Earlier this month I was fortunate enough to attend the NRF retail conference in New York. It was my first time attending the “Big Show” and what immediately struck me on arrival at the Jacob Javits Convention Centre in New York, was the sheer scale of the event. The great and the good from retailers from around the world were in attendance, 16k retailers in total! Along with nearly a thousand agencies and technology companies exhibiting their products. It takes a day or so to get your bearings and understand how to get the most from the trip, but by the time we jumped into a taxi to catch our return flight, we agreed that it had been an extremely valuable few days.
Whilst much has already been written on what it all means for retail in 2019, I thought I’d hop on the bandwagon and provide an insight analyst’s view on what the key take-aways from the NRF conference were:
The US retail market is in far ruder health than the UK one
Target CEO, Brian Cornell, took to the main stage on Day 2 and declared that “we can all agree, that 2018 had been a great year for retail”. He backed that up with figures from Mastercard that showed a 5.1% YoY increase in sales for the 2018 holiday peak period. More surprisingly a 3% growth in store sales; sales growth that isn’t just coming from online is not in-keeping with the usual narrative. If we contrast that with the UK, where 2018 Q4 non-food retail grew by just 2.7% (source: ONS) and both trade and mainstream press is littered with stories of struggling retailers, profit warnings and administration, one wonders whether there are lessons to be learned from our friends from over the pond?
One theme was how traditional retailers can use their scale and footprint to their advantage against pureplay online retailers, as customers still value physical experiences and interacting with real people. Indeed, a customer journey project we recently completed with a prominent US retailer showed that positive experiences with store staff drove an 11% uplift in spend amongst online shoppers, compared to 4% amongst offline shoppers.
Another topic, called out by Alex Gourlay from Walgreens Boots Alliance, was the importance of investing in local communities. Again, this is supported by findings from our own research, that suggests customers who feel that a retailer is “linked to my local community” spend an extra 3.5% a year.
Everyone is agreed on the challenges that retail is facing
Omni-channel, path to purchase, always-on, customer-centric, digital transformation, innovation. Almost all of the presentations featured at least one of these buzzwords. And after 3 days of them, they can start to blur into one big PowerPoint water torture. But this consistency of message does at least show that there’s some key themes around the challenges facing retailers in 2019 and beyond. And that theme is that advances in technology are leading to a more-demanding, empowered and less-loyal consumer, one that has non-stop access to connected devices and huge amounts of information. What wasn’t consistent was the potential solutions to meeting the needs of today’s consumer. Most of the technology companies obviously think that their product is the solution, but there were very few examples of retailers that have nailed it.
With technology changing and improving so quickly, picking the right partners is key
With that in mind, something I took away from walking through the three floors of exhibitors is that, with technology advancing at an ever-increasing speed, it is becoming increasingly specialised. And with that specialisation comes great levels of expertise that retailers could never hope to replicate in-house, which means selecting the right technology partners will be vital for future success. Outsourcing your technology developments frees up time to focus on what’s important for retailers: their customers. Be clear on your objectives and that should help you clearly define your requirements, which in turn should help you select the right partner, or partners, to realise your ambitions for meeting the needs of increasingly demanding customers.
Disruption will continue and it won’t just be for retailers
Representatives from Kantar Consulting & CloudRaker held a talk on “What retail will look like in 2030”, highlighting how Smart Homes & Smart Stores will shape the future challenges retailers will face (as an aside, I’d love to go back to NRF 2009 and see the predictions for retail in 2020!). What has become clear is how automated the shopping trip will become, and how algorithms will be far more likely to make many of our purchase predictions. Obviously, the companies that exploit and master these technological developments will continue to disrupt those that get left behind, but the biggest impact could be for brands and manufacturers. With Amazon practically giving away their Echo smart speakers, as the tech giants face a land grab to have a presence in our homes, they’re doing this to become the default option for all our purchases. So, when you’re saying “Alexa, add washing up liquid to my shopping list”, you’re no longer checking your purse for Fairy coupons, or scanning the aisles for the cheapest option, instead Alexa, or more accurately Amazon’s recommender algorithms, are choosing what they think is the most appropriate option for you. Within an instant, the millions of £ that P&G spent trying to build brand equity has been wasted.
When did we stop calling it data science and start calling it AI?
After 11 years working with big data and customer insights, I’ve seen the lexicon of terms used by data professionals expand and change, and “AI” is the new black. With over 130 exhibitors showcasing some sort of AI-related product, it got me wondering when we moved on from calling it Data Science, and why? Perhaps “AI” is just sexier, with images of Tom Cruise in a futuristic blockbuster, as opposed to the reality – a geek writing code in Python. Either way, what was previously a niche, back-office practice has now become mainstream, with AI or Data Science informing & improving all aspects of the retail value chain, from stock & supply to chain, to forecasting sales performance, to optimising prices and product ranges.
Fashion & Apparel appear to be leading the way here, with examples like Alibaba & Guess’s collaboration on an AI store in Hong Kong, and smart dressing rooms providing personalised content and offers.
Personalised offers were a widely discussed topic, in what is an increasingly competitive market, but we were reminded by Precision Marketing specialists, ciValue, that any machine-learning algorithm is only as good as what goes into it; if you don’t start with an offer pool full of products that appeal to a broad range of customers, you won’t see the results you expect.
What disappointed me when discussing the advances in data science was the lack of innovation in using a wider range of data sources. Aside, from Nielsen, who extolled the virtues of using their panels to get a 3600 customer view, everyone else still appeared to be heavily reliant on retailer sales & loyalty data. Whilst this is a rich source of customer insight, it is, by definition, bias towards the retailer’s historic activity. Not having a consistent view on overall market trends means you risk making the same mistakes year after year by being blissfully unaware of what customers need and when they need it.
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