How analytics can revolutionise your online business

Retail Sector spoke to Greg Fletcher and Tom McKenna, the co-founders of Ocula Technologies about how retailers can fully optimise their new access to data, how analytics can help you know both your customers and competitors, and the ways to target more personalised user experiences

One side-effect of the pandemic on the retail industry is to accelerate the shift to online, as brick and mortar stores were closed all around the UK and retailers and customers alike flocked to the digital sphere. Trends were already moving in that direction, but the unprecedented speed of the shift may have caught retailers off-guard. As physical customers became digital ones, retailers gained access to floods of data which could hold the key to success. However, there is a difference between tracking the data and understanding how to fully utilise it to the retailers benefit.

Retail Sector spoke to Greg Fletcher and Tom McKenna, the co-founders of Ocula Technologies, a tech-startup which utilises analytics to help “challenger retail brands” compete with bigger retailers, such as Amazon. They set up the company with the aim of “democratising AI for the majority of the retail market”, after seeing what they saw as an underutilisation of the area in the market for mid-sized companies.

The pair explain how retailers can fully optimise their new access to data, how analytics can help you know both your customers and competitors, and the ways to target more personalised user experiences.

New shop window

One of the key accelerators for the importance of analytics has been the move to online. While this trend was already taking place pre-Covid, during the pandemic, a “huge flight” to digital occurred which granted retail companies an even greater insight into their consumer habits.

“It’s suddenly your shop window, and when you create a digital shop window, you get more data than ever. Essentially it’s your window to the world, your digital shop, and you can see the things that your consumers are interacting with” McKenna explains, emphasising the opportunities this afforded retail companies as the online front-page took the place of the traditional ‘shop window’. However, with the help of analytics, retailers were able to look back at their customers and analyse their habits, preferences and what they were or were not responding to on the homepage.

McKenna and Fletcher also emphasise the importance of analytics as consumers become even more digitally savvy. “With the increasing shift to online, people are becoming far more savvy around comparing prices, because it’s very easy to do from one provider to the next,” McKenna adds.

While your homepage has become your shop window, customers are able to instantly compare it to competitors. But even more importantly, for McKenna and Fletcher, it provides companies the opportunity to analyse not only their own homepage, but how it matches up against these competitors. Acquiring the data is one thing, but utilising it is another.

‘Understanding’ the data

As such, often, they suggest, it is not acquiring the data but understanding it where companies are under-utilising their analytical approach. “When we worked with what we call “challenger” retailers and brands, we were seeing the same problems coming up over and over again; they had a problem with getting their data into a good state” Fletcher says. Looking at the market, they ran into companies whose analytics “had given them lots of graphs that felt intelligent and gave them more insight, but they couldn’t translate them into actual action that drove decisions”.

Companies might have access to the data, but as McKenna outlines, “where do you start? Do you make your product display pages more efficient and with better quality images? Do you change prices? Do you do better digital marketing? It’s incredibly challenging to disseminate all the different information, pull it all back in, and then prioritise for your team’s efforts”.

So how do companies optimise their analytics when they’re drowning in data? For McKenna and Fletcher, the difference is simply priority, and understanding that to maximise optimisation from your analytical approach, there needs to be a dedicated place for it within the company, not just as an add-on in another department. They outline how they are “just seeing such a difference between the few who really invested in this space, and were then really rapidly pulling ahead of the market”, identifying Amazon as a key example. And they believe that this is not out of reach, even for smaller companies.

For these companies, the pair argue the key is to look into analytics driven companies. Without the resources of Amazon to have a dedicated in-house team, attempting to fit it all under the one smaller umbrella leads to the data overload. “While they haven’t got the internal capabilities, despite having a relatively large team to actually build this themselves and produce it, because they’re just not a software house, right? They’ve got enough to worry about being a retailer” Fletcher notes. “Data analytics companies can create a plug-and-play easy to use AI platform for the majority of the retail market that gives them this Amazon style capability against the key modules that matter for a retailer.”

The benefits

For McKenna and Fletcher, the ultimate benefit behind analytics is simply to provide a clear understanding for all employees in the company, from e-commerce director to sales assistants on the shop floor, to aim towards a common and understood goal.

They outline “if you’re an e-commerce director there’s three things you can do; You look at what looks good in the market across different areas. You’re then trying to benchmark yourself in all of those areas against your competitors. And then once you’ve got a benchmark and found, for instance, we’re worse than our main competitors in 100 areas, you then have to apply a pound value to it and work out how you’re going to deploy all your team across these areas”.

It’s about retail brands identifying clearly the areas to improve, responding to market changes, and adapting their retail strategy appropriately. “If you look at things like pricing and stock optimisation and their digital marketing, they’re all incredibly data driven. But they’re algorithmically driven, which means they’re constantly optimising, constantly getting smarter and responding to market changes” Fletcher says.

“So, using things like AI and machine learning, in order to understand the customer better, to be able to target more personalised recommendations to tailor an experience that’s far more personalised, is a very important aspect that a lot of retailers are struggling with.”

Bridging the gap between data and utilising the data is where analytics companies are aiming to come into play in order to address the gap.



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