behavior analysis product designed for enterprise clients. It supports private deployment and comprehensive data collection and modeli"},{"id":"885336","string_id":"0ZunmSux","status":"1","parent_id":"885334","slug":"0ZunmSux","order":"0","name":"What can Sensors Analytics do?","type":"2","cover":"","cover_alt":"","desc":"","website":"","contact":"","seo_title":"","seo_keyword":"","seo_desc":"","share_switch":"0","share_code":"","author":"","published_time":"1785825078","add_time":"1785825078","update_time":"1785828228","corp_auth_id":"0","verify_code_auth_id":"0","editor_type":"3","file_type":"","auth_id":"[\"885336\", \"885334\", \"885333\"]","content":{"content_id":"885336","content":"\n
In the previous article, we learned that Sensors Analytics provides solutions to the pain point of \"low data efficiency\" for enterprises. These solutions include enabling business personnel to independently acquire and analyze data to solve problems without needing support from product\/data engineers.
Below, we will explain the role of Shence Analysis by focusing on specific roles and issues.<\/p>
Assisting each role in achieving self-service analysis<\/span><\/h2>
Sensors Analytics supports data analysis for business personnel in various roles, including product, operations, channel placement, and technology, and provides solutions for the analysis and evaluation of the following issues.
<\/span><\/p>
Support product feature evaluation to improve conversion rates<\/span><\/h3>
As product planners, product roles focus on ensuring that the product's workflow and functional design provide a good user experience and that users fully experience the product's core value. The practical challenges products face include the following:<\/p>
How to evaluate the effectiveness of a function<\/li>
How to assess content popularity<\/li>
How to improve core process conversion<\/li>
How to optimize product experience<\/li>
How to assess feature retention?<\/li>
How to optimize the onboarding process to improve user retention?<\/li><\/ul>
<\/span><\/p>
Support operations in segmenting users to achieve precise marketing.<\/span><\/h3>
The operations role focuses on the current state and changes in user demographics, and analyzes user activity and churn from a behavioral perspective. Strategies are implemented to incentivize different user segments, aiming to improve the product's core performance indicators (KPIs). The practical challenges faced by operations include the following:<\/p>
How to assess user composition<\/li>
How to improve user retention rate<\/li>
How to evaluate new\/existing user retention performance<\/li>
How to evaluate the impact of different ad placements on promotional effectiveness?<\/li>
How to assess the impact of campaign operations on user activity levels<\/li><\/ul>
<\/span><\/p>
Support channels in controlling both the quantity and quality of new customer acquisition.<\/span><\/h3>
Channel roles should focus on the quantity and quality of new user acquisition through different channels, and compare the ROI of different channel campaigns.
The practical problems faced by channel deployment include the following:<\/p>
How to evaluate the number of new users acquired through each channel<\/li>
How to evaluate the quality of new users acquired through channel promotion?<\/li>
Are there differences in subsequent behavior among users acquired through different channels?<\/li><\/ul>
<\/span><\/p>
Provide solutions for data acquisition technicians<\/span><\/h3>
Technical roles focus on how to quickly, accurately, and comprehensively collect and integrate data, fully understand the analytical needs of business personnel, and collaborate to achieve product performance growth targets.
