Not all organizations with occasional users look the same. The distribution of who uses the tool, how often, and in what pattern varies — and that variation determines which pricing model fits and what the realistic cost difference looks like. Before evaluating specific tools, it's worth spending a few minutes identifying which usage archetype describes your organization. The three archetypes below cover the patterns that come up most often.
How to Identify Your Archetype
The input you need is simple: 90 days of login data from your current BI tool, grouped by frequency. Most BI tools have an admin console that exposes this. Export the list of users and their login counts over the last three months. Group them into buckets — logged in more than 10 times, 3–10 times, 1–3 times, never — and look at the shape of the distribution.
That shape tells you your archetype. For a detailed methodology on pulling and analyzing this data, see the cost audit chapter in our BI pricing guide. This chapter covers the archetypes themselves — what each looks like and what it implies for tool selection.
Archetype 1 — Core-Heavy
A small group of daily users — analysts, operations staff, finance team members — does the bulk of the analytical work. Behind them is a larger population of managers, department leads, and executives who log in once a month to review metrics before a leadership meeting, pull a number for a board presentation, or check progress against a quarterly goal.
The login distribution looks like a steep drop-off: a handful of users with dozens of monthly logins, and a long tail of users with one to three. The 80/20 principle in its purest form — 20 percent of users driving 80 percent of logins, with the other 80 percent spread thin across the tail.
This is the most common archetype in SMBs and mid-market organizations. The core is small enough that per-seat pricing for just that group would be manageable — but the moment the organization tries to extend access to the larger occasional population, the per-seat model starts charging for activity that won't materialize.
Best fit: MAU pricing. Pay for the active core at the rate they actually use the tool. Extend access freely to the occasional population — accounts cost nothing until they're used. The bill reflects the actual usage distribution rather than the full roster.
Archetype 2 — Broadcast
A very small number of people — sometimes two or three, rarely more than five — build all the reports. Everyone else is a pure consumer: they log in to see dashboards, not to create anything. The consumers may number in the dozens, but each logs in rarely. A regional sales team that checks a performance dashboard monthly. A franchise network where franchisees log in to review their own location's numbers. An operations function where frontline managers check KPIs at the start of each shift.
The login distribution here is bimodal: a tiny cluster of heavy builders, a large cluster of light consumers, with very little in between. The builders are easy to price for. The consumer population is where per-seat pricing causes problems — it charges the same rate for a franchisee who logs in twice a month as for an analyst who runs queries every day.
Best fit: MAU pricing or a flat-tier tool with broad viewer access included. The key requirement is that consumer accounts can be provisioned freely without each one being a cost decision. For external consumer scenarios — clients, franchisees, partner organizations — also evaluate tools that include Public View Licenses or unauthenticated viewer access as part of the tier structure.
Archetype 3 — Seasonal
Usage spikes around specific events and collapses in between. A retailer where the entire organization is in the BI tool through Q4, then barely touches it in January and February. A professional services firm where utilization reports get heavy attention during annual performance reviews and lie dormant most of the year. An agricultural operation tracking planting and harvest cycles. A company where budget planning season brings in every department head, who then disappear until next year's cycle.
In these organizations, the usage distribution changes dramatically by month. The same 60 people who needed access in October genuinely needed it — and the 8 people using the tool in March are the actual daily core. On a per-seat model, you pay for the October headcount all year. The off-peak months are largely paying for idle seats.
Best fit: MAU pricing, specifically because the bill contracts naturally in slow months. A seasonal organization benefits directly from paying for what's actually used rather than what's licensed — the tool becomes more cost-efficient in the slow periods automatically, without any manual seat management.
Start a free 7-day trial — no credit card required. Connect your data, provision your team, and see what your actual active user count looks like in practice.
A Note on MAU Pricing Mechanics
All three archetypes point toward MAU pricing as a better fit than per-seat for organizations with occasional users. If you're not familiar with how MAU pricing works in detail — what counts as an active user, how overages are handled, how to model your cost at different usage levels — the MAU pricing chapter in our BI cost guide covers the mechanics thoroughly. This chapter stays focused on the organizational picture; the pricing mechanics are covered there.
The next chapter moves from the organizational analysis to the vendor evaluation: the specific questions to ask any BI vendor before you sign, calibrated for the occasional-user buyer.
