For many retailers, there is a significant gulf between wanting to become a data-driven organisation and the reality of becoming one. Despite the explosion of online shopping and the ability to derive data from multiple sources and touchpoints, including e-commerce, POS and reward schemes, businesses must achieve an advanced level of data maturity before they can begin to convert their data into valuable insights.

Companies often struggle with the first phase of the data analytics process: to clean any incoming data. Even if they have taken the time and effort to get this laborious process right, they are still often unable to convert their data into game-changing insights. A key reason for this is that the carefully gathered data rarely makes it into the hands of the people who need it.

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Often, data is left to atrophy in a stand-alone IT department when it should be available in an accessible form to everyone in the organisation, exactly when and where they want it. The key to unlocking data’s superpower is creating a robust, accessible and well-designed  data analytics platform.

By combining embedded analytics with data visualisation and design, each decision-maker across the business can have instant access to the insights that matter most to them. This includes anything from at-a-glance top-line trends to drilling down into significant growth metrics.

Andrew Dunbar, General Manager EMEA of global digital consultancy Appnovation

Here are three ways an effective analytics strategy can unlock the value of data, backed by examples of retailers that have cracked the code.

  1. Insights-driven, scalable data decisions for rapid use: Embedded analytics is about delivering meaningful data insights fast. Rather than struggling to interpret vast amounts of data, business intelligence software does the hard work, breaking data down into bite-sized insights to meet strategy needs and presenting them instantly on a mobile device or via voice-enabled tech. Starbucks is a global leader in this respect, crunching disparate data streams such as location, demographics, nearby offices and colleges, to test the potential success of new stores. Data analytics also proved invaluable during the Covid-19 lockdowns when Starbucks created a summary report that delivered key data points to leadership teams, so they had a snapshot of what was happening in near real-time.

  2. A joined-up approach to digital innovation: No longer the domain of analysts alone, data-driven intelligence is most effective when approached from a team perspective – involving everyone from the CEO to junior staff. Rather than isolating data teams from the people who rely on their insights, a well-executed analytics platform uses design that puts people first to keep everyone updated at the same time. This allows employees to act promptly, encouraging them to become proactive and empowered participants in the datasphere.

    As a retailer, part of ASOS’s success is down to its organisational approach, with cross-functional data teams working together to achieve powerful business outcomes. By enabling execs to tap into insights from colleagues across different departments, ASOS has eliminated isolated pockets of data and fostered a culture of data-driven decision-making, continuous experimentation and always-on marketing – all supported by tracking tools and real-time dashboards.

    Once again, data visualisation can be a key part of this process, with tech, UX (user experience) design and a robust data strategy coming together to deliver transparent and easily understandable intelligence that can be distributed and accessed by all.

  3. Improved employee wellbeing and retention: As well as understanding the needs and wants of customers, integrated data software can optimise internal processes. This includes breaking down goals such as employee engagement (something that will prove incredibly challenging yet essential to capture in an age of remote working). For example, businesses can design an application that inputs data around employee satisfaction levels, then categorise those findings according to location or department. Armed with this information, configured into charts and interactive maps, managers can quickly identify trends, dig deeper into individual threads (such as employee benefits) and respond to any emerging staff issues in real-time.

    US supermarket chain Wal-Mart has a team dedicated to studying people analytics and always seeks insights that might deliver additional business value. The company uses analytics to explore the impact of new processes on the business and employees. It also monitors employee turnover as part of its effort to retain staff and improve customer experience. As a result, over a quarter of a million people have been with the company for more than ten years, which is partly attributed to data insights.

And finally – new income streams.

Embedded analytics can help companies in myriad ways, from enhancing employee wellbeing to increased efficiency, improved customer engagement, and the identification of game-changing opportunities.

It can also help retailers to monetise their data. Embedded analytics can extract such valuable insights from a company’s data pool that these become a viable commercial operation in their own right – a retailer’s data becomes robust market intelligence that third parties are willing to subscribe to.

When data platforms reach this level of business activation, they become valuable assets that can transform an organisation from the inside out, building on new and unprecedented growth tangents and providing a springboard for game-changing new ideas.

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