I can help you determine your customer lifetime value
- 3.9
- (5)
Project Details
Why Hire Me?
I help e-commerce businesses discover how much each customer is really worth and what drives their long-term profitability. With my CLV service, you can optimize marketing spend, improve customer retention, and focus your efforts on high-value customers.
What Sets Me Apart:
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7+ years of experience analyzing CLV across various industries
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Proficient in R, Python, Excel, and SQL for statistical modeling
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Customized approach tailored to your business model (D2C, subscription, marketplace, etc.)
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Actionable insights beyond CLV metrics—segmentation, strategy, and retention playbook
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Clear communication and fully documented reports/dashboard outputs
What I Need to Start Your Project
To accurately calculate and interpret your customer lifetime value, I will need the following:
1) Business and Customer Profile
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Your product/service structure (one-time purchases, subscriptions, etc.)
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Customer acquisition channels and retention goals
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Target markets or customer segments (if defined)
2) Customer and Transactional Data
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Purchase history (customer ID, date, amount, product/service)
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Customer metadata: join date, channel, location, etc.
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Returns, refunds, and churn data (if available)
3) Revenue and Cost Breakdown
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Average order value and profit margins
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Customer acquisition cost (CAC)
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Any recurring costs or loyalty program incentives
4) Retention and Engagement Metrics
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Churn rate or retention rate by month/quarter
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Email/marketing engagement (if available)
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Repeat purchase behavior and order intervals
5) Business Goals for CLV
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Whether you want to identify high-value customers, improve retention, optimize CAC, or refine targeting
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Preference for CLV models: historical, predictive, cohort-based, etc.
6) Implementation & Output Needs
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Final deliverables: dashboard, presentation, or technical report
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Whether the results will be used for fundraising, marketing strategy, or internal planning
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Timeline and milestones
Portfolio

Predictive CLV Modeling for a Meal Subscription Business
See how a meal delivery service used predictive analytics to calculate customer lifetime value, segment subscribers, and improve retention. A case study in data-driven customer profitability and strategic targeting.

Historical CLV and RFM Segmentation for a D2C Skincare Brand
Learn how a D2C skincare brand used RFM segmentation and historical CLV to identify profitable customer cohorts and optimize loyalty strategies. A case study in data-driven retention and customer value analysis.

CLV Forecasting and CAC Payback Modeling for B2B SaaS Segments
Explore how a B2B SaaS company calculated segment-wise customer lifetime value, CAC payback, and profitability to guide marketing, pricing, and retention. A data-driven case study in enterprise SaaS financial modeling.
Process

Customer Reviews
5 reviews for this Gig ★★★★☆ 3.9
I thought our loyal customer base was strong, but the CLV model showed we were losing money on repeat buyers. We revamped our offers and saw improvements in average order value. Highly recommend this if you're working with real data.
I run a mid-sized clothing brand, and this was the first time we looked at customer value in such detail. The clarity it brought to our marketing decisions was worth every rupee. Great insights, no fluff.
The CLV breakdown was a game changer. We segmented our users based on value, adjusted pricing for some segments, and built a loyalty flow that’s already showing results. Solid experience working with someone who knows what they’re doing.
I used to guess which customers were most valuable, but this analysis gave me exact figures. We now prioritize high-value cohorts and removed campaigns that weren't working. It made a real difference to our profit margins.
This CLV service helped me realize that 20% of our customers were driving 70% of the revenue. With those insights, I was able to focus our retention strategy and reduce ad spend wastage. The report was simple but actionable.