“Our collaboration has been nothing short of a revolution. Dema's solution is now the beating heart of our operation and we'll keep evolving and growing together.”
Elin Glommen
CEO
A Northbeam alternative that runs the commercial side, not just the measurement
Northbeam is a strong measurement platform. Dema measures too, but it also carries cost of goods, fulfilment, returns, inventory and sell-through, so the answer arrives as profit per product per market, and agents can act on it.
GP3per product, market and campaign
215 metrics110 dimensions, one definition each
1M+ actionsrun by agents every month
Dema or Northbeam: which should you pick?
Choose Northbeam if what you want is a measurement platform. Their attribution modelling is genuinely deep, and Apex (feeding their first-party signal back into Meta) is something we do not do at all. Choose Dema if you are an e-commerce or retail business trying to improve profitability: you need product-level margin after real operational costs and returns, inventory and sell-through in the same model, and agents that turn the answer into work. Northbeam tells you what your marketing did. Dema is built to run the commercial decisions that follow.
Where Northbeam is the better choice
They are good at what they are for, and two of these we simply do not offer.
Apex: pushing first-party signal back to Meta
Northbeam Apex feeds their own attribution data back into Meta so you can run Custom Attribution campaigns optimised against Northbeam's numbers rather than Meta's. It is a genuinely clever piece of plumbing and Dema has no equivalent. If closing that loop inside Meta is your priority, they are the better tool.
Depth of attribution modelling
Their Clicks + Deterministic Views model, configurable attribution windows, and a model comparison tool that can hold their models against outside data: this is a more elaborate attribution toolkit than ours, and it is the centre of their product rather than one input among several.
A measurement specialist, if that is the brief
If your team's job is media measurement and someone else owns margin, inventory and merchandising, a specialist is a reasonable choice. Dema's breadth is only an advantage if you actually need the commercial side of it.
Dema vs Northbeam, feature by feature
Rows where Northbeam wins are marked as such. Everything about their product below comes from their own documentation and product pages.
What the product is for
DemaRunning the commercial side of an e-commerce or retail business: marketing, buying, merchandising and finance on one model.
NorthbeamMarketing measurement and media decisions.
Attribution
DemaAd platform data and MTA are inputs, weighted by causal factor attribution against experimental evidence per channel, funnel stage and market.
NorthbeamFirst-party multi-touch attribution is the core, including their Clicks + Deterministic Views model, configurable windows and a model comparison tool.
A deeper attribution toolkit than ours, and the centre of their product.
Pushing signal back to ad platforms
DemaNot offered.
NorthbeamApex feeds Northbeam's first-party data into Meta for Custom Attribution campaigns.
MMM and incrementality
DemaMMM run on profit and on LTV, plus geo-based incrementality testing whose results calibrate the model and set the attribution weights.
NorthbeamMMM+ for budgeting and forecasting, and an incrementality product, with automated incrementality testing described as forthcoming.
Comparable breadth. The difference is the outcome being modelled: ours is contribution margin, theirs is media ROI.
Profit depth
DemaGP3 per product, per market, per campaign, after cost of goods, shipping, toll, pick-and-pack and transaction costs, with actual and estimated return rates.
NorthbeamProfit Benchmarks and product analytics on the marketing side.
Inventory and sell-through
DemaInventory history as of any past date, and sell-through forecasting per size, in the same model as spend and margin.
NorthbeamNot part of the product.
Omnichannel and offline
DemaOnline, retail and wholesale in one model, with separate warehouses each able to carry its own cost structure.
NorthbeamBuilt around paid media and e-commerce orders.
Data model you work against
DemaA semantic layer: 215 metrics and 110 dimensions, each defined once, combinable almost freely, so inventory, margin and campaign sit in one query.
NorthbeamAttribution and metrics surfaces with exports and a Data Export API.
Agents and automation
DemaBuild agents with their own tools, integration access and memory, running on a schedule, with an app surface, and executing changes back through MCP where you allow it.
NorthbeamNo agent or automation-building capability appears in their documentation or product pages.
API access
DemaGraphQL over the semantic layer, on every plan.
NorthbeamOrders, Spend and Data Export APIs, with export documented as a higher-tier plan feature rather than entry-level.
Where AI inference runs
DemaChoose EU or US, with residency control. LLM subprocessors are optional.
NorthbeamNot stated in their public documentation.
| Dema | Northbeam | |
|---|---|---|
| What the product is for | Running the commercial side of an e-commerce or retail business: marketing, buying, merchandising and finance on one model. | Marketing measurement and media decisions. |
| AttributionA deeper attribution toolkit than ours, and the centre of their product. | Ad platform data and MTA are inputs, weighted by causal factor attribution against experimental evidence per channel, funnel stage and market. | First-party multi-touch attribution is the core, including their Clicks + Deterministic Views model, configurable windows and a model comparison tool. |
| Pushing signal back to ad platforms | Not offered. | Apex feeds Northbeam's first-party data into Meta for Custom Attribution campaigns. |
| MMM and incrementalityComparable breadth. The difference is the outcome being modelled: ours is contribution margin, theirs is media ROI. | MMM run on profit and on LTV, plus geo-based incrementality testing whose results calibrate the model and set the attribution weights. | MMM+ for budgeting and forecasting, and an incrementality product, with automated incrementality testing described as forthcoming. |
| Profit depth | GP3 per product, per market, per campaign, after cost of goods, shipping, toll, pick-and-pack and transaction costs, with actual and estimated return rates. | Profit Benchmarks and product analytics on the marketing side. |
| Inventory and sell-through | Inventory history as of any past date, and sell-through forecasting per size, in the same model as spend and margin. | Not part of the product. |
| Omnichannel and offline | Online, retail and wholesale in one model, with separate warehouses each able to carry its own cost structure. | Built around paid media and e-commerce orders. |
| Data model you work against | A semantic layer: 215 metrics and 110 dimensions, each defined once, combinable almost freely, so inventory, margin and campaign sit in one query. | Attribution and metrics surfaces with exports and a Data Export API. |
| Agents and automation | Build agents with their own tools, integration access and memory, running on a schedule, with an app surface, and executing changes back through MCP where you allow it. | No agent or automation-building capability appears in their documentation or product pages. |
| API access | GraphQL over the semantic layer, on every plan. | Orders, Spend and Data Export APIs, with export documented as a higher-tier plan feature rather than entry-level. |
| Where AI inference runs | Choose EU or US, with residency control. LLM subprocessors are optional. | Not stated in their public documentation. |
Northbeam details verified against docs.northbeam.io and northbeam.io in August 2026, plus third-party pricing reporting for plan tiers. Their product changes and pricing moves, so if something here is out of date, tell us and we will correct it.
