When CFO Steve Umpelby arrived at the London offices of top interdealer broking firm Compagnie Financière Tradition in 2010, he found all the right things going on in terms of legal and regulatory requirements.
But when it came to analysis there was a yawning gap. The organisation, which acts as an intermediary in trading derivatives and other instruments, depends on the performance of its brokers for its financial health. Yet despite plenty of figures on how much individuals and teams were bringing in, there was little fact-based understanding of their true contribution against costs.
His immediate answer was to hire an Excel specialist, who set about looking at broker performance, laboriously correlating data such as the 400 to 500 brokers' pay and bonuses from HR, with travel and entertainment costs and the numbers held in IT timesheets.
The resulting reports appeared two or three times a year, at first taking up to two-and-a-half months to produce each time with a large amount of figure-checking, according to Tradition EMEA CFO Umpelby.
"Which is OK if everything's going well, but not great if you're in a declining market and you need to take action quickly," he said.
Umpelby explained that one of the reasons for needing to do things faster is that many of the brokers have contract clauses stipulating a failure to produce certain revenues can lead to a salary cut and ultimately the sack at no cost to the company. But to enforce the performance clauses the broker has to be notified within either 14 or 28 days of the end of period.
"Given it was taking us over two months and then down to a month and a half to produce the reports, we could never enforce them," he said.
Business intelligence to speed analysis
With the manual Excel process becoming increasingly cumbersome, Umpelby began to look for better technology to speed up the analysis and make it more robust.
"We wanted to produce a profit-and-loss account by broker. We needed to know who was profitable and who wasn't, so that when it came to contract renegotiations we were doing it based on data rather than just gut feel and bluster," Umpelby said.
"But the Excel was getting too unwieldy. There were occasions where we were finding errors in it. As well as taking a long time to produce, it also took a long to time to check because the data was coming from different sources and changed each month."
Umpelby stipulated the functionality the new analytics software would have to offer, while his colleagues in IT ensured that possible products would fit the firm's IT infrastructure and allow datafeeds from existing systems.
In the end Tradition, which employs about 2,400 people in 27 countries, worked with analytics specialist Cubewise and opted for IBM's Cognos Express business intelligence software.
"In very simplistic terms, it felt to me like Excel. It had the flexibility of Excel — because all accountants love Excel — but at the same time it had a lot more robustness in terms of its calculating capacity and its ability not to fall over. It also had a far better ability to take in the data from the various different systems that we needed to," he said.
Hardware demands and cleaning data
The hardware demands for Cognos Express were relatively light — a new production server and a backup — and there were few teething problems cleaning existing data and getting it into the new system. But now Umpelby can press a button to generate a report showing brokers' profit and loss accounts, which after a quick check can go out to business heads.
In a market with falling revenues, the business intelligence software has played its part in providing more accurate data for the cost and job reductions that have taken place at the company.
"It wasn't particularly pleasant last year but [the system] helped us cut the cost of our business in the right places because we knew exactly which people were producing and performing last year and exactly which people weren't," Umpelby said.
"Previously it would have been down to gut feel from the business management. Gut feel might have got 80 percent of it right but it might have got some things wrong as well. The management know who's doing what but previously they didn't have the data to back it up."
With the business shifting more towards electronic broking, the company is also using the Cognos system to get a more precise picture of the platform development costs. The old spreadsheet and timesheet files became too big to manage after six or seven months and offered no method for recharging costs accurately to the businesses that had incurred them.
In fact the Cognos system is now used not just for IT timesheets but for cost allocations across all the business lines and generates reports for the board and executive management committee.
Client and transaction analysis
The system is also flexible enough to take on other new tasks, according to Umpelby, who wants to be able to use it for more client and transaction analysis, including developing a more detailed picture of how the brokers override ratecards for commission on particular trades.
"We have an AS/400 system that's got loads of data, but it's just difficult to analyse and produce reports. So one of the things we want to be able to do is dump that data into Cognos and analyse it and set up parameters so that it throws out the exceptions for us to look at. That will help us both on revenue leakage and our compliance aspect," he said.
Umpelby added that Tradition is starting to use the system as part of its stress testing and analysis for financial regulations. He pointed out that it is all these new capabilities that justify the acquisition of the system, rather than the broker profit and loss accounts and the IT cost analysis.
"We wouldn't have got it just for those two things. It's debatable whether it's covered its own costs based on those two things or whether me just hiring another spreadsheet jockey could have achieved the same, albeit with less reliability," he said.
Now the Asian arm of the business is looking at the system and may go down the Cognos route, according to Umpelby, who has also shown it to his US colleagues.
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When businesses make investments in new technologies, they usually do so with the intention of creating value for customers and stakeholders and making smart long-term investments. This is not always an easy thing to do when implementing cutting-edge technologies like artificial intelligence (AI) and machine learning. Business intelligence case studies that show how these technologies have been leveraged with results are still scarce, and many companies wonder where to apply machine learning first (a question at the core of one of TechEmergence’s most recent expert consensuses.)
Artificial intelligence and machine learning have certainly increased in capability over the past few years. Predictive analytics can help glean meaningful business insights using both sensor-based and structured data, as well as unstructured data, like unlabeled text and video, for mining customer sentiment. In the last few years, a shift toward “cognitive cloud” analytics has also increased data access, allowing for advances in real-time learning and reduced company costs. This recent shift has made an array of advanced analytics and AI-powered business intelligence services more accessible to mid-sized and small companies.
In this article, we provide five case studies that illustrate how AI and machine learning technologies are being used across industries to help drive more intelligent business decisions. While not meant to be exhaustive, the examples offer a taste for how real companies are reaping real benefits from technologies like advanced analytics and intelligent image recognition.
