Home Data Retailers unlock value everywhere with analytics, says SAS executive

Retailers of all sizes and across all industries are beginning to reap the benefits of data analytics, underpinned by the availability now of strong, and easier-to-use, tools.

Dan Mitchell, director of the Global Retail and Consumer Packaged Goods team for global analytics giant, SAS, spoke exclusively to iTWire, explaining the value retailers are unlocking out of an increased use of analytics. It’s an area Mitchell knows well, as he also has spent considerable time engaging with retailers on high performance, big data analytics, Internet of Things, personalisation and recommendation, customer intelligence, geo-location, text analytics and social media analytics.

Mitchell says it’s been his observation that “retailers are really interested in talking to me about how to pull all their data together. They know they need to consolidate and manage, following the trend of big data. They have a lot of data on hand — transactions, products, customers, inventory — and already live in the big data world, and have an interest in doing something with the data to make the customer experience better, reduce cost, and predict what customers want and maintain that inventory on hand".

“Also driving this is outside online competitors pressing on every retailer,” he said.

The call to action to retailers then is to “use analytics to get their arms around the customer experience and make it better, tracking if they’re doing a good job, not just test something and pray something big comes out of it,” Mitchell says.

An example of applying analytics is optimising palettes leaving the warehouse, minimising excess inventory and maximising the “right” inventory that people are buying in the target region.

“Most of retailers capital is locked up in the supply chain, all the way upstream, trying to determine the best products to buy. Retailers are striving to get better, using receipts and sales data but now they’re starting to get a more sophisticated view of demand,” Mitchell said.

“They know the purchasing journey is no longer [that] someone just walks in, picks up an item, and buys it with cash. Instead, consumers look online, or their friend tells them they bought something, they might see TV ad, or they are shopping for the best price - it’s all a lot of data that can be collected to bring in and give a more sophisticated view of demand,” he said. “You can look at intents to buy as an early warning system.”

Meanwhile, in the consumer packaged good space, analytics can be applied to demand prediction when selling to retailers. Their ability to know what the consumer wants is shrouded by the retailer — you give products to them and get re-orders back without knowing how quickly items sold or if the demand exceeds supply — but Mitchell says blending receipt data with syndicated sales data across other industries can recreate the demand pattern.

Analytics can also be applied in many other ways, Mitchell says. For example, whether to buy advertisements and measuring the attribution and conversion of sales, helping retailers be thoughtful about which products they carry even to localising products in specific stores, identifying which categories sell best in certain geographies, pricing and price elasticity, the expected impact of promotions and many more.

What’s more, Mitchell says, “a lot of technology and capabilities exist to accelerate modelling and simulations. They can be done in an hour or less. SAS can perform incredibly fast analytics, modelling against all data not just against samples".

The retailer needs to identify their business goals, but the potential is almost limitless. “There’s keen interest in instrumenting stores with sensors to get an idea of traffic – you can use Wi-Fi tracking and Bluetooth to understand the path through the store,” Mitchell says. “Customers don’t need to log into Wi-Fi, just have their phone turned on. Shopping malls use cellular sensing technology to track foot traffic.”

“The next wave is other instrumentation in the store. Video is a good example. As computer vision becomes more scalable it can be used to track people.”

What does it mean for privacy and consent in such a scenario where consumers’ path through the store is closely observed?

“Only through analytics can we secure customer privacy,” Mitchell says. “We can take a data set, train it to see a problem - like too many people in the store, then deploy real-time analytics in the edge so it raises an alert and then discards the data. There’s no need to save it. We need to be really responsible for using analytics and securing data in a meaningful way.”

“It’s our role to make consumers feel better about this."

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David M Williams

David has been computing since 1984 where he instantly gravitated to the family Commodore 64. He completed a Bachelor of Computer Science degree from 1990 to 1992, commencing full-time employment as a systems analyst at the end of that year. David subsequently worked as a UNIX Systems Manager, Asia-Pacific technical specialist for an international software company, Business Analyst, IT Manager, and other roles. David has been the Chief Information Officer for national public companies since 2007, delivering IT knowledge and business acumen, seeking to transform the industries within which he works. David is also involved in the user group community, the Australian Computer Society technical advisory boards, and education.

 

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