Measuring the spend is half the question
Northbeam can tell you a channel drove £40,000. It cannot tell you that most of it came from a product with a 62% return rate, that the sizes people actually wanted sold out eleven days ago, or that the same budget in another market would have earned more margin. Those answers need cost of goods, fulfilment, returns, inventory and sell-through in the same model as the spend, which is a different kind of product, not a deeper attribution model.
And then something has to happen
This is the gap that widened most in the last year.
From answer to action
A measurement platform ends at a recommendation. Dema agents run more than a million actions a month, mostly on a schedule, and write back through the same MCP integrations the platform uses (negative keywords into Google Ads, collection membership in Shopify, budget moved between Meta and Google) inside limits you set per agent.
Systems, not dashboards
Because the semantic layer carries inventory, cost and forecast alongside spend, you can build things a measurement tool has no data for: a replenishment engine, a sell-through tracker for the buying team, a search-term optimiser. Each can carry its own app surface for the people who use it.
One team's tool becomes everyone's
Attribution answers belong to marketing. Margin after returns, stock cover and sell-through belong to finance, buying and merchandising too. Putting them in one model is what lets those teams argue from the same numbers instead of three different exports.
What switching involves
Connect your ad platforms and e-commerce platform with OAuth, which takes minutes each. The longer part is cost data: cost of goods, fulfilment rates and expected returns, which is what makes margin reporting real and usually needs someone from finance rather than an API key. Most teams are connected within a day, and history goes back as far as each source allows. Plenty of brands also run both for a period while they compare the numbers, which we would encourage rather than discourage.
Trusted by category leaders.
Frequently asked questions
For measurement, yes. Dema runs attribution, marketing mix modeling and incrementality testing, and reconciles them. But it is not a like-for-like swap, in either direction. Northbeam's attribution modelling is deeper than ours and Apex has no Dema equivalent. Dema carries cost of goods, fulfilment, returns, inventory and sell-through, so it answers commercial questions Northbeam is not built for. Which one is the alternative depends on whether your problem is measuring media or running margin.
Northbeam is a measurement platform; Dema is a commercial platform that includes measurement. Practically, that shows up as scope: Northbeam models what your marketing caused, while Dema models what it earned after cost of goods, shipping, pick-and-pack, transaction costs and returns, alongside inventory and sell-through, at product and market level. That is why buying, merchandising and finance use Dema as well as marketing.
Yes: MMM+ for budgeting and forecasting, and an incrementality product, with automated incrementality testing described as forthcoming. So measurement breadth is not really the difference between us. The difference is what gets modelled: Northbeam optimises media ROI, while Dema's MMM runs on contribution margin and on LTV, and incrementality results feed both the model and the attribution weights. Two channels can deliver identical incremental revenue and very different profit.
Apex sends Northbeam's first-party attribution data back into Meta so you can run Custom Attribution campaigns optimised against their numbers rather than Meta's. Dema has nothing equivalent, and it is a real advantage for a team whose main lever is Meta. Dema's approach to the same problem is different: get the profit number right, prove causality with incrementality tests, then let agents act on the result across platforms, which is broader but does not close the loop inside Meta the way Apex does.
No agent or automation-building capability appears in their documentation or product pages. Their output is measurement, recommendations and exports, which someone then acts on. Dema agents are built by you, run on a schedule, can carry an app surface, and write changes back through MCP integrations within limits you set, which is a different category of product rather than a better version of the same one.
Both are custom-quoted above an entry tier, so the honest answer is that you should get both numbers for your data volume. Two structural things are worth knowing: Northbeam's Data Export API is documented as a higher-tier feature rather than entry-level, while Dema's GraphQL API is on every plan, and Dema is usually replacing more than one tool because it covers analytics, measurement and the agent layer, so compare against your whole stack rather than line by line.
Yes, and for a period it is often sensible. They read from the same sources, so running both while you compare numbers is straightforward, and if Apex or their attribution depth is doing real work for your Meta buying there is a coherent case for keeping it. What we would not recommend long-term is two systems each holding a different definition of profit, because that is how teams end up arguing about whose number is right instead of what to do.
Everything stated about Northbeam here comes from their own documentation and product pages, checked in August 2026, plus third-party reporting for plan tiers. Pricing and product pages move, so if something here has gone out of date, tell us and we will fix it. A comparison page that quietly rots is worse than no comparison page.
Measurement, margin and the work that follows, in one place.