Tuesday, June 02, 2015
It’s just over two years since I started writing about Customer Data Platforms. One thing that’s become clear since then is that only big companies will purchase a marketing database by itself. Everyone else wants to combine the database with a practical application. B2B CDPs have favored analytical applications like lead scores and churn predictions. B2C CDPs have often included campaign engines that manage triggers, query-based segmentations, and multi-step program flows in addition to predictive models. But even the B2C CDPs rely on external systems such as email agents and Web content managers to deliver the campaign messages.
Blueshift fits nicely into the B2C CDP mold: it builds a multisource database, incorporates machine learning-based predictive models, uses filters to create segments, and runs multi-step campaigns that are executed by external systems in email, SMS, mobile apps, and display and Facebook retargeting. What sets Blueshift apart – and this is typical of later entrants to a new market – are a lower price point and simpler operation than early B2C CDPs like RedPoint and AgilOne.
How low? Pricing for the most basic version of Blueshift starts at $999 per month. The most advanced version starts at $3,999 per month for all features and up to 1 million “active users” across all channels. (The company says that most clients are in fact larger than one million users, with the largest at 100 million.) The fact that prices are published is itself a mark of a later entrant.
How simple? Well, one measure is implementation time. Blueshift says can be operational in as one day (if data is loaded through an existing Web page tag or push-button integration with Segment) or under two weeks if some work is required. Technically, this is plausible: the system has JSON API that can accept pretty much anything and will put it into MongoDB and/or Postgres with minimal data modeling.
Another measure of simplicity is the campaign building interface. Blueshift lets users specify a sequence of steps by filling out forms to define the segment, channel, and content template for each step and time between steps. This is nowhere near as pretty or flexible as graphical flow charts, but does qualify as simple.
Segments are also built using forms to define one or more filters. Again, nothing fancy but it gets the job done. What’s more important is that the segments can use a wide range of data including online behaviors, attributes from CRM and other systems, predictive model scores, and product information from catalogs. This is what gives the system its power. Content templates do incorporate some visualization, as well as tokens for personalization and machine learning-based product recommendations. Split testing, ecommerce integration, and predictive models for activation, churn and repeat purchase are available in advanced versions of the system. Reports show model performance and attributes, segment counts, and campaign results using several basic attribution methods.
So, apart from some missing bells and whistles, what doesn’t Blueshift do? The main limit is that it works only with known individuals (i.e., those reachable through an email or SMS address, app registration, or similar identifier) and primarily in outbound channels. This means that Web display ads, site personalization, and anonymous visitor targeting aren’t part of the mix, aside from retargeting. And, while data and models are updated continuously, the system isn’t designed to manage real-time interactions.
Blueshift was launched earlier this year. It has more than ten clients, who are mostly multi-channel marketers with a majority of revenue from mobile payments.
In sum, Blueshift isn’t the fanciest marketing system available but it provides a solid mix of highly usable features at a reasonable price. B2C marketers will find it worth a look.