Analytics
Understand your spending patterns with automatic charts, category breakdowns, and month-over-month trends — computed entirely on your device from your transaction history.
Monthly Spending — 6 Month Trend
$1.8k
$2.1k
$2.5k
$1.9k
$2.0k
$2.2k
$2,091
6-month average
↓ 6%
vs last month
Food
highest category
What analytics shows you
Analytics transforms your raw transaction log into meaningful patterns. The more consistently you log, the more accurate and useful the insights become. Spend Sense's on-device computation means no raw financial data ever leaves your phone.
Spending Breakdown — This Month
Charts and views
| Chart | What it shows | Best used for |
|---|---|---|
| Monthly trend bar chart | Total spend per month for the last 12 months | Spotting seasonal patterns and long-term trends |
| Category donut / breakdown | Percentage of spend per category for a selected period | Identifying where most money goes |
| Day-of-week heatmap | Which days you spend most on average | Recognising habitual spending days |
| Merchant frequency | Most frequently visited merchants and average spend per visit | Evaluating subscription and restaurant habits |
| Rolling 30-day spend | Running total for the past 30 days updated daily | Real-time budget awareness outside a calendar month |
| Category comparison | Side-by-side bar chart for two selected categories across time | Comparing dining vs groceries, etc. |
Automatic insights
Spend Sense generates plain-English insight cards on your dashboard based on your transaction patterns. These update every time you log a transaction.
Pattern
"Your food spending is 18% higher this month than your 3-month average."
Merchant frequency
"You've logged 4 transactions at Starbucks this week totalling $28."
Projection
"At this rate, you'll exceed your Entertainment budget by $42 before month end."
Positive
"This is your lowest Transport spend in 6 months — nice work!"
Recommendations for better analytics
Log consistently for at least 30 days
Analytics is most valuable after a full month of consistent logging. Month-over-month comparisons need at least 2 months of data.
Keep categories stable
Avoid renaming or reorganizing categories frequently — it breaks historical trend lines. Plan your category structure before you start logging.
Use subcategories for precision
Breaking "Food" into "Groceries" and "Dining Out" gives you much more actionable insights than a single merged category.
Tag one-time anomalies
Big one-off expenses (medical bills, flights) skew averages. Tag them as "One-time" in the notes field so you can filter them out when reviewing trends.
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