Understanding who responds to your ads helps you refine targeting, adjust messaging, and allocate budget more effectively. ConvertMate's audience analysis reveals demographic patterns, device preferences, and geographic performance to improve your targeting decisions.
Before you start
You need:
- A connected Google Ads account with active campaigns
- At least 2-4 weeks of conversion data for meaningful insights
- Starter plan or higher to access audience analysis
For demographic data, your campaigns need sufficient volume. Low-traffic campaigns may show limited demographic breakdowns.
What audience insights reveal
ConvertMate helps you understand:
Demographics:- Age ranges that convert best
- Gender performance differences
- Household income insights (where available)
- Parental status (where available)
- Desktop vs. mobile vs. tablet performance
- Device-specific conversion rates
- Cost differences across devices
- User behavior patterns by device
- Country, region, and city-level results
- Location-specific conversion rates
- Market opportunities by geography
- Areas to exclude or emphasize
- Best days of week for conversions
- Hour-of-day performance trends
- Seasonal behavior changes
This data helps you bid more on valuable segments and less on segments unlikely to convert.
Analyzing demographics
To view demographic performance:
- Go to Insights → Google Ads
- Select Audience demographics
- Choose your date range
- Select dimension (age, gender, or both)
- Optionally filter by campaign
- Click Analyze
This costs 12 credits and takes 15-20 seconds.
Age insights
Age ranges show distinct patterns:
18-24 year olds:- Often price-sensitive
- Mobile-first behavior
- Respond to social proof and trends
- May have lower average order values
- Higher engagement with visual content
- Often in prime spending years
- Balance of mobile and desktop usage
- Career-focused purchases common
- Respond to convenience and quality
- Building households (furniture, electronics, services)
- Typically highest income bracket
- Family-related purchases
- Value quality and reliability
- Research purchases more thoroughly
- Higher average order values
- Established careers and purchasing power
- Desktop usage still common
- Brand loyalty stronger
- Longer consideration periods
- High-ticket purchases more likely
- Different priorities (health, leisure, grandchildren)
- More cautious with new brands
- Prefer detailed information
- Phone support more important
- Disposable income available
- Distinct product needs
- Less likely to impulse buy
- Trust and security paramount
- May need larger text and clearer layouts
- Often researches extensively
Use age data to:
- Write age-appropriate ad copy
- Adjust landing page messaging
- Set bid adjustments for high-value age ranges
- Create age-specific campaigns for very different segments
- Exclude age ranges that consistently underperform
Gender insights
Gender performance differences often indicate:
Product relevance - Some products naturally appeal more to one gender. Performance differences are expected and normal. Messaging effectiveness - Same product, different gender response suggests your messaging resonates more with one audience. Consider testing different ad copy. Market opportunity - Unexpectedly strong performance with one gender might reveal untapped markets. A product you thought was for women might sell well to men. Bias in targeting - If one gender dominates impressions but the other converts better, your targeting might be too narrow. Expand to capture the high-converting audience.Actions based on gender data:
- Create gender-specific ad groups with tailored messaging
- Adjust bids up for higher-converting gender
- Test products with unexpected gender appeal
- Ensure landing pages don't alienate either gender unintentionally
- Consider separate campaigns for products with clear gender preferences
Device performance analysis
To review device behavior:
- Go to Insights → Google Ads
- Select Device performance
- Choose your date range
- Optionally filter by campaign
- Click Analyze
This costs 10 credits.
