Big success stories of big data analytics

Big success stories of big data analytics

Rod Newing reports on three organisations that have successfully deployed big data analytics which is now driving value and revenues forward

Big success stories of big data analytics

 

German mail order company Otto Group is very much a data-driven company. With revenues of more than €11 billion from over 1,800 online catalogues around the world, including Freemans and Grattan in the UK, up to 100 million data records or11 gigabytes are fed into its systems daily.

Its business intelligence department analyses this data for decision-support. It uses NeuroBayes, a predictive analytics tool from associated group company Blue Yonder, to identify any relevant complex interconnections that exist within these mass volumes of data to deliver up to 40 million separate, highly accurate product forecasts.

“The fact that NeuroBayes teaches itself is one of the great strengths,” says Matthias Stüben, division manager for merchandise processes at Otto. “The system recognises the importance of constant changes in influential factors throughout the product life cycle, such as customer and product rankings, page segmentation or catalogue output numbers in printing.”

These forecasts help the purchasing department to keep the number of items ordered from suppliers as close as possible to the actual amount needed, ultimately increasing the company‘s profits. “As a self-learning system, it matches our dynamic business model perfectly,” says Michael Sinn, the group’s vice president of buying division support. “Our sales forecasting has been up to 40 per cent more accurate, depending on the type of catalogue.”

This allows the group to plan its promotions early, have significantly reduced surplus stock at the end of the season, improve delivery reliability for the customer, and increase efficiency and profitability.

“We now view big data analytics as a critical part of our operations and have integrated it into our overall corporate strategy,” says Mr Sinn. “The impact on Otto’s revenue brought on by more accurate forecasts runs to a figure worth tens of millions.”

Analytics enable the YO! Sushi operations team to meet every morning to discuss analysis of the previous day’s trading in its 68 restaurants around the world, comparing it with budget, the previous week and the same week a year ago. The team can then formulate action plans for each restaurant based on the information.

“As we expand rapidly, a lot of factors affect our business at any one time,” says Billy Waters, the company’s IT manager. “[Analytics] enables us to see and react to any revenue increases, declines or unexpected patterns.”

Founded in 1997, YO! Sushi brings the concept of a Japanese kaiten sushi bar to serve 80 different dishes to 3.5 million customers every year. It uses IBM Cognos Business Intelligence software to look at trends over weeks, months and years, looking for specific events that trigger abnormal trading patterns. It also monitors the performance of the company as a whole on a like-for-like basis, taking out any new restaurants which have opened and consequent start-up losses which might affect the picture of how the core business is faring.

More accurate sales projections deliver more visibility of the sales in individual stores, he adds. Predicting changes or reacting quickly allows the business to reduce staff in a lull, to save costs or to recruit in a peak to maintain customer service.

It also allows the company to track customer habits, and plan its price and product promotions throughout the year, taking into account quieter periods and peak-sales times. By analysing daily patterns, the company can tell if an anomaly is a one-off or likely to continue for several days.

“We can now react much more quickly,” says Mr Waters. “We are so used to having this information that, if it was taken away, it would be like losing our right arm.”

In 2010, the law governing building societies changed. Nationwide was required by its regulator to reduce the time it takes to transfer individual savings accounts (ISAs) between providers from 23 to 15 working days, or it would face fines. The system involved moving three million pieces of paper through the system annually, which had to be physically transferred between more than 700 branches, distribution centres and its three main offices.

“Problem customers got stuck in the process and we couldn’t understand the aged accounts to respond when complaints came in,” says Martin Boyle, director of business transformation at the world’s largest building society. The company introduced a new system from OpenText to digitise the paper and manage its processes. Critical analytics were provided by SAP Business Objects and SAS.

Analytics from the new system allow managers to focus on the flow of cases, both internally and with other ISA providers. “The analytics get information to people who can address it in near real-time, as opposed to waiting for monthly reports after complaints have been filed,” says Mr Boyle. “We can now understand our case pipeline in a way that is actionable and use the information to drive behaviour.”

Gerry Coppell, head of group programmes, explains that managers can drill down to individual cases that are stuck in the system and transfer them to more experienced staff or chase the other ISA provider. Most important of all, rather than have to explain delays to the regulator, by monitoring the analytics on a daily basis, the company can ensure that it not only meets its deadlines, but can also prove it.

“We are using information to drive value, so we will never reach an end-point,” says Mr Boyle. “Continued investment in building appropriate analytical tools is moving us from retrospective investigation to forward-looking management.”

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