Customer life cycle-based management method and system

Abstract

The invention discloses a management method and system based on a customer life cycle, and the method comprises the steps: recognizing a high-potential customer group through market behavior data, combining a low-threshold conversion path, remarkably improving the first-time purchase conversion rate of potential customers, precisely positioning a high ROI channel through data burying point and attribution analysis, optimizing an advertisement putting strategy, and reducing the customer obtaining cost. By automatically triggering a guide course and real-time behavior information, the period from registration to first use of a new customer is shortened, core function advantages are highlighted, and cognition and trust of the customer to a product are quickly established.

Description

Technical Field

[0001] The present invention relates to the field of customer management, and particularly to a management method and system based on the customer life cycle. Background Art

[0002] In the prior art, the life cycle management of customers usually includes aspects such as account creation, transaction monitoring, and anomaly detection. Common methods mainly rely on a single data source and traditional statistical analysis techniques, and discover and prevent abnormal behaviors through regular audits and manual inspections. Some advanced methods introduce rule-based automated monitoring systems that can automatically identify and alarm some common violations.

[0003] However, the prior art has obvious deficiencies in many aspects. A single data source cannot comprehensively reflect various behaviors of customers, resulting in incomplete monitoring data. Secondly, traditional statistical analysis methods are unable to cope when facing complex behavior patterns and are difficult to accurately identify potential abnormal behaviors. In addition, the existing rule-based monitoring systems lack flexibility and intelligence and cannot effectively handle new and complex abnormal behaviors. Therefore, the existing customer life cycle management methods need to be improved in terms of comprehensiveness, accuracy, and intelligence.

[0004] In summary, a management method and system based on the customer life cycle are needed to solve the deficiencies existing in the prior art. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides a management method and system based on the customer life cycle, aiming to solve the above problems.

[0006] To achieve the above object, the present invention provides the following technical solution: A management method based on the customer life cycle, comprising the following steps: Step S1: Identify high-potential customer groups through market behavior data, design a low-threshold conversion path, attract potential customers to convert into first-time purchase users, and record the customer source and the first interaction behavior; Step S2: Push a guided tutorial or automatically send a message based on behavior through email or instant messaging tools, highlight the core functions or service advantages, and accelerate the first use experience of new customers; Step S3: Recommend relevant products or services based on the purchase history or usage data to enhance customer stickiness and explore long-term value; Step S4: Mark low-active customers through the RFM model, identify customers at risk of churn, and send customized offers or investigate the reasons for churn, and provide one-on-one recovery for high-value customers; Step S5: Design a reward program to guide customers to share evaluations or cases, convert loyal customers into brand promoters, and provide exclusive activities or early product experiences.

[0007] Optionally, the conversion strategy for customers in the method is as follows: Obtain the chat records between the buyer and the seller, import them into the lifecycle management system, extract the quotes from the chat records. If multiple contact persons of the seller are in contact with the contact persons of the buyer, the system will give a pop-up prompt for the contact person of the seller with a higher quote; If the contact person of the seller is in contact with multiple contact persons of the buyer, the system will give a pop-up prompt for the initial contact quote information of the seller.

[0008] Optionally, the conversion strategy for customers in the method is as follows: If multiple contact persons of the seller are respectively in contact with multiple contact persons of the buyer, the system will give a pop-up prompt for the contact person of the seller with a higher quote.

[0009] Optionally, the conversion strategy for customers in the method is as follows: If the levels of multiple contact persons of the seller are different, judge the levels of the contact persons. The system will give a pop-up prompt that a contact person with a lower level has been in contact with a contact person with a higher level, and give a pop-up prompt to the person with a higher level to ask whether to disclose the chat records.

[0010] Optionally, step S1 is implemented in the following way: Step A1: Data collection and tagging. Obtain customer data through customer interaction behaviors and third-party data, and label the customer data; Step A2: Modeling and clustering. Build a clustering model and cluster customers through label classification; Step A3: Lower the decision-making threshold through price anchors, zero-risk commitments, and simplified processes, and reduce the conversion path through behavior guidance design; Step A4: Data embedding and attribution. Track the user's first open behavior through UTM parameters and SDK integration, establish an attribution model, and record the first interaction and the final click.

[0011] Optionally, step S2 is implemented in the following way: Step B1: Define key trigger behaviors and message types through customer behaviors; Step B2: Build an automated process, push messages through communication tools, and conduct behavior tracking; Step B3: Directly associate with customer behaviors through pain point stimulation, and send solutions and / or links through instant messaging tools; Step B4: Detect customer behavior metric data, conduct A / B recommendation tests, and handle negative feedback.