The practical problems facing the technology include the following:<\/p>
How to collect data<\/li>
How to implement data tracking across all platforms<\/li>
How to import existing historical data<\/li>
How to improve efficiency, avoid redundant development, and reduce R&D investment?<\/li><\/ul>
<\/span>
In the next article, we will take the e-commerce industry as an example to demonstrate how Sensors Analytics can help product, operations, and channel marketing colleagues quickly understand the current situation and pinpoint problems through various analytical functions.<\/p><\/div><\/div><\/div><\/body><\/html>","pure_content":"In the previous article, we learned that Sensors Analytics provides solutions to the pain point of \"low data efficiency\" for enterprises. These solutions include enabling business personnel to independently acquire and analyze data to solve problems without needing support from product\/data engineers. Below, we will explain the role of Shence Analysis by focusing on specific roles and issues. Assisting each role in achieving self-service analysis Sensors Analytics supports data analysis for business personnel in various roles, including product, operations, channel placement, and technology, and provides solutions for the analysis and evaluation of the following issues. Support product feature evaluation to improve conversion rates As product planners, product roles focus on ensuring that the product's workflow and functional design provide a good user experience and that users fully experience the product's core value. The practical challenges products face include the following: How to evaluate the effectiveness of a function How to assess content popularity How to improve core process conversion How to optimize product experience How to assess feature retention? How to optimize the onboarding process to improve user retention? Support operations in segmenting users to achieve precise marketing. The operations role focuses on the current state and changes in user demographics, and analyzes user activity and churn from a behavioral perspective. Strategies are implemented to incentivize different user segments, aiming to improve the product's core performance indicators (KPIs). The practical challenges faced by operations include the following: How to assess user composition How to improve user retention rate How to evaluate new\/existing user retention performance How to evaluate the impact of different ad placements on promotional effectiveness? How to assess the impact of campaign operations on user activity levels Support channels in controlling both the quantity and quality of new customer acquisition. Channel roles should focus on the quantity and quality of new user acquisition through different channels, and compare the ROI of different channel campaigns. The practical problems faced by channel deployment include the following: How to evaluate the number of new users acquired through each channel How to evaluate the quality of new users acquired through channel promotion? Are there differences in subsequent behavior among users acquired through different channels? Provide solutions for data acquisition technicians Technical roles focus on how to quickly, accurately, and comprehensively collect and integrate data, fully understand the analytical needs of business personnel, and collaborate to achieve product performance growth targets. The practical problems facing the technology include the following: How to collect data How to implement data tracking across all platforms How to import existing historical data How to improve efficiency, avoid redundant development, and reduce R&D investment? In the next article, we will take the e-commerce industry as an example to demonstrate how Sensors Analytics can help product, operations, and channel marketing colleagues quickly understand the current situation and pinpoint problems through various analytical functions.","is_draft":"0","editor_type":"3","file_path":"","add_time":"1785825080","update_time":"1785828228"},"pure_content":"In the previous article, we learned that Sensors Analytics provides solutions to the pain point of \"low data efficiency\" for enterprises. These solutions include enabling business personnel to indepe"},{"id":"885337","string_id":"UowzOrfG","status":"1","parent_id":"885334","slug":"UowzOrfG","order":"0","name":"How does Sensors Analytics solve problems?","type":"2","cover":"","cover_alt":"","desc":"","website":"","contact":"","seo_title":"","seo_keyword":"","seo_desc":"","share_switch":"0","share_code":"","author":"","published_time":"1785825081","add_time":"1785825081","update_time":"1785828229","corp_auth_id":"0","verify_code_auth_id":"0","editor_type":"3","file_type":"","auth_id":"[\"885337\", \"885334\", \"885333\"]","content":{"content_id":"885337","content":"\n
In the previous article, we saw that Sensors Analytics can support the problem analysis process for different business roles.
Here, we take the e-commerce industry as an example to introduce how Sensors Analytics helps business personnel in product, operations, and channel placement to quickly conduct data analysis.<\/p>
Standard e-commerce core processes<\/span><\/h2>
E-commerce users typically go through the following core behavioral processes:<\/p>
Launch App<\/li>
Start browsing the homepage<\/li>
Click and browse the product details page<\/li>
Once you see a suitable item, add it to your shopping cart.<\/li>
If you intend to purchase, submit an order.<\/li>
Decide to make payment, pay for the order.<\/li><\/ol><\/blockquote>
The core product process can be described as follows:<\/p>
Launch the App - Browse the homepage - Browse the product details page - Add to cart - Submit order - Pay for order<\/p><\/blockquote>
Common Data Analysis Problems in the E-commerce Industry<\/span><\/h2>
In the specific work of the e-commerce industry, channel placement personnel focus on the quantity and quality of customers acquired through each channel, operations personnel focus on how to segment users and conduct targeted operations, and product personnel focus on the conversion of each core process and the user experience of each function.
We will list the main analytical requirements as follows:<\/p>
How many new customers are acquired through each promotional channel? Which channel has the strongest customer acquisition capability?<\/li>
Are there differences in the behavior of users from different channels from app activation to order payment?<\/li>
What could be the reasons for user churn during the payment process?<\/li>
What can I do about losing users through payment services?<\/li>
How can we comprehensively monitor the quantity and quality of new user acquisition across all channels?<\/li><\/ul>
Let's now learn how to find the answers to the questions above one by one.<\/p>
Sensors Analytics Solution<\/span><\/h2>
View the number and proportion of new customers from each channel.<\/span><\/h3>
First, we want to understand the customer acquisition capabilities of each channel, and then calculate the ROI based on the advertising costs.
