Disclosed | Services

OUR SERVICES

Marketing data that means something

Our data capabilities become yours, helping you to cut through reporting noise and metrics that measure activity rather than impact. See what is really working, who is and is not converting, what the future holds and where you can make the biggest difference.

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OUR SERVICES

Start with the question, not the technique

Sometimes a careful analysis is enough. Sometimes the question needs a model, an experiment or better-connected data. The method follows the question.

KNOW

Reveal what matters.

Understand customers, demand and opportunities hidden in the data.

Search, content, campaigns, web analytics and CRM each tell part of the story. We bring the signals together to reveal what people need, where they're getting stuck and which opportunities your reporting overlooks.
Bring together behaviour, customer history and audience modelling to identify valuable customer and prospect groups, including those you don't yet know by name.
Use what people search for, read and do to understand their needs, intentions and what's getting in their way.
Connect search, website and market signals to see where demand is emerging, where you're capturing it and what you may be missing.
Connect digital, campaign and CRM interactions to reveal where people move towards conversion, where they stall and where they leave.
Look beyond familiar audiences, markets and headline metrics to discover overlooked sources of demand and growth.

PROVE & IMPROVE

Establish what works. Change what does not.

Separate genuine impact from attributed credit and invest accordingly.

Activity isn't impact. We test what marketing actually changed, measure its contribution to business results and help you decide what to invest in, improve or stop.
Use experiments and credible comparisons to distinguish additional demand from conversions that would have happened anyway.
Connect marketing to commercial outcomes and establish how much additional value it creates, rather than relying on attributed credit alone.
Compare channels, audiences and activities by the additional value they generate, not just the conversions they claim.
Find where customers hesitate or leave, test improvements and measure whether they genuinely improve outcomes rather than just engagement.
Agree success measures, fair comparisons and decision rules before results arrive, so convincing explanations never take the place of reliable evidence.

PREDICT

See what is coming. Change what is possible.

Anticipate outcomes early enough to make better decisions.

A target is an ambition, not a forecast. We use early signals to anticipate demand, revenue and customer behaviour, helping you see where things are heading and what you can still do to change the result.
Forecast demand and revenue independently of your plans, so you can identify likely shortfalls while there's still time to act.
Use search, behavioural and market signals to identify emerging needs and opportunities before they appear in revenue.
Use validated predictive models to identify likely converters, then test whether acting on those predictions actually improves results.
Compare plausible scenarios for spend, timing and channel mix to understand the potential outcomes and where further testing would help.
Turn forecasts into practical decisions about investment, targeting and timing, while there's still an opportunity to influence the outcome.

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"Thanks to your work, we can use data to answer so many more strategic questions."

Social Media Marketing Manager

Global Asset Manager

+34%

More orders than expected against a modelled counterfactual over three months.

Financial Services Educator

“I’m impressed that your team managed to find such clear opportunities in the patchy data.”

Head of Digital and Marketing Technology

Global Asset Manager

“Thanks to your innovative models, our team have made continuous and meaningful progress.”

Marketing Strategy Director

Financial Services Educator

+59%

More users engaged with website content by year-end.

Global Asset Manager

“The forecasting is amazing and a huge leap forward for us in our approach to data driven marketing.”

CMO

Financial Services Indicator

"We had no idea our advertising spend was having such an impact on demand."

Commercial Leadership

Online Retailer

$270k

Incremental revenue proven through a controlled geographical experiment.

Financial Services Educator

“Thank you for finding an alternative for this. We really couldn't have done it without your expertise.”

Vice President, Global Digital

Global Asset Manager

CASE STUDIES

What changed for our clients?

Not every good result is an uplift. Sometimes the value is an opportunity revealed, an investment challenged or a better decision made.

Problem

The marketing team at a professional education provider knew broadly who to target, but could not identify the most responsive prospects or the best moments to reach them for the highest likelihood of conversion. Furthermore, they didn’t know what data was available to help them overcome the problem.

What We Did

We looked at the professional profiles of thousands of our client’s customers and clustered them based on patterns in education and employment, in order to understand their receptiveness over time.

Outcome

We revealed the traits, needs and behaviour of people that would respond better to marketing and sales than others, and showed when they were the most receptive. Factors such as number and type of jobs, education history and extracurriculars. This gave our client a clearer strategy for personalisation, prioritisation and experimentation.
Problem

Our client’s teams could not agree on what was contributing to conversion the most. Marketing felt that their work was being undervalued by last-touch reporting in comparison to sales. This is an issue we often see: B2B tends to overcredit sales teams and B2C tends to over credit lower-funnel channels

What We Did

We built a multi-touch attribution model, connecting online and offline touchpoints and incorporating third-party data. The customer journey was far more complex than teams had expected and marketing was contributing significantly to sales opportunities. 34% of conversions were re-attributed to marketing and 9% to upper-funnel channels.