[0012] Optionally, step S3 is implemented in the following manner: Step C1: Collect customer historical purchase data, behavioral data, and user portraits, and extract the behavioral characteristics and interaction characteristics of customers; Step C2: Select an appropriate algorithm type according to the applicable scenario, make recommendations based on association rules through the order confirmation page, member center, or email marketing, and perform lifecycle adaptation at the same time; Step C3: Recommend popular products in the corresponding category for user cold start through the interest tags selected during registration, or perform product cold start by manually tagging for similar product clustering.

[0013] Optionally, step S4 is implemented in the following manner: Step D1: Build an RFM model and segment customers. Build an RFM based on historical data to calculate RFM scores, segment customers through RFM scores, and obtain an RFM matrix; Step D2: Divide customers into high-value customers and medium-low-value customers according to RFM scores. For high-value customers, conduct one-on-one exclusive recovery through customized solutions and manual outbound calls. For medium-low-value customers, push preferential links through automated preferential and survey strategies; Step D3: Execute and track the effects. Detect the short-term execution effects through the short-term coupon redemption rate and survey recovery rate, and detect the long-term execution effects through detecting the repeat purchase rate of recovered customers and the retention rate of high-value customers within one month.

[0014] Optionally, step S5 is implemented in the following manner: Step E1: Select an appropriate reward type according to the applicable scenario, reward customers through a hierarchical reward rule, lower the participation threshold and incentivize high-value content, and guide customers to share evaluations; Step E2: Conduct early product experiences for VIP exclusive activities online or offline, and display brand ambassador medals on the account page.

[0015] A customer lifecycle management system that adopts the management method based on the customer lifecycle, characterized by including a data integration module, a lifecycle stage engine module, an automated marketing execution module, a personalized recommendation engine module, and an analysis and optimization module; The data integration module is used to collect customer data from multiple channels, construct a customer view, integrate the behavior data throughout the lifecycle, and output a customer portrait and a behavior event stream; The lifecycle stage engine module is used to dynamically divide the lifecycle stage where the customer is located, define the stage transition conditions, update the customer stage status in real time, trigger the actions of downstream modules, and output the customer lifecycle stage label and the stage forward warning; An automated marketing execution module, which is used to automatically trigger personalized marketing actions according to the lifecycle stage and customer behavior, support the automated process orchestration of multiple channels, integrate the A / B testing function, optimize the reach content and timing, and output the automated marketing pipeline and channel effect reports; A personalized recommendation engine module, which is used to generate recommended content based on collaborative filtering, content similarity and time series models, update the recommendation results in real time, and output the personalized recommendation list and recommendation effect indicators; An analysis and optimization module, which is used to identify high-value channels and strategies according to the core indicators of each stage, and output the lifecycle stage health report and strategy optimization suggestions.

[0016] The beneficial effects of the present invention: 1. In the present invention, high-potential customer groups are identified through market behavior data, combined with a low-threshold conversion path, significantly improving the first purchase conversion rate of potential customers. Data buried points and attribution analysis can accurately locate high-ROI channels, optimize advertising placement strategies, reduce customer acquisition costs, and shorten the cycle from new customer registration to first use by automatically triggering guided tutorials and real-time behavior messages, highlighting the core functional advantages, and quickly establishing customers' recognition and trust in the product; 2. In the present invention, personalized recommendations based on purchase history and usage data enhance cross-selling and repeat purchase rates. Through loyalty programs and regular health checks, the risk of customer churn is reduced, the customer lifecycle is extended, the RFM model is used to dynamically identify at-risk customers, and retention strategies are implemented in layers. High-value customers are provided with one-on-one outbound customized solutions, and medium and low-value customers are provided with automated discounts and churn surveys to accurately reduce retention costs; 3. In the present invention, an incentive-based recommendation program and a sharing reward guide are designed to acquire high-quality new customers at low cost. The VIP privilege new product experience enhances customers' sense of belonging and promotes the brand's word-of-mouth communication effect. Unified multi-source customer data is integrated to eliminate information silos, providing a real-time and complete customer view for decision-making at each stage, supporting personalized reach across multiple channels, optimizing content through A / B testing to improve open rates and conversion rates, and adjusting recommended content in real time based on algorithms to adapt to the customer's current lifecycle stage. Description of the Drawings

[0017] Figure 1 It is a schematic flowchart of a method of the present invention.

[0018] Figure 2 It is an internal flowchart of step S1 of the present invention.

[0019] Figure 3 It is an internal flowchart of step S2 of the present invention.

[0020] Figure 4 It is an internal flowchart of step S3 of the present invention.

[0021] Figure 5 This is an internal flow chart of step S4 of the present invention.

[0022] Figure 6 This is an internal flow chart of step S5 of the present invention.

