By Sona Abaryan, Senior Manager, Ekimetrics
The pandemic has forced retailers to adapt, re-evaluate and transform their offering like never before. It was the catalyst for rapid digital transformation, and ecommerce became an essential channel for brands to engage with their customers. But now that some of the dust has settled, what position is the industry in? And how can retail brands use data to stand out in an increasingly crowded and rapidly evolving space?
Online experiences as an expectation
Retail across all sub-sectors will never go back to its pre-pandemic state. With more convenient ways to access services and shop, products on shelves awaiting footfall are, for most, now part of a much broader sales and distribution mix, from websites and apps to social and marketplaces, automated warehouse delivery to click and collect. Levelling up the whole shopping experience across every channel is crucial in winning brand loyalty in a world of endless choice.
The rapid shift to online was particularly prominent within areas such as the beauty sector. Traditionally reliant on in-store samples to get customers to try new products or switch brands, lockdown meant that this experience had to move online – and quickly.
Some brands had already invested heavily in this space, putting them ahead of the curve. Take L’Oreal’s Skin Genius, an AI skin analysis tool that offers tailored skincare recommendations based on a single selfie. It’s a great example of reimagining in-store experiences in an online world, recreating a vital part of the skincare purchase journey when stores were closed. And as data showed consumers moving away from makeup and towards skincare, it triangulated perfectly with changing customer needs and priorities.
For others, the move online was anything from a rapid acceleration of existing plans to a complete reinvention of their model.
Data science can play a key role in this adaptation of services to suit changing customer demands. By analysing the trends, challenges and opportunities it is possible to find new and data-backed solutions.
Elsewhere, those who haven’t adapted have suffered. Ecommerce giant Boohoo bought out the 243-year old retailer Debenhams, after it went into liquidation in late 2020 – finally beaten by online competition.
Cultivating the omnichannel experience
Of course, there will still be a place for brick-and-mortar stores. But alone they are no longer enough. A key facet of brand loyalty is an integrated, omnichannel experience: it has become the expectation of consumers as purchase journeys mutate and become more complex, spanning different forms of media. By analysing data to identify audience drivers and capture new audiences, brands can respond to behaviour changes in online shopping to attract new customers and ensure they continue to serve their existing client base.
Gen Z, for example, are keyed into social selling and livestream shopping, fuelled by brand communities that include them in the marketing process. They are likely to buy via social media – through Instagram and TikTok brand promotions and in-app shopping but will still visit stores for the social experience. But their purchasing journey still ends online, where they can buy and return easily, and in high volumes.
Baby-boomers and Gen X are typically less likely to use social media to shop but will research online to plan store visits and purchases. The pandemic has seen online shopping rise in this age bracket, but without a simple, intuitive online shopping experience, customers are not likely to convert to purchase.
And so, while the shopping habits of different ages are markedly different, without a seamless experience to support different audiences and their needs across all platforms, consumers will look elsewhere. Understanding and responding to data around shopper habits and needs is vital to successfully achieving this.
Creating tailored experiences through data
Building an omnichannel experience that truly stands out also relies on creating a customer-centric experience. That is, a highly personalised experience that speaks directly to your audience’s evolving values and needs – all on the right platforms.
Strategically, data science can come to the fore in the tracking and measurement of the right leading indicators for the individual business. By taking this data and analysis and applying it to forecasting, business leaders can keep a watchful eye on the biggest change drivers and make decisions based on robust data, rather than guess work. This approach affords leaders confidence in their decisions and the ability to respond to forecast changes, long before they arrive.
For example, it took the pandemic and series of poor financial results for Marks & Spencer to launch its “Never the Same Again” programme – aimed at turbocharging its online presence. The company has also started to sell external clothing brands to try an offset its own underperforming labels and appeal to the changing preferences of its consumers. While profits have improved in recent months, the transition would have been less painful had M&S adapted to what the data was telling them earlier.
Meanwhile some brands are bringing personalisation to the next level. ASOS offers different deals depending on if the customer or new to their site, a returning customer or a regular, while US retailer Nordstrom remembers your clothing size each time you logon. Gucci even lets consumers customise their clothes with their own initials.
Providing the seamless, personal customer experiences that the modern consumer expects can come with a high price tag. So, to achieve a good return on investment, it is vital to have an in-depth understanding of your data, whether that’s through working with data science partners or investing in data and analytics skills internally. However, fundamental to this is ensuring that data is translated into human, actionable terms that will drive change and advantage for the business.
Armed with an in-depth understanding of your customers, the investment priorities will become clear – whether the result is an AR shopping experience, a slicker checkout process, an algorithm that serves customers products in their perfect shade, or a brand partnership with a key influencer on Instagram.