Outcome

The new model gave teams a better understanding of their customers: prospects were undertaking substantial self-directed research across fund, product and forecasting content before a sales conversation even began. This made channel investment and targeting more effective, and also helped marketing to prove their value within the business.
Problem

An e-commerce company wanted to improve their performance in an online marketplace by increasing first order value, as they felt that their own direct-to-consumer website was too expensive.

What We Did

We built our client a pricing engine for the marketplace, but we also found opportunities for them to increase their margin elsewhere. Average order value was 20% higher through D2C, so we investigated customer lifetime value (CLV) once factors like repeat purchases and market costs had been accounted for.

Outcome

Our CLV model showed that customers using the website were 32% more valuable in the long run than those using the marketplace. This encouraged our client to shift their focus away from first order value to client relationships.

Problem

A professional education provider used business targets and last-touch attribution to understand their performance, but it could not see where new revenue was coming from.

What We Did

We identified leading indicators of revenue, such as first-time website visits in this case, and used them to create a ~98% accurate rolling forecast. No future metrics were included.

Outcome

The forecast gave leadership a forward-looking basis for investment decisions. Alongside marketing-mix analysis, it helped them explore budget scenarios and prioritise new activity to test.
Problem

We built a forecast for our client to help them understand where new revenue would come from. However, it proved to be too accurate.

What We Did

The trajectory of performance was barely changing in response to the marketing team’s hard work. Despite slowly improving revenue, they were not able to affect the business as much as they hoped.

Outcome

This lack of impact became a recurring topic for leadership. We suggested that, because of their business model, performance was largely pre-determined by the market, and that they should aim to make fewer, bigger bets that could be thoroughly tested.
Problem

An institutional asset manager reported on website metrics like ‘visits’, but they wanted to know if the visitors to their website were actually valuable and, in turn, if their marketing was reaching the right people.

What We Did

Using demographic and behavioural patterns, we built an AI model that estimated with +93% accuracy which anonymous visitors resembled relevant people from our client’s database.

Outcome

This gave marketing a way to assess likely audience quality beyond the visitors it could identify directly, providing a true representation of campaign impact and strengthening retargeting.
Problem

Our client’s marketing team could not measure their incremental uplift: the contribution to performance from their activities beyond what would have happened naturally.

What We Did

We built a marketing-mix model (MMM) that linked revenue to channel activity and market changes. We used it to establish baseline expectations for performance.

Outcome

This allowed our client to prove the contribution of their campaigns and channels in commercial terms, and explore the impact of different budget and channel allocation scenarios.

Problem

An investment manager’s awareness campaign report was wrongly claiming post-view conversions as incremental uplift in demand, but no experiment had been set up to prove that claim; to distinguish between what happened and what would have happened anyway.

What We Did

We developed the capability to estimate retrospectively the expected performance of post-view conversions, solving a fundamental problem: “how can we know what impact this campaign had when we didn’t build a control?”

Outcome

In this case, our tests found no detectable incremental lift from the team’s campaign, prompting discussions on how to build clearer success measures and improve future activities.
Problem

Our client found it difficult to isolate the impact on demand of their out-of-home marketing campaigns – something which is notoriously difficult. Instead of simply reporting on impressions, they wanted to understand the campaign’s incremental impact on upper funnel metrics.

What We Did

We identified leading indicators of future demand and used them to estimate what would have happened without the campaign, comparing outcomes across matched test and control cities.

Outcome

We identified a 22% uplift in demand among key audiences across test cities, giving the client evidence of incremental brand impact and identifying larger engaged audiences for future activation.
Problem

After our client launched a new website, building an entire experiment was impractical, and ordinary before-and-after reporting could not separate its effect from seasonality and other factors.

What We Did

We were able to retrospectively build a model that allowed us to understand what the website would have got in terms of orders had it not been relaunched. By comparing to actuals we were able to reveal the lift.

Outcome

We could show impact without a costly experiment: over three months, actual orders were 34% above the modelled baseline, with approximately $10m in revenue above expectation.
Problem

Our client wanted to invest in a major personalisation program but did not know if they were ready for the technology or whether it would substantially improve results.

What We Did

Using existing tools like our Single Customer View, we created pilot versions of the program’s changes: five controlled personalisation tests across messages, markets and customer groups.

Outcome

Two variants significantly reduced click-through rates while another showed an 8% directional increase, with conversion inconclusive. Our tests also exposed gaps in the content and processes needed to run personalisation reliably. The client avoided investing too soon in a multi-million pound tool.
Problem

Our client could not reliably protect their profit margin because they had no clear way of understanding the impact of pricing and external factors on revenue and orders.

What We Did

We built a pricing engine that brought together cost, stock, demand and competitor data to determine where prices could be adjusted to improve margin.

Outcome

Over the one-month period, the new approach delivered a 70% uplift in gross margin. The business also gained greater control over product-level profitability, with a more consistent and evidence-led approach to pricing decisions.