How to Improve Retention with Cohort Analysis: A Guide to Segmenting and Reaching Filtered Phone Numbers

Is batch sending after number filtering your go-to strategy? Many overseas marketing teams fall into this trap early in customer acquisition. Bulk filtering, bulk importing, bulk sending—it seems efficient at first, but after a few months, you’ll notice your number pool thinning, reply rates dropping, and even platform risk controls kicking in. The root cause isn’t that number quality has degraded; it’s that you’ve been treating all leads with a “undifferentiated traffic mindset.”

To break out of this dilemma, you need to shift from a traffic mindset to a retention mindset. The key tool that connects these two mindsets is Cohort Analysis. This article will walk you through how to use the output data from the KK-DATA filtering platform to segment filtered phone numbers, design differentiated reach-out cadences, and continuously optimize your acquisition efficiency by reviewing retention data.

Why Does the Simple “Filter → Batch Send” Model Fail?

The traditional workflow is: collect numbers from various channels → use KK-DATA to check validity → directly import valid numbers into Telegram/WhatsApp bulk send tools → send in batches. This assembly-line approach can bring a wave of traffic initially, but as reach frequency increases, problems gradually emerge:

• User fatigue: All numbers receive the same messages at the same frequency. Once users feel disturbed, they directly block or report.
• Conversion rate plummets: Numbers with different activity levels have different tolerance for messages. A number inactive for 30 days receiving high-frequency private messages will only accelerate churn.
• Data distortion: You cannot determine whether a failed outreach campaign is due to “poor number quality” or “overly aggressive cadence.” Mixing all numbers together for analysis leads to vague conclusions.
• Rapid consumption: Every batch send consumes the “lifespan” of your number pool. Without targeted allocation of outreach resources, high-value numbers are also covered by low-quality strategies.

The root cause is: you have not layered your filtered phone numbers. And cohort analysis is the data-driven solution to this problem.

What Is Lead Cohort Analysis and Its Relationship with Retention Outreach?

In a nutshell: Cohort analysis doesn’t just look at “how many numbers are valid”; it groups numbers by time, behavior, and attributes, then tracks each group’s retention performance after the first outreach.

For example, in the first week of January, you used KK-DATA to generate 5,000 US numbers and performed a Telegram activity check. You obtained two cohorts:

• Cohort A: Generated in the first week of January + active within 7 days + female
• Cohort B: Generated in the first week of January + active within 30 days + male

Then you test different outreach cadences on these two cohorts, recording how many remain active or interact with you on day 7, day 15, and day 30. You’ll quickly discover which type of numbers suits “fast-paced, aggressive outreach” and which needs “slow-burn nurturing.”

Step 1: Obtain and Prepare Basic Leads for Cohort Analysis

To perform meaningful cohort analysis, you first need traceable number data. Each group of numbers must be “tagged” with its generation time, detection results, activity level, gender attributes, etc.

Use the “Generate → Filter” Pipeline to Accumulate Comparable Number Groups

KK-DATA’s global number generation and multi-platform filtering functions provide natural raw materials for cohort analysis. Here’s how:

1. Set a time dimension: Execute a number generation task every week. For example, generate 1000 US numbers every Monday. Mark this batch as “Week 1 - US”.
2. Perform multi-platform filtering: Use KK-DATA’s Telegram filtering function to check registration status, activity level (7 days / 15 days / 30 days), and gender identification results.
3. Save original task information: When exporting results, be sure to include fields like “task creation time”, “detection completion time”, “detection result (active/valid/invalid)”, and “gender”.

Using KK-DATA’s global number generation → filtering pipeline, you can accumulate multiple weekly batches of number groups with different attributes at low cost and scale. These batches are the foundation for subsequent cohort analysis.

Keep Full Attribute Fields When Exporting

When exporting CSV from the KK-DATA console, don’t just check “Phone Number” and “Is Valid”. Be sure to include:

• Detection time (ensures it maps to a week or month batch)
• Activity level (e.g., “active within 7 days”, “active within 30 days”, “valid only”)
• Gender identification result (male / female / unknown)
• Platform detection result (Telegram / WhatsApp, etc.)

These fields are the cornerstone for subsequent cohort segmentation and retention analysis.

Step 2: Perform Cohort Segmentation on Filtered Numbers

Once you have the CSV file with complete fields, you can perform segmentation in Excel, Google Sheets, or more professional analysis tools. There are two main segmentation dimensions.

Segment by First Outreach Time (Time Cohort)

This is the most basic cohort division. Group numbers imported and started outreach in the same week or month together. For example:

Cohort Name	Number Range	First Outreach Week
Week 1 of Dec	US + active 7 days	Week 1 of 2024
Week 2 of Dec	US + active 7 days	Week 2 of 2024
Week 3 of Dec	US + active 30 days	Week 3 of 2024

Time cohorts let you observe whether there are periodic fluctuations in retention performance across different weekly batches (e.g., retention might be higher or lower during holidays).

Segment by Number Activity and Gender (Behavior + Attribute Cohort)

This is a more refined and effective segmentation. Split the same batch of numbers by “activity level + gender” combinations. For example:

Cohort Name	Activity Level	Gender	Number Count
High-activity Female	Active within 7 days	Female	200
High-activity Male	Active within 7 days	Male	250
Medium-activity Female	Active within 30 days	Female	300
Medium-activity Male	Active within 30 days	Male	350

This segmentation directly answers two key questions: “Which type of numbers should we prioritize?” and “Should messaging be adjusted for different genders?”

