HSBC Bank USA, a member of the global HSBC Group, serves more than 1.4 million retail banking customers, providing a variety of checking, investment, loan and other financial products through 380 New York banking locations. HSBC Bank USA, with $35 billion in assets, also serves business and commercial customers.
A typical branch of a neighborhood bank might have up to a half dozen or more competitors in close proximity, creating a continuous competition to attract - and keep - potential customers in the surrounding area. To maintain high customer acquisition and retention rates, and keep operations profitable, bank goals often include:
With SPSS, HSBC Bank USA effectively mines an ever-growing file of customer data, creating predictive models to uncover cross-selling and "roll over" sales opportunities. Focusing on the best prospects for each product helps maximize sales and minimize marketing costs, and SPSS' ease of use helps researchers deliver intelligence faster to decision makers.
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Retail banking is a highly competitive business. In addition to other banks in the neighborhood constantly vying for customers' checking, investment and loan business, recent deregulation also means intense competition from financial services companies of all kinds, from stockbrokers to mortgage companies.
With so many organizations working the same customer base, the value of customer retention is greater than ever before. As a result, many banks' sales strategies now strongly focus upon enticing existing customers to "roll over" maturing products, or on cross-selling new ones.
In the past, HSBC Bank USA often promoted its products to both new and existing customers using lifestyle segmentation information purchased from market research companies, such as studies that predict income and buying behavior based upon neighborhood or subdivision.
"That kind of external segmentation scheme has its place and can be valuable when soliciting new customers. However, we realized that we already had much more specific and potentially valuable information on the buying habits and needs of our 1.4 million customers already locked in our database," explained Joe Somma, HSBC Bank USA's manager of customer acquisition and research. "It was just a matter of mining the data and analyzing the patterns to learn more about who needs what and when. This predictive analysis helps us contact the right people at the right time with the right offer. And, SPSS has given us the power to do just that."
Predictive analysis helps us contact the right people at the right time with the right offer. SPSS has given us the power to do just that.
According to Somma, the key is using SPSS to find customers that "look like" those that have demonstrated a particular buying behavior in the past. Take investment products, for example.
"Which personal characteristics and patterns in deposits to a checking account indicate a customer might be interested in a higher yielding investment? This buying behavior has occurred thousands of times in the past, and can help us predict buying behavior in the future, if we know where to look," said Somma. "Mining sales data with SPSS helps us uncover statistical relationships, and more importantly, shows us the strength of those relationships, so we can instantly see what is meaningful. This helps us optimize our resources in building an effective marketing strategy."
Indeed, that vital last piece of the puzzle was where Somma has seen traditional OLAP tools fall short of his needs.
"OLAP is nice way to get a sense of the characteristics of a dataset, but I can't find the strength of the associations and I can't do predictive modeling, and that's the meat of what I need," he said. "OLAP is a good reporting tool, but without a statistical engine, it can only tell me where I've been, not where I need to go."
With SPSS helping point the way, Somma and his colleagues in the bank's individual product departments have created successful marketing strategies based upon the software's predictive models. In just three years, he reports, sales are up by up to 50 percent across the bank's numerous product lines.
Somma notes that by targeting customers more accurately, he not only uncovers the most promising prospects for a particular product, he also saves money by not contacting those customers who do not fit the predictive profile.
"If I'm doing a direct mail campaign, for example, I can mail out fewer pieces in a more targeted manner, get a higher percentage response rate and bring in about the same amount of money," he said. "Avoiding the traditional 'shotgun' approach, in one recent promotion, we reduced a mail program by more than a third, saving thousands in postage and printing costs, but still brought in 95 percent of the revenues of the previous mailing. So our ROI was enormously improved."
Indeed, he says, this ability to directly save on previously fixed costs is a major benefit of SPSS.
"If you run just one good model, you can easily pay for the software." In addition, Somma notes there is also a strong customer care "bonus" to his predictive modeling efforts.
"No one likes to be inundated with messages about products they don't want or need. So, by data mining with SPSS, we are less likely to annoy our customers with what they might consider junk calls or mail," he said. "And that, I believe, pays off in customer loyalty for the long term."
According to Somma, SPSS not only helps him uncover new business opportunities, but also do so faster than many of his competitors.
"Customers ready to buy a CD can buy it from us or from the bank down the street, so speed is absolutely critical in these situations. The faster we can get to market with a new promotion, the better we'll do." SPSS, he said, is geared toward marketers that want to move both effectively and quickly.
"SPSS provides a combination of ease of use and analytical power that lets me create models quickly," he explained. "All the depth and breadth of analytical functionality we need is right there, letting us perform the most complex correlations or work with the largest data sets with ease. And, once we have the models completed, presenting them to product line decision makers is easy. You can walk them through it and demonstrate the logic, and they don't have to know anything about statistics to quickly see the relationship between customers who have bought certain products in the past and the universe of customers who 'look like' them. SPSS models help decision makers see the opportunity clearly, so they can move quickly, confidently and decisively.
"SPSS is an ongoing strategic partner," Somma concluded. "We work with them because they give us a competitive edge."
Read more about SPSS@work for HSBC Bank USA in the Gomez report Interactive Marketing
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