Data Strategy

Machine Learning

Utilising the power of automation to increase operational efficiencies by implementing predictive algorithms and intuitive software applications and minimising the need for human intervention.

Empowering Automation

Machine learning (ML) is a core capability within artificial intelligence (AI) that focuses on enabling automation to put data to work.  Similar to our predictive analytics and forecast modelling our machine learning models use historic data to predict behaviour and incite change.  The additional benefit of using machine learning algorithms is thus the ability for the software and systems to learn from the data and form patterns and trends without or with minimal intervention of data analysts. 

 

Once set up, machine learning offers a powerful tool to study customer behaviour and optimise the journey.  By empowering automation using both supervised and unsupervised machine learning models, this artificial intelligence technique is a powerful tool within our suite of data science and analytics services and combined with the expertise of our Centre of Excellence team, can be used to tackle a variety of business challenges. 

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Machine Learning Solutions

Our machine learning solutions can be applied to a number of business challenges and scenarios.  Some examples of machine learning models are outlined below. 

Propensity Modelling: 

 

By applying a scorecard approach to predictive modelling, propensity modelling determines the probability of future behaviours or outcomes through allocating a specific weighting to certain outcomes.

 

Propensity modelling is particularly advantageous when determining customer behaviour and can be used to predict customers likely to churn (a.k.a. attrite) or recover customers likely to abandon their shopping basket.  

 

With this insight, businesses and marketers can tailor their email communications, optimise conversion rates and the online journey, or implement other tactics including promotions to reduce the probability of negative outcomes.  Propensity modelling can also be used by businesses in the financial services and regulated sector to predict financial crime or fraud. 

 

Customer Lifetime Value Modelling:

 

Customer lifetime value models tell you how much value you can extract from any given customer over their entire engagement with your brand.​ 

 

Although businesses understand the value of retaining customers over developing relationships with new prospects, it is also important to recognise that some customers may be lost and here the value is retaining and optimising media spend to drive cost efficiencies rather than wasting resources unnecessarily.

Challenging the Machine Learning Approach

Our clients are implementing a variety of machine learning models to unlock valuable insights about their businesses, which allows them to make more strategic decisions more quickly through automated data-driven strategies.

 

However, as artificial intelligence and machine learning models play an increasingly significant role in the business decisions of companies today, all businesses must consider some critical issues around the governance of data and AI in particular:

 

How do you ensure your models are delivering ethical and explainable outcomes?

 

Historical data can often contain the recipe for creating unintentional bias that unwittingly leads to your business treating people differently or unfairly.  By focusing on ‘how business has always done it’ there is a chance businesses will not evolve or adapt to the changing needs of their customers. Read our insights on Bias in Machine Learning and AI where our experts consider this issue and how to ensure businesses are not missing opportunities for transformational growth.
 

How do you build and maintain processes around AI models?

 
Businesses must also consider what safety nets they should have in place to monitor the performance and degradation of their models over time.  Big Data is ever-evolving and thus the solutions in place to capture the best insight from this asset must evolve too. This includes refreshing the data input into the predictive models.

 

Our ethical approach to delivering data-driven strategies, ensures that we allow our clients to put data to work while still considering the importance of operating with quality data governance and best practise at the heart of what we do. We can help you assess your AI and Machine Learning solutions for bias and provide the solutions to remedy this.

Review

Independant Review

Independent review of your models to identify and assess bias.

Discuss

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Facilitate internal conversations about bias and the outcomes.

Fix

Independant Review

Targeted modifications to model or data sources to address bias.

Monitor

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Implement process of regular review and assessment to check for bias for internal reporting.