Step 3: Design Outreach Cadence and Retention Metrics

Once each cohort has a clear label, design a专属 outreach strategy. Take “High-activity Female” and “Medium-activity Male” as examples:

Cohort	First Outreach Content	Follow-up Cadence	Criteria for Second Outreach
High-activity Female	Friendly community invitation	Send second message 2 days after first outreach	If no reply within 2 days, switch to once a week for 2 weeks
Medium-activity Male	Value-based content (e.g. industry report summary)	Send second message 5 days after first outreach	If message not opened within 5 days, send again after 2 weeks, then mark as low activity

At the same time, define clear retention metrics. Commonly used ones:

• Weekly retention rate: Number of people who still actively interact (e.g., reply to messages, join community) on day 7 / number of people first reached.
• Monthly retention rate: Number of people still reachable and not unsubscribed on day 30 / number of people first reached.
• Friend request acceptance rate: If you reach out via friend requests, this metric is more core than “open rate”.
• Private message reply rate: Conversion metric for direct private message scenarios.

Step 4: Execute Outreach and Record Retention Data for Each Cohort

This step requires the most patience and discipline. Do not send all numbers at once. Instead, strictly send in batches per cohort, and record detailed data for each outreach.

Use A/B Testing to Verify Optimal Outreach Cadence

Select two cohorts with similar attributes (e.g., both “active within 7 days + male”):

• Cohort X: Send a message every other day, 3 times in a row.
• Cohort Y: Send a message once a week, 3 times in a row.

Compare retention counts on day 10 and day 30 for these two cohorts. If Cohort Y’s retention rate is double that of Cohort X, you’ve found a better cadence. You can repeat this test until you find the best model for your business.

Avoid Cross-contamination Between Cohorts

This is the most overlooked but most impactful part of the entire analysis. If the same number appears in both the “High-activity Female” and “Medium-activity Female” cohorts, and you reach out to it simultaneously, its retention performance cannot be correctly attributed to any single cohort.

How to solve? Before segmentation, deduplicate the number pool. KK-DATA has a built-in data deduplication warehouse function that helps you remove duplicate numbers across tasks, ensuring each number is assigned to only one cohort. This ensures your retention data is clean and trustworthy.

Step 5: Interpret Cohort Retention Rates to Optimize Future Lead Acquisition and Outreach

After you have executed 2-3 rounds of cohort outreach, your retention data table should look like this:

Cohort Name	First Outreach Count	7-day Retention	15-day Retention	30-day Retention
Week1-High-activity Female	200	60 (30%)	40 (20%)	25 (12.5%)
Week1-Medium-activity Male	350	70 (20%)	45 (12.8%)	20 (5.7%)
Week2-High-activity Female	220	75 (34%)	50 (22.7%)	35 (15.9%)

To interpret these data, ask yourself several questions:

• High-activity Female retention is generally higher than Medium-activity Male. Does this mean we should allocate more resources in future number filtering to the “active within 7 days + female” detection dimension?
• Week2 retention is higher than Week1. Is it because of optimized outreach messaging, or because the numbers this week are inherently better quality? If the latter, should you increase investment in the source of Week2 batch numbers?
• The Medium-activity Male cohort decays rapidly after 15 days. Does this indicate that for this type of number, you should shorten the outreach cycle, or change the outreach method (e.g., from private message to group invitation)?

When you can answer these questions clearly, you have formed a data review flywheel: discover which attribute numbers have the best retention → increase generation and filtering investment for those attribute numbers → verify effects again through cohort analysis. With each cycle, your acquisition efficiency and retention rate improve.

Frequently Asked Questions

Q: What is the minimum sample size for cohort analysis? Can I analyze with too few numbers?

A: It is generally recommended that each cohort has no fewer than 200 valid numbers (e.g., “active within 7 days + female” group). Groups with fewer than 50 have weak statistical significance and may mislead decisions due to random fluctuations. If total numbers are limited, you can merge time dimensions (e.g., monthly cohorts instead of weekly cohorts).

Q: When doing A/B testing of outreach cadence, do different cohorts need to start at the same time?

A: Yes. Cohorts with different cadences should complete their first outreach within the same week to avoid errors caused by “different time → natural decay of user activity.” It’s best to randomly assign numbers filtered in the same period to two cohorts and then start the test.

Q: I keep sending private messages but get no replies. Can cohort analysis determine whether it’s a number quality issue or an outreach method issue?

A: Yes. If all cohorts (regardless of activity/gender) have extremely low retention, the problem is likely in the messaging or sending frequency. If only one specific active cohort has low retention while another active cohort has high retention, the problem points to number quality and activity filtering standards—you need to raise the activity threshold for filtering (e.g., from 30-day valid to 7-day active).

Q: Do I need to do cohort analysis every month? How often should I review?

A: At least formal reviews once per quarter (recommended to do lightweight analysis once a month). If your outreach volume is large (over 10,000 numbers per month), it’s recommended to export filtering results from KK-DATA every month for cohort tracking, so you can promptly detect decay trends in certain number segments or gender groups and adjust direction ahead of time.

Q: Can numbers for different cohorts come from the same filtering task?

A: Yes. It’s recommended to split numbers from the same task into different cohorts based on attributes like “activity + gender.” This allows you to directly compare retention differences between different attribute numbers within the same time window, avoiding external variables from different batches. For example, from the same TG filtering task, export “active within 7 days + female” and “active within 7 days + male” as two separate files, each as an independent cohort.

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All functions mentioned in this article can be experienced in the KK-DATA Application Console. For more information about billing and detection types, visit the official billing page. For custom needs or partnership inquiries, feel free to contact official customer service via Telegram @kkdata_robot.