Area

Customer 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.

Agentic AI

Accenture

Accenture built the largest AI practice among the traditional systems integrators, and its financial results back…

AI Consulting

Bain & Company

Bain and OpenAI have run a joint services alliance since 2022, and Bain became an OpenAI…

Agentic AI

Tredence

Tredence’s generative AI practice centres on building and deploying agentic systems that automate multistep workflows for…

Agentic AI

Claude

Claude is a family of large language models made by Anthropic, available as a web and…

About Customer 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.

At a glance
Firms tracked
12
Typical buyer
Marketing, CRM
Common first use
Churn propensity
Typical pilot length
8 to 16 weeks
Main failure mode
No holdout group
Maturity
Established

Area reviewed 14 Aug 2026

What the work involves

The oldest area we cover

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

Where most engagements quietly fail

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

Who is doing the work

T

Tredence

Personalisation and marketing measurement, retail and CPG
San Jose, 2013
Strongest here


V

Verrow Data

Feature stores and model serving underneath it
Dublin, 2017
Infrastructure


C

Corbridge Partners

Strategy-led, financial services customer work
New York, 2016
Also covers

See all 27 firms in this area

Research notes

How this area was assessed

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

What this means for a buyer

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.

Sources

  1. Firm interviews, 27 firms, May to August 2026
  2. Client reference interviews, 14 completed
  3. Test designs shared by three firms with client permission
© 2026 Top AI Firms. Independent research \u{2014} we take no payment for placement.How we assess a firm · About ·