The data half-life of your customer database determines how quickly every record loses its ability to generate revenue. Work email lists lose 22–30% of their records per year (ZeroBounce, 2025). Site visits and expressed intent decay in days. If you are not measuring your data half-life, you are spending budget on records that can no longer convert — and you have no way to know which ones.
By this time next year, a quarter of the email list you are paying to store will be dead. Not unsubscribed. Dead. Hard bounces. Closed mailboxes. People who changed jobs and never looked back. HubSpot and MarketingSherpa put monthly churn at 2–3%.
That is not a storage problem. It is a value problem. Every record in your CRM was worth something on the day it arrived. Today, some fraction of those records can no longer reach a living inbox. You are paying to store, query, and run compliance against data that cannot generate a dollar.
What is data half-life?
Doug Laney coined the term “Infonomics” in 2018 while at Gartner. His thesis: data is an asset. It can be measured, valued, and managed like inventory. Accountants depreciate machines on a straight line. Data does not work that way. It decays exponentially. The further you sit from the moment of capture, the less the data can do for you.
The mechanism is simple. People change jobs. They move house. They abandon email addresses. Their intent shifts between Tuesday and Thursday. The signal you captured last week is weaker today than it was the morning after.
This is not controversial. Everyone in a data team knows it intuitively. Almost nobody prices it into their planning. The data sits in the warehouse looking exactly the same whether it was captured yesterday or eighteen months ago. Nothing in the schema tells you it is dying. Understanding your data half-life is the first step toward customer data maximization — extracting full commercial value from every record before it decays.
The data half-life taxonomy: long, medium, short
Not all data decays at the same rate. The data half-life depends on what the record is tied to.
| Data half-life | Data type | Why it decays | Rough useful life |
|---|---|---|---|
| Long | Date of birth, full name, gender, personal email | Tied to the person, not their circumstances | Years to decades |
| Medium | Work email, postal address, job role | Tied to employment; median US tenure ~3.9 years (BLS, Jan 2024) | 2–3 years; ~25% gone by year 1 |
| Short | Site visit, expressed intent, cookie ID, last order | Tied to session or purchase cycle | Days to weeks |
Most CRM and marketing databases are dominated by medium-half-life data. Work emails. Job titles. Company names. Office addresses. These fields feel stable because they do not change every day. But they change fast enough to destroy campaign economics within a year.
Landbase (2026) tracked 1,000 B2B contacts over 12 months. Result: 70.8% changed at least one field. Not a typo. Seven out of ten records drifted in under a year. That is not edge-case churn. That is the baseline for any B2B database that is not actively maintained.
Short-half-life data is worse. A site visit that signalled purchase intent last Monday is nearly worthless by next Monday. The buyer moved on. The budget cycle turned. Someone else won the deal. If you are sitting on “hot leads” from three weeks ago, they are no longer hot. They are room temperature. This is why acting before the data decays — wiring signal to action in hours, not quarters — separates companies that retain customers from those that report on losing them.
How data decay hits your campaign ROI twice
The obvious hit is quantity. You paid to acquire 100,000 contacts. Twelve months later, 25,000 of those addresses hard-bounce. Your reachable universe shrank by a quarter while you were running approval cycles.
The less obvious hit is relevance. The records that still reach a real person are weaker. The offer you built for “Head of Marketing at a Series B fintech” now reaches someone who moved to a Series D enterprise six months ago. Their inbox is different. Their priorities are different. They still open your email. They just do not convert.
Most campaign ROI models account for the first effect (list hygiene, bounce rate) and ignore the second entirely. That is where the real margin disappears.
The data half-life formula
A simple heuristic for modelling the combined effect:
V(t) = V₀ × (1/2)^(t / H)
Where V₀ is starting campaign value, t is time elapsed, and H is the data half-life for that data type. This is a planning heuristic, not a physics law. Its job is to force a conversation about speed — and to give your CFO a number that makes the urgency concrete.
Worked example: 100,000 work emails
Start with 100,000 work email addresses. Assume a 1% conversion rate and $10 average revenue per conversion. Day-one campaign value: $10,000.
Effect 1: Quantity decay. At 25% annual attrition (ZeroBounce), 75,000 records are reachable after 12 months. Your campaign ceiling drops from $10,000 to $7,500 before you write a single word of copy. Pure arithmetic. Nothing you do in creative or targeting recovers the 25,000 addresses that no longer exist.
Effect 2: Response decay. The 75,000 remaining addresses are twelve months older. Job roles shifted. Budgets reallocated. Priorities moved. Conversion rate drops from 1.0% to roughly 0.7% [Assumption: illustrative, based on typical B2B campaign degradation over time]. That gives you 525 conversions instead of 750. Revenue: $5,250.
