Vizury Combines Web Page Personalization with a Customer Data Platform

One of the fascinating things about tracking Customer Data Platforms is the great variety among the vendors.

It’s true that variety causes confusion for buyers. The CDP Institute is working to ease that pain, most recently with a blog discussion you’re welcome to join here.  But for me personally, it’s been endlessly intriguing to trace the paths that vendors have followed to become CDPs and learn where they plan to go next.

Take Vizury, a Bangalore-based company that started eight years ago as an retargeting ad bidding platform. That grew into a successful business with more than 200 employees, 400 clients in 40 countries, and $30 million in funding. As it developed, the company expanded its product and, in 2015, released its current flagship, Vizury Engage, an omnichannel personalization system sold primarily to banks and insurance companies. Engage now has more than a dozen enterprise clients in Asia, expects to double that roster in the next six months, and is testing the waters in the U.S.

As often happens, Vizury’s configuration reflects its origins. In their case, the most obvious impact is on the scope of the system, which includes sophisticated Web page personalization – something very rare in the CDP world at large. In a typical implementation, Vizury builds the client’s Web site home page.  That gives it complete control of how each visitor is handled. The system doesn't take over the rest of the client's Web site, although it can inject personalized messages on those pages through embedded tags.

In both situations, Vizury is identifying known visitors by reading a hashed (i.e., disguised) customer ID it has placed on the visitor’s browser cookie. When a visitor enters the site, a Vizury tag sends the hased ID to the Vizury server, which looks up the customer, retrieves a personalized message, and sends it back to the browser.  The messages are built by templates which can include variables such as first name and calculated values such as a credit limit.  Customer-specific versions may be pregenerated to speed response; these are updated as new data is received about each customer. It takes ten to fifteen seconds for new information to make its way through the system and be reflected in output seen by the visitor.

Message templates are embedded in what Vizury calls an engagement, which is associated with a segment definition and can include versions of the same message for different channels. One intriguing strength of Vizury is machine-learning-based propensity models that determine each customer’s preferred channel. This lets Vizury send outbound messages through the customer’s preferred channel when there’s a choice. Outbound options include email, SMS, Facebook ads, and programmatic display ads. These can be sent on a fixed schedule or be triggered when the customer enters or leaves a segment. Bids for Facebook and display ads can be managed by Vizury’s own bidding engine, another vestige of its origins. Inbound options include on-site and browser push messages.

If a Web visitor is eligible for multiple messages, Vizury currently just picks one at random. The vendor is working an automated optimization system that will pick the best message for each customer instead. There’s no way to embed a sequence of different messages within a given engagement, although segment definitions could push customers from one engagement to the next. Users do have the ability to specify how often a customer will be sent the same message, block messages the customer has already responded to, and limit how many total messages a customer receives during a time period.

What makes Vizury a CDP is that it builds and exposes a unified, persistent customer database. This collects data through Vizury's own page tags, API, and mobile SDK; external tag managers; and batch file loads.  Data is unified with deterministic methods including stitching of multiple identifiers provided by customers and of multiple applications on the same device. The system can do probabilistic cross-device matching but that's not reliable enough for most financial service applications.  Vizury doesn’t do fuzzy matching based on customer names and addresses, which is not a common technique in Asia.

The system includes standard machine learning algorithms that predict product purchase, app uninstalls, and message fatigue in addition to channel preference and ad bidding. Results can be applied to tasks other than personalization, such as lead scoring.  Algorithms are adapted for each industry and trained on the client’s own data. Users can't currently apply machine learning to other tasks.

Vizury uses a typical big data stack including Hadoop, Hive, Pig, Hbase, Flume, and Kafka. Clients can access the data directly through Hadoop or Hbase.  Standard reports show results by experience, segment, and channel, and users can create custom reports as well.


Pricing for Vizury is based on the number of impressions served, another echo of its original business. Enterprise clients pay upwards of $20,000 per month, although U.S. pricing could be different.