[0023] Figure 7 The figure is a schematic diagram of a system structure of the present invention. DETAILED DESCRIPTION

[0024] In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] like Figures 1 to 6 As shown in the figure, a management method based on customer life cycle includes the following contents: Step S1: Identify high-potential customer groups through market behavior data, design low-threshold conversion paths, attract potential customers to convert into first-time buyers, and record customer sources and first interaction behaviors; Step S2: Push guidance tutorials or behavior-based automatic messages via email or instant messaging tools to highlight core features or service advantages and accelerate the first-time user experience for new customers; Step S3: Recommend related products or services based on purchase history or usage data to enhance customer stickiness and tap into long-term value; Step S4: Use the RFM model to mark low-activity customers, identify customers at risk of churn, and send customized offers or investigate the reasons for churn, and provide one-to-one recovery for high-value customers; Step S5: Design a reward program to guide customers to share reviews or cases, convert loyal customers into brand promoters, and provide exclusive activities or early product experiences.

[0026] The customer conversion strategy in the method adopts the following approach: Obtain the chat records between the buyer and the seller, import them into the lifecycle management system, and extract the quotes from the chat records. If multiple contacts of the seller are connected to the contact person of the buyer, the system will pop up a window to prompt the contact person of the seller with the higher quote.

[0027] If the seller's contact person connects with multiple contact persons on the buyer's side, the system will pop up a window to prompt the seller's initial connection quotation information.

[0028] If the seller has multiple liaison persons who are respectively matched with multiple liaison persons on the buyer's side, the system will pop up a window to prompt the seller's liaison person with the higher offer.

[0029] If the seller's multiple contact persons have different levels, the system will determine the level of the contact persons and give a pop-up window to remind the contact person with a lower level that a contact person with a higher level has been connected, and give the contact person with a higher level a pop-up window to remind whether to make the chat records public.

[0030] Customer acquisition stage Goal: Attract potential customers and convert them into first-time buyers.

[0031] Implementation content: Precise positioning: Identify high-potential customer groups through market segmentation (such as demographics, behavioral data).

[0032] Channel optimization: Place personalized ads (such as programmatic ads) on social media, search engines, content marketing and other channels.

[0033] Conversion strategy: Design low-threshold conversion paths (such as free trials, first-order discounts), and use A / B testing to optimize landing pages.

[0034] Data integration: Record customer sources and first interaction behaviors through CRM systems (such as Salesforce).

[0035] Promotion stage Goal: Accelerate the first-time user experience for new customers and increase activity.

[0036] Implementation content: Onboarding process: Push guidance tutorials via email / APP (such as Nike Run Club’s introductory training plan).

[0037] Triggered communication: Automatic messages are triggered based on behavior (such as reminders for unpaid shopping carts).

[0038] Rapid value delivery: highlighting core features or service advantages (e.g. Slack’s “build a team workspace in 10 minutes”) Mature stage Goal: Enhance customer stickiness and tap into long-term value.

[0039] Implementation content: Personalized recommendations: Recommending related products or services based on purchase history or usage data (e.g. Amazon’s “Frequently Bought Together”).

[0040] Loyalty programs: Design a points, tier, or membership system (such as Starbucks Rewards).

[0041] Customer Success Management: B2B enterprises can prevent churn through regular health checks (Quarterly Business Reviews).

[0042] Decline Stage Objective: Identify at-risk customers for churn and implement retention strategies.

[0043] Implementation Details: Early Warning Mechanism: Mark low-activity customers through the RFM model (Recency, Frequency, Monetary).

[0044] Retention Activities: Send customized offers (such as "We Miss You" discount coupons) or investigate the reasons for churn.

[0045] Differentiated Strategy: Provide one-on-one retention for high-value customers (such as mobile phone bill subsidies by telecom operators).

[0046] Loyalty Advocacy Stage Objective: Convert loyal customers into brand promoters.

[0047] Implementation Details: Incentive-based Referral: Design a "Invite Friends - Both Benefit" program (such as Dropbox's storage space rewards).

[0048] UGC Encouragement: Guide customers to share reviews or cases (such as TripAdvisor's review rewards).

[0049] VIP Privileges: Provide exclusive events or early product experiences for top customers (such as Apple's developer preview).

[0050] Technical Support and Key Tools CRM System (such as HubSpot): Integrate full-lifecycle data and enable automated processes.

[0051] Data Analytics Platform (such as Google Analytics + CDP): Predict customer behavior and segment lifecycle stages.

[0052] Marketing Automation Tools (such as Mailchimp): Trigger personalized emails / sms.

[0053] Key Success Factors Dynamic Adjustment: Regularly update customer segmentation criteria (such as changes in consumption habits after the pandemic).

[0054] Cross-departmental Collaboration: Sales, customer service, and marketing teams share a unified customer view.

