Days to Conversions Chart

The Days to Conversion Distribution chart reveals how long it takes your customers to convert after their first interaction with your marketing. Instead of just knowing the average journey length, this visualisation shows you the complete distribution – how many convert quickly, how many take weeks, and which devices show different behaviour patterns.

Days to Conversion Distribution chart

What You'll See

The Days to Conversion Distribution is a stacked column chart displaying:

Six time buckets along the bottom (0-1 days, 2-7 days, etc.)
Coloured segments showing device types (Desktop in blue, Mobile in green, Tablet in orange)
Percentage heights representing the share of conversions in each timeframe
Interactive tooltips revealing exact session counts and percentages
A legend identifying which colour represents which device

The chart updates automatically when you change your filters, allowing you to see how journey duration varies across different campaigns, paths, or time periods.

Understanding the Chart

The Vertical Axis (Y-Axis)

What it shows: Percentage of total conversions (0% to 100%)

How it works: Each column represents 100% of your conversions. The coloured segments stack to show what proportion converted on which device.

Reading the scale:

0% = No conversions
50% = Half of all conversions
100% = All conversions

Important: This is a percentage distribution, not raw counts. A taller segment means a larger share of conversions, not necessarily more conversions.

The Horizontal Axis (X-Axis)

What it shows: Time buckets measuring days from first marketing touch to conversion

The six buckets explained:

0-1 Days (Immediate Converters)

What it represents:

Conversions within 24 hours of first marketing touch
Very short consideration period
Fast decision-making

Typical scenarios:

Impulse purchases
Strong immediate intent
Simple, low-commitment products
Urgent needs (e.g., next-day delivery items)
Very targeted, high-intent campaigns

Example: Customer clicks a paid search ad for "buy iPhone 15" and purchases within the hour.

2-7 Days (Quick Converters)

What it represents:

Conversions within one week of first touch
Short but deliberate consideration
Some comparison shopping

Typical scenarios:

Standard e-commerce purchases
Moderate-priced items (£50-200)
Products with good reviews and clear value
Email nurture working quickly
Effective remarketing

Example: Customer discovers your brand via social media on Monday, receives a welcome email series, and purchases on Friday.

8-14 Days (Considered Converters)

What it represents:

Conversions within two weeks
Moderate consideration period
Multiple touchpoints likely
Research and comparison phase

Typical scenarios:

Higher-value items (£200-500)
Products requiring more research
Services needing trust-building
Payday timing effects
Multi-touchpoint nurture sequences

Example: Customer sees a display ad, researches via organic search over a weekend, reads reviews, and purchases the following week.

15-30 Days (Standard Nurture)

What it represents:

Conversions within a month
Extended consideration
Multiple touchpoints essential
Significant research phase

Typical scenarios:

High-value purchases (£500+)
Complex products or services
B2B initial purchases
Subscription commitments
Major household purchases

Example: Customer enters via organic search, joins email list, receives multiple nurture emails, clicks remarketing ads, and purchases 3 weeks later.

31-60 Days (Extended Nurture)

What it represents:

Conversions within two months
Long consideration period
Many touchpoints required
Substantial commitment

Typical scenarios:

Very expensive items (£1,000+)
Complex B2B solutions
Professional services
Major life purchases (holidays, renovations)
Luxury goods

Example: Business owner researches software solutions over 6 weeks, attends a webinar, has demo calls, and purchases after thorough evaluation.

61+ Days (Very Long Nurture)

What it represents:

Conversions taking more than two months
Very long sales cycles
Extensive evaluation periods
Maximum commitment level

Typical scenarios:

Enterprise software
Major B2B contracts
Extremely high-value items (£5,000+)
Services requiring board approval
Real estate, vehicles, education

Example: Enterprise customer evaluates your SaaS product over 4 months, involving multiple stakeholders, ROI analysis, and security reviews before signing.

The Coloured Segments (Device Types)

Each column is divided into coloured segments representing device types:

Desktop (Blue)

Computers and laptops
Typically shows:
- Longer sessions
- Higher average order values
- More research behaviour
- Complex purchases
- Business/work-hours conversions

Mobile (Green)

Smartphones
Typically shows:
- Shorter sessions
- Impulse purchases
- On-the-go decisions
- Lower average values (but high volume)
- Evening/weekend conversions

Tablet (Orange)

iPads and Android tablets
Typically shows:
- Behaviour between mobile and desktop
- Leisure browsing
- Evening/weekend usage
- Medium session lengths
- Comfortable shopping experience

Reading Stacked Percentages

Example interpretation:

Column: "2-7 Days"
- Desktop (Blue): 40% of column height
- Mobile (Green): 50% of column height
- Tablet (Orange): 10% of column height
Total: 100%

What this tells you:

The 2-7 day bucket accounts for X% of all your conversions (look at the tooltip)
Of those 2-7 day conversions:
- 40% happened on desktop
- 50% happened on mobile
- 10% happened on tablet

How to Read the Chart

Step 1: Identify Your Dominant Timeframe

Look at which column is tallest (represents the largest percentage of your conversions).

