Old Answers to New Questions Won’t Work: Retail’s 21st Century Data Problem

By Manjit Johal 

The big promise of digitalisation was the ability to gain greater insight into customer behaviour based on the data clues at every touch-point. But the reality for many retailers is that just because data is collected doesn’t necessarily mean it can be analysed – well not easily at least. The promise of ‘a click of a button’ utopia has been under-minded by the quagmire of disparate datasets, legacy silos, time-intensive data ingestion and data quality issues – making deep, quick analysis and data-driven decisions difficult.  

For many retailers, the old ways of analysing data are outdated in the modern environment. Today, data comes in a vast array of forms and languages; it is spread over multiple systems and is collected on a variety of spreadsheets. It resides in systems that aren’t always visible across all departments from merchandising and promotions to returns and logistics.

For all the benefits that digital disruption has given us, the truth is data analytics can be cumbersome, time-consuming and costly. It can involve collecting samples of data at the end of the week and analysing them to find historical insights that can be applied in the future. This rear-mirror approach lacks the immediacy of real-time analysis. Meanwhile, bringing all the data together by hand on spreadsheets is a slow, grinding process. Many retailers struggle to find tools that will turn the insights trapped inside their servers and silos into actions.

Every time a shopper buys an item from a store or ecommerce site, a complex flow of data is triggered. The purchase tells the retailer that the item needs replacing, triggering a replenishment process that will move products from warehouse pallets onto the shelf. Alongside this, the data contains numerous signals that retailers can use to sharpen their sales tactics. Was the item on promotion? What was the size, colour and style? Was the purchase related to a public holiday, life milestone or the weather? Was a loyalty card used? What was the profile of the purchaser?

Knowing common trends around shopping cart abandonment, the return on existing customers versus new customers, and the impact of personalised promotions is still a pipedream for many smaller players that lack the IT budgets of the retail giants.

Historically, the answer has been to overload data analysts or data scientists with requests for numerous reports – not a scalable or timely option and not a great use of their time or talent.

Reinventing retail operations for the digital age requires the ability to unify data from across different business operations and across different data sources to make it easy to access. From store managers to merchandisers and marketers, everyone responsible for boosting sales and improving margins should have access to real-time data about sales performance.


That’s where augmented analytics, which is the use of machine learning and natural language processing to automate data preparation and enable data sharing, comes into its own by offering retailers the ability to unite a huge variety of data, providing a full overview of activities and sales performance.

Retailers can now save considerable time by automating this data ingestion, melding sources such as live sales data, demographic information on consumers, partners’ performance and stock levels. This not only significantly improves stock levels, but also makes those metrics actionable so you can get on with the task at hand - building sales and increasing revenues.

One weekly rolled up metric does not exist in ecommerce anymore. Modern reporting demands that updates of activities in stores, ecommerce and direct mail channels are delivered with one voice. This means seamless multichannel insights, advanced segmentation of customers and analysis of competitor offers, and a view of the supply chain. Today, retailers must be able to read and slice data in different ways and in some cases multiple times a day. Drilling down into data in a timely fashion and knowing with certainty whether there is a need to change and react, and/or spend more or less in online promotions.

Companies such as Figleaves are now able to adopt a narrow and deep approach to merchandising, allowing teams to see at a glance and in real-time which items are selling best, and stock the most popular items based on which products are selling fast and which need replenishing.

One of the great shopping trends of our time is buy-to-try as people order online, try items at home and get free returns. Some items such as clothing have return rates of 70%, and it is calculated that some retailers must sell an item three times for it to remain sold. An augmented analytics approach can allow real-time measurement of which items are in stock, which are being returned and which are being kept. By stocking the most popular items, returns are reduced creating a faster throughput of merchandise. It also gives granular insights into any correlations or trends around returns and consumer demographics, colours or sizes.


Using machine learning algorithms to offer smart alerts, augmented analytics enables users to keep on top of specific metrics whether it’s promotional sales or turnover of a specific product line. They can set custom alerts which help them understand what is driving changes to key metrics through root cause analysis. This instantly examines the reasons for a change in metrics, saving users time as they investigate these trends. 

By taking the guesswork out of retail data, augmented analytics helps retailers get in shape for a world of multichannel selling by sharpening their data analytics for the digital age.

Manjit Johal is Chief Technology Officer for augmented analytics specialist, Avora.

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