The company, which counts Orange, Sony and MetroPCS among its customers, has created a big data analytics tool that can sift through a client’s customer data and identify “purswayers,” the often-hidden people who are able to prompt others to buy products or use services. The company said that these influencers, about 7 to 15 percent of a company’s users, are not necessarily the customers that spend the most or the loudest users, and they may only influence a handful of people. But they can be very valuable to a particular company because they help influence purchase decisions, for good and bad, in their vertical.

Pursway says that information can be used to provide better customer service, tweak marketing campaigns or to extend special offers to the influencer. That can ultimately mean less customer churn, better adoption of products, faster acquisition of new customers and, ultimately, more revenue, it says. Pursway said the top 10 percent of customers with a lot of influence can impact more than 50 percent of a company’s revenues.

Here’s how it works: a company turns over its customer lists and sales data, usually 12 months worth, to Pursway. Pursway taps online and social information along with its own data to establish existing “shopping relationships” and tries to understand the strength of those relationships. Then it looks for actual purchase behavior that appears to have a causal relationship, like if one person buys a product and then a friend buys a similar product the next month. It gives users a score to represent that person’s purchasing influence.

Pursway co-founder Ran Shaul told me that there’s only about a 25 percent overlap in a company’s heaviest spenders and its  most-influential customers. Some influencers are only trusted in specific categories. ”Instead of focusing on displaying and pushing ads, this is about whom do you push the ads to,” Shaul said. “Social media is important, but word of mouth is everything.”

Companies who use Pursway don’t tell their influencers they know who they are. Pursway said it can increase marketing ROI for companies by five to 10 times by just directing their work toward these influencers.

Pursway’s work highlights the power of transactional data. Former PayPal CTO Mok Oh told me that transaction data, combined with consumer behavior information, can be used by merchants to identify and interact with their best customers.

Pursway was founded three years ago as Datanetis and has taken on $6 million from Battery Ventures. We’re just getting started thinking about how to measure influence and how that impacts the bottom line. It’s not easy work, blending offline actions and online data — it involves analyzing terabytes of data. It also potentially runs into some privacy issues, though Shaul said all the data is legally obtained. I would like to see more case studies that prove out Pursway’s proposition, showing how they can home in on real-world influencers.

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That doesn’t exist yet for courtship (to my knowledge at least), but New York-based Custora is trying to do something like it for commerce. With its predictive analytics software, the Y Combinator-backed startup, which launched last year, helps popular commerce sites, such as Fab, Etsy and Birchbox, get an early read on their customer relationships to figure out which ones will be the most valuable over time – and how they can keep those high-value customers happy.

“In general, online marketing is very visitor- and session-centric – they convert or they don’t,” said Corey Pierson, co-founder of Custora. “We’re really trying to expose that longer-term way of thinking about each and every customer.”

Pierson, who met his co-founder Jon Pospischil while getting an MBA at Wharton a few years ago, said the company grew out of a business school class on the lifetime value of customers and using behavioral data to forecast it.

On its own, Pierson said, thinking about long-term relationships with customers is “beautiful” because it isn’t so transactional. But he realized that the modeling technique used to calculate customer lifetime value (CLV) came with another positive side effect: it was simple enough to inform algorithms that could power a software-as-a-service product.

“If you had a hundred data scientists looking at every single variable in a business, you very likely could create a solution that is totally optimized for every single nook and cranny of that company,” said Pierson. “But with this probabilistic notion of marketing we can get really close to that ultimate solution and leaps and bounds above where they are today.”

Searching for patterns among purchasers

That means for reasonable monthly fee (about $200 to more than $1,000 a month, depending on the size of a company’s customer base), growing commerce startups like Fab and Birchbox (and a few dozens more) get to have data insights close to those generated by full teams of Ph.D. data scientists at bigger online retail sites like Amazon, Pierson said.

Based on so-called Bayesian probabilistic theories, Custora’s algorithms analyze the purchasing behavior of a retailer’s customer database to look for patterns among shoppers. When a new shopper enters the site and makes a purchase, the software analyzes information like where she came from, what she bought, how much she sent and more, and then matches her up against similar customers to generate insights.

For example, after just one purchase, Custora can tell that a given customer of a commerce site is on track to make six purchases, totaling $275, over the upcoming year, placing her among the top 5 percent ot his customer base. The program could also predict that half of her purchases will be art-related, 25 percent food-related, with the rest falling into other categories, Pierson said. As for warning signs, Custora can learn that if the customer doesn’t order an item for 3 consecutive months, they can assume that she has only a 10 percent chance of returning.

Even though the program analyzes each consumer’s individual behavior (anonymously), it gives clients an intuitive way to understand it in aggregate. Each dashboard screen answers a specific question with a design-sensitive, easy-to-understand infographic. An opening screen, for example, might show a client that there’s a $250,000 opportunity in targeting a certain percentage of customers who have only purchased once and then give recommendations for how to achieve it. Another screen might display how much a company should spend to acquire a customer.

No more ‘batch and blast’ emails

It also lets clients segment customers based on whether they are one-time purchasers, repeat purchasers, at-risk or lost. It then provides a list of recommended strategies for communicating with them, but doesn’t take what Pierson called a “batch and blast approach.” For example, instead of sending all at-risk users the same “haven’t seen you in a while” email message, he said the software looks at each person’s unique shopping habits and tailors messages specific to their circumstance.

For now, the program integrates with a client’s email provider so that it can directly monitor and analyze their email communications’ impact on consumer behavior, but Pierson said it could eventually integrate with other touch points, like social media and mobile applications.

While other companies, such as startups GoodData and SumAll or Google Analytics and Adobe’s Omniture, might compete against Custora in different parts of its analytics business, Pierson said no other company operates across as much of the consumer experience spectrum or provides the same level of CLV insights.

In a New York Times article last month, Custora got a shout out for helping Fab forecast that over the next two years iPad users would generate more than 25 percent of the company’s revenue. And as subscription commerce service Birchbox tries to get more subscribers to purchase full-price items from its online store, it said Custora helped uncover strategies for increasing profit per customer by more than 70 percent.

Earlier this year, the nearly 10-person company announced that it had raised $850,000 in funding from SV Angel, Founder Collective, Highline Venture Partners, Valhalla Partners, the Start Fund and other angel investors.

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Since the acquisition, Jigsaw has been re-architected to work better within the Salesforce application, including a deep integration with Chatter, Salesforce’s real time status update service. With the new application, sales leads, changes of contact information and organizational profiles can be viewed within the salesforce application, all generated from up to date data created by real business interactions.

While the real time, highly accurate nature of the Jigsaw data is appealing, there is a significant barrier to the adoption of a crowdsourced approach. Many organizations consider the contact data for their customers to be an inherent part of their intellectual property and competitive advantage. Changes in the role of an important contact offer the business an opportunity to steal a lead on the opposition, and businesses are unlikely to be comfortable with leveling the playing field such that all their competitors share that same information.

Similarly, with so much data accruing about individuals on the social web, the fact that Jigsaw limits the data it collects to data entered by sales people misses a huge opportunity to be gained through the combination of contact information with social web content. New entrants in this market such as Etacts and Rapportive, both covered previously, are doing a great job of serving up social information about a contact within Gmail – services of this kind are attempting to span the divide between the social web and CRM – so-called social CRM offerings.

At $29 per user per month Jigsaw will appeal to sales teams that make use of contact lists. However for more progressive companies, aware of the value that lies within the social web, Jigsaw will offer little of value beyond the data cleansing aspects.

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Ben Kepes is an independent consultant and contributing writer for GigaOM. Please see his disclosure statement in his bio.