Customer acquisition is a challenge for lenders—if you offer a loan at the right moment, it’s easy to win new business; but if you misjudge the customer’s needs, you can waste time and money.
acuteIQ is using cognitive computing to predict when someone is most likely to need a loan, helping financial services companies increase their conversion rates and capture new types of business.
Opens upnew markets by revealing prospects that lenders previously overlooked
4xgrowth in customer acquisition rates boosts revenues for lenders and brokers
95%accuracy in lead data quality, compared to an industry average of 60%
Business challenge story
Customers in both the consumer and business-to-business sectors are overwhelmed by choice, which puts sellers under more pressure than ever to proactively capture business. But indiscriminate selling simply isn’t effective—often wasting time for both parties, and sometimes doing more harm than good to the relationship between a seller and a potential buyer.
Ian Foley, CEO of acuteIQ, comments: “Successful sales is all about timing: if a customer is in the market for what you are selling, then they will usually respond positively when you get in touch. However, if the offer isn’t relevant to them, a sales pitch wastes both their time and yours, and can even discourage them from doing business with you in the future.”
In the financial services sector, banks and other lenders have often depended on their branch network to identify and entice new customers. But most organizations are taking advantage of the advent of online and mobile banking to cut down on their number of physical locations, compelling them to take the initiative and reach out to potential customers via other means.
“Many lenders and brokers purchase lead lists to help with customer acquisition,” says Ian Foley. “For example, they will buy details on people or companies that might be interested in a loan, and their sales teams will work through the list, calling or emailing to offer their services.
“There are a number of issues with this approach. Firstly, the data is often regurgitated and the data accuracy is 60 percent, so much of it is almost worthless. Secondly, due to privacy restrictions in the financial services industry, much of customer-specific data is severely limited and cannot be commercialized.
“Worst of all, lead lists usually include data from the same 20 percent share of the market, which typically focuses on high street stores, restaurants and other traditional businesses, and is already hugely saturated.
“This doesn’t help lenders tap into the vast opportunity offered by non-traditional borrowers, such as Internet-only merchants who are trying to build new businesses via platforms such as eBay or Amazon. We wanted to connect all the players in the marketplace, and help our clients reach out to these new customers at just the right time.”
Finding a smarter way
To help it develop a more effective approach to customer acquisition, acuteIQ looked to machine learning.
“Previously, we used content-based, contextual clues to build up a picture of the typical customer looking who had previously bought a product, and we used those profiles to identify other potential customers who had the same characteristics,” explains Ian Foley. “But with our machine learning coupled with IBM® Watson Analytics™, we saw the chance to zone in on the true predictors that a customer is looking to buy—which would really change the game.”
To support this predictive analysis, acuteIQ has built a database of more than 19 million businesses, which it continues to expand. The company layers its clients’ own data on top of this vast dataset, and also enriches it with data from third-party sources, such as credit card organizations.
“Our database combines layers of different types of data that continue to evolve over time,” describes Ian Foley. “Watson Analytics uncovers the patterns and trends that signal that a particular business might represent a useful lead. We can then continually refine and optimize our models as new data comes in to work out which of these predictors are the most effective.
“For instance, some of our clients may state that they wish to focus their customer acquisition activities on restaurants. Using Watson Analytics, we come up with the hypothesis that a restaurant with a liquor license that is about to expire may be in the market for a $75,000 working capital loan.
“We can identify leads that fit this hypothesis, and put them up as packages in our auction platform for lenders and brokers to bid on. Then we track whether these leads actually convert into sales, which gives us a complete end-to-end view of how effective the predictor is. Finally, we can feed this information back into our predictive models to enhance them further—so our results improve all the time and we can increase the bid rate for the leads in the auction.”
By significantly improving the quality of the leads that its clients’ sales teams are chasing, acuteIQ is helping them target their customer acquisition activities more effectively, and achieve better results than ever before.
Ian Foley elaborates: “Backed up by Watson Analytics, we have seen clients increase their conversion rate for new customers by as much as a factor of four. This gives them much greater return on their investment in customer acquisition. Using proven technology to identify potential customers at just the right point in the buying cycle, we are driving dramatically higher conversion rates for both lenders and brokers.”
acuteIQ is also improving on industry averages in data accuracy and market coverage.
“Our lead lists are between 90 and 95 percent accurate, compared to the usual 60 percent, meaning our clients don’t waste their time on unusable leads,” says Ian Foley. “We can also embed our clients’ requirements in our models, so we only provide leads that fit their specific criteria for lending. Because of this approach, the lenders that we work with can increase their coverage in industries where they are under-indexed, and the brokers can increase the numbers of leads that actually turn into funded loans.”
He adds: “We are also able to provide insight into the non-traditional borrowers that major lenders have generally overlooked in the past—potentially opening up an unsaturated segment of the market that could be four times larger than the stores, restaurants and other traditional small businesses that they usually target. This gives our clients a huge opportunity to increase their market share and get ahead of competitors.”
Leaving the old approach behind, acuteIQ is helping its clients enhance the results of their customer acquisition activities without putting relationships at risk.
Ian Foley concludes: “Client relationships are delicate, and aggressive sales tactics can leave them irreparably damaged. By applying Watson Analytics to work out when it’s best to contact a potential customer, we can cut down on unwanted contact while driving higher sales. The result? Happier lenders, brokers and customers.”
acuteIQ specializes in customer acquisition powered by artificial intelligence, with a focus on helping clients in the financial services sector. Its technology works by identifying signals that a potential customer might want to buy a product, and then scaling and optimizing the customer profile from its database of 19 million using machine learning. The company has locations in Tiburon, California, and New York City, New York.
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