In Sensors Analytics, we can do the following:<\/p>
View the total number of new customers, and then drill down by dimensions such as date and channel.<\/p>
<\/span><\/p>
View the proportion of new customers acquired through each channel to the total number of new customers acquired.<\/p>
<\/span><\/p>
View the core process conversion rate of users across different channels<\/span><\/h3>
Examine the overall conversion rate of the core customer acquisition process across various channels, as well as the conversion rates between each step, to identify areas for improvement in the overall conversion rate.<\/p>
<\/span><\/p>
Multi-dimensional analysis of user payment behavior from various channels<\/span><\/h3>
In the previous question, we saw the conversion rates of users from various channels towards the product's intended target behavior. At the same time, we also hope to obtain more data to evaluate the quality of customer acquisition across channels.<\/p>
View the conversion time from app activation to order payment for users from various channels.<\/p>
<\/span><\/p>
Examine the distribution of average payment order amount and total payment order amount among users from different channels to determine the impact of channels on users' spending power.<\/p>
<\/span><\/span><\/p>
Analyze the reasons for user churn during the payment process<\/span><\/h3>
Payment is typically a targeted user behavior for e-commerce products, but a significant number of users inevitably churn at this stage. We want to understand the subsequent behaviors of users who churn during the payment process. Is their payment behavior affected by interference from other stages?
Sensors Analytics allows users to view the historical behavior sequences of specific user groups, identify order submissions, and manually annotate subsequent behaviors to infer why payments were not made.<\/p>
<\/span><\/p>
Analyze user activity from various channels<\/span><\/h3>
We want to understand the activity level of users from various channels, as well as the frequency of the target behavior\u2014payment order behavior.<\/p>
View user activity across different channels<\/p>
<\/span><\/p>
Observe the frequency of payment order behavior of users across different channels<\/p>
<\/span><\/p>
Obtain the list of churned users<\/span><\/h3>
We want to obtain a list of churned users to implement targeted marketing to specific groups. Sensors Analytics supports syncing a list of specific user devices to JPush\/Xiaomi, enabling precise in-app push notifications to churned users in order to reactivate them and win them back.<\/p>
<\/span><\/p>
Comprehensive monitoring of the quantity and quality of customer acquisition through various channels<\/span><\/h3>
If channel business personnel want to comprehensively monitor the channel's performance but don't want to reconfigure the metrics one by one each time, Sensors Analytics supports adding the analysis results to the overview, allowing business analysts to quickly obtain the status of the metrics they are interested in without having to configure them.<\/p>
<\/span><\/p>
This allows you to build an overview of channel analysis, helping you quickly understand the current state of channel performance.<\/p>
<\/span><\/p>
This case study helps you quickly understand a simple analytics scenario, involving Sensors Analytics models such as event analysis, funnel analysis, retention analysis, distribution analysis, and interval analysis. You can further explore more features. For more advanced scenarios, please refer to industry practice examples<\/a> to learn more about use cases and advanced usage techniques.<\/p>
The above data are all simulated data; please refer to the e-commerce demo.<\/a>\u00a0<\/span>Experience. The next article will answer the question, \"Where does Sensors Analytics' data come from?\"<\/p>

<\/p><\/div><\/div><\/div><\/body><\/html>","pure_content":"In the previous article, we saw that Sensors Analytics can support the problem analysis process for different business roles. Here, we take the e-commerce industry as an example to introduce how Sensors Analytics helps business personnel in product, operations, and channel placement to quickly conduct data analysis. Standard e-commerce core processes E-commerce users typically go through the following core behavioral processes: Launch App Start browsing the homepage Click and browse the product details page Once you see a suitable item, add it to your shopping cart. If you intend to purchase, submit an order. Decide to make payment, pay for the order. The core product process can be described as follows: Launch the App - Browse the homepage - Browse the product details page - Add to cart - Submit order - Pay for order Common Data Analysis Problems in the E-commerce Industry In the specific work of the e-commerce industry, channel placement personnel focus on the quantity and quality of customers acquired through each channel, operations personnel focus on how to segment users and conduct targeted operations, and product personnel focus on the conversion of each core process and the user experience of each function. We will list the main analytical requirements as follows: How many new customers are acquired through each promotional channel? Which channel has the strongest customer acquisition capability? Are there differences in the behavior of users from different channels from app activation to order payment? What could be the reasons for user churn during the payment process? What can I do about losing users through payment services? How can we comprehensively monitor the quantity