The customer data platform (CDP) market is confusing — but it doesn’t have to be. “What is the BlueConic CDP?” is part 6 in a series of blog posts exploring different types of CDPs, how they differ from other technologies and what you can do to select the right vendor for your enterprise company. Check out part 1 on the Adobe Marketing Cloud CDP, part 2 on the Salesforce Marketing Cloud CDP, part 3 on the Segment CDP, part 4 on the Amperity CDP and part 5 on the Treasure Data CDP.
The BlueConic CDP — intended to help brands personalize experiences for website visitors — has been repositioned to appeal to the broader teams responsible for managing customer experiences.
Similar to other website personalization CDPs — such as Lytics — BlueConic was designed to help businesses manage tailored content and offers across websites and mobile applications, testing them over time to improve performance.
But because BlueConic’s heritage prioritizes speed, it comes at the cost of advanced segmentation and scalability.
Enterprise brands must ask themselves if it’s the right solution for their long-term business needs. We’re here to help you navigate its pros and cons.
Who is BlueConic for?
BlueConic is geared toward business users —often found on e-commerce teams — who are focused on personalizing offers for site and app visitors.
Because it’s a pre-packaged application — as opposed to a highly configurable platform — it lacks the enterprise-grade customization necessary to manage complex omnichannel use cases. This is why it tends to appeal most to mid-size businesses that are looking to kick start their personalization efforts.
Its straightforward, visually appealing user interface makes it attractive to business users, but its graph database infrastructure lacks the computing power enterprise brands need to deliver truly personalized, cross-channel customer experiences.
Watch our CDP 101 video below for more information on how CDPs can fit into your marketing technology strategy:
How Do You Use the BlueConic CDP?
BlueConic is used by business teams to personalize website content and product recommendations based on visitors’ individual attributes or a combination of these attributes. Built-in A/B testing allows users to test and optimize content and recommendations for optimization.
However, despite its intended use by business teams, BlueConic’s speed-focused data infrastructure means users must depend on technical professionals to transform data to fit its rigid data model, build complex customer attributes and assist with micro-segmentation and nested audiences.
What Are the Pros & Cons of the BlueConic CDP?
BlueConic excels at providing contextually relevant recommendations to site and app visitors in real time, as well as helping organizations determine which variations of content provide the most value.
But, as industry analysts often point out, website personalization CDPs don’t stand up to enterprise organizations’ requirements.
With no access to highly granular and historical customer data, business teams are unable to customize attributes, audiences or customer journeys without assistance from technical teams.
This results in relentless reliance on IT, which must query data in a big data platform and deploy the results into BlueConic for activation. This leads to an increase in IT support tickets and operational bottlenecks that stall speed to market.
And as with data science CDPs, brands using BlueConic are forced to rely on pre-packaged analytic models. These one-size-fits-all models not only suffer from lower predictive power, but they also offer little insight into why a particular recommendation was served.
A lack of feature engineering — combined with restrictive data models that prevent brands from storing every customer interaction that would serve as an input for predictive modeling — results in less accurate content affinities, next-best-action recommendations and churn predictions.
Arguably the biggest obstacle for enterprise brands is related to BlueConic’s computing power. Since it only stores a subset of granular interaction data in order to power site personalization in real time, users must sacrifice the ability to support complex omnichannel use cases.
These and other limitations lead to friction-filled processes, slower time to market, unending technical support costs and, perhaps most damaging, irrelevant customer experiences.
Website personalization tools are important additions to a marketing technology stack, but their narrow focus prevents them from delivering on the full capabilities of an enterprise CDP. Website personalization CDPs may attempt to be good at everything, but their limited capabilities mean they’re not great at anything.
How Does the ActionIQ CDP Compare to the BlueConic CDP?
Like BlueConic, ActionIQ enables brands to personalize customer experiences and optimize performance via testing. But unlike BlueConic, it’s not meant to be used as a website personalization tool. Brands should invest in a dedicated solution for website personalization, similar to the 56% of martech buyers who prefer a best-of-breed approach to building their tech stacks.
As an enterprise CDP, ActionIQ is designed to help brands operationalize both complex customer journeys and real-time customer experiences across online and offline touchpoints using customers’ full profile histories.
With a business-friendly, no-code interface and intuitive drag-and-drop dashboard, marketing, sales and customer service teams are empowered to access, analyze and take action on customer insights without technical assistance when using ActionIQ.
Additionally, our proprietary, InfiniteCompute composable infrastructure gives organizations unlimited computation power. This means brands can use the full breadth of their customer data for audience segmentation, model creation and journey orchestration without having to make tradeoffs or request time-consuming database updates from IT.
Since ActionIQ was designed to help brands personalize customer experiences via orchestrated journeys across all channels without relying on IT or data teams, organizations can expect a 25% increase in marketing efficiency and a 40% decrease in data analytics workload, helping to drive a 522% return on investment.