A VALUED CUSTOMER OF THIRDEYE DATA
Service Corporation International: pricing across a national network
Service Corporation International is the largest provider of funeral and cemetery services in the world, operating at a scale that spans hundreds of local markets. Pricing across a network that size is genuinely hard. What a market will bear varies by location and changes with economic and social conditions, and a national price list is either leaving money on the table somewhere or pricing itself out somewhere else.
LOCATION
Houston, Texas, United States
COMPANY SIZE
Fortune 500 Company
INDUSTRY
Retail › Explore solutions for this industry
Price optimization across products and services
The work began with analysis rather than modelling. Before anything could be predicted, we needed to establish which datasets actually influence pricing, both the company’s own and the external data that moves what a market will support. That analysis determined everything that followed.
From there we built the pipelines that bring those enterprise and external datasets together on an ongoing basis, so that pricing decisions rest on current information rather than on a study that was accurate when it was commissioned.
On top of that sits a market intelligence dashboard with analytical depth, which is the tool the commercial team actually uses. Machine learning models built on the enterprise data, then augmented with external data, produce pricing recommendations, and the programme is operationalising those recommendations across the whole enterprise rather than leaving them as a report.
The sequencing was deliberate. Pricing recommendations that appear without the people receiving them understanding where the numbers come from get overridden, and a model that is routinely overridden is an expensive way to produce a document nobody reads.
Pricing recommendations moving from analysis into operational use across the enterprise.
Related solution: price optimization and revenue analytics
Price optimization fails more often for organisational reasons than technical ones. The model produces a recommendation, the person responsible for that market disagrees, and the recommendation is quietly ignored. The failure is usually not the model being wrong. It is that nobody built the path from a number to a decision someone is prepared to stand behind.
ThirdEye Data builds both halves. The analytical half brings together internal data with the external signals that actually move what a market supports, and produces recommendations that account for both. The organisational half is the dashboard, the explanation, and the ability for a commercial lead to interrogate a recommendation rather than simply receive it. We have built this for a services business operating across hundreds of local markets and for distribution businesses where inventory and price are the same decision viewed from two sides.
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