Element Group builds and runs AI systems inside sales, marketing and operations. We spent 20+ years growing revenue for global brands, so we know which part of the funnel pays. That is where the AI goes.
Marketing track record: brands we have run search and paid media for
Polaroid
Sony
Alibaba
Xiaomi
Yves Rocher
GE Money Bank
Cisco
Subaru
Nespresso
River Island
PolaroidSonyAlibabaXiaomiYves RocherGE Money BankCiscoSubaruNespressoRiver Island
PolaroidSonyAlibabaXiaomiYves RocherGE Money BankCiscoSubaruNespressoRiver Island
Problem
Most companies have bought AI. Few can show what it earned.
The tools are there and the experiments happened, yet the P&L looks the same. We see the same four reasons almost everywhere.
1
Experiments that never leave the demo.
Nobody owns them in production.
2
Tools that don’t talk to each other.
CRM, ads, calls and finance each tell a different story.
3
Your team doing a machine’s job.
Call write-ups and reports still done by hand.
4
Costs that didn’t move.
A lead, a call and an article still cost what they cost before.
Results
AI in production, with the numbers.
Three systems we built and still run inside our own group’s businesses. Each one moved a number the business already tracked: wasted ad spend, time per sales call, content output. Every figure is our own measurement.
Group company: consumer electronics brand, three marketplaces
29%
of audited ad spend was going to campaigns with no sales. A single audit run found it.
Data lived in three marketplace accounts, accounting, the bank and spreadsheets. Leadership saw revenue but not which product was losing money. Now daily syncs feed unit economics per product, a morning ad audit and a dashboard. Changes to live ad accounts wait for human approval. 869 automated tests keep it honest.
Group company: B2B sales department
From 60 minutes down to 5605min
to write up a sales call, with 100% of calls analysed.
Every call is transcribed and summarised straight into the deal history, with coaching notes for the manager and a team digest of objections: the reasons buyers give for saying no, and how often each comes up. Script changes now start from that digest.
Group company: B2B services website
250
articles shipped by two AI content pipelines: 58 for a commercial blog, 192 for a reference wiki.
Topics come from live search demand. Agents draft, facts are checked against primary sources, and nothing is published until it passes a multi-layer quality gate. Our experts now review drafts. They no longer write them.
If it can’t tell a revenue story, we don’t ship it.
Hence the name: revenuestory.com.
What we build
Six places we put AI to work first.
Each one is tied to a process you run today and a number you already track. A first pilot is planned for four weeks.
Sales
Sales call intelligence
Every call transcribed, summarised and written into the CRM. Objections counted, not guessed.
Advertising
Ad spend audit
A daily check that flags spend on campaigns with no sales, before the month’s budget is gone.
Content
Content pipelines
Topics from live search demand, drafts by AI agents, facts checked against primary sources.
Leadership
Leadership dashboard
Unit economics across channels in one view, refreshed without an analyst.
Customer service
Customer replies in your voice
Reviews and questions answered in your brand’s tone. A person approves what goes out.
Team
An AI toolkit your team runs itself
Agents and playbooks handed over to your own people. You get the method as well as the system.
Method
Built like software. Governed like finance.
We start with an audit and reserve the right to say “don’t automate this”.
Phased delivery
A written plan with acceptance criteria. You sign off before we write code.
Review at every phase
Code is reviewed as each phase closes. Anything that touches money, logins or personal data gets a security review as well.
Result checks
Each automation checks its result, not just that it ran. Empty output counts as a failure.
Approval before money moves
Actions with financial impact need explicit approval, and every one is logged.
Data protection
Personal data is anonymised in-house before it reaches any third-party model.
The people on your project
A strategist decides what to tackle first. A marketer knows where the customer journey breaks. An AI engineer builds the agents and the tests around them. An analyst checks the numbers before and after.
Track record
Two decades of marketing results for brands you know.
Before AI, our work was search, performance and product marketing, including for the brands below. It taught us where revenue is won and lost in a funnel. That is why our AI goes into the ad account, the sales call and the content pipeline first.
Selected marketing work and results
Brand
Sector
Work
Result
Polaroid
Cameras
Paid search and content
4.2× revenue from paid search
Sony
Online store
Search, paid and display mix
1.9× sales from paid traffic
Alibaba
E-commerce
Search strategy and technical spec for 1M+ pages
+386% organic traffic
Xiaomi
Consumer electronics
Search visibility for the US site, Google and Yahoo
+378% organic traffic
Yves Rocher
Beauty
Organic search
+193% organic traffic
GE Money Bank
Banking
Organic search
+345% site visits
Cisco
Web conferencing software
Search programme on Baidu
+87% site traffic
Subaru
Automotive
New model launch campaign
3.5× average CTR
Nespresso
Coffee
Organic search for the online store
31% more queries ranked no. 1
River Island
Fashion
SEO across 3,000 pages
4,108 keywords in the top 10
Marketing engagements delivered before our AI practice. Figures come from our own case studies. All trademarks belong to their owners; no endorsement implied.