BlueSky International is a marketing research and consulting company specializing in the financial services sector. Based in Paris, France, the company provides a range of fact-based diagnostic services to banks, brokers, private banks, and investment houses in all of the world’s major financial centers.
BlueSky’s mission is to help clients determine their strategic options and support their tactical plans through a deep understanding of what customers consider important in their products and processes. This enables BlueSky clients to increase their profitability by increasing the satisfaction and retention of their most valuable customers.
BlueSky wished to provide a range of superior diagnostics from the data it collected through its rigorous market research and customer satisfaction studies. These diagnostic tools needed to draw together a multitude of measurements and criteria and establish norms for comparison, yet present the results in ways that were understandable and business-relevant. In the fast-moving world of investment, particularly online investment, it was also essential that these models could react rapidly to changes in norms and perceptions.
BlueSky adopted PASW Statistics to analyze and model the data, and worked closely in association with SPSS Worldwide Services to create a range of unique and powerful diagnostic tools. Critical to the decision was the comprehensive support offered in PASW Statistics to apply statistical models of association and comparison to nominal and ordinal data.
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BlueSky International is part marketing research company and part consulting firm to financial services companies and investment specialists around the world. From its base in Paris, France, BlueSky provides a range of diagnostic tools to help investment banks, brokers, and private bankers. Designed to determine customers' strategic options and help develop their tactical plans, BlueSky’s diagnostic services are based on facts derived from original market research and customer satisfaction measurement.
The company wished to develop a superior diagnostic service based on more accurate and revealing tools and measurements, and, by doing so, transform research data into information. Key to this was the ability to use powerful statistical methods such as regression and correlation to develop models that could react to changes in the competitive landscape of the market and respond to increasing client expectations.
Suzan Nolan, BlueSky's president, explained: “We help our customers understand if they are lucky or good. What we are pioneering is getting people to focus on value-based objectives.”
A problem for BlueSky was that most statistical software is not well equipped to handle so-called “soft” data based on rating scales and discrete criteria, the kinds of data that statisticians refer to as ordinal data.
“We knew we would have a lot of data,” Nolan related, “and we would want to compare multiple clients across hundreds of variables. We knew we needed a powerful tool to handle this. PASW Statistics was the standard in the market and, because there was an installed base of users, we knew we would be able to share our data more easily.”
Another attraction for BlueSky was the experience SPSS Worldwide Services could bring to the table in handling this type of data. Nolan commented: “We knew what answers we wanted to get but did not necessarily know how to get them. Consultants were able to show us how to come up with the tools to apply another dimension to the data, beyond simple crosstabulations.”
Sue Hlady, satisfaction research manager at BlueSky, was aware of the technical limitations of other solutions, and endorsed working with PASW Statistics to develop BlueSky's new diagnostic product. She worked closely with SPSS Inc. consultants to develop the models and processes BlueSky needed to launch its new service.
She commented: “We mostly deal with customer satisfaction, which often is expressed as ordinal data. PASW Statistics is very good at handling ordinal data—it has a lot of different regression-based techniques that support ordinal data analysis. There is a feature of PASW Statistics called optimal scaling, for example. Without that, you simply would not be able to run regression with the data that we collect.”
Though Hlady had experience with PASW Statistics when studying for her MBA, applying SPSS to research data called for a different approach. “What was positive about the processes was that the training SPSS Inc. provided us was highly customized,” she said. “Rather than a normal training class, we had SPSS Inc. come in and work with us, on our specific needs. We were able to hit the problems together, with our data as the context, and that really helped us.”
The new diagnostic service has enjoyed considerable success over the year since its launch. Nolan finds that the business it generates now represents a third of BlueSky's revenues. “This is new money,” she stated. “The service we developed using PASW Statistics enabled us to increase revenues by 30 percent.”
The service is continuing to attract new clients and, among existing customers, BlueSky's services are being recognized at a higher level. At one major investment house, for example, eight mainly middle-ranking executives were in attendance at the fist presentation from BlueSky, using the new diagnostic tools. One year later, thanks to the sophistication of the analysis and the relevance of the data presented, the presentation was packed with 32 attendees, including the CEO and entire senior management team.
The tools have proved productive for BlueSky to use. Before PASW Statistics was fully adopted, BlueSky produced a series of reports that took around 90 days to create. Now they are completed in 30 days. Both Nolan and Hlady feel that, in PASW Statistics, they have chosen a solution that will grow with their business. Already they are adding new techniques based on factor and cluster analysis, again on ordinal data, and finding the solutions are readily available within PASW Statistics.
A year after the service launched, BlueSky is continuing to gain clients in what has subsequently become a very difficult market.
Nolan concluded: “Using ordinal data lets us measure a range of emotions which we can afterwards translate into hard numbers and into a model for analyzing the performance of our clients' businesses. This is why the CEOs will sit in on our presentations—because we can relate otherwise fuzzy market research to the very hard top- and bottom-line numbers that matter.”
These are revenues that did not exist. It's new money that has let us increase our revenues by 30%.
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