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Robert Bates, head of decision sciences, Dixons Carphone

Robert Bates, head of decision sciences, Dixons Carphone

How is your organisation using data and analytics to support the corporate vision and purpose?

 

Within Dixons, our aim is to help everyone enjoy amazing technology – from helping find the right product solutions to how to pay for it and keep it working through support and services – and data is analytics sits at the heart of this.

 

Over the past few years, I’ve seen a major shift in the way the business works with data – not just in day-to-day activities such as product ranging, pricing and personalised marketing, but also across the major transformation initiatives and customer journey analysis.

 

Now, data sits at that start of project definition and discussions, allowing us to really explore what insight is needed to drive actions or change the direction of projects, and build out clear hypotheses to investigate – or, if we’ve never done it before, trial. This helps drive the business forward and, crucially, track the impact of the changes so we can iterative and continually improve.

 

2020 was a year like no other - how did it impact on your planned activities and what unplanned ones did you have to introduce?

 

In some ways, 2020 saw us go "back to basics". The early stages of lockdown brought multiple requests in terms of performance questions, store to online sales migration and how customer behaviour was changing. This led to moving resources and priorities towards smaller, shorter term pieces of work to quickly triage and respond to the business needs of the week. I’m proud of how the teams adapted and were able to change the style of working towards pragmatic, focused output and identifying new opportunities as a result.

 

These changes also saw us having to deal with new datasets and we engaged with parts of the business we’d not worked with as much before. Suddenly we were working with property and finance, reviewing store catchment dynamics and the impact of product and customer segment on drive times and online penetration, models we’ve continued to develop and refine.

 

It’s also helped that we built upon the core foundations – we’ve spent time reviewing the data models to simplify processes so we can more easily benchmark performance and identify opportunities for improvement and we’re benefitting now from this as we return to projects we had to park.

 

Looking forward to 2021, what are your expectations for data and analytics within your organisation?

 

In one word – growth! Data and analytics is a key part of the transformation journey we are on and we will be supporting projects across the whole business. As we support transformation through data, I’m expecting to be involved in all levels of the data journey – from refining the underlying data models to improve efficiency, sharing and translation between functions to optimising how we deploy, measure, and refine the models. It will be another exciting and interesting year.

 

Is data for good part of your personal or business agenda for 2021? If so, what form will it take?

 

Outside of Dixon’s I work with our local Neighborhood Forum, supporting development of the Village Local Plan. It’s a different area to that which I am used to dealing with at DC, looking at how we can bring open source population, traffic, and other geospatial data together to influence planning strategy.

 

Within Dixons, we’re continuing to work with a variety of business schools, from giving guest lectures and mentoring students through their MSc projects to helping develop the data scientists of tomorrow.

 

What has been your path to power?

 

I took the more winding route into data science and analytics, commencing with a PhD in Semiconductor Physics. It was there I realised what really interested me wasn’t necessarily the specific topic, but rather the challenges of identifying how systems interacted, what the causal factors were and how to optimise and solve problems.

 

After time working in management consulting, I moved into commercial finance where I developed the understanding of the day-to-day commercial world (and CIMA qualifications), being drawn towards the pricing, marketing, and customer data aspects of the roles. It was here that I learnt the value of data science isn’t in finding the perfect answer, rather being able to translate the findings into stories and actions the business can understand and take – the translation role between the two worlds being critical to success.

 

I joined Dixons 10 years ago and since then have become known across the business for bringing the right mix of detail, pragmatism, and appropriate level of challenge for problems spanning all areas of customer and operations.

 

What is the proudest achievement of your career to date?

 

There are two different answers to this – firstly, any time we see those "wow" moments when stakeholders see the power of the insight and recommendations we have brought them and the way it transforms how they operate on a daily basis. It’s great when they come back to request advice and input on other problems.

 

Secondly, developing the team, seeing them grow and taking on wider roles in the organisation is an achievement to be proud of. Recruitment into data science is challenging and having an environment people want to join and which allows them to excel is always satisfying.

 

Tell us about a career goal or a purpose for your organisation that you are pursuing?

 

This year the focus is upon improving how we explain the outputs of machine learning tools capturing those insights which – all too often – are hidden deep inside models to use elsewhere across the wider business.

 

Take affinity modelling for example – hidden within the recommendations are factors such as price sensitivity for the product affinity. Being able to tease this out opens up a whole new range of applications and levers for implementation.

 

By focusing upon explainability and sharing these stories with the commercial teams, we’ll be able to create new analytics products which empower decision making and enhance the customer offer.

 

How closely aligned to the business are data and analytics both within your own organisation and at an industry level? What helps to bring the two closer together?

 

Within Dixons, there is good alignment in terms of the value data can bring to all parts of the business – in fact, we’re regularly seeing more requests for support than we can service on a day-to-day basis and I don’t see this ending anytime soon.

 

This brings its own set of challenges as we have to balance the competing pressures of shorter-term support and insight delivery with producing those models and analytics which will be needed for the long term. To manage these, we rely upon the expertise of our senior team working in close partnership with the business to translate the business needs and questions into an analytics project.

 

It is this translation layer which keeps the analytics and operating functions of the business close, ensuring the analytics team get to the crux of the ask and link the output of the work and models to the actual levers the business has to play with for that specific customer journey. This improves speed to action and delivers the right level of solution – from simple time series analysis to detailed propensity models – for the business, all the while keeping the customer journey at the heart of it.

 

This has led to the senior team becoming trusted advisors within Dixons, keeping us closely aligned and working together.

 

What is your view on how to develop a data culture in an organisation, building out data literacy and creating a data-first mindset?

 

First and foremost, there has to be a partnership between the data and analytics team and the wider business, recognising the needs and motivations of those in the different parts of the business.

 

For me this starts with making sure that everyone has access to the same solid foundations of consistently good data and reporting on which the right questions can be asked and trust built.

 

Once in place it is easier to then develop the more complex models and solutions, linking carefully into the operation levers the business has at its disposal and gradual iteration – demonstrating the benefits at each stage securing demand and buy in for the next.

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