Duplicate customer records. Triple-mailed catalogs. Inflated CLV that makes your retention model a fantasy. We clean, deduplicate, and standardize your customer data so every dollar goes where it should. It's our data cleaning service tuned for e-commerce and DTC.
Every duplicate record and bad address is money walking out the door.
Same customer, three records, three catalogs to the same address. At $2–$5 per catalog, that's thousands per mailing.
When one customer looks like three, your Customer Lifetime Value is fantasy. You over-invest in acquisition and under-invest in retention.
Marketing segments built on duplicate and malformed data mean your “VIP customers” list is 30% ghosts. Ad spend gets wasted on audiences that don't exist.
100,000 customers? Or 75,000 real ones and 25,000 duplicates? Your board deck tells a different story once the data is clean.
Lookalike audiences trained on dirty customer data attract the wrong prospects. Your cost per acquisition balloons while conversion tanks.
Bounce rates from bad emails tank your sender score. Your legitimate campaigns land in spam instead of inboxes.
Fuzzy matching across name, email, phone, and address. We merge duplicates into single golden records while preserving all order history.
USPS-verified formatting, missing ZIP code completion, apartment/suite normalization. No more catalogs returned as undeliverable.
Deliverability scoring, syntax validation, disposable email detection, role-based address flagging. Protect your sender reputation.
Standardize product names, SKU formats, and category mappings. Make your reporting consistent across platforms and time periods.
Rebuild your VIP, at-risk, churned, and new customer segments on clean data. Finally trust what your dashboard tells you.
Once duplicates are merged, we recalculate true CLV per customer so your retention and acquisition budgets are based on reality.
A DTC skincare brand discovered 23% of their “customers” were duplicates — same person, different email variants. Their CLV was inflated by $180 per customer. Marketing was spending against phantom audiences.
After cleaning, they saved $14,000/month in wasted catalog mailings and rebuilt their retention model on real numbers. Their ad ROAS improved 22% once lookalike audiences were trained on clean data.