Build a Single Source of Truth with Google Sheets + Apps Script (Marketing SSOT Guide 2025)

Intro

Marketers lose an absurd amount of time because their numbers live in different universes. Google Ads shows one conversion count, Facebook Ads shows another, the CRM insists on a different opportunity total, and your email tool quietly tracks its own revenue. Every reporting meeting becomes a forensic investigation: which system is “wrong” this week?

The deeper problem is not that tools disagree; it is that there is no single place where the business truth lives. Ads platforms optimise for their own attribution logic. CRMs care about contacts and stages. Analytics tools are obsessed with sessions and events. None of them is designed to be a neutral referee.

That is what a Single Source of Truth (SSOT) is supposed to fix. Instead of trying to make every tool agree with every other tool, you create one central, structured layer that receives data from all of them, reconciles conflicts and becomes the place everyone references. The good news: in 2025, you can do this with something you already have—Google Sheets plus Apps Script—before you ever touch heavyweight ETL or warehouse platforms.

This guide will walk you through how to turn Sheets into a marketer-friendly SSOT and how to use Apps Script (often miswritten as AppScript) as your lightweight sync engine. If you later upgrade into dedicated data pipeline tools built for marketers, this foundation will still be valuable; you’re designing the schema and logic first, then the tooling can grow with you.

💡 Nerd Tip: If you currently spend more time copying numbers into decks than actually changing campaigns, you don’t have a data problem—you have a “truth” problem. Fix that first.

📘 What Exactly Is a Single Source of Truth (SSOT) for Marketers?

Marketers sometimes confuse SSOT with “fancy dashboard” or “yet another reporting tool.” It’s neither. A dashboard is a view. An SSOT is a data layer. You can build ten different dashboards on top of the same SSOT, and they may emphasise different metrics or visuals, but they pull from one underlying set of reconciled numbers.

A marketing SSOT is not a replacement for Ads Manager, Analytics or your CRM. It is the place where you consolidate, normalise and decide how metrics should be interpreted. Think of it as the contracts your numbers sign before they are allowed into decision-making conversations. If Facebook claims 120 conversions, Google says 95, and your CRM shows 70 closed deals, the SSOT is where you decide how to log those events, which definition you’re using for “conversion” and how they line up by date and campaign.

This is also why a SSOT is not the same as an ETL tool or BI platform. ETL helps you move and transform data between systems. BI helps you visualise it. The SSOT is the canonical table or set of tables that everything else reads from. If you change a formula in that truth layer, your dashboards and automations should update accordingly, instead of each tool having its own version of reality.

A practical SSOT for marketing usually focuses on a small number of core entities: campaign performance, contacts or customers, and messaging events like emails or automations. Each row in those tables is defined once. You resist the temptation to let every tool invent its own names and fields. Over time, this reduces the classic arguments about “why does this report show different ROAS than that one” and lets you spend more energy on actions instead of reconciliation.

Eric’s Note:

I’m no fan of buzzwords, but “SSOT” earns its keep when it quietly kills three recurring reporting fights on your team. For me, that is the real metric that matters.

📊 Why Google Sheets Is an Ideal SSOT for Solo Marketers & SMBs

Enterprise teams often jump straight to warehouses and dedicated ETL tools. Solo marketers and small teams usually don’t need that complexity at first. Google Sheets offers a surprisingly strong sweet spot: it is free, ubiquitous and programmable, yet simple enough that everyone already knows how to open, filter and sanity-check a number.

Sheets works well as a SSOT backbone because it is structurally flexible. You can start with one “Campaign Performance” table and expand into two, three or ten related sheets as your needs grow. You can add columns for new networks or metrics without waiting for a data engineer. For 95% of non-enterprise teams, the volume of daily marketing data is small enough that a well-designed Sheet can hold months or even years of snapshots without buckling.