1 – Global Tech LED: Google Analytics Instant Activation of Remarketing
Image credit: SearchStar
Company description: Headquartered in Bonita Springs, Florida, Global Tech LED is a LED lighting design and supplier to U.S. and international markets, specializing in LED retrofit kits and fixtures for commercial spaces.
How Google Analytics is being used:
- Google Analytics’ Smart Lists were used to automatically identify Global Tech LED prospects who were “most likely to engage”, and to then remarket to those users with more targeted product pages.
- Google’s Conversion Optimizer was used to automatically adjust potential customer bids for increased conversions.
- Remarketing campaigns triggered by Smart Lists drove 5 times more clicks than all other display campaigns.
- The click-through rate of Global Tech LED’s remarketing campaigns was more than two times the remarketing average of other campaigns.
- Traffic to the company’s website grew by more than 100%, and was able to re-engage users in markets in which it was trying to make a dent, including South Asia, Latin America, and Western Europe.
- Use of the Conversion Optimizer allowed Global Tech LED to better allocate marketing costs based on bid potential.
2 – Under Armour: IBM Watson Cognitive Computing
Image credit: UA Record
Company description: Under Armour, Inc. is an American manufacturer of sports footwear and apparel, with global headquarters in Baltimore, Maryland.
How IBM Watson is being used:
- Under Armour’s UA Record™ app was built using the IBM Watson Cognitive Computing platform. The “Cognitive Coaching System” was designed to serve as a personal health assistant by providing users with real-time, data-based coaching based on sensor and manually input data for sleep, fitness, activity and nutrition.The app also draws on other data sources, such as geospatial data, to determine how weather and environment may affect training.Users are also able to view shared health insights based on other registered people in the UA Record database who share similar age, fitness, health, and other attributes.
- The UA Record app has a rating of 4.5 stars by users; based on sensor functionality, users are encouraged (via the company’s website and the mobile app) to purchase UA HealthBox devices (like the UA Band and Headphones) that synchronize with the app.
- According to Under Armour’s 2016 year-end results, revenue for Connected Fitness accessories grew 51 percent to $80 million.
3 – Plexure (VMob): IoT and Azure Stream Analytics
Company description: Formerly known as VMob, Plexure is a New Zealand-based media company that uses real-time data analytics to help companies tailor marketing messages to individual customers and optimize the transaction process.
How Azure Stream Analytics is being used:
- Plexure used Azure Stream to help McDonald’s increase customer engagement in the Netherlands, Sweden and Japan, regions that make up 60 percent of the food service retailer’s locations worldwide.
- Azure Stream Analytics was used to analyze the company’s stored big data (40 million+ endpoints) in the cloud, honing in on customer behavior patterns and responses to offers to ensure that targeted ads were reaching the right groups and individuals.
- Plexure combined Azure Analytics technology with McDonald’s mobile app, analyzing with contextual information and social engagement further customize the user experience. App users receive individualized content based on weather, location, time of day, as well as purchasing a and ad response habits. For example, a customer located near a McDonald’s location on a hot afternoon might receive a pushed ad for a free ice cream sundae.
Restaurant of the future – A successful IoT strategy for marketing from Plexure on Vimeo.
- McDonald’s in the Netherlands yielded a 700% increase in customer redemptions of targeted offers.
- Customers using the app returned to stores twice as often and on average spent 47% more than non-app users.
4 – Coca-Cola Amatil: Trax Retail Execution
Image credit: Trax Retail
Company description: Coca-Cola Amatil is the largest bottler and distributor of non-alcoholic, bottled beverages in the Asia Pacific, and one of the largest bottlers of Coca-Cola products in the region.
How Trax Image Recognition for Retail is being used:
- Prior to using Trax’s imaging technology, Coca-Cola Amatil was relying on limited and manual measurements of products in store, as well as delayed data sourced from phone conversations.
- Coca-Cola Amatil sales reps used Trax Retail Execution image-based technology to take pictures of stores shelves with their mobile devices; these images were sent to the Trax Cloud and analyzed, returning actionable reports within minutes to sales reps and providing more detailed online assessments to management.
- Real-time images of stock allowed sales reps to quickly identify performance gaps and apply corrective actions in store. Reports on shelf share and competitive insights also allowed reps to strategize on opportunities in store and over the phone with store managers.
- Coca-Cola Amatil gained 1.3% market share in the Asia Pacific region within five months.
5 – Peter Glenn: AgilOne Advanced Analytics
Image credit: AgilOne
Company description: Peter Glenn has provided outdoor apparel and gear to individual and wholesale customers for over 50 years, with brick-and-mortar locations along the east coast, Alaska, and South Beach.
How AgilOne Analytics is being used:
- AgilOne Analytics’ Dashboard provides a consolidated view across online and offline channels, which allowed Peter Glenn to view trends between buyer groups and make better segmentation decisions.
- Advanced segmentation abilities included data on customer household, their value segment, and proximity to any brick-and-mortar locations.
- Peter Glenn used this information to launch integrated promotional, triggered, and lifecycle campaigns across channels, with the goal of increasing sales during non-peak months and increasing in-store traffic.
- Once AgilOne’s data quality engine had combed through Peter Glenn’s customer database, the company learned that more than 80% of its customer base had lapsed; they were able to use that information to re-target and re-engage stagnant customers.
- Peter Glenn saw a 30% increase in Average Order Value (AOV) as a result of its automated marketing campaigns.
- Access to data points, such as customer proximity to a store, allowed Peter Glenn to target customers for store events using advanced segmentation and more aligned channel marketing strategies.
Image credit: DSCallards