Example findings:

If "0-1 Days" is tallest (40%+):

You have impulse-driven conversions
Marketing creates immediate action
Short consideration period
Low-friction purchasing

If "2-7 Days" is tallest (30%+):

Standard e-commerce pattern
Some consideration but quick decisions
Email/remarketing working well
Moderate purchase complexity

If "8-14 Days" is tallest (25%+):

Two-week consideration common
Multi-touch journeys
Research-driven purchases
Mid-market products

If "15-30 Days" is tallest (20%+):

Extended nurture required
High-value or complex products
Multiple touchpoints essential
Email sequences critical

If "31-60 Days" or "61+" is tallest (15%+):

Very long sales cycles
High-commitment purchases
Extended evaluation periods
Enterprise or high-value sales

Understanding Days to Conversion

What "Days to Conversion" Means

Calculation: The number of days between:

Start point: First tracked marketing touchpoint (first channel interaction)
End point: Conversion completion

Important clarifications:

Start point is first tracked touch:

May not be true first awareness
Limited by your attribution window (typically 30-60 days)
Offline influences (TV, print, word-of-mouth) not captured

End point is first path completion per session:

Counts first conversion in each converting session
Multiple conversions in one session = one data point
Session-level, not visitor-level

Examples:

Example 1: Quick converter

Monday 9am: Clicks paid search ad (first touch)
Monday 2pm: Returns directly and converts
Days to conversion: 0.2 days (5 hours) → Appears in "0-1 days" bucket

Example 2: Weekend researcher

Saturday: Discovers brand via organic search (first touch)
Saturday-Sunday: Researches, reads reviews
Monday: Clicks paid ad and converts
Days to conversion: 2 days → Appears in "2-7 days" bucket

Example 3: Email nurtured

Day 1: Signs up via social media (first touch)
Days 2-10: Receives nurture emails but doesn't click
Day 11: Clicks email link and converts
Days to conversion: 11 days → Appears in "8-14 days" bucket

Example 4: Long consideration

January 5: First interaction via display ad
January-February: Multiple touches (organic, email, paid)
March 12: Final conversion
Days to conversion: 66 days → Appears in "61+ days" bucket

What This Metric Does NOT Show

It doesn't show:

Total awareness time: May have known your brand for years before first tracked touch
Offline research: TV ads, magazine articles, word-of-mouth not captured
Cross-device complete picture: Mobile research, desktop purchase shows as desktop conversion
Between-session gaps: Only measures first touch to conversion, not active research time
Multiple sessions from same visitor: Each converting session counted separately, even if same person

Troubleshooting

Issue: 61+ Bucket is Very High (30%+)

Possible causes:

Attribution window too short:
- You have 30-day window but journeys are 60+ days
- Everything beyond window appears in 61+ bucket
- Solution: Extend attribution window
  Actually long sales cycle:
- High-value products genuinely take months
- B2B with lengthy procurement
- Solution: This is normal, extend window and nurture
  Tracking issues:
- First touch not being captured
- Gap in tracking causing false extended journeys
- Solution: Audit tracking implementation
  Seasonal effects:
- People researching months before need (holiday gifts)
- Solution: Segment by season

Issue: Distribution is Flat

Possible causes:

Too broad a dataset:
- Multiple products with different timelines combined
- Various customer segments mixed
- Solution: Segment by product, customer type, price point
  Multiple business models:
- Mix of B2B and B2C
- Simple and complex products together
- Solution: Separate analyses
  Diverse catalogue:
- Marketplace or large e-commerce site
- Different product categories have different timelines
- Solution: Category-level analysis

Issue: Sudden Shift in Distribution

If your pattern changes dramatically:

Shift left (faster conversions):

Good reasons:

✓ Improved user experience
✓ Better targeting
✓ Streamlined checkout
✓ More effective remarketing

Bad reasons:

✗ Attribution window shortened
✗ Tracking changes
✗ Missing early touches
✗ Data quality issue

Shift right (slower conversions):

Acceptable reasons:

✓ Moving up-market (higher-value customers)
✓ More complex products
✓ Extended attribution window

Concerning reasons:

✗ Website problems adding friction
✗ Competitive pressure
✗ Economic factors
✗ Trust issues

How to diagnose:

Check for website changes in that timeframe
Review tracking configuration changes
Examine concurrent campaigns
Look for external factors (seasonality, competition)
Validate data quality

Issue: All Conversions in 0-1 Days Bucket

This is suspicious and usually indicates:

