Robin Coles, Director at Inovretail looks at the challenge for traditional retailers keen to use data to transform their operations, and how conventional high street retailers, such as Marks and Spencer are saddled with unwieldy legacy systems and playing a desperate game of catch-up with agile digital disruptors who have based their entire business models […]
Robin Coles, Director at Inovretail looks at the challenge for traditional retailers keen to use data to transform their operations, and how conventional high street retailers, such as Marks and Spencer are saddled with unwieldy legacy systems and playing a desperate game of catch-up with agile digital disruptors who have based their entire business models on the bedrock of data science.
In a world of increasingly digitised commerce it seems that everyone now wants to be a data scientist. And why not, when you consider the success of data behemoths such as Amazon, ASOS and Ocado?
The challenge for traditional retailers keen to use data to transform their operations is capturing the right information, processing it at the right speed, ensuring insights are readily available and then taking the appropriate action. This all sounds easy on paper, but conventional high street retailers saddled with unwieldy legacy systems are playing a desperate game of catch-up with agile digital disruptors who have based their entire business models on the bedrock of data science.
The target for retailers is to focus on the total end-to-end customer journey. As the customer moves through the organisation, data should be collected at every touch point (store, app, website, contact centre, email, social media etc).
With the right data analytics this information can then be refined into retail gold. For example, retailers can create targeted engagement strategies by accurately identifying customer segments. Having successfully divided a customer base into defined groups sharing common demographics, buying power and needs, it is then possible to implement specific strategies that are highly targeted to each group, increasing overall campaign effectiveness.
But to make such insights truly actionable, retailers need to create a much fuller and richer single view of individual customer datasets, capturing and processing relevant data in real time. These actionable insights then need to be made immediately available to front-line colleagues in the form of alerts on subtle, preferably wearable, next-generation devices which avoid the kinds of barriers created by tablets and smartphones. Imagine the motivating influence of being able to check whether you’re on target to meet KPIs such as sales targets by quickly glancing at a wearable or the time saved by reviewing stock availability on a smart watch.
Fashion brand G Star has already harnessed the power of data science accessed via wearable tech. Sales performance has been doubled and its intra-day target has been improved by 10%.
Only when front-line store associates can readily access real-time data in this way can insight be used to its fullest extent, empowering the retailer to enrich the end-to-end customer journey.
Not just any data science project…
Marks and Spencer is one such conventional high street retailer struggling with market share in the digital age. Part of its preferred solution was to open a new data science academy last summer. Its aim is to turn staff from every store function into data scientists in a bid to make the retailer more digital savvy.
Employees are being encouraged to enrol on the 18-month in-work data skills programme, during which they are being taught about machine learning and programming languages, such as R and Python.
M&S is the first publicly listed retailer in the UK to create such a data skills academy, but it is convinced that it needs a raft of data-skilled leaders and data scientists to lead digital transformation across its organisation. It has its sights on training more than 1,000 employees in the first 18 months of the academy’s operation. In fact, Steve Rowe, chief executive goes as far as to suggest the initiative could be the key to M&S’s survival.
Hi-tech changing rooms
Tommy Hilfiger is one of several premium fashion brands introducing smart fitting rooms in their stores.
Using RFID technology, the touchscreens here provides customers information, recommendations and styling advice. Through conveniently located buttons on the screens, on-going conversations between customers and in-store staff can be facilitated, and items can be added to digital wish-lists that can be shared via social or email.
This kind of interaction, enriched by technology, delivers the immediacy, inspiration and intimacy that customers crave.
Data? Nike just did it
The Nike Live concept shows what a high street store driven by data science really looks like. The first concept store opened in Melrose, Los Angeles last summer and data provided by NikePlus members’ shopping habits in LA shapes exactly what the store stocks. The brand describes it as ‘an experimental digital-meets-physical retail pilot’.
Every product offered at the store is selected based on local buying patterns, app usage, and engagement. Nike’s data analysis already show that local NikePlus members are running- and style obsessed, like basketball and are competitive. The store is re-stocked bi-weekly to ensure its stock faithfully reflects customers’ data.
The store is also surrounded by a geo-fenced area that uses GPS to deliver special offers to customers’ phones as they enter. There are also vending machines where loyal customers can collect free products – when the store first opened there were long queues to pick up complimentary socks, for example.
Nike now has plans to open flagship stores in New York and Shanghai and will use the Melrose store as a test bed for a more data-driven approach when deciding on product assortment for these locations as well as data-reliant technology that the brand can roll out worldwide. There are also plans for the Melrose store to be replicated in Tokyo.
When it comes to data ‘every little helps’
Of course, data analytics hasn’t suddenly been discovered by retail. More than a decade ago Tesco, for example, had the vision and business sense to see how data analysis can boost retail profit margins.
Many of its senior managers today are the same data analysts, computer programmers and maths graduates the firm hired in the early 2000s, when the burst dotcom bubble opened up a new labour market. With their help, Tesco uses analytics to save £100m a year in supply chain costs. But it’s not so easy now to find this critical combination of analytic talent and business insight.
McKinsey’s report on ‘Big data: the next frontier for innovation, competition and productivity’, suggests a shortage of 1.5 million managers and analysts with the know-how to use big data to make effective decisions in the US alone.
When you have the size and resources of M&S, Nike and Tesco it’s possible to nurture in-house talent on a meaningful scale. But for many retailers this approach is considered too costly takes too long to provide a return on investment and expertise in this field, particularly applied to fashion retail, is in very short supply. That’s why increasing numbers of high street retailers are forming partnerships with third-party organisations specialising in the application of data science in retail – not just to personalise their product offering but to optimise processes right across their operation.
By Robin Coles
Director