Importantly, Sheets sits inside the Google ecosystem. This makes it trivial to link your SSOT into Looker Studio dashboards, data-driven slides and internal documents. If you have non-technical stakeholders who prefer a friendly interface, they never need to see the Apps Script code; they simply get a live dashboard powered by the Sheet you maintain. NerdChips has seen many teams evolve from spreadsheet-only reporting to full marketing dashboards for non-techies with very few structural changes in the underlying SSOT.

Apps Script is the second half of the equation. Because it runs natively with Sheets, you don’t have to maintain separate servers or authentication layers. With a handful of script files, you can fetch data from Google Ads, Meta, your CRM and email tool, map it into your unified schema and schedule it to run on timers. For a solo marketer, that’s the difference between “I should really update this spreadsheet” and “it updated itself while I was in a meeting.”

💡 Nerd Tip: If your first instinct is “we’ll grow out of Sheets in six months,” treat that as a design constraint. Build your schemas and naming conventions as if they might later live in a warehouse. Future migrations become much less painful.

🧩 The Architecture — Your SSOT in One Diagram

Before writing any code, visualise the architecture you’re about to build. The core idea is that your SSOT is not a monolithic monster; it’s a small graph of inputs, a central schema and outputs.

At the top, you have source systems: ad platforms like Google Ads and Meta, your CRM, email service provider, maybe a payment system or subscription tool. Each of these exposes APIs or export files that contain the metrics you care about. They don’t know about each other, and they have their own conventions for naming and attribution.

Next, you introduce Apps Script data pullers. These are Google Apps Script functions that call the APIs, fetch raw data for a defined timeframe (for example, “yesterday’s performance”), transform field names and formats, and prepare arrays that match your SSOT schema. These scripts run either on manual triggers (for testing) or on time-based triggers you configure inside the Apps Script UI.

The heart of the system is the unified schema in Google Sheets. This is where you define consistent columns such as date, platform, campaign, spend, clicks, conversions, revenue, ROAS and so on. Every source system has to “speak” this language before its data is allowed in. Supporting sheets store mapping tables—campaign aliases, channel mappings, normalised UTMs—and validation logic.

From there, you maintain daily snapshots: instead of constantly overwriting today’s numbers, you append rows for each day and platform. This gives you a historical trail that isn’t subject to the quirks of changing attribution windows as time passes. Your dashboards and reports read from these snapshots, not the raw API outputs.

Finally, downstream tools consume the SSOT. Looker Studio connects directly to the sheet for dashboards. Workflow tools or scripts read from it to trigger alerts. Even more advanced stacks that rely on workflow automation software for mapping processes and scaling actions can treat your sheet as a simple but reliable upstream.

💡 Nerd Tip: Print this architecture or sketch it on a whiteboard. When something breaks, ask “which layer is misbehaving?” before changing three things at once.

🧱 Step 1 — Design Your SSOT Schema (The Most Important Step)

If you only did one step from this guide, this would be it. The schema—the columns you choose and the meanings you assign to them—is what separates a random spreadsheet from a Single Source of Truth.

Start with Campaign Performance. This table is your cross-platform heartbeat. Each row should represent performance for a specific date, platform and campaign. Useful fields include a date column, a platform label (Google Ads, Meta, LinkedIn, etc.), a campaign_name_raw straight from the source, and a campaign_canonical reflecting your own naming standard. Add numeric columns for spend, impressions, clicks, conversions, revenue and ROAS. The point is to make it trivial to group by platform or campaign and compare apples to apples.

Next, define CRM Events. This could be a separate sheet where each row represents a key lifecycle moment: lead created, opportunity won, churn event, upsell. Core fields might be contact_id, acquisition_source, first_touch_campaign, last_touch_channel, lifecycle_stage, status and timestamps for when each stage changed. Aligning these fields with your campaign table lets you trace which campaigns are not just generating clicks, but actually moving pipeline.

Then add an Email / Automation table. Here, rows can represent email sends or automation steps. Fields could include email_id, campaign_canonical, send_date, emails_sent, unique_opens, unique_clicks, unsubscribe_count and any attributed_revenue from post-click behaviour. When this table uses the same canonical campaign naming as your performance table, you can evaluate full-funnel impact rather than siloed “open rate” vanity metrics.