[0055] Closed-loop feedback: Feed back insights on lost customers to the optimization strategy in the customer acquisition stage.

[0056] By systematically implementing CLM, enterprises can reduce customer acquisition costs, increase customer LTV (Lifetime Value), and build a customer-centric growth model.

[0057] Identifying high-potential customer groups through market behavior data and designing low-barrier conversion paths requires combining data-driven strategies and user experience optimization. The following are specific implementation steps and tool examples: Identifying high-potential customer groups Data collection and tagging Data sources: Website / APP behavior: Browsing duration, page depth, frequently visited categories (e.g., Google Analytics event tracking).

[0058] Ad interaction: Click-through rate, video view completion rate (Facebook Pixel or TikTok Ads data).

[0059] Social media: Like / comment / share behavior (collected through Hootsuite or Sprout Social).

[0060] Third-party data: Industry reports or data from cooperative platforms (e.g., Nielsen consumer profiles).

[0061] Tag system: Behavior tags: Such as "visited the pricing page for 3 consecutive days without placing an order", "favorited the product but did not purchase".

[0062] Value tags: Based on the RFM model (for new customers, focus on "interest intensity" rather than amount).

[0063] Modeling and clustering Tools: Use Python (Scikit-learn) or SaaS tools (such as Adobe Analytics) to build clustering models.

[0064] Typical high-potential groups: "Hesitant type": Viewed product details multiple times but did not place an order → May need price incentives.

[0065] "Benchmark type": Competitor users (identified through SimilarWeb traffic analysis) → Highlight differentiated advantages.

[0066] Designing low-barrier conversion paths Strategy design Lowering the decision-making threshold: Price anchor: 50% off for the first order + free shipping (e.g., exclusive offer for new users on Shein).

[0067] Zero - risk commitment: 7 - day no - reason return + free trial (such as Warby Parker glasses).

[0068] Simplified process: Guest Checkout option (to avoid registration barriers, such as ASOS).

[0069] Behavior - guiding design: CTA optimization: Change "Buy now" to "Unlock benefits now" (psychological hint).

[0070] Progress incentive: Show "70% completed" (such as the progress bar for the first Uber ride).

[0071] Technical implementation A / B testing tools: Optimizely or VWO to test different landing page versions.

[0072] Example path: Ad click → Load the optimized Landing Page (highlighting the first - order benefits) → Time - limited pop - up window ("Enter mobile number to get a 10 - yuan coupon") → Jump to the checkout page with pre - filled shopping cart Record the customer source and first interaction Data tracking and attribution UTM parameters: Add source tags to the ad links.

[0073] SDK integration: Use Firebase or Adjust on the APP side to track the user's first - open behavior.

[0074] CRM field design: Required fields: First - contact channel, first - interaction time, promotion code that triggers conversion.

[0075] Automation rules: Such as setting "Automatically record the source ad campaign when a new customer is created" in HubSpot.

[0076] Closed - loop analysis Attribution model: First Touch: Suitable for brand - exposure channels (such as YouTube ads).

[0077] Last Touch: Suitable for performance - based channels (such as feed ads).

[0078] Dashboard setup: Use Tableau or Looker to visualize the CAC (Customer Acquisition Cost) and conversion rate of each channel.

[0079] Case: The new - customer acquisition process of a DTC brand Identifying High-Potential Users: It is found through Meta Ads data that among female users aged 25 - 35, the conversion rate of the group that clicks on the "sustainable materials" label is 200% higher.

[0080] Low-Threshold Path Design: Push a dedicated Landing Page of "50% off for the first order over 100 + free eco-friendly bag" to this group.

[0081] Data Recording: Link the GCLID parameter of Google Ads in the Shopify backend to automatically label the customer source as "Meta eco-friendly material group".

[0082] Trap Avoidance Data Silos: Ensure that CRM (such as Salesforce), advertising platforms (Google Ads), and website data (GA4) are integrated (using a CDP such as Segment).

[0083] Over-Reliance on Discounts: Replace price cuts with "value first" (such as providing free samples but with postage fees).

[0084] Ignoring Long-Tail Channels: Record niche sources (such as KOC product promotions), which may hide high-potential groups.

[0085] Through the above steps, enterprises can achieve a closed-loop from data insight to conversion improvement, while accumulating a reusable customer behavior knowledge base.

[0086] Pushing guided tutorials and trigger messages via email or instant messaging tools (such as WhatsApp, WeCom, APP push) is a key strategy to improve the activation rate of new customers. The following is a detailed implementation plan step by step, covering technical configuration, content design, and data closed-loop: Implementation Framework Goal: Complete core actions (such as placing an order, using core functions) through automated message guidance within the golden 72 hours after a customer first contacts the product.

