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How retailers can use data to drive success

By James Boother

Fashion retail is exciting, fast-moving and innovative, with a huge diversity of organisations working within it. Yet despite this diversity, our research has uncovered a number of universal ways of working that use data as a valuable business asset.

We conducted in-depth interviews with experienced fashion retail experts and influencers to discover the methods successful retailers are using to harness their data and reinvent their working culture. These contributors came from a variety of businesses throughout the sector, including high-volume high street vendors, online flash retailers and high-end luxury goods retailers. Despite their differences, their experiences brought up seven common themes that are necessary for success:

  1. Know your customer
  2. A cultural shift
  3. What is a retailer?
  4. The bottom line never goes out of style
  5. The finishing touches
  6. Omnichannel takes time
  7. A tailored approach

These key findings have been broken down into bite-sized takeaways below. For a more thorough analysis, be sure to read our whitepaper: ‘Data is the New Black’.

Know your customer

From boutiques to eBay, the emphasis is on a highly personalised experience based on knowledge of customer preference, which is moving beyond simply tracing previous orders.

New entrants such as Dressipi are using machine learning and algorithms to help retailers – for example, John Lewis use their solution to offer an online shopping experience that can make recommendations based on a customer’s neckline and hemline preferences.

And it’s working too – in A/B tests, Dressipi’s technology is proven to deliver 5-8% increase in net incremental revenue per visitor.

Phone and candles

A cultural shift

Data is becoming a board-level issue.

With the volume and density of data available leading to greater opportunities to directly influence a sale, we’re seeing a rapid change in the organisational structure of fashion retailers and the introduction of new roles and reporting lines. This, in turn, is influencing the organisational culture with data being viewed as a key asset by all aspects of the business.

In organisations with a strong data culture, there is a proactive shift towards, or the aspiration to implement, a model which includes data mastery and creates a wealth of new opportunity. We see this structure and attitude to data offering fashion retailers the ability to identify new points of differentiation that are not visible under the traditional model.

What is a retailer?

Since the introduction of ecommerce, manufacturers have been increasingly cutting out aspects of the retail process they do not own, while online retailers are connecting customers directly to wholesalers. This movement is prompting a bigger question: ‘what is a retailer?’.

In the online space – where Amazon is leading the way – retail is becoming more of a service. Customers are looking for a cheaper price point than the high street can offer, low or no-cost delivery, and an easy returns process.

However, for premier, heritage, and niche brands, being a retailer is about building relationships with customers and offering an experience that encompasses more than just buying clothes. Because of an increasing focus on direct communication with customers – particularly through social media - content creation experts are becoming as important to the industry as fashion experts.

High street

The bottom line never goes out of style

Artificial intelligence and machine learning present great opportunities for innovation and differentiation, but one thing will hold true for every retailer – every investment decision needs to stand up to scrutiny in the context of the bottom line. An investment needs to bring measurable benefits to a business, such as reducing costs by improving efficiency.

For example, one fashion retailer used Power BI to determine that an item previously thought to be a best-seller was only being sold at full-price 30% of the time. Although volume was high, profits were low and so the management team were able to discontinue the item and focus on more profitable stock.

The finishing touches

Smart bricks-and-mortar retailers are treating their stores as showrooms and using data analytics to make the in-store experience as personalised as possible to the local demographic. This includes incorporating data such as long-range weather forecasts, average property prices and household incomes. This enables retailers to choose the goods that best suit local markets or – in the case of one retailer – select clothing to sell based on the weather forecast for a particular Bank Holiday.

We are also seeing previously online-only and catalogue retailers use their data to inform decisions on where to invest in stores as a marketing asset. One well-documented example is Bonobos’ ‘guideshop’, where customers browse products with a dedicated assistant before placing an order that will be shipped to their home.

Showroom

Omnichannel takes time

Omnichannel is a form of multi-channel sales that provides customers with the same experience no matter where they interact with a business.

Our experience shows us that the more sales channels a fashion retailer has, the longer the transition to omnichannel can take. The same applies for high profile bricks-and-mortar brands who are taking their first steps into ecommerce.

You can find out more information on transitioning to omnichannel in our previous IMRG blog.

A tailored approach

The people we spoke to all recognised that out-of-the-box enterprise resource planning (ERP) systems rarely meet requirements without some customisation. The people we spoke to all recognised that out of the box ERP systems rarely meet requirements without some customisation. Most fashion retailers find that undertaking some in-house development offers a better fit when it comes to mastering data.

Data is the New Black

The whitepaper shares several Coeo case files, giving details about the ways data platform modernisation, artificial intelligence, advanced analytics, and automated reporting have led to real increases in profit in this highly competitive sector. From using local weather data to make stock decisions store-by-store, to spotting counterfeiting through applying basket analysis, these examples give insight into how data mastery can transform your business.

These technologies present huge opportunities for innovation and differentiation, but we also understand that in the real world, every investment decision needs to stand up to scrutiny in the context of the bottom line.

To see the detail of these seven transformative retail trends, and understand how these can be implemented, download the whitepaper today: Data is the New Black.

James Boother, Sales and Marketing Director, Coeo

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