Finally, build Mapping / Alias tables. One sheet might map raw campaign names from each platform to a standardised canonical naming convention. Another can store channel mappings for UTMs, translating raw utm_source and utm_medium pairs into tidy channel buckets. This is where you encode your conventions rather than relying on memory or ad hoc rules hiding in formulas.

If you’ve ever fought with messy spreadsheets, you’ve already felt why this matters. A marketer on X recently put it bluntly:

“90% of ‘data problems’ in marketing are just naming problems, wearing a more expensive hat.”

Your SSOT schema is where you decide those names once and stop reinventing them in every report.

🧵 Step 2 — Pull Data Automatically with Google Apps Script

With your tables defined, you can start wiring up the pipes that keep them fresh. Google Apps Script is essentially JavaScript with deep hooks into Google products. You can open the Script Editor from your Sheet and write functions that call external APIs, process responses and write rows directly into the right tabs.

A typical pattern is to create one script file per platform: GoogleAds.gs, MetaAds.gs, CRM.gs and so on. Inside each, you define a function like pullGoogleAdsCampaigns() that fetches yesterday’s stats and merges them into your Campaign_Performance sheet. For a REST API, the structure is usually: build a URL, attach headers with access tokens, send the request, parse the JSON and then transform it into an array of arrays ready for Sheet.getRange().setValues().

A simplified pseudo-code sketch might look like this:

In a real setup, you’d handle pagination, errors and rate limits more carefully, and you’d centralise authentication so tokens are stored securely. You’d also repeat the pattern for Meta Ads, your CRM and email service, adjusting field mapping each time so they all end up speaking your SSOT schema.

As your stack grows, you may eventually graduate to dedicated data pipeline tools built specifically for marketers to handle heavier loads or more complex destinations. But starting with Apps Script means you can ship value fast, learn what you actually need and keep ownership of your logic.

💡 Nerd Tip: Begin with just one platform, usually the one you spend the most on. Get that sync stable before wiring in additional sources. Complexity compounds fast.

🧽 Step 3 — Clean & Normalize Data (Stop Garbage In → Garbage Out)

Pulling data is the easy part. The hard part—and the thing that truly makes this a SSOT—is normalising it. That means aligning definitions, formats and naming so that rows from different systems can sit in the same table without starting a fight.

Campaign naming is a classic example. Google Ads, Meta and LinkedIn may all use different naming conventions, and human marketers love ad hoc abbreviations. If you let raw names leak into your primary analysis, your SSOT will devolve into chaos quickly. Instead, use your mapping table to convert raw names into canonical ones with a mapToCanonical() function. That may involve simple lookups or pattern-based rules that strip prefixes and normalise country codes.

Attribution windows are another subtle trap. One platform may define a conversion within seven-day click and one-day view, another might default to 28 days. Your SSOT should be explicit about which window you’re logging. In some cases, you’ll choose to pull only certain types of conversions; in others, you’ll translate them into a consistent “model” that your stakeholders understand. Write that decision down in a README sheet so new teammates aren’t guessing.

You also need to treat dates, currencies and numbers consistently. Dates should use the same timezone and format across all sources. Currencies should be converted into a single reporting currency before you compute ROAS, otherwise you end up comparing euros to dollars like they’re the same. If one platform reports micro-values (for example, multiplying by 1,000,000), your scripts should handle that conversion centrally.

This is where simple data validation rules in Sheets can help. You can restrict allowed values in certain columns, use conditional formatting to flag negative spends or unreachable ROAS, and keep consistent number formats. Unlike raw spreadsheets, your SSOT is not a scratchpad; it’s a curated environment where bad data is either fixed or prevented from entering.

From the NerdChips perspective, this is also where SSOT thinking overlaps with broader automation of data entry and spreadsheet-heavy workflows. The more you formalise cleaning and mapping rules, the less you rely on human copying and manual fixes, and the less fragile your reporting becomes.