[0087] Core Logic: Behavioral Trigger + Personalized Content + Multi-Channel Collaboration. Specific Implementation Steps

[0088] 1. Define key trigger behaviors and message types as shown in the following table:

[0089] Technical Configuration: Set up an automated process Tool Selection: Email / Push Automation: Mailchimp (Simple), Braze (Advanced Personalization), Enterprise WeChat SCRM (Domestic).

[0090] Behavior Tracking: Mixpanel / Amplitude (APP Behavior), Google Tag Manager (Web Behavior).

[0091] Content Design: Highlight Core Value Email / Message Template Formula: Pain Point Stimulation + Solution Demonstration + Low-Threshold Action Instruction Title / Opening: Directly Related to Customer Behavior (Improve Open Rate) "Haven't you experienced [Core Function] yet? 90% of users save 2 hours / day with it." Body: Tutorial Type: Step-by-Step GIF / Short Video (e.g., Recorded with Loom) + Concise Text Explanation.

[0092] Promotion Type: Create a Sense of Urgency with Countdown / Stock Tension ("Only 83 50% Off Coupons Left").

[0093] CTA Button: Avoid "Click Here", Use Action Result Description "Immediately Unlock Time-Saving Function" > "Click to Try" Instant Messaging Tools (e.g., Enterprise WeChat): Avoid Promotions in the First Message, Provide Help First: "Hello! I'm your exclusive consultant, Xiao Wang. Here is the 'Newcomer's Guide to Avoiding Pitfalls' or [Link] prepared for you." Data Closed-Loop and Optimization Must-Monitor Metrics: Email / Push Open Rate, Completion Rate of Tutorial Videos, Function Usage Rate after Triggering.

[0094] A / B Test Suggestions: Test Different Trigger Times (1 hour vs 6 hours after registration).

[0095] Test Message Formats: Plain Text vs Graphics vs Short Videos.

[0096] Negative Feedback Handling: Set up monitoring for "Unsubscribe Keywords" (e.g., "Don't Need"), Automatically Transfer to Human Customer Service.

[0097] Industry Case References Case 1: Notion New User Onboarding Trigger Action: Create the first page after registration.

[0098] Message Flow: Email 1 (1 hour after registration): "Quick Start with 5 Templates" + GIF demonstration.

[0099] APP Push (3 days of inactivity): "Your friend @Lisa has just shared a collaboration space with you".

[0100] Case 2: Activation of the Uber driver app Triggering behavior: Completing the profile but not accepting orders.

[0101] Strategy: SMS + APP Push: "There are high-demand areas near you, and the order acceptance reward is +20%" (triggered by real-time LBS data).

[0102] Pitfall Avoidance Guide Avoid over-disturbance: Set an upper limit on the message frequency (e.g., no more than 3 pushes within 72 hours).

[0103] Manual assistance during the cold start period: For high-value customers (such as B2B trial customers), add a 1-on-1 call after the automated message.

[0104] Cross-channel consistency: Ensure the consistency of product terms in emails, pushes, and customer service scripts (to avoid confusion).

[0105] The above methods can increase the activation rate of new customers by 30% - 50% (industry benchmark). The key lies in: precise triggering timing design + presenting content that reduces customer thinking + optimizing real-time data feedback.

[0106] Recommending relevant products or services based on purchase history and usage data (i.e., personalized recommendation system) is the core means to improve customer retention and LTV (Lifetime Value). The following is a step-by-step implementation plan, covering data preparation, algorithm selection, recommendation strategy, and effect optimization: Recommendation Strategy Design 1. Recommendation Position Layout Key positions: Order confirmation page: "Recommended matching products for you" (e.g., recommend phone cases and screen protectors when buying a phone).

[0107] Membership center: "Exclusive preview of new products based on your historical purchases".

[0108] Email marketing: "Guess You Like" section (open rate increased by 20% - 30%).

[0109] 2. Strategy Rules Cross-category recommendation: Based on association rules (Apriori algorithm): For example: 60% of users who buy cat food will purchase cat litter within 30 days → Recommend combined offers.

[0110] Lifecycle adaptation: New customers: Recommend best-selling products (high-conversion products to build trust).

[0111] Existing customers: Recommend high-margin or long-tail products (to explore needs).

[0112] 3. Cold start solutions User cold start: Select interest tags during registration → Recommend popular products in the corresponding categories.

[0113] Product cold start: Manual tagging + clustering of similar products (e.g., newly launched lipsticks are grouped into the "matte lipsticks" category).

[0114] Effect monitoring and iteration 1. Core metrics Click-through rate (CTR): Number of clicks on the recommendation position / Number of impressions.

[0115] Conversion rate: Number of purchases of recommended products / Number of clicks.

[0116] Recommendation penetration rate: Proportion of orders generated through recommendations.