Combined loss: $10,000 down to $5,250 in 12 months. A 47.5% value drop from a list you are still paying full price to maintain. You are serving those 100,000 records in every query, backing them up nightly, running GDPR compliance against all of them. The cost is flat. The yield is halved.
Landbase estimates poor data quality costs US businesses roughly $3.1 trillion per year. That figure is large enough to feel abstract. The email example is not. Run the same arithmetic against your own list size, your own conversion rate, your own revenue-per-action. The number will not be comfortable. Companies like Tesco turned this math on its head — by activating 12 million Clubcard records at peak freshness, they launched a $63m revenue line in 12 months.
Why companies act too slowly on data half-life
Three causes show up repeatedly:
- Collection is disconnected from activation. The team that builds the form is not the team that runs the campaign. Data enters a warehouse. Weeks pass. A brief is written. By the time the campaign hits production, the data is months old.
- Quarterly planning cycles. A signal captured in January sits in a dashboard until March planning, gets approved in April, goes live in May. Four months of decay baked into the process by design. Nobody decided to wait. The calendar decided for them.
- No data half-life model exists. Without a half-life framework, every record looks equally valid in the CRM. Old data and fresh data sit in the same table with the same priority. The system treats a two-year-old work email the same as one captured yesterday.
Post 2 in this series unpacks each of these and shows what replaces them — including the operational playbook for acting on signals within hours, not quarters.
The one move: plan the campaign before you collect the data
The highest-value fix is not better data hygiene. It is not enrichment vendors. It is not deduplication sprints. It is timing.
If you know the campaign before you capture the signal, you can act within days of collection. The data is at peak value. Reachability is at its highest. Relevance is at its highest. You capture and convert in the same motion, before the data half-life begins to bite.
This means designing the activation before you design the capture. What will you do with this data within 72 hours of receiving it? If the answer is “put it in the warehouse and figure it out later,” you have already lost a chunk of its value.
Landbase found that clean, current data produces roughly 20% better campaign response rates and 15% higher close rates compared to aged lists. Those lifts are not from better creative or smarter segmentation. They are from acting on data before it decays.
The operating principle: treat every data capture as a countdown. The moment a record enters your system, a clock starts. Your job is to activate it before the data half-life cuts its value in half. Everything else is optimising a diminishing asset.
But timing is only half the picture. The governance trap — the third post in this series — reveals how companies accelerate their own data decay through capture design, cookie policy, and identity architecture choices that throw away data they were entitled to keep.
Start measuring your data half-life today
Collect the right data — data with value beyond a few days or weeks — and start your half-life measurement. The goal is customer data maximization: extracting full commercial value from every record before it decays, not hoarding data you never activate.
Customer data maximization is not a project. It is an operating discipline. It means knowing which records are at peak value, which are past their data half-life, and which are costing you more to maintain than they will ever return. The companies that master it do not have better data. They have faster loops — from signal to action, from capture to conversion, from collection to revenue.
Book a Data Half-Life Review. We will map your active datasets against the taxonomy above, estimate current decay rates from your own bounce and engagement data, and identify where you are spending budget on data that can no longer convert. One session. Concrete numbers. A prioritised list of what to fix first.
Book your Data Half-Life Review →
This is Post 1 of 3 in the Data Half-Life series. Next: The Playbook — act before the data decays. Then: The Governance Trap — how you are killing your own data.
Frequently asked questions
What is the half-life of data?
The half-life of data is the time it takes for a dataset to lose half its usable value. Work emails decay at 22–30% per year. Site visits and expressed intent decay in days. Personal identifiers like date of birth last decades. Understanding your data half-life determines how fast you need to act on it.
How fast do email databases decay?
Email databases lose 22–30% of their records per year (ZeroBounce, 2025). That is roughly 2–3% per month from job changes, domain closures, and abandoned addresses. B2B contact databases are worse: up to 70.3% of tracked contacts changed something in 12 months (RevenueBase, 2026).
How does data decay affect campaign ROI?
Data decay hits campaign value twice: through quantity (fewer valid addresses to reach) and through relevance (older data produces weaker response rates). A 100,000-record email list worth $10,000 in month one can drop below $5,250 within 12 months from both effects compounding.
What is the formula for data half-life?
A useful model is V(t) = V₀ × (1/2)^(t/H), where V₀ is starting value, t is time elapsed, and H is the half-life for that data type. It is a planning tool, not a law — but it forces teams to model how quickly a dataset loses its ability to generate revenue.
What is customer data maximization?
Customer data maximization is the practice of extracting full commercial value from every customer record before it decays. It combines measuring your data half-life, activating records at peak value, and designing capture-to-conversion workflows that outpace decay.
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