Last month’s post on Oracle Real Time Decisions offered a brief overview of real-time interaction management products. I won’t repeat that here, except to summarize that these systems use data from multiple source systems to feed centrally-managed, real-time decisions to multiple touchpoints. The most common application has probably been product recommendations in customer service call centers, where there’s a substantial opportunity to sell something to a customer once you’ve solved their problem. Another frequent use has been selecting offers on Web sites, such as the familiar book recommendations on Amazon.com.
You’ll note that both of these are single-channel examples. That may seem odd, since coordinating treatments across channels is a key selling point. I believe the explanation is that most buyers purchase interaction management systems to get more powerful decision engines than those provided with their call center and Web site products.
Indeed, effective interaction management requires a sophisticated mix of predictive modeling, business rules, flow management, response capture, data integration, real-time processing, simulation, and analytics. The simple scripting and personalization engines built into call center and Web products don't provide all this. Equally important, the results of an interaction management deployment are immediately and precisely measureable – so it’s clear when one product works better than another. This means specialist vendors with superior products have a good chance to survive.
But you’ll also notice that these products don’t have many customers. I haven’t done a proper census but doubt there are five hundred implementations among all vendors combined. One reason is the sophistication itself: only a highly knowledgeable set of users can deploy the required rules and models effectively. Another is cost: you’re looking at the price of a 50 foot yacht (about a quarter million dollars if you haven’t bought one lately), plus a sister ship or two for implementation. Few firms with the resources and business volume needed to justify this expense.
(Alternate interpretation: the tools built into standard call center and Web applications are pretty good, so dedicated interaction managers offer only a small percentage gain. A company must be quite large for this to cover the interaction manager's cost.)
Swyft provides a low-cost alternative – more like a 30 footer (around $100,000).
The comparison is inexact because traditional interaction management systems are sold as licensed on-premise software, while Swyft is a Software-as-a-Service product, billed monthly. Pricing for agent-based applications (call centers, field sales, etc.) runs about one dinghy per user ($50 to $80 per month). But even small clients buy a fleet of 100 or more. Web site applications are priced on number of customers but come to roughly the same total.
Implementation is around $15,000 to $25,000, with data connections handled through standard Web Services. The company says a typical deployment takes 30 to 90 days, usually closer to 30.
Functionally, Swyft offers a pretty full set of interaction management capabilities. Decision rules can take into account capacity constraints such as call center workload; customer propensities; current and previous interactions; channel distinctions; offer eligibility; and event-based triggers. Interactions can kick off complex back-end workflows for follow-up treatments.
Call center integrations monitor agent activities and flash an alert if the system has an offer to make. The system then guides the agent through transition statements, probing questions, objections, offers, closing statements, and disposition capture. It can present different messages depending on the agent’s skill level. Web site implementations can present offers, collect data, and run champion/challenger and multivariate tests. The system will automatically adjust offer frequencies based on test results.
One feature that Swyft lacks is built-in predictive modeling. The company says it has found that most clients already have models in place. Rules can use model scores as inputs.
Like other interaction managers, Swyft relies primarily on data stored in external systems. Again like other products, it creates its own database of offers made and responses received for each customer. Less typically, it also stores marketing contents internally and provides a content builder to create these. The system can import and store additioinal information if real-time access is not appropriate.
The current version of Swyft lacks an interface that lets business users create their own rules. The company addresses this largely by doing the work for its clients, providing a “concierge” service that includes content and rule management as part of the base price. Clients do have the option to do this work for themselves; the company says it can be done after a couple weeks of training. A simpler end-user interface is planned for future development.
Swyft was founded in 2004 and launched its product in 2006. It has about ten clients spread among financial services, insurance, communications and media. The largest are mid-sized firms, with a several million customers. Intriguingly, the company offers its product on the Salesforce.com App Exchange, specifically offering a smartphone-enabled version that can use geolocation to identify a salesperson’s current location and recommend the most efficient prospects to visit. It has not yet deployed this at an actual client.
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