[0117] 2. A / B testing Test different algorithm combinations: Control group: Collaborative filtering.

[0118] Experimental group: Collaborative filtering + real-time behavior weighting.

[0119] Test UI presentation methods: Carousel vs. static list.

[0120] 3. Bad case analysis Establish a manual review sample library and conduct regular checks: Unreasonable recommendations (e.g., recommending steaks to vegetarians).

[0121] Repeated recommendations (the same product appears multiple times).

[0122] Industry case reference Case 1: Amazon's "Frequently Bought Together" Data: Association rule mining based on billions of orders.

[0123] Strategy: Automatically calculate bundled discount prices when making combined recommendations (e.g., $10 off for phone + phone case).

[0124] Case 2: Netflix's Horizontal Scroll Recommendations Algorithm: Collaborative filtering + video metadata (director / actor) similarity.

[0125] UI Design: Automatically play the trailers of recommended videos (reduce the user's decision-making cost).

[0126] Trap Avoidance Over-personalization: Reserve 10%-20% of the traffic to explore new categories to avoid the "information cocoon".

[0127] Data latency: Real-time recommendations require a stream processing architecture (such as Kafka + Spark Streaming).

[0128] Explanatory: Increase the reasons for recommendations (such as "because you have bought XX") to enhance trust.

[0129] Through the above steps, enterprises can increase the revenue contributed by the recommendation system by 15%-40% (industry benchmark). The core lies in: replacing guesswork with behavioral data and replacing manual experience with algorithms.

[0130] Identifying at-risk customers of churn through the RFM model and implementing a hierarchical recovery strategy is the core method to improve customer retention. The following are the specific implementation steps, combining data modeling, strategy design, and execution tools: RFM Model Building and Customer Segmentation 1. Data Preparation Required fields: Recency (time since last purchase): The number of days since the customer's last order.

[0131] Frequency (purchase frequency): The total number of orders in the past 12 months.

[0132] Monetary (consumption amount): The total consumption amount in the past 12 months (excluding refunds).

[0133] Data source: Order database (SQL export) + CRM system (such as Salesforce).

[0134] 2. RFM Score Calculation Segmentation rules (example), as shown in the following table:

[0135] 3. Customer Segmentation RFM matrix (8 typical groups):

[0136] Hierarchical Recovery Strategy Design 1. High-value customers (R low + F high + M high) Strategy: One-on-one exclusive recovery.

[0137] Execution steps: Manual outbound call: Script template: "Mr. Wang, we noticed that you haven't shopped recently. We have reserved an [exclusive benefit] for you. Would you like to talk about your changing needs?" Customized solution: Provide solutions based on historical purchases (e.g., for maternity and baby users: "Your child is now 2 years old. Do you need to upgrade the milk powder stage?").

[0138] Permission opening: The account manager can approve special discounts (e.g., 15% OFF + free shipping).

[0139] 2. Medium and low-value customers (low R + medium-low F / M) Strategy: Automated discounts + surveys.

[0140] Execution and effect tracking 1. Implementation toolchain

[0141] 2. Core metric monitoring Short-term: Coupon redemption rate, survey response rate.

[0142] Long-term: Repurchase rate of recovered customers within 30 days (target: ≥ 80% of normal customers).

[0143] Retention rate of high-value customers (compared with the non-intervened group).

[0144] 3. Iterative optimization A / B testing: Test the impact of different discount levels (¥30 vs ¥50) on the recovery rate.

[0145] Test communication channels (pure email vs email + SMS combination).

[0146] Bad Case analysis: Screen customers who "received the discount but did not purchase" and optimize the product recommendation strategy.

[0147] Industry case reference Case 1: A maternity and baby e-commerce RFM threshold: R > 60 days + F ≥ 3 times + M ≥ ¥3000 → Define high-value lost customers.

[0148] Recovery actions: The account manager made a phone call and found that the user had left due to the child's weaning → Recommended new children's clothing products + gave an early education experience class.

[0149] Results: 30% of retained customers repurchased within 60 days, and LTV increased by 2.5 times.

[0150] Case 2: SaaS Enterprise RFM Adjustment: R=number of days without logging in, F=frequency of function usage, M=ARR (annual recurring revenue).

[0151] Strategy: For customers with R>30 days, we will provide the "Old User Data Backup Guide" + free 1-on-1 consultation.

[0152] Converting loyal customers into brand promoters requires designing a complete "incentive-participation-reward" closed-loop system. The following are the specific implementation steps, covering reward program design, UGC (user-generated content) guidance, exclusive privilege operation and effect tracking: Rewards Program Design 1. Clarify goals and reward types Goal: Improve word-of-mouth communication, obtain high-quality UGC, and increase repeat purchases.

