Plain-language marketing report interpreter for non-analysts

A marketing manager or business owner opens a dashboard and sees numbers. They don't know if those numbers are good. They don't know which one to act on. The complaint 'ease of use can have users draw wrong conclusions' is the most dangerous version of this — the tool gives no warning that someone is misreading their own data. The user feels like they understand, acts on a wrong signal, and finds out later when results don't match expectations.

This gap exists because analytics tools are built to display data accurately, not to interpret it for a specific context. Interpretation requires knowing what industry you're in, what your conversion benchmarks are, what changed recently in your campaigns, and what matters to your business goals — none of which the tool knows. The vendors don't build this because it's genuinely hard and because their core buyers (analysts, data teams) don't want the tool to 'dumb things down.' So the interface stays neutral and precise, which is correct for power users and actively harmful for everyone else.

The specific complaint 'the software is overwhelming for users without a data background, making it difficult to interpret the information without hiring a data analyst' names the real cost: companies hire an analyst they wouldn't otherwise need, or they make decisions blind. Neither is sustainable for small teams.

This isn't about simplifying the charts. It's about adding a layer that sits above the data and says: 'Your CPL went up 40% this week, your CTR dropped on brand terms, here's what that typically means and here's what to check next.' That kind of plain-language interpretation doesn't exist inside these tools because no tool has the business context to do it — that context lives with the human who set up the account.

What to build

Build a reporting add-on that connects to a marketing analytics data source via API, lets an admin configure business context (goals, benchmarks, channel priorities), and then generates a weekly plain-language narrative summary — with specific flagged anomalies and suggested next actions — formatted for a non-analyst reader.

Where to start

Start with e-commerce store owners on Shopify using Google Ads, where the data model is simpler, the benchmarks are more established, and the owner is highly motivated to understand performance without hiring help.

The hard part

Calibrating the plain-language interpretation so it's genuinely useful rather than generically obvious is technically hard — getting it wrong means producing summaries that either state the obvious or confidently misdiagnose the data, which destroys trust immediately.

How it makes money

Flat monthly subscription per account, priced below the cost of a single hour of analyst time — around $49–$99/month — with pricing tiers based on the number of connected ad accounts or data sources.