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e-commerce and marketing

Smarter, more profitable, customer-oriented decisions.


complexity with ease

Data is the central nervous system of a business, telling you what's happening both internally and in your external environment.

Like any nervous system: it needs a brain, so it can make sense of input and help you make decisions. ​

Marketing successfully involves making a complex array of business decisions to achieve efficiency and deliver improved customer satisfaction.


Pattern recognition, as provided by machine learning, is the best available tool for marketers looking to make the most accurate business decisions fast and at scale.

Don’t miss growth opportunities

Don’t let your marketing ROI efficiency fall behind your competitors. Make smarter, more profitable, customer-oriented decisions.

Meaningful engagement

People expect meaningful engagement before making a purchase or decision about a brand, they want relationships, not just transactions.

Combine multiple data sources

Most retail analytics focus on insights in silo. Combine all metrics related to sales, inventory or stock optimisation, customer behaviour, and other pertinent aspects of business, all into a single decision oriented view of business.

Hyper-personalise customer experiences

Successfully mine underlying customer data, businesses can now hyper-personalise user experience based on age, gender and other key segments. The only way to thrive in the new business reality is to understand customers. Those who don’t risk being left behind.

Why Machine Learning?

You're data-rich, so spend it 

e-Commerce is an ecosystem primed for machine learning applications

Mining and connecting vast data sources help you discover hidden patterns that reveal what is happening in your company, inform who your customers are, what they’re doing and what they will do in the future.


All so you can make better decisions.


e-Commerce is an inherently data-rich activity. Accelerated by the global pandemic, it represents a greater proportion of retail activity than ever and shows no sign of slowing. 

Now is the time to ensure you're prepared for a data-lead commercial world. Now is the time to make complex decisions, with ease.

Revisiting the P's of Marketing

Applications of Artificial Intelligence, Applied to Marketing Theory

The P's of Marketing


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Process and Planning

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People and Customers

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product and service 'recommender' engines

Possibly the most affordable machine learning solution with an instant impact - like Netflix for your e-store

Imagine a site or service that knew what people were likely to buy based on live engagement, so presented it to them.


Actually, you don't need to imagine.

Boost conversion rates with a world-class recommendation system built on Amazon Personalize.


Our suppliers handle the setup, training and integration to have you up and running within 30 days, just as one of our partners did for 'the Netflix of Germany' RTL Interactive.

Recommender Engines

One-to-One Commerce

Personalised Content Means Better Engagement

Amazon Personalise

Tried and tested

In 2020, recommender systems are nothing new. Fairly recently, Amazon released 'Amazon Personalise' through AWS to make it even easier to install a personalised web service. 

Whilst Amazon's marketing efforts suggest "an engineer with no prior experience in Machine Learning" can install it, we beg to differ.

Machine Learning mostly fails not from the maths, but from the engineering. 

Real-time Event Integration

It's alive

Consider the time you spend browsing an online shop. You navigate around and, with each click, you're revealing your intentions.

Often, these intentions go uncultured; however, even when they are captured, this information is meaningless by the time its analysed by research teams.

Real-time event integration means that by the time you've clicked the back button on your browser, the system has already clustered you and changed what the home page presents. Creepy or cool, we'll leave your bottom line to decide.  

Engineering First

Kaizen, by default

Recommendation systems re-learn as often as you tell them to. 

Continual optimisation is in-built with every installation, so contextual recommendations improve with every engagement (or lack thereof).

Harness the power of Goliath. Amazon's highly advanced deep learning kit sits behind every solution.

Increase engagement

Presentation from propensity

"With Amazon Personalize, you provide an activity stream from your application – clicks, page views, signups, purchases, and so forth – as well as an inventory of the items you want to recommend, such as articles, products, videos, or music."
-Amazon Personalise website


Use Cases

Real Client Examples From the Collective

Find the best placement for a storefront with city wide passer-by analysis


Cloud based content management and media library


An intelligent chatbot for ensuring responsible gaming practice


Fast food order preparation tracking, an investigation

Confidential (top food delivery in the US)

Player segmentation for online gaming


In-house intelligent audience analytics platform


Portfolio optimisation for a beer wholesaler's retail clients

Confidential (world's major beer company)

Recommender system


Understanding impact of Catalogue Mailings on sales


Customer segmentation and entire user-history to predict purchase outcome


Multi-device media attribution

Optimise pricing per-user to maximise sale value without loss of customers


Evolutionary algorithms to power a dynamic web environment to improve conversion rates

Multi-touch attribution analysis to increase marketing effectiveness

Prediction model to recommend correct growth strategy to 95% accuracy.

Automated speech recognition based solution


Media optimisation response to covid in fashion retail

Ted Baker

Accurate product recommendations to increase revenue and reduce churn


Customer experience monitoring system


Segment online customers

AAA Auto

In-house intelligent audience analytics platform

Group M, USA; TVS Motors, India

Minimise perishable good returns with accurate sales predictions


Boost engagement with personalized recommendations

RTL Interactive

Detecting product ingredients from images

Confidential (Polish retail giant)

Multi touch attribution model for cross device marketing

Predict design attractiveness to increase the speed and quality of designers


Identify food dishes from social media to assign to menu items

Best Place

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