📆 Step 4 — Build Daily Snapshots Automatically

One mistake many teams make is treating their SSOT as a constantly updated “current state” instead of a time series of snapshots. If you keep overwriting the same rows whenever you pull fresh metrics, you lose historical consistency as attribution catches up or platforms retroactively adjust numbers.

A more robust pattern is to capture daily snapshots. For each day and platform, you append a new row with that day’s view of the world. When you pull “yesterday” from each source, you log exactly what those platforms reported at that time. Over weeks and months, you build stable historical data that isn’t retroactively rewritten unless you deliberately run backfills.

Apps Script makes this easy because your fetch functions already know which date they’re pulling for. You can set up time-based triggers in the Apps Script editor—say, every day at 03:00—to run the pullGoogleAdsCampaigns() and other functions that append rows. Each run adds yesterday’s snapshot to the Campaign_Performance sheet and moves on.

Over time, you may want to keep snapshots for different granularities: daily at minimum, but maybe also weekly or monthly aggregates in dedicated sheets. You can generate these automatically from the daily table, either via formulas or by scripting aggregation jobs that run less frequently.

Storage is cheap at the scale most marketers operate. A year of daily campaign snapshots across a handful of platforms rarely hits any serious limit in Sheets. If it does, you can archive older months into separate files and point your dashboards at the “current year” for speed, keeping historical archives for reference or migration.

💡 Nerd Tip: Lock your snapshot sheets once they’re populated for a date. If numbers need correction, do it through a controlled backfill process, not by freehand editing rows. Treat your SSOT like a database, not a flexible note.

🧪 Step 5 — Validate with Automated Checks

A SSOT is only as trustworthy as its weakest sync. APIs break, tokens expire, and occasional anomalies slip through. Instead of discovering these in a board meeting, you can make Apps Script run validation passes after each sync and surface problems early.

Start with simple completeness checks. For each day you expect data, ensure that every major platform has at least one row. If yesterday has Meta and Google Ads but no email metrics, that’s a yellow flag. Apps Script can read the snapshot sheet, count rows per platform and, if counts fall below thresholds, log errors or send alert emails.

Then add field-level sanity checks. If spend jumps from 500 to 50,000 in one day without a planned budget change, that’s worth investigating. The same goes for days with spend but zero impressions, or days with impressions but zero reported clicks. These patterns often indicate tracking issues or configuration problems rather than true performance changes.

You should also watch for broken tokens and API errors. If a request fails, your fetch functions should catch the error, log it to an Error_Log sheet and optionally send a notification. A basic pattern is to wrap API calls in try–catch blocks and record error messages, response codes and timestamps.

Once you have good validation practices in place, your SSOT becomes more than a passive store; it acts like a guardrail. It doesn’t just collect data; it tells you when that data can’t be trusted and when to dig deeper. That is exactly the mindset that underpins mature webhook-based automation flows for growth teams, where error handling is part of the design rather than an afterthought.

💡 Nerd Tip: Start with just one alert: “No data for any platform yesterday.” Getting that right will catch 80% of catastrophic failures. Then layer on more nuanced checks as you go.

📈 Step 6 — Use SSOT as the Source for Dashboards

Once your SSOT is alive and validated, you can treat it as the single feed for everything visual and narrative. Instead of connecting tools directly to multiple platforms, point them to your Sheets tables and let that truth layer handle reconciliation.

Looker Studio is usually the easiest starting point because it integrates natively with Sheets. You connect your Campaign_Performance snapshot sheet as a data source, declare the right field types, and then build charts and tables. Because your schema already unified metrics across platforms, you can build cross-channel ROAS views, blended performance by country or funnel dropout analyses without juggling multiple connectors.

For stakeholders who don’t want to touch dashboards, you can generate scheduled PDF reports or slide decks where key charts are embedded and refresh from the SSOT. Some teams even write Apps Script functions that assemble summary lines—“This week’s blended ROAS was X, up Y% versus last week”—and inject them into emails or docs.

