1. How can a robust business analytics (BA) solution assist overall business performance?
A robust solution (rather than just a single tool) is designed to help organisations become 'analytics-driven'. That is, to move from ‘sense and respond’ to ‘predict and act’, without requiring that everyone in the organisation be an analytical expert.
These solutions allow companies the unique ability to anticipate and shape business outcomes based on insights. Some of the business benefits enabled by these systems include: informed decision making, anticipating patterns and trends and enabling increased agility to respond rapidly to market opportunities.

2. How can predictive modelling be leveraged for businesses to project and develop insightful strategies?
Predictive analytics can be used to drive decisions that optimise customer experience and business goals at each and every interaction.
The problem with traditional analytic techniques is that it counts on the human to ask the question. The science of predictive analytics is to point a machine at your data and let it find what is interesting. It takes the data and groups it into segments that look similar, embodying them in what is referred to as a predictive model. The mathematics behind this allows businesses to predict the attributes of a campaign or strategy in spite of limited access to information.
Predictive analytics can determine the most appropriate strategy to:

• Predict an undesirable event before it happens;
• Identify the driving attributes related to the desired/undesired outcomes;
• Determine the risk of an event; and
• Develop and test risk mitigation strategies.

The high-value data we are seeing clients add to the mix is attitudinal data and interactional data. This non-traditional data includes the opinions and preferences of customers, often in form of open-ended survey results or even as feedback, or from unconventional ‘big-data’ sources such as social media.
Coupling your traditional enterprise data warehouse with this type of data hones insights significantly and gives organisations the ability to develop and deliver smarter, more insightful strategies.

3. Given the advent of geo-spatial technologies, how do you view these impacting the data collected through BA systems?
Almost all customer, sales and marketing data have a spatial component. This could be as basic as an address, or something more complex like predicting the catchment area for a new branch based on travel times and the proximity to competitor’s branches.
This type of query is difficult with a traditional structured query, and usually requires additional coding to your data elements. For example, we often store postcodes in the address of a supplier or customer - but that actually tells us very little about the location of that person or business.
For example, travelling west from the city of Sydney, I would drive from Sydney (postcode 2000), through Pyrmont (2009), across the ANZAC bridge to Balmain (2041) and so on. If I wanted to find a petrol station within five kilometres by road from the centre of the city, postcodes are almost useless. How would I do this simple task without local knowledge, or specialised tools?
This geo-spatial query is the sort of thing most people take for granted from the GPS in our car, but fail to use in our corporate lives. Without ‘geo-tagging’ our data, we cannot effectively ask this type of question, and so we miss out on a wealth of information and analysis.

4. Personalisation is becoming increasingly important within the realm of marketing. How does BA enhance opportunities for this?
Personalisation adds depth by giving a richer view of the analysis and a more complete picture of client.
There is a breadth and depth to the information available today that was unheard of even a few short years ago - from new sources such as social media, real-time interaction data (like verbatim chat transcripts, instant messaging, crowd-sourcing and web clickstreams). We also have access to far greater analytic capabilities for deeper understanding of the data such as predictive analytics.
The more data you add, the richer your analysis, and the more accurate your predictions. Done properly, predictive analytics can help you address marketing and sales specific issues such as:

• Poor direct marketing campaign results; customers opting out due to irrelevant (not tailored) messaging.
• Lack of quick and easy access to campaign effectiveness metrics and analysis on customer.
• Difficulty identifying your best customers and prospects.

Supplied courtesy of Power Retail (www.powerretail.net), Australia’s information resource for the online and multichannel retail community.

Background
Gloria Jean’s Coffees is a leading global coffee house operator and sells hand-crafted signature hot and cold coffee drinks, traditional espresso drinks, a range of single origins, blends and estate whole-bean coffees and specialty teas, along with pastries and coffee accessories in a comfortable and friendly environment.
As one of Australia’s leading coffee specialists, Gloria Jean’s Coffees has played a major role in the development and growth of the retail coffee market in Australia. Since opening in 1996, the company has opened over 500 coffee houses and operates in every state and territory of the country. Gloria Jean’s Coffees has also expanded internationally to become a global coffee retailer, serving an estimated 102 million guests each year in 37 countries around the globe.

Challenges
In previous years, Gloria Jean’s Coffees utilised an antiquated ERP system and spreadsheet-based reporting system that took at least 10 days to process monthly reports, plus an additional day per amendment. Employees in the financial department could not generate their own reports, and the slow, inflexible system made it cumbersome for them to utilise the data to make a meaningful impact on the business in a timely fashion.
Gloria Jean’s Coffees oversees hundreds of franchises globally and about 10 different legal entities, all with varying IT and reporting systems. Different business units also relied upon different Excel-based systems to produce reports on sales and franchise fees, and the reporting processes were often unclear, difficult to manage, and costly to maintain. Gloria Jean’s Coffees needed to find a way to consolidate these processes into a more cost-effective, time-efficient and productive system.
With domestic and international business growth, monthly financial reporting processes and franchise fee calculations were becoming a major pain-point for Gloria Jean’s Coffees. Slow, manual spreadsheet-based processes were hindering business agility, and the company was spending tens of thousands of dollars annually on consultancy, just to get the information it needed.

Execution/Solution
Gloria Jeans Coffees tackled this by leveraging IBM® Business Partner, Cubewise’s expertise and product recommendations to develop a system specifically tailored to its business needs. The aim was to centralise data onto one platform through IBM Cognos, consolidate reporting analysis and empower employees with the ability to develop exactly the type of reports they need.
The company implemented IBM Cognos® Express Xcelerator – providing a single source of truth for business analytics and substantially simplifying reporting processes. The solution will also be integrated with the company’s new ERP system, providing enterprise-wise insight.

Outcomes
Using Cognos Express, Gloria Jean’s Coffees is now able to generate financial analyses and sales reports more quickly and efficiently, reducing reporting time from over 10 days to five.
The implementation reduced accounting costs by $120,000 per year and has eliminated the need for Gloria Jeans Coffees to spend $60,000 to $80,000 annually on consultancy to support franchise-related business analytics processes.
The solution has allowed Gloria Jeans Coffees to significantly reduce business expenses by cutting redundant roles and systems, such as the previous macro-driven franchise fee calculation process. It has also enabled the company to upgrade to a new ERP system tailored to its supply chain needs, with full confidence that IBM Cognos will be able to manage the necessary reporting functions.
IBM Cognos Express has enabled the creation of consistent, logical reports on a single system, while drawing data from a variety of entities. As the system is centralised and consistent for all users, allowing easily and effective management, it offers employees more time to analyse the data, understand its implications, and make better business decisions.