[0153] 2. Tiered Reward Rules Basic rewards: All users can get it by participating (e.g. share a review to get a ¥10 coupon).

[0154] Advanced rewards: cumulative rewards based on content quality or dissemination effect (e.g., an additional ¥50 reward for high-quality video reviews).

[0155] Ranking competition: The top 3 monthly sharers will be given rare benefits (such as pre-sale rights for co-branded products).

[0156] 3. Participation threshold design Low threshold to start: Post a review with ≥3 pictures + 20 words → get a small reward.

[0157] High-value content incentives: Case submission (such as "My Usage Story") → Reward high-value benefits + official signature.

[0158] UGC guidance and content production 1. Trigger scenario design Post-purchase timing: The order completion page has a reminder embedded in it: "Post your order to win ¥20, share now" → jump to the review page.

[0159] When the product is in use: APP push trigger: "You have used XX function for 7 days, share your experience and win a prize" → guide to community posting.

[0160] 2. Content Template Assistance Evaluation Guidance: Provide structured questions (to prevent users from being lazy to write): "What is your favorite product feature? ______" (in fill-in-the-blank form) Case Submission Tool: Provide an online editor template (such as Canva's customized template library) to reduce the difficulty of creation.

[0161] 3. Social Sharing Optimization One-click Sharing Button: Integrated into the evaluation submission page (supporting WeChat / Weibo / TikTok).

[0162] Pre-generated Promotion Copy: "I discovered this amazing product on [Brand]. Click to see my real review or [Link]" Exclusive Privilege Operation 1. VIP Exclusive Activities Online: Closed-door live broadcasts for loyal customers (such as CEO Q&A + new product spoilers).

[0163] Offline: Invite to factory tours / new product experience sessions (such as NIO's "User Day").

[0164] 2. Early Product Experience Beta Testing Right: Screen high-active customers to participate in the internal test (such as Xiaomi Community's "Honor Development Group").

[0165] Pre-sale Privilege: Open for purchase 48 hours in advance (such as Apple's education discount early access).

[0166] 3. Identity Symbol Design Exclusive Logo: Display the "Brand Recommender" medal on the account page.

[0167] Customized Benefits: The annual TOP promoter names the product color scheme (such as OPPO's "Fan Co-branded Model"). The present invention works by identifying high-potential customer groups through market behavior data, combined with a low-threshold conversion path, significantly improving the first purchase conversion rate of potential customers. Data buried points and attribution analysis can accurately locate high-ROI channels, optimize advertising placement strategies, reduce customer acquisition costs, shorten the cycle from new customer registration to first use through automated trigger-guided tutorials and real-time behavior messages, highlight the core function advantages, and quickly establish customers' recognition and trust in the product.

[0168] Personalized recommendations based on purchase history and usage data can improve cross-selling and repurchase rates. Through loyalty programs and regular health checks, the risk of customer churn can be reduced and the customer life cycle can be extended. The RFM model can be used to dynamically identify customers at risk of churn and implement recovery strategies in a layered manner. High-value customers can be customized through one-to-one outbound calls, and medium and low-value customers can be accurately reduced in terms of recovery costs through automated discounts and churn surveys.

[0169] Design incentive-based referral programs and reward guides for posting reviews to acquire high-quality new customers at a low cost. VIP privileges and new product experiences enhance customer sense of belonging and improve the brand word-of-mouth communication effect. Unify multi-source customer data, eliminate information silos, provide real-time and complete customer views for decision-making at all stages, support multi-channel personalized reach, optimize content through A / B testing, improve open rate and conversion rate, and adjust recommended content in real time based on algorithms to adapt to the customer's current life cycle stage.

[0170] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent substitution or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A management method based on the customer life cycle, characterized in that, The following steps are involved: Step S1: Identify high-potential customer groups through market behavior data, design low-threshold conversion paths, attract potential customers to convert into first-time buyers, and record customer sources and first interaction behaviors; Step S2: Push guidance tutorials or behavior-based automatic messages via email or instant messaging tools to highlight core features or service advantages and accelerate the first-time user experience for new customers; Step S3: Recommend related products or services based on purchase history or usage data to enhance customer stickiness and tap into long-term value; Step S4: Use the RFM model to mark low-activity customers, identify customers at risk of churn, and send customized offers or investigate the reasons for churn, and provide one-to-one recovery for high-value customers; Step S5: Design a reward program to guide customers to share reviews or cases, convert loyal customers into brand promoters, and provide exclusive activities or early product experiences.

2. The management method based on the customer life cycle according to claim 1, wherein The customer conversion strategy in the method described is as follows: Obtain the chat records between the buyer and the seller, import them into the lifecycle management system, and extract the quotes from the chat records. If multiple contacts of the seller are connected with the contact person of the buyer, the system will pop up a window to remind the contact person of the seller who has the highest quote. If the seller's contact person connects with multiple contact persons on the buyer's side, the system will pop up a window to prompt the seller's initial connection quotation information.