and quality of new user acquisition across all channels? Let's now learn how to find the answers to the questions above one by one. Sensors Analytics Solution View the number and proportion of new customers from each channel. First, we want to understand the customer acquisition capabilities of each channel, and then calculate the ROI based on the advertising costs. In Sensors Analytics, we can do the following: View the total number of new customers, and then drill down by dimensions such as date and channel. View the proportion of new customers acquired through each channel to the total number of new customers acquired. View the core process conversion rate of users across different channels Examine the overall conversion rate of the core customer acquisition process across various channels, as well as the conversion rates between each step, to identify areas for improvement in the overall conversion rate. Multi-dimensional analysis of user payment behavior from various channels In the previous question, we saw the conversion rates of users from various channels towards the product's intended target behavior. At the same time, we also hope to obtain more data to evaluate the quality of customer acquisition across channels. View the conversion time from app activation to order payment for users from various channels. Examine the distribution of average payment order amount and total payment order amount among users from different channels to determine the impact of channels on users' spending power. Analyze the reasons for user churn during the payment process Payment is typically a targeted user behavior for e-commerce products, but a significant number of users inevitably churn at this stage. We want to understand the subsequent behaviors of users who churn during the payment process. Is their payment behavior affected by interference from other stages? Sensors Analytics allows users to view the historical behavior sequences of specific user groups, identify order submissions, and manually annotate subsequent behaviors to infer why payments were not made. Analyze user activity from various channels We want to understand the activity level of users from various channels, as well as the frequency of the target behavior\u2014payment order behavior. View user activity across different channels Observe the frequency of payment order behavior of users across different channels Obtain the list of churned users We want to obtain a list of churned users to implement targeted marketing to specific groups. Sensors Analytics supports syncing a list of specific user devices to JPush\/Xiaomi, enabling precise in-app push notifications to churned users in order to reactivate them and win them back. Comprehensive monitoring of the quantity and quality of customer acquisition through various channels If channel business personnel want to comprehensively monitor the channel's performance but don't want to reconfigure the metrics one by one each time, Sensors Analytics supports adding the analysis results to the overview, allowing business analysts to quickly obtain the status of the metrics they are interested in without having to configure them. This allows you to build an overview of channel analysis, helping you quickly understand the current state of channel performance. This case study helps you quickly understand a simple analytics scenario, involving Sensors Analytics models such as event analysis, funnel analysis, retention analysis, distribution analysis, and interval analysis. You can further explore more features. For more advanced scenarios, please refer to industry practice examples to learn more about use cases and advanced usage techniques. The above data are all simulated data; please refer to the e-commerce demo.\u00a0Experience. The next article will answer the question, \"Where does Sensors Analytics' data come from?\"","is_draft":"0","editor_type":"3","file_path":"","add_time":"1785825084","update_time":"1785828229"},"pure_content":"In the previous article, we saw that Sensors Analytics can support the problem analysis process for different business roles. Here, we take the e-commerce industry as an example to introduce how Sens"},{"id":"885338","string_id":"Kw4qXExT","status":"1","parent_id":"885334","slug":"Kw4qXExT","order":"0","name":"Data sources for Sensors Analytics","type":"2","cover":"","cover_alt":"","desc":"","website":"","contact":"","seo_title":"","seo_keyword":"","seo_desc":"","share_switch":"0","share_code":"","author":"","published_time":"1785825084","add_time":"1785825084","update_time":"1785828229","corp_auth_id":"0","verify_code_auth_id":"0","editor_type":"3","file_type":"","auth_id":"[\"885338\", \"885334\", \"885333\"]","content":{"content_id":"885338","content":"\n
In the previous article, we...\u00a0<\/span>E-commerce Demo\u00a0<\/span><\/a>Through virtual data simulations and analysis within the environment, we learned how Sensors Analytics helps users quickly understand the quantity and quality of new user acquisition across various channels. In actual use, Sensors Analytics utilizes data sourced from the client's own real user data.
In this article, we will briefly introduce the issues of data sources and data security.<\/p>
Data source issues<\/span><\/h2>
All data in Sensors Analytics comes from clients' own data access.
Sensors Analytics primarily supports the collection of three types of customer-owned data: front-end operations, back-end logs, and business data (including historical data). Furthermore, Sensors Analytics provides various data access solutions and tools for data from different sources.
