Martin Squires, director of advanced analytics, Pets At Home

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

Pets At Home has established "using data and the VIP loyalty scheme to better serve customers" as one of our four strategic pillars, intended to keep us ahead of the curve in the pet care industry. We recognise we cannot rest on our laurels, so we have taken a forward-looking approach and placed data and analytics at the heart of the business, particularly around our successful VIP programme. We know if we can leverage our data far more than we have historically, then we can harness the power of our data to open up new opportunities and support our continued growth.

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

It’s definitely been a year like no other, that’s for sure. I think I’d struggle to even remember what our planned activities looked like way back in March.

Covid-19 obviously meant lots of new work to understand what impact the virus and various lockdowns, etc, were having on consumer spending and purchase behaviour, customer attitudes and channel choice but we also had to spend time understanding the impact of these changes on our capabilities and how we needed to respond to that changing behaviour in terms of our stores and distribution centres.

It also meant big changes to ways of working and not just in terms of not being inside the office anymore. Everything was changing so fast that we had to truly embrace agile working methods, so that we could really get to practical answers fast rather than perfect answers slow.

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

I’m excited about data and analytics within Pets At Home in 2021. We’ve spent a lot of time and effort this year upgrading our systems and tech stack and building the team, so we’ve now got all the building blocks in place and this year will really be about showing just what we can do. We’ve already started to show where we can deliver value from data and analytics, but we now have a massive list of potential projects to get stuck into.

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

Data for good would definitely be a big think for both me personally and for Pets At Home. Pets At Home has long been a major supporter of a wide variety of pet care charities, to give just one example, we donated £300,000 in March 2020 as part of our VIP Lifelines scheme to over 750 animal charities across the UK. On a personal note, I’m a part of the team in Pets At Home building out our diversity and inclusion programme and my role in that will definitely include looking at where we can use data and analytics to improve our diversity and inclusion efforts.

What has been your path to power?

It’s been a long journey as I’ve been around a while now! I started out after university as an economist and moved into customer analytics in the early 1990s as part of the original CRM boom that Peppers & Rogers started off. I’ve always been fascinated with numbers and with understanding customer behaviour, so marketing and customer analytics has predominantly been my “home” for the past 25 years, either with banks and building societies, credit card companies, retailers and now in pet care. Over time, that’s shifted from being the person in the corner crunching the numbers and building the models to building and leading teams.

What is the proudest achievement of your career to date?

Being part the DataIQ 100, of course. Actually, looking at the lists the last couple of years, I think the thing I’m proudest of has been that I’ve always tried really hard to develop people, wanting to make sure anyone who works for me would always have a better CV at the end of the year than at the start of it. Seeing people I’ve worked with over the years go on to achieve some truly stellar success gives me a real buzz. I hope somewhere along the line I helped impart a bit of wisdom that’s helped people along the way.

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

I’m not sure I should put this out there, but I’ve been thinking of writing a book. When I first mentioned that to a friend of mine, he thought I was volunteering to try to help George RR Martin finish" Game of Thrones", but sadly not. Mine is hardly likely to be a best-seller, but I’ve some thoughts on building and leading analytics teams that I’ve an itch to set down on paper properly at some stage.

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?

I’m lucky enough to work for an organisation where it’s far more aligned than most. We’ve put in place a great CDO in Rob Kent, so we have a voice right at the top table in leading Pets At Home and I think that makes a massive difference.

As for what helps bringing the two together, I’ve one tip from the analysis team perspective. Stop your team using the words “the business”. I HATE IT. With an absolute passion. Every analysis team I’ve ever worked in was a part of the business, not some strange entity from another dimension. Analysis teams need to have skin in the game, they need to see themselves as part of their business. “But the business won’t use my model” should never be an excuse. Communicate the model better - we’re all part of the same team. Sorry, rant over.

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

I think you need to remember that most people don’t actually like data and are often more than a little frightened by it. You just have to look at how many people are bewildered by the sorts of graphs and charts that either deliberately or accidentally mislead people under the banner of fake news.

This means patience is key. This isn’t an easy thing to achieve and you need to bring people along on the journey at their pace, building trust in the data and analysis and confidence in the tools over time. Trying to rush people along with technical solutions rarely works, for example, just dropping a self-service reporting tool on someone’s desktop and saying, “there you go, you can self-serve now,” has rarely been a recipe for success.

Remember very few people are actually as excited about data as we are, but we can make them more excited if we talk more about what analysis and insights can do for them and less about the mechanics of machine learning algorithms and if we show a little patience. A sense of humour and not taking ourselves too seriously helps too!