3. The management method based on the customer life cycle according to claim 2, characterized in that, The customer conversion strategy in the method described is as follows: If the seller has multiple liaison persons who are respectively matched with multiple liaison persons on the buyer's side, the system will pop up a window to prompt the seller's liaison person with the higher offer.

4. The management method based on the customer life cycle according to claim 2 or 3, characterized in that, The customer conversion strategy in the method described is as follows: If the seller's multiple contact persons have different levels, the system will determine the level of the contact persons and give a pop-up window to remind the contact person with a lower level that a contact person with a higher level has been connected, and give the contact person with a higher level a pop-up window to remind whether to make the chat records public.

5. The management method based on the customer life cycle according to claim 1, wherein The step S1 is implemented in the following manner: Step A1: Data collection and labeling: obtain customer data through customer interaction behaviors and third-party data, and label the customer data; Step A2: Modeling and clustering: building a clustering model and clustering customers by label classification; Step A3: Lower the decision threshold through price anchors, zero-risk commitments and simplified processes, and reduce the conversion path through behavior-guided design; Step A4: Data tracking and attribution: Track the user's first opening behavior through UTM parameters and SDK integration, establish an attribution model, and record the first interaction and last click.

6. The management method based on the customer life cycle according to claim 1, characterized in that The step S2 is implemented in the following manner: Step B1: Define key trigger behaviors and message types through customer behaviors; Step B2: Build an automated process, push messages through communication tools, and track behavior; Step B3: Directly connect customer behavior through pain point stimulation and send solutions and / or links via instant messaging tools; Step B4: Detect customer behavior indicator data, conduct A / B suggestion testing, and handle negative feedback.

7. The management method based on the customer life cycle according to claim 1, characterized in that The step S3 is implemented in the following manner: Step C1: Collect customer historical purchase data, behavior data and user portraits, and extract customer behavior characteristics and interaction characteristics; Step C2: Select the appropriate algorithm type according to the applicable scenario, make recommendations through the order confirmation page, member center or email marketing based on association rules, and perform life cycle adaptation at the same time; Step C3: Recommend popular products in the corresponding category through the interest tags selected during registration for user cold start, or perform product cold start by manually tagging for clustering similar products.

8. The management method based on the customer life cycle according to claim 1, characterized in that The said step S4 is implemented in the following manner: Step D1: Build the RFM model and segment customers. Build RFM based on historical data to calculate the RFM score, segment customers through the RFM score to obtain the RFM matrix; Step D2: Divide customers into high-value customers and medium-low value customers according to the RFM score. For high-value customers, conduct one-on-one exclusive recovery through customized solutions and manual outbound calls. For medium-low value customers, push discount links through automated discount and survey strategies; Step D3: Execute and track the effects. Detect the short-term execution effects through the short-term coupon redemption rate and survey recovery rate, and detect the long-term execution effects by detecting the repeat purchase rate of recovered customers and the retention rate of high-value customers within one month.

9. The management method based on the customer life cycle according to claim 1, characterized in that The said step S5 is implemented in the following manner: Step E1: Select the appropriate reward type according to the applicable scenario, reward customers through a hierarchical reward rule, lower the participation threshold and incentivize high-value content, and guide customers to share and evaluate; Step E2: Conduct early product experiences for VIP exclusive activities online or offline, and display the brand recommender medal on the account page.

10. A customer lifecycle management system that adopts the management method based on the customer lifecycle as described in any one of claims 1-9, characterized in that, It includes a data integration module, a life cycle stage engine module, an automated marketing execution module, a personalized recommendation engine module, and an analysis and optimization module; The data integration module is used to collect customer data from multiple channels, construct a customer view, integrate the behavior data throughout the life cycle, and output a customer portrait and a behavior event stream; The life cycle stage engine module is used to dynamically divide the life cycle stage where the customer is located, define the stage transition conditions, update the customer stage status in real time, trigger the actions of downstream modules, and output the customer life cycle stage label and the stage forward warning; The automated marketing execution module is used to automatically trigger personalized marketing actions according to the life cycle stage and customer behavior, support the automated process orchestration of multiple channels, integrate the A / B test function, optimize the reach content and timing, and output the automated marketing pipeline and the channel effect report; The personalized recommendation engine module is used to generate recommendation content based on collaborative filtering, content similarity, and time series models, update the recommendation results in real time, and output a personalized recommendation list and recommendation effect indicators; The analysis and optimization module is used to identify high-value channels and strategies according to the core indicators of each stage, and output the life cycle stage health report and strategy optimization suggestions.

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