For example, we provide a client SDK<\/a> , a server SDK<\/a> , an external data import tool<\/a> , and...\u00a0<\/span>Methods such as API import<\/a> .<\/p>
Data security issues<\/span><\/h2>
Except for the SaaS version service, where data storage is provided by Sensors Data, for the privately deployed standalone\/clustered versions, all data is stored on the customer's server. Furthermore, Sensors Data will not provide user data from customers it has served to build a user tagging system for targeted marketing.
Meanwhile, in terms of technical implementation, we ensure the security of customer data from various angles ( related blog links<\/a> ). For example:<\/p>
Support using HTTPS as the transport protocol<\/li>
Encrypt the transmitted content<\/li>
Backend data collection from Sensors Data will not expose customer data.<\/li>
Antispam after collection<\/li><\/ul>
This concludes our beginner's guide. In the next chapter, we will detail how to use the various analytical models offered by Sensors Data.
Welcome to Sensors Analytics\u00a0<\/span>E-commerce Demo\u00a0<\/span><\/a>Follow the instructions to practice.<\/p><\/div><\/div><\/div><\/body><\/html>","pure_content":"In the previous article, we...\u00a0E-commerce Demo\u00a0Through virtual data simulations and analysis within the environment, we learned how Sensors Analytics helps users quickly understand the quantity and quality of new user acquisition across various channels. In actual use, Sensors Analytics utilizes data sourced from the client's own real user data. In this article, we will briefly introduce the issues of data sources and data security. Data source issues All data in Sensors Analytics comes from clients' own data access. Sensors Analytics primarily supports the collection of three types of customer-owned data: front-end operations, back-end logs, and business data (including historical data). Furthermore, Sensors Analytics provides various data access solutions and tools for data from different sources. For example, we provide a client SDK , a server SDK , an external data import tool , and...\u00a0Methods such as API import . Data security issues Except for the SaaS version service, where data storage is provided by Sensors Data, for the privately deployed standalone\/clustered versions, all data is stored on the customer's server. Furthermore, Sensors Data will not provide user data from customers it has served to build a user tagging system for targeted marketing. Meanwhile, in terms of technical implementation, we ensure the security of customer data from various angles ( related blog links ). For example: Support using HTTPS as the transport protocol Encrypt the transmitted content Backend data collection from Sensors Data will not expose customer data. Antispam after collection This concludes our beginner's guide. In the next chapter, we will detail how to use the various analytical models offered by Sensors Data. Welcome to Sensors Analytics\u00a0E-commerce Demo\u00a0Follow the instructions to practice.","is_draft":"0","editor_type":"3","file_path":"","add_time":"1785825086","update_time":"1785828229"},"pure_content":"In the previous article, we...\u00a0E-commerce Demo\u00a0Through virtual data simulations and analysis within the environment, we learned how Sensors Analytics helps users quickly understand the quantity and q"}],"content":{"icon":"","desc":"\n
Welcome to Sensors Analytics.<\/p>
In this chapter, we provide beginners with a quick introductory guide to Sensors Analytics.<\/p>
Follow us to learn more about Sensors Analytics and gain a deeper understanding of its significance for enterprises in achieving data-driven operations.<\/p>
<\/p>
What is Sensors Analytics?<\/a><\/div>
What can Sensors Analytics do?<\/a><\/div>
How does Sensors Analytics solve problems?<\/a><\/div>
Data sources for Sensors Analytics<\/a><\/div><\/div>
<\/p><\/div><\/div><\/div><\/body><\/html>\n","content_id":"885334","add_time":"1785825075","update_time":"0","content":"\n
Welcome to Sensors Analytics.<\/p>
In this chapter, we provide beginners with a quick introductory guide to Sensors Analytics.<\/p>
Follow us to learn more about Sensors Analytics and gain a deeper understanding of its significance for enterprises in achieving data-driven operations.<\/p>
<\/p>
What is Sensors Analytics?<\/a><\/div>
What can Sensors Analytics do?<\/a><\/div>
How does Sensors Analytics solve problems?<\/a><\/div>
Data sources for Sensors Analytics<\/a><\/div><\/div>
<\/p><\/div><\/div><\/div><\/body><\/html>","is_traffic_less":"0","file_type":""},"hot_list":[],"user_read_num":"0","is_auth":"0"},"msg":"Success","code":200}