Understanding device patterns
Desktop/laptop computers:Typical behaviors:
- Longer sessions and more pages viewed
- Higher conversion rates for complex purchases
- Better for B2B and high-consideration products
- More comfortable for form fills and detailed information
- Shopping cart values often higher
When desktop wins:
- Complex products requiring research
- B2B purchases (especially during work hours)
- High-ticket items ($500+)
- Products targeting older demographics
- Purchases requiring multiple form fields
Typical behaviors:
- Quick sessions and immediate actions
- Better for simple, known purchases
- Impulse buying more common
- Location-based intent strong
- Voice search increasing
When mobile wins:
- Local searches (restaurants, stores, services)
- Simple purchases (apps, subscriptions)
- Reorders of known products
- Younger demographics
- Evening and weekend shopping
Typically perform between desktop and mobile:
- Often used for leisure browsing
- Evening usage common
- Conversion rates similar to desktop
- Lower volume than phones or computers
Optimizing for device
If desktop converts better:
- Increase desktop bid adjustments (10-30%)
- Ensure landing pages work well on large screens
- Provide detailed product information
- Don't oversimplify for mobile assumptions
If mobile converts better:
- Increase mobile bid adjustments (10-30%)
- Optimize landing pages for mobile (speed, simplicity)
- Reduce form fields to minimum
- Use click-to-call buttons
- Test mobile-specific ad formats
If mobile gets clicks but doesn't convert:
- Landing page isn't mobile-optimized
- Too many form fields or complicated checkout
- Page loads too slowly on mobile
- Consider decreasing mobile bids until you fix the experience
If desktop gets clicks but doesn't convert:
- Ad messaging might over-promise
- Landing page experience doesn't match expectations
- Competitors may offer better options
- Review search terms for irrelevant traffic
Geographic insights
To analyze location performance:
- Go to Insights → Google Ads
- Select Geographic performance
- Choose your date range
- Select location type (country, region, or city)
- Optionally filter by campaign
- Click Analyze
This costs 12 credits.
Country and region patterns
Geographic data reveals:
Market opportunity - Strong performance in specific regions suggests room for growth. Consider region-specific campaigns with increased budgets and localized messaging. Competitive intensity - High costs in some regions often indicate strong competition. You might find better ROI in less competitive markets. Cultural fit - Some products resonate in certain regions due to climate, culture, or local preferences. Language considerations - Regions with different primary languages need translated or culturally adapted ads. Shipping and logistics - Remote or difficult-to-serve areas might have lower conversion due to shipping costs or delivery times.Use location data to:
- Exclude unprofitable regions
- Increase bids 10-50% in top-performing areas
- Create location-specific ad copy
- Test local offers or pricing
- Schedule ads for local time zones
City-level insights
Drilling down to cities reveals:
Urban vs. rural - Cities typically have higher search volume but more competition. Rural areas have lower volume but may convert better due to fewer options. Local competition - Strong local competitors in specific cities can drive up costs. Consider whether you can compete effectively in those markets. Income levels - Affluent areas often have higher average order values but may be more competitive. Industry clusters - B2B products may perform exceptionally well in cities where target industries concentrate.Actions for city data:
- Create campaigns for high-value cities
- Exclude expensive cities where you can't compete
- Test location extensions for nearby searchers
- Adjust budgets based on city population and performance
- Build remarketing lists by location
Time-based audience patterns
To see performance by time:
- Go to Insights → Google Ads
- Select Time performance
- Choose your date range
- Select granularity (hourly or daily)
- Optionally filter by campaign
- Click Analyze
This costs 10 credits.
