As more and more of us shop online, the retail sector is becoming an increasingly data (opens in new tab) rich environment. Last year, the retail sector experienced the fastest quarterly growth in the UK, allowing for retail businesses to gain greater insights and analytics and global retail sales grew more than 25%, and with major shopping events like Black Friday (opens in new tab), Cyber Monday (opens in new tab) and Prime Day causing online shopping (opens in new tab) frenzies for consumers.
This shift to online retail (opens in new tab) means that it is critical for retailers to be able to access and analyse data for activities across their organisation, from marketing activity and promotions to optimising stock levels.
The challenge for every retailer is: how can they approach, own and analyse this data? And all at a fast pace to ensure immediate action. The customer journey (opens in new tab) at every touch point is a vital data opportunity; to collect insightful analytics and identify key performance activity to employ a data-driven strategy in a highly competitive industry.
Data challenges in the retail industry
The way in which consumers purchase goods is dramatically different than even five years ago, and with new technologies being developed to further personalise and simplify consumers experience online, retailers must be more efficient and smarter with their data to remain up to date and scale their business.
The proportion of online retail skyrocketed to a record level in the first month of 2021 at 35.2%, and the trend has continues, despite many in person retailers reopening.
Shoppers demand a hyper-personalised and faultless customer experience through features such as smarter search browsers, reviews, ratings, and competitively priced promotional codes, and in order for retailers to match and manage these expectations, they must implement data strategies that can access and capture analytics and insights. According to Deloitte, 96% of UK and European retailers state that data management was a key priority for 2020 to improve their marketing functionalities.
Data query limitations can hinder retailers' success; for example, the ability to query problems like return rates (reason, specific product lines with a high return) or card abandonment insights. The average cart abandonment rate is 69.57% in the retail industry: and one way to overcome this high percentage, is to capture and analyse customer behavioural data.
A critical data pain point is enabling the exploitation of this customer data whilst staying fully compliant with regulatory frameworks such as the General Data Protection Regulation (GDPR). Retailers must ensure they are storing customer’s highly personal and sensitive information in accordance with regulation, with transparency well communicated with users on how their data is stored, used, and retained to maximise trust and individual permissions.
Maximising and innovating data with Data Virtualisation
Accessibility and availability of data at scale can be a challenge for any organisation, with data commonly found in data siloes supporting the operation of online applications or simply due to historic data warehouses designed for discrete purposes, which causes bottlenecks and can make it difficult to gain clear data analytics; and this is especially acute in organisations that run support both bricks and mortar and online operations.
As the amount of online data increases and therefore the ability to generate more comprehensive customer insights, it is key to select a data strategy that is focused on removing the impact of inefficient silos and focused on decentralised data for maximum operability and efficiency.
This is where data virtualisation can make a significant positive impact, and even more so when it’s part of the adoption of a data mesh based approach.
Benefits of Data Mesh
A Data Mesh approach can help retailers hyper personalise the customer journey and enhance analytics and insights via a data-driven strategy. Data Mesh decentralises data management and abates the impacts of silos and bottlenecks by giving teams across their organisation ownership, control, and access to their own data, this includes controlling access and authorisation to data to ensure its usage stays well within regulatory boundaries.
End users can query and access data where it lives without having to transport it anywhere, gaining better management and serving abilities. This more agile approach directly translates into greater discoverable insights, more agile decision making and reduced time to value of the investment in data related initiatives.
For retailers, this is essential to keep up with market trends and regulatory changes, especially within the auspices of GDPR, by removing inefficient barriers of central IT. By empowering data and product teams, companies can adapt faster to keep up with the market and regulatory changes by removing inefficient blockers of central IT. Data Mesh reduces organisational bottlenecks, simplifying access and removing unnecessary movement of data.
With the increase of online shopping showing no signs of stopping, data volumes in the retail industry continue to increase as firms gain greater insights and analytics on every aspect of the customer relationship at each touch point. Data Mesh architecture closes the gap between these transactions and the process of analysis with data ownership granted to individual teams, allowing them to make quick, real-time decisions without the need for data transfer.
The way we shop online has changed drastically, and in a hyper-competitive industry, it is vital for quick action, adaption and acceleration towards innovative data driven strategies.
Adopting Data Mesh can unlock further success, granting greater independence and access for data teams. With regulations constantly changing and more and more seasonal events, like Black Friday, growing in popularity every year, retailers can bypass organisational data inefficiencies and access data with transparency and agility.
Andy Mott, EMEA Head of Partner Solutions Architecture and Data Mesh Lead at Starburst Data (opens in new tab).