Personalised Shoppers – Retail and the Future of Data

In a customer-driven world, retailers deal with a hugely variable commodity. No successful business would dare assume it can cater to everyone at once, but each tries its hardest to tailor to as many individuals as possible. Artificial Intelligence (AI), paired with big data, brings a long-lost asset back to e-commerce – knowing your customer. It has already been adopted by 38 per cent of the retail sector to leverage customer insight, but legacy infrastructure is in no shape to handle the coming surge of data dependency.

Machine learning (ML) has seen even greater uptake than AI. 48 per cent of retail executives use it to boost sales figures: by predicting trends, tailoring special offers and generally gaining invaluable insight into customer’s behaviour. As organisations shift from a product-focused strategy to a customer-focused one, the number of moving parts increases exponentially. Data aggregation is exploding, and legacy warehouses are simply not equipped for it.

Retail is a driver of technological trends and spurs on software development. Those at the forefront of this innovation are reaping the rewards: 54 per cent of shoppers have bought something based on automated recommendations or cart-reminders, while over 70 per cent have been nudged into purchases through coupons and discounts. To support continued growth and viability of these techniques, the data sector needs to lead with innovation of its own – it must build itself into the cloud.

AI and ML are incredibly promising technologies, but are being held back by data bottlenecks. The demand on legacy warehouses – used by most of the industry – exceeds the capacity they are capable of handling efficiently. Already sluggish, this will be exacerbated by investment in Internet of Things (IoT) technology, which is expected to be seen as a priority for at least 30 per cent of organisations within two years.

Modern retail outlets take technology as seriously as stock and sales. Data analytics, the science of drawing predictions and conclusions from data collected through various channels, has seen a surge of growth in the sector over the past decade. Erick Roesch, director of business intelligence at fashion retailer Rue La La, states that big data has brought “the power to conceptualise what we want and get results in seconds”.

The benefits data analysis offers has made it a principal element of the commerce toolkit. At the foundation is an intricate system of predictive modelling, automated product sourcing and shipping, and tailored marketing. The latent issues set to cripple legacy warehouses must be addressed to free up the data these AI and ML processes depend on, enabling the continued, remarkable growth of the retail sector.

[easy-tweet tweet=”Many retailers struggle to handle data from fragmented services and departments” hashtags=”Retail, Data”]

It has all come in the blink of an eye and companies can crumble just as quickly if they’re not able to meet scalability demands. Success for a retailer in 2017 means exponential growth, with customer base and orders snowballing month-by-month – quickly demanding expensive upgrades to existing systems. Not only is the cost of a system overhaul out of reach for most small businesses, but collating data across departments poses a near impossible feat.

Many retailers struggle to handle data from fragmented services and departments – in the cloud they can be co-ordinated in a way that enables operational fluidity, which is simply not available under the legacy model. In fact, only Walmart successfully unified data throughout its supply chain pre-cloud. Reliant on traditional data warehouses, even established retailers can find their success is limited by the inability to meet a sudden influx in customer sales. This can be anticipated for seasonal sales, but is often a result of completely unexpected trends. What data analytics offers is a “360-degree view” of your customers and the sheer size of an online customer base means this can only be made possible by AI.

Most retailers are aware of the advantages of targeted data and seek to, as Roesch says, “extend what [they’re] doing in a more elastic manner.” To make the most of the latest tools and technologies at their disposal, retailers need more flexibility from their storage solution – the only way to achieve this is in the cloud. The ability to meet peak demand would ensure full advantage is taken of every market whim, while being able to roll back data caps during quieter periods would mean loss margins are kept to a minimum.

Most AI/ML solutions we see today are in stock management and supply chain, but they extend to customer relations, the use of chat-bots, and tailored services. Through bespoke marketing, brands can see low-loyalty customers become significantly more engaged and likely to make a purchase. Forecasting accuracy, operational efficiency, excess and depleted inventory, are all better managed by implementing these tools, but running them from a traditional data centre reduces effectiveness.

The fast-growing fashion retailer Rent the Runway (RTR) calls out “We’re in the fashion-technology-engineering-supply-chain-operations-reverse logistics-dry cleaning-analytics business.” This is the new model – multi-faceted service-based businesses. Technology and retail are growing together and strengthening the case for one-another’s development. Using ML, a service such as RTR can be tailored to the individual and offer an unprecedented degree of customer insight. To support this ‘Closet in the Cloud’, RTR turned to a cloud-based data warehouse to provide a system that boasted scalability at the click of a button.

By adopting a true-cloud data warehouse to support the organisation, businesses such as RTR can avoid drowning in their own success. Legacy warehouses are inflexible, offering one option for retailers who hope to succeed – ‘overprovision or bust’. This creates a culture of duplicate data filling up capacity, as concurrency is virtually non-existent. As AI is dependent on a vast amount of resources, such wastefulness can quickly lead to redundancy. Optimisation is the trend of today – the leaner your data strategy, the more effective your predictive analysis will be.

There is no singular version of the truth in a legacy warehouse, rather many half-truths. Any attempt at personalised marketing or sales predictions is thrown horribly off the mark and effectiveness trends to zero. A cloud solution eradicates this in its inherent ability to support a virtually infinite number of concurrent users – any changes to the data will only be in service of creating a more complete image of the customer, stock, sales, etc., empowering the business as it grows.

Young businesses are fighting a good fight against established industry giants, but they will need every possible resource in their favour, with AI and ML as their powerhouse. They need a holistic view of their customers so as to leverage every advantage and close every sale. They need flexibility and scalability in real-time to minimise overheads and meet demand. They need concurrency, accuracy and organisation across every department. Cloud-based data warehousing is the only viable solution for those who seek a level playing field. As legacy-loyal competitors find themselves tethered to outdated systems, a new breed will strategically fill the niche.

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