Day of week patterns
Common patterns:
B2B products - Peak Monday-Friday during business hours (9am-5pm). Weekends often see low volume and poor conversion. Consider pausing ads or reducing bids 50-100% on weekends. B2C products - Evening and weekend performance often strong. People shop when they have free time, not during work hours. Impulse purchases - Late night shopping (10pm-2am) can work for entertainment, food delivery, or simple purchases. Paycheck patterns - Early in month often sees higher spending, especially for discretionary purchases. Weekly routines - Thursday and Friday often strong for weekend planning (restaurants, events, travel).Use day-of-week data to:
- Set ad schedules to show only on profitable days
- Adjust bids by day (increase 20-50% on best days)
- Pause ads completely on consistently unprofitable days
- Plan promotions for high-traffic days
Hour of day patterns
Hourly analysis reveals when your audience is active:
Business hours (9am-5pm) - B2B searches peak, especially late morning and after lunch. Evening (6pm-10pm) - Consumer shopping peaks as people relax after work. Late night (10pm-2am) - Mobile usage high, impulse purchases common, often younger demographics. Early morning (6am-9am) - Lower volume but high-intent searches during commute times. Middle of night (2am-6am) - Typically low value unless you serve specific industries (healthcare, hospitality, emergency services).Optimize hourly bids:
- Increase bids 30-50% during peak conversion hours
- Decrease bids 30-50% during low-performing hours
- Pause ads during consistently unprofitable hours
- Match bid schedules to when your customer service is available
- Consider time zones if you serve multiple regions
Combining audience insights
The most powerful insights come from combining data:
Young mobile users - May need simpler checkout, mobile payment options, social proof. Older desktop users - Provide detailed information, trust signals, customer service contact info. Urban mobile shoppers - Emphasize fast shipping, local availability, convenience. High-income desktop users from major cities - Premium positioning, quality focus, detailed product information.Build audience segments:
- "Prime audience" - Demographics + devices + locations that convert best
- "Growth opportunity" - Segments with good performance but low impression share
- "Needs improvement" - High volume but poor conversion (landing page issues)
- "Exclude" - Consistently unprofitable segments
Bid adjustments strategy
Stack bid adjustments based on insights:
Start with 100% base bid, then adjust:
- Device: +20% for best device, -30% for worst
- Location: +30% for top regions, -50% for poor performers
- Day of week: +20% best days, -40% worst days
- Hour of day: +30% peak hours, -50% off-peak
Maximum combined adjustment typically ±300%. Be careful not to adjust so aggressively that you stop showing ads entirely for some segments.
Monitor regularly:
- Check monthly to ensure adjustments still make sense
- Look for seasonal changes in patterns
- Adjust gradually (10-20% changes) rather than dramatically
- Allow 1-2 weeks for data after changes before adjusting again
Privacy and data limitations
Google limits demographic data to protect privacy:
Unknown demographics - Many users fall into "unknown" categories where Google can't determine age or gender. Minimum thresholds - Demographic data only shows when you have sufficient volume to maintain anonymity. Estimation - Google estimates demographics based on behavior, not actual user data. Treat as directional, not absolute. Coverage varies - Display and video campaigns have better demographic data than search campaigns.If you see mostly "unknown" demographics, you need more volume before patterns emerge.
Testing audience hypotheses
Once you identify patterns:
- Create hypothesis - "30-40 year old women on mobile convert best for our product"
- Build test campaign - Target that specific segment with tailored messaging
- Set appropriate budget - Enough to get statistically significant results
- Run for 2-4 weeks - Collect sufficient data
- Compare to control - Does targeted campaign outperform broad campaign?
- Scale or iterate - If successful, expand budget; if not, try different audience
Don't over-segment with limited budget. Better to optimize one broad campaign than run ten tiny targeted campaigns.
Credit costs
Audience analysis reports cost:
- Audience demographics: 12 credits
- Device performance: 10 credits
- Geographic performance: 12 credits
- Time performance: 10 credits
Run monthly to identify trends and quarterly for strategic decisions.
Common questions
My demographics show mostly "unknown" - is something wrong?No. Search campaigns have limited demographic data. This is normal if you have lower volume or primarily search campaigns. Display and video campaigns provide richer demographic data.
Should I exclude demographics that underperform?Generally no. Use bid adjustments instead. Exclusions prevent any impressions, which limits your data and future optimization. Lower bids let you still reach those audiences at profitable costs.
How much data do I need for reliable insights?Aim for:
- 100+ conversions before making major audience decisions
- 30+ conversions per demographic segment for reliability
- 1000+ clicks for device and location analysis
- 2-4 weeks of data minimum
Small data sets lead to false conclusions.
My mobile traffic is high but conversions are low. What should I do?First, fix your mobile experience (page speed, simplicity, mobile-optimized design). Then lower mobile bids while you improve the experience. Once mobile converts better, increase bids again. Don't just cut off mobile traffic without improving the experience first.
More questions? Use the chat widget in the bottom-right corner or email support@convertmate.io.