Q&A: Gabriel Hughes, CEO and founder of Metageni
Gabriel is the CEO and founder of Metageni, designing algorithms for customer journey optimisation and data-driven decision making. With over 20 years experience in quantitative analytics research, Gabriel believes we are at a turning point where intelligent analytical systems can unlock the full value of big data. Before founding Metageni, Gabriel was VP of Analytics for two years at Elsevier, the world’s largest science publisher, and before that was Head of Attribution at Google for six years. At Google, he was instrumental in the development of new marketing attribution features for Google Analytics and enterprise platforms.
Can you tell us a bit about your background?
Before Metageni my career has been in data science, research and product development, with key highlights being R&D activity in analytics while at Google, including patents for attribution tools, and running the Analytics function globally for Elsevier, the worlds largest science publisher. I have a PhD in Economics from Imperial – basically I enjoy creating research analytics solutions.
What does your company do? / What is your USP?
Metageni provides custom machine learning ROI solutions for data-driven marketing, including attribution, econometrics and conversion optimisation. Our USP is that we produce highly accurate transparent models which are customised to the businesses we work with. Our goal is to support marketing and analytics teams with smart decision making as they are often short of expertise to leverage their data fully.
What’s special about the platform and your approach?
We model entirely on first party data – there is no adding tagging or tracking – instead we focus on helping our clients unlock the hidden value in the data they have. Our approach is custom and collaborative which is why we get such good results. The best models are those which ate tailored to the unique challenges of each business.
What advantage does it add?
It means our clients get to own their attribution and get closer to their own customers. For example, a custom model can adapt the value given to each touch point depending on whether they are marketing to their new or their existing customers. Ultimately a custom model means greater accuracy and relevance which maximises the opportunity to make precise decisions to reach peak marketing ROI.
How does a product/service implementation actually look like and how do you measure success?
Implementation is collaborative process whereby we run increasingly more accurate and relevant models in response to analytical learnings and client feedback. The final implementation is a complete set of custom dynamic reports with different view for key marketing executives such as channel owner, and also for some clients, direct links to bidding or targeting solutions. Success is when clients grow faster, save costs and come back to us for further work.
How are retailers using your systems to gain competitive advantage and what does best practice look like? Can you share a case study with us?
Retailers know they need an edge in highly competitive online marketplace whereby sales can be won online through channels like paid search and affiliate but with very tight margins. When we see that this analysis drive actions on both the strategic and tactical fronts, down to campaigns and keywords for example, we know it’s working. One client who have been driving growth with our custom data driven attribution is the eCommerce giant AO.com who have worked with us to extend custom data driven attribution and econometrics form the German market following success in the UK.
Are there other companies you partner with?
We have collaborated with complimentary business such as Cynozure, the data consultancy, and Nest, who specialise in social marketing for ecommerce where huge opportunity combines with huge measurement challenges.
What challenges and opportunities do you see in UK retail for 2021 / What challenges are retailers facing in 2020?
Well of course in 2020 COVID continues to disrupt and accelerate the transition to digital marketing and retail, and my view is that this will lead to sustained change in 2021, as consumers will not fully revert to the old ‘normal’. To adapt companies naturally invest digital channels to grow but eventually marketing efficiency and precise customer targeting and engagement becomes critical to maintain viable returns. Getting the best ROI means rapidly adopting data driven approaches such as machine learning (AI) in order to manage all stages of the customer journey – and here lies the opportunity.
How will you address these challenges and turn them into successes?
Our business has supported both offline and online retail environments, but our strengths are greater in digital, so we can help companies with this transition and despite the pandemic, we are growing. The most promising new area for us the provision of ‘next best action’ at user level, which uses machine learning to identify precisely which actions will nudge potential customers to convert. We have seen huge success already with eCommerce brands.
What is on the horizon for you as a company?
We now have a full live platform to underpin our hands-on consultative approach, so we are poised to accelerate growth. We see old and new eCommerce platforms keen to take control of their own data resource where before they relied on the big tech platforms to do the work for them, at the cost of a looser connection to their customer base. We want to help be part of the customer centric data revolution in retail!
Any final thoughts?
If anyone is reading this and thinking, ‘do we need our own attribution models?’ – just be aware than you already have one! Any time you decide to put money into your multi-channel marketing pot, you are implicitly valuing each touch point. Perhaps it is time to shine a light on these assumptions…you may be surprised at how much hidden inefficiency you can uncover.
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