idio Recommends Content Based on Customer Interests


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Systems in our new Guide to Customer Data Platforms range from B2B data enhancement to campaign managers to audience platforms. This may lead you to wonder whether there’s anything we actually left out. In fact, there was: although the final choices were admittedly a bit subjective, I tried to ensure the report only included systems that met specific critieria including a persistent database, customer-level data, marketer control, and marketing-related outputs to external systems. In most cases, I could judge whether a system fit before doing a lot of detailed research. But a few systems were so close to the border that I only made the final call after I had evaluated them in depth.

idio was one of those. The company positions itself as a tool to deliver “personalized and relevant multi-channel communications”, which sure sounds like a CDP. Indeed, it meets almost all the critieria listed above, including the most important one of building and maintaining a persistent customer database. But I ultimately excluded idio because it is tightly focused on identifying the content that customers are most likely to select, a function I felt was too narrow for a proper CDP. The folks at idio didn’t necessarily agree with this judgment, and pointed to planned developments that could indeed change the verdict (more about that later). But, for now, let’s not worry about CDPs and take idio on its own terms.

The full description on idio’s home page reads “idio understands your customer’s interests and intent through the content they consume and uses this to deliver personalized and relevant multi-channel communications” and that pretty much says it all. What idio does is ingest content – typically from a publisher such as ESPN, Virgin Media, Guardian Media, or eConsultancy (all clients) – but also from brands with large content stores such as Diageo, Unilever, and C Spire (also all clients). It uses advanced natural language processing to extract entities and concepts from this content, classifying it with the vendor’s own 23 million item taxonomy.

The system then monitors the content selected by its clients’ customers in emails, Web pages, mobile platforms, and some social platforms and builds an interest profile for each customer. This in turn lets the system recommend which existing content the customer is most likely to select next. The recommendations are typically fed back to execution systems, such as email generators or Web content managers, which insert links to the recommended content into Web pages, emails, or newsletters. Reports show selection rates by content, segment, or campaign, and can also show the most common topics published and the most commonly selected. Pricing is based on recommendation volume and starts around $60,000 per year for ten million recommendations.

Describing idio’s basic functions makes it sound similar to other recommendation systems, which doesn’t really do it justice. What sets idio apart are the details and technology.

• Content can include ads, offers and products as well as conventional articles.

• The natural language system classifies content without users tagging each item, a huge labor savings where massive volumes are involved, and can handle most European languages.

• idio’s largest client ingests more than 1,000 items per day and stores more than one million items, a scale far beyond the reach of systems designed to choose among a couple hundred offers or products.

• Interest profiles take into account the recency of each selection and give different weights to different types of selections – e.g., more weight to sharing something than just reading it.

• Users can apply rules that limit the set of contents available in a particular situation.

• The system returns recommendations in under 50 milliseconds, which is fast enough to support online advertising selection.

• It stores customer data in a schema-less system that can make any type of input available for segmentation and reporting, although not to help with recommendations.

• It can build a master list of identifiers for each individual, allowing systems to submit any identifier and access a unified customer profile.

• It can return a content abstract, full text, images, or HTML, or simply a pointer to content stored elsewhere.

• It captures responses directly as the content is presented.

Most of these capabilities are exceptional and the combination is almost surely unique. The ultimate goal is to increase engagement by offering content people want, and idio reports it has doubled or even quadrupled selection rates vs. previous choices. All this explains why a small company whose product launched in 2011 has already landed so many large enterprises among its dozen or so clients.

Impressive as it is, I don’t see idio as a CDP because it is primarily limited to interest profiles and content recommendations. What might yet change my mind is idio’s plan to go beyond recommending content based on likelihood of response, to recommending content based on its impact on reaching future goals such as making a purchase. The vendor promises such goal-driven recommendations in about six months.

Idio is also working on predicting future interests, based on behavior patterns of previous customers. For example, someone buying a home might start by researching schools, then switch to real estate listings, then to mortgages, then moving companies, and so on. Those predictions could be useful in their own right and also feed predictions of future value, which could support conventional lead scoring applications. Once those features become available, idio may well be of interest to buyers well beyond its current customer base and would probably be flexible enough to serve as as Customer Data Platform.

Republished with author's permission from original post.


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