The March 2026 update to Oracle Analytics Cloud has a lot of great new features introducing new capabilities across visualisations, AI, and data preparation. In this article we are going to look at the metric-based colouring for heatmaps.
Let’s show it in action using a fictional UK coffee chain called Daynes Coffee House UK, with 48 stores spread across the country. I have pre-built a dataset of sales locations, revenue and margins and we will use it to build up three separate views to demonstrate the power of switching the colouring metric.
Firstly let’s take a quick look at one of the things you can to do when loading location data into OAC.
Telling OAC that your columns are location data
Our dataset includes Latitude and Longitude columns for each store location. When you load this data into OAC, these will initially come in as plain numeric attributes as OAC doesn’t automatically know they represent geographic coordinates.
To do this, go into the dataset and click on the Latitude column. In the column properties panel you will see a field called Location Details as this is where you change the classification from Attribute or Measure to the “location-aware” types.
For our Latitude column, set this to Latitude, and for Longitude set it to Longitude. It also validates them for us. Once you do this, OAC gives these columns a special indicator icon (a small pin as we see above) and from that point it recognises these as geographic coordinates.
This is the same approach I used in a previous article on the AI Assistant, and it is worth doing properly before you start building any map-based visualisations.
Once both columns are flagged, let’s create a workbook. Add a Map visualisation and drag and drop the Latitude and Longitude into the grammar panel as the map location source, also add in the Store name and add a heatmap overlay.
The Dataset of Daynes Coffee House UK
The dataset has 48 fictional stores across the UK. Each row is a single store, and for each one we have:
- Store_Name, City, Region, Latitude, Longitude, Zone_Type the identity and location fields
- Annual_Revenue_(£), Gross_Margin_% the performance metrics we will use for colouring
- Avg_Daily_Covers, Rating_(out_of_5) supporting context
- Rent_Grade — Premium / High / Mid / Low which is key to understanding the margin story
This is the city level summary which will act as a kind of answer key as we build up the heatmap.
Using The Heatmap
By default (with the Colour field left empty in the grammar panel) this is a density heatmap. OAC counts how many store locations are in each geographic area and colours accordingly, so more stores means a brighter representation. London stands out as it’s got 12 stores so really stands out. If you were just to look at this map as it is, you would probably conclude that London is really dominant.
We will discover this isn’t the complete picture, by using different colourings for the heatmap.
Let’s Investigate with Revenue as the Colour Field
This is the key point of the March 2026 update for Heatmaps. Instead of leaving the Colour field empty, we drag a metric onto it. Let’s start with Annual_Revenue_(£).
The heatmap updates immediately and the picture is noticeably different.
Three stores in Edinburgh are generating nearly half the combined annual revenue of twelve stores in London. Bath, York, Cambridge, and Oxford also being prominent.
These are “heritage” city locations where tourist footfall is high, average spend per visit is high, and competition for a premium coffee experience is lower than it is in central London. A single Royal Mile store in Edinburgh turns over £418,000 a year.
Compare that to a busy London location:
Edinburgh’s three stores comfortably outperform London’s top three on both revenue and margin.
The density map said London was the “leader” but the metric map says, let’s now consider Edinburgh, Bath, and York.
Swap to Gross Margin and check the results
Revenue is interesting, but it’s not the full picture. Our London stores have “premium” rent. Edinburgh and Bath are on high rent. The question is what happens if we switch the Colour metric to Gross_Margin_%.
To do this we can just drag the new metric onto the Colour field.
Edinburgh and Bath hold up well as they have good margins too. If we also look North, we see that Manchester’s Northern Quarter and Ancoats leap to the top of the colour scale. We see below that alll Manchester’s stores have high intensity with that metric.
( Sheffield’s Kelham Island also lights up as does Bristol’s Clifton shop ).
These are all Low to Mid rent locations with (we can assume) a loyal local customer base. They are not revenue leaders, but at something like 42–46% gross margin they are our most profitable stores per cup.
Manchester and Bristol significantly outperform London on margin, despite lower total revenue figures.
London’s Premium rent is likely eating into the margin. At 24–31% gross margin versus Manchester at 43–46%, it’s clear that high revenue does not mean high profit. One metric says Edinburgh is the top market. A different metric points to Manchester, and both are correct as they are answering different questions. This is exactly why being able to swap the Colour field gives us so much power to analyse the data visually.
The Seaside Coldspots?
Let’s take a look at the South coast.
On the density heatmap, Brighton, Bournemouth, Southend, and Margate were visible. Five locations, a reasonable cluster but nothing that indicated any issues, but if we switch to the revenue or margin metric heatmap and the South coast goes almost invisible.
All South coast locations are achieving 13–18% gross margin.
These are seasonal tourist locations. Summer weekends are likely to be busy, but in the winter it really will quieten down and the seasonal customer profile means average spend is lower. We likely have holidaymakers having a quick coffee rather than the repeat regulars who drive dense urban margin in Manchester or Bristol.
The metric map indicates these seaside locations are candidates for review, and you would only spot that clearly by colouring the map with the right metric.
Many Additional Controls
There are many additional controls available in the properties and i’d recommend creating a visualization and experimenting with it. Two we will take a look at are “Metric Calculation” and Intensity.
We can control how the metric values are aggregated within each area of the heatmap.
Intensity is the a contrast dial. Increasing it makes the difference between hotspots and coldspots more visually dramatic which are very useful for presentations to an executive audience where you perhaps want to the story to “pop”. For detailed analytical work, keeping it lower lets you see the subtler regional variations. In the example about we turn the intensity upto the max and also nudge the radius up a bit too.
Summary
To summarise what metric-based heatmap colouring gives us ….
- The density map tells you where your data exists. The metric map alls us to see different stories about how where it performs.
- You can swap the metric at any time by dragging a different measure onto the Colour field without rebuilding the visualisation.
- Different metrics tell genuinely different stories. In out example, Revenue highlights Edinburgh, Margin highlights Manchester and Density highlights London.
- The hotspots that matter are not always where the most stores are and the coldspots that need action can be completely invisible until you colour by the right metric.
It is a genuinely useful addition to the map capability in OAC and one that I think will get a lot of use in not just retail but any other scenario where geographic performance matters.