Non-techies benefit the most from this approach, because they can work in friendly interfaces that sit on top of a robust truth layer. When you design dashboards in the spirit of marketing dashboards that are approachable for non-technical users, the SSOT is what keeps those dashboards from drifting apart as data grows.

If you’re more technically inclined, you can pull the same SSOT into Python notebooks, Streamlit apps or BI suites. The point remains the same: they all draw from one canonical set of tables, not from a patchwork of direct API pulls each doing its own mapping.

💡 Nerd Tip: Any time someone asks for a new report, ask “can this live on top of the SSOT?” If the answer is no, either your schema is missing something or the request is too one-off to justify code.

🚨 Step 7 — Advanced: Trigger Workflows Based on SSOT Data

Once your data is centralised and trusted, the obvious next move is to act on it automatically. Your SSOT becomes not just a reference, but a control panel.

A straightforward use case is low ROAS alerts. A small Apps Script function can run daily, read yesterday’s snapshots, identify campaigns whose blended ROAS has fallen below a threshold and send you a concise summary by email or Slack. Over time, you can tie these alerts into automation tools that adjust bids or pause campaigns, especially if you’re also experimenting with external workflow automation platforms that map and trigger processes at scale.

You can also use SSOT data for budget pacing. If your monthly budget for a platform is fixed, a script can compare month-to-date spend versus a linear or custom pacing curve and flag whether you’re over or under-spending. That same logic can update a “pacing” column inside your Sheet and highlight problem rows with color formatting.

CRM-related workflows are another fertile area. When your SSOT knows which campaigns create high-value contacts, you can respond to lifecycle signals—like a surge in churn-risk events—by firing off webhook calls or using patterns from webhook automation for low-latency growth integrations to inform other systems. For example, a spike in churn-related events tied to a specific channel could trigger diagnostics on that channel’s messaging or offer mix.

At the simplest level, you can start by colouring rows. When certain conditions are met—negative ROAS for seven days in a row, extreme CPI outliers, or sudden drops in open rate—your scripts update cell formats so the issues visually stand out. That alone can meaningfully change how your team reviews data in weekly meetings.

The key is to build slowly. Every new automation should be traceable back to a clear rule in your SSOT. If something misfires, you want to be able to read the row and immediately see why the system acted as it did.

💡 Nerd Tip: Automation is not about trusting scripts blindly. It’s about encoding decisions you already make repeatedly into reliable, transparent rules that live next to the data itself.

🧠 Nerd Verdict

A marketing Single Source of Truth is not a luxury reserved for teams with warehouses and data engineers. It is a discipline: deciding which numbers matter, how they are defined and where they live. Google Sheets plus Apps Script gives you enough power to practice that discipline today, without waiting for a bigger budget or a new tool.

From a NerdChips lens, the biggest unlock is psychological. Once your data lands in one schema every day, arguments shift from “whose export is right” to “what will we do about this trend.” Dashboards become less fragile. Automation becomes more believable. You stop screenshotting Ads Manager in panic five minutes before a meeting.

If your future includes more sophisticated data pipelines or complex workflow engines, your SSOT will come with you. The schema, mappings and validation rules you baked into Sheets are portable; the value of thinking in terms of a central truth layer is not tied to any one tool.

In other words: building a SSOT in Sheets isn’t a detour. It’s your first real step into operating your marketing data like an asset instead of a recurring headache.

❓ FAQ: Nerds Ask, We Answer

💬 Would You Bite?

If you started your marketing SSOT today, which table would you design first—campaign performance, CRM events, or email metrics?
And once your Google Sheets + Apps Script setup is live, what’s the first workflow you’d trigger from it: low-ROAS alerts, budget pacing, or lifecycle-driven CRM nudges? 👇

Crafted by NerdChips for creators and growth teams who are done letting scattered spreadsheets decide their next move.