Agentic AI
Deloitte
Deloitte built Zora AI, an agentic product platform shipping ready-to-deploy AI agents that perceive, reason, and…
Area
Customer analytics is the practice of modelling who buys what and why, then measuring whether an intervention changed it. In enterprise use it covers segmentation, propensity, personalisation and marketing measurement.
Agentic AI
Deloitte built Zora AI, an agentic product platform shipping ready-to-deploy AI agents that perceive, reason, and…
Agentic AI
Cognizant positions itself as an AI Builder and delivered more than $21 billion in 2025 revenue,…
Agentic AI
Capgemini reported full-year 2025 revenue of 22.47 billion euros, or roughly $26.65 billion, beating its own…
Customer Analytics
ZS, founded in 1983, operates as a global management consulting and technology firm with deep roots…
Customer Analytics
Fractal, established in 2000 in Mumbai, grew into a multinational AI and analytics firm serving multiple…
Customer Analytics
Tiger Analytics, founded in 2010, built its strategy around open intellectual property rather than proprietary tooling,…
Customer Analytics
Merkle operates as a global, data-driven customer experience management firm built around proprietary consumer data and…
Customer Analytics
EXL runs a data, AI, and digital solutions practice spanning marketing, supply chain, and operations, with…
Customer Analytics
TCS packages its customer analytics work into Customer Intelligence & Insights (CI&I), a combined real-time customer…
Customer Analytics
A ranked guide to the top 10 customer analytics service providers in 2026, covering specialist analytics…
Customer analytics is the practice of modelling who buys what and why, then measuring whether an intervention changed it. In enterprise use it covers segmentation, propensity, personalisation and marketing measurement.
Area reviewed 14 Aug 2026
What the work involves
This work predates the current AI cycle by two decades, which makes it the most mature area in the directory and the one where vendor claims are easiest to check. Propensity models, segmentation and lifetime value are well-understood problems with established methods. The AI layer mostly changes how personalisation is delivered, not how it is measured.
The measurement problem
A personalisation model will almost always appear to work, because the customers it targets were more likely to buy anyway. Without a holdout group you are measuring selection, not impact. This is the single most common flaw we see, and it is why we weight measurement rigour above model sophistication when assessing firms here.
Firms in this area
See all 27 firms in this area
Research notes
We asked every firm one question first: describe the holdout design on your most recent personalisation engagement. Seven of 27 could describe a clean one without prompting. Eleven described something partial. Nine could not, or described a pre-post comparison with no control, which is not measurement.
| Holdout design described | Firms | Our read |
|---|---|---|
| Clean control group, unprompted | 7 | Assessable, shortlist candidates |
| Partial or inconsistent | 11 | Ask for the test design in writing |
| Pre-post with no control | 9 | Treat reported uplift as unverified |
Make holdout design a shortlist criterion rather than a technical detail settled later. A firm that cannot describe one on a first call will not build one for you, and every accuracy number in the engagement afterwards will be unfalsifiable.