Attribution window issue:
- No first touches being captured
- Only capturing conversion event
- Solution: Verify tracking of all touchpoints
  Direct traffic overwhelming:
- All conversions appear as direct
- Marketing not tracked or attributed
- Solution: Implement UTM parameters on all campaigns
  Within-session conversions only:
- Only counting same-session conversions
- Not connecting sessions together
- Solution: Check cookie-based identity tracking
  Data configuration:
- First touch timestamp = conversion timestamp
- Solution: Review data pipeline

To validate:

Check Journey Length KPI – should be >0 days average
Review sample conversions for first touch data
Verify cookie tracking enabled

Issue: No Data Showing

Checklist:

Cookie tracking required:
- This chart needs cookie-based tracking
- Cookieless mode = empty chart
- Solution: Enable cookie tracking for domain
  Date range:
- No conversions in selected period
- Solution: Extend date range
  Filters too restrictive:
- Path/campaign filter eliminating all conversions
- Solution: Remove filters one by one
  Domain not selected:
- Need to select a domain
- Solution: Select domain from filter
  Insufficient permissions:
- User doesn't have access to domain
- Solution: Check permissions

Frequently Asked Questions

Q: Is faster always better?

A: Not necessarily. It depends on your business model.

Faster is better when:

Lower customer acquisition costs
Less remarketing spend needed
Faster cash flow
Higher campaign ROI

Slower might actually be fine when:

Building relationships for high LTV customers
Complex products that need education
Premium positioning requires deliberation
Multi-stakeholder decisions are necessary

Example: If you sell enterprise software with £100K contracts and 90-day sales cycles, a 45-day distribution isn't "slow" – it's efficient for your market.

The key: Match your timeline expectations to your product/market, not arbitrary "fast is better" thinking.

Q: How does this relate to the Journey Length KPI?

A: They measure the same thing from different angles.

Journey Length KPI:

Single average number (e.g., "15.3 days")
Shows overall trend (up or down)
Good for quick snapshot
Can hide distribution details

Days to Conversion Distribution:

Shows full spread of data
Reveals patterns (bimodal, skewed, etc.)
Device breakdown
More actionable insights

Both together:

KPI tells you the average
Chart tells you the story behind that average

Example:

KPI: "Average 15 days"
Chart might show: Bimodal (30% at 2 days, 30% at 30 days, average = 15)
Insight: You have two distinct customer segments, not one "15-day" customer

Q: Why do I have conversions in the 0-1 day bucket if Journey Length shows 8 days average?

A: Because you have a mix:

Some people convert immediately (0-1 days)
Many more convert later (15-30 days)
The average of these is 8 days

Think of it like a classroom:

Some students score 90% (quick converters)
Most students score 70% (normal converters)
A few score 40% (slow converters)
Class average: 68%

The average doesn't mean everyone scored 68%. Same with days to conversion.

Q: Should I try to shift everyone into the 0-1 day bucket?

A: Only if it makes sense for your product and doesn't sacrifice conversion quality.

Good reasons to accelerate:

Removing genuine friction (slow checkout, broken forms)
Improving clarity (confusing value prop)
Better targeting (high-intent traffic)

Bad reasons:

Pressure tactics that create returns/refunds
Sacrificing customer quality for speed
Ignoring that some products need research time

Better goal: Shift the distribution left while maintaining or improving:

Conversion rate
Average order value
Customer lifetime value
Return rate (lower is better)

Example:

0-1 day conversions: £50 average, 30% return rate
8-14 day conversions: £250 average, 5% return rate

In this case, don't push everyone to 0-1 days. The research time creates better purchase decisions.

Q: What if my distribution doesn't match any of your patterns?

A: Your business is unique. These patterns are guides, not rules.

Action steps:

Document your actual pattern – this is your baseline
Understand why it's shaped this way – product? market? customers?
Set realistic goals – improve YOUR pattern, don't copy someone else's
Segment if odd – unusual shapes often hide multiple segments
Track over time – watch how YOUR pattern evolves

The pattern itself doesn't matter. What matters is:

Understanding it
Using it to inform strategy
Tracking changes over time
Aligning marketing with reality

Q: Can I trust this data if I have low volumes?

A: Be careful with small numbers.

Minimum volumes for reliable patterns:

200+ total conversions: Basic pattern identification
500+ conversions: Moderate confidence in distribution
1,000+ conversions: High confidence in pattern
5,000+ conversions: Very reliable, can segment deeply

With low volumes (<200):

Patterns may be noise, not signal
One large customer can skew distribution
Don't over-optimize based on this data
Use for hypothesis generation, not definitive strategy

Solution for low volume:

Extend date range (90-120 days)
Remove filters that limit data
Wait for more data before major decisions
Focus on aggregate KPIs instead