Export MX Rewards Balances to a Google Sheet for Points Portfolio Analysis

The Scenario

You are a travel rewards enthusiast — not a professional, but serious about it. You have six credit cards connected via MX, each with a different rewards program. Three times in the past month, you have tried to figure out your total points value across all cards before booking a trip. Three times, you have ended up on six different bank websites, writing numbers on a sticky note, and doing the math yourself. The sticky note is never current by the time you check.

The bad version:

Log into the MX portal, find the members list, note each member GUID, then call the rewards API for each one — six separate API calls because rewards are scoped per member, not per user.
Handle the inconsistency: some cards return points, some return miles, some return cash-back in dollars — the unit is different per card and MX returns whatever the institution reports.
Paste six rows into your sheet, manually add a cash-value column with 0.015 multiplied by the raw balance for the points-denominated cards, and leave the cash-back cards alone because they don't need conversion.

You want to look at one number. The process to get there currently takes 25 minutes.

The Easy Way: One Prompt in SheetXAI

SheetXAI is an AI agent that lives inside your Google Sheet. It reads the sheet, connects to MX Technologies, and writes your rewards data across all members without requiring you to look up a single member GUID.

For MX user USR-abc123, list all members, trigger a rewards fetch for each, then list every rewards record with member name, rewards type, and current balance in this sheet, and in column D multiply the balance by 0.015 to estimate cash value — show the total estimated value at the bottom of column D.

What You Get

One row per rewards record, one per card.
Column A: member name or institution name associated with the card.
Column B: rewards type as returned by MX (e.g., "points", "miles", "cash_back").
Column C: current rewards balance.
Column D: estimated cash value (balance multiplied by 0.015 for points/miles; balance used directly for cash_back).
A total row at the bottom of column D showing aggregate estimated value.

What If the Data Is Not Quite Ready

Some cards use miles instead of points and should use a different conversion rate

For MX user USR-abc123, pull all rewards balances and write member name, rewards type, and balance to columns A, B, C — in column D apply 0.015 for type "points", 0.012 for type "miles", and use the raw balance for type "cash_back", then total column D at the bottom.

A rewards fetch for one member returned stale data

For MX user USR-abc123, trigger a fresh rewards fetch for each member before reading the balance, then write member name, rewards type, balance, and last-updated date to columns A through D — flag any row where the last-updated date is more than 7 days ago in column E.

For MX users USR-abc123 and USR-xyz789, pull all rewards balances for each, write user ID, member name, rewards type, and balance to columns A through D, and in column E calculate the estimated cash value using 0.015 for points and miles and the raw balance for cash_back — total by user in a Summary tab.

You want to clean up null balances, convert everything to a single unit, and see which card is closest to a redemption threshold

For MX user USR-abc123, pull all rewards records, remove any rows where balance is null or zero, convert all balances to estimated cash value (0.015 per point/mile, raw for cash_back), and in column E show the gap between each card's current estimated value and its nearest 25-dollar redemption threshold.

The cleanup and the threshold calculation belong in the same prompt — that way SheetXAI only makes one pass through the data.

Try It

Get the 7-day free trial of SheetXAI and open any Google Sheet with your MX user GUID, then ask it to pull all rewards balances and calculate estimated cash value. For a fuller picture of your financial accounts across all institutions, see the net worth snapshot article.