Some of the most impressive marketing systems I’ve seen have been developed for mobile phone marketing, especially for companies that sell prepaid phones. I don’t know why: probably some combination of intense competition, easy switching when customers have no subscription, location as a clear indicator of varying needs, immediately measurable financial impact, and lack of legacy constraints in a new industry. Many of these systems have developed outside the United States, since prepaid phones have a smaller market share here than elsewhere.
Flytxt is a good example. Founded in India in 2008, its original clients were South Asian and African companies whose primary product was text messaging. The company has since expanded in all directions: it has clients in 50+ countries including South America and Europe plus a beachhead in the U.S.; its phone clients sell many more products than text; it has a smattering of clients in financial services and manufacturing; and it has corporate offices in Dubai and headquarters in the Netherlands.
The product itself is equally sprawling. Its architecture spans what I usually call the data, decision, and delivery layers, although Flytxt uses different language. The foundation (data) layer includes data ingestion from batch and real-time sources with support for structured, semi-structured and unstructured data, data preparation including deterministic identity stitching, and a Hadoop-based data store. The intelligence (decision) layer provides rules, recommendations, visualization, packaged and custom analytics, and reporting. The application (delivery) layer supports inbound and outbound campaigns, a mobile app, and an ad server for clients who want to sell ads on their own Web sites.
To be a little more precise, Flytxt’s application layer uses API connectors to send messages to actual delivery systems such as Web sites and email engines. Most enterprises prefer this approach because they have sophisticated delivery systems in place and use them for other purposes beyond marketing messaging.
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And while we’re being precise: Flytxt isn’t a Customer Data Platform because it doesn’t give external systems direct access its unified customer data store. But it does provide APIs to extract reports and selected data elements and can build custom connectors as needed. So it could probably pass as a CDP for most purposes.
Given the breadth of Flytxt’s features, you might expect the individual features to be relatively shallow. Not so. The system has advanced capabilities throughout. Examples include anonymizing personally identifiable information before sharing customer data; multiple language versions attached to the one offer; rewards linked to offers; contact frequency limits by channel across all campaigns; rule- and machine learning-based recommendations; six standard predictive models plus tools to create custom models; automated control groups in outbound campaigns; real-time event-based program triggers; and a mobile app with customer support, account management, chat, personalization, and transaction capabilities. The roadmap is also impressive, including automated segment discovery and autonomous agents to find next best actions.
What particularly caught my eye was Flytxt’s ability to integrate context with offer selection. Real-time programs are connected to touchpoints such as Web site. When a customer appears, Flytxtidentifies the customer, looks up her history and segment data, and infers intent from the current behavior and context (such as location), and returns the appropriate offer for the current situation. The offer and message can be further personalized based on customer data.
This ability to tailor behaviors to the current context is critical for reacting to customer needs and taking advantage of the opportunities those needs create. It’s not unique to Flytxt but it’s also not standard among customer interaction systems. Many systems could probably achieve similar outcomes by applying standard offer arbitration techniques, which generally define the available offers in a particular situation and pick the highest value offer for the current customer. But explicitly relating the choice to context strikes me as an improvement because it clarifies what marketers should consider in setting up their rules.
On the other hand, Flytxt doesn’t place its programs or offers into the larger context of the customer lifecycle. This means its up to marketers to manually ensure that messages reflect consistent treatment based on the customer’s lifecycle stage. Then again, few other products do this either…although I believe that will change fairly soon as the need for the lifecycle framework becomes more apparent.
Flytxt currently has more than 100 enterprise clients. Pricing is based on number of customers, revenue-gain sharing, or both. Starting price is around $300,000 per year and can reach several million dollars.