AgilOne recently released its Predictive Marketing Cloud, AgilOne 5, to deliver relevant omni-channel experiences and boost customer loyalty and revenues. With this blog we want to elaborate why existing marketing cloud solutions from vendors such as Salesforce.com and Adobe aren’t cutting it and why there is a need for a next-generation, predictive marketing cloud.
In a nutshell, we believe that existing marketing cloud solutions still leave marketers facing the challenges of siloed data and batch-and-blast campaigns because most solutions are too difficult to setup, integrate and use. There is a need to make omni-channel marketing and predictive targeting more accessible to all marketers – at companies large and small. This means that marketing clouds have to become more integrated, rather than cobbled together from multiple acquisitions. Existing marketing clouds are more akin to a toolbox for omni-channel marketing and require a lot of professional services to setup and use, whereas a predictive marketing cloud comes with out-of-the-box data quality, predictive models, audiences and campaigns.
Here are three specific ways in which we believe marketing clouds need to improve:
A predictive marketing cloud offers a 360-degree profile of each customer that is validated every day, not just an empty database
Challenge: Siloed data & data quality
Current Solution: While most marketing clouds offer some sort of data management, most solutions are still lacking in this area. For example, Salesforce.com and Oracle only focus on digital information, can only de-dupe based on an exact match of the email addresses, and do not store or cleanse physical address information. Without checking addresses with the National Change Of Address database, and others, these solutions are useless for direct mail campaigns. Other marketing cloud solutions, such as Adobe Campaign (through their acquisition of Neolane) include data cleansing for online and offline campaigns, but configuring cleansing rules is completely manual and often involves professional services. As a result, cleansing may happen only on an ad-hoc, weekly, or monthly basis.
The Evolution: Predictive marketing clouds leverage machine learning and processes originally used by catalog marketers to not just verify information such as postal addresses, but to recognize similarities in customer information, connect the dots, and offer a clean foundational layer of data that captures both the demographics behind individual customers and their personal engagement.
A predictive marketing cloud comes with built-in models and reports that can be instantly accessed by everyday marketers.
Challenge: Finding the signal in all the noise
Current Solution: Whether it’s via APIs, partnership, acquisitions, or manual imports/exports, every marketing cloud is allowing for more and more data to be brought together. But even if the data is clean and in one place (which usually requires waiting on IT), there is so much noise! Marketers need to react in real time and make smarter decisions on how to engage their customers. They can’t wait on data scientists or professional services engagements to build a custom model, such as likelihood to buy, and then come back and configure or run a new report. Information needs to be accessible instantly, and waiting for weeks or months for reports is not acceptable
The Evolution: A predictive marketing cloud leverages machine learning to take an unbiased look at all customer information across all channels, and clearly surface the different personas. A predictive marketing cloud comes with out of the box predictive models and reports that tell you how likely someone is to respond to your email or buy your product with proven algorithms, not arbitrary scoring models. A predictive marketing cloud tells you where your biggest gaps and opportunities are so you can plan strategically.
A predictive marketing cloud offers an all-in-one solution including built-in audience and campaign templates.
Challenge: Complexity in execution
Current Solution: For the most part, existing marketing clouds are a combination of several products, cobbled together from various acquisitions. Many marketing clouds started off as point solutions. For example, Salesforce.com started off as CRM software, Adobe’s marketing offering was built around its Omniture web analytics product and Oracle started as a database company. All these companies have spent billions acquiring other technologies, including leading email service providers. However, this means marketers have to import and export lists, combine data via relational tables, create campaigns in different modules, execute, and then pull multiple reports from different areas into a visualization solution just to combine marketing performance with financial metrics, not to mention the effort and services required to create segments and queries.
The Evolution: A predictive marketing cloud offers native execution across channels, including native email and web personalization, and comes with turnkey campaigns based on industry best practices to hit the ground running. Predictive attributes and dynamic content are available for use in campaigns through a drag-and-drop interface!
Of course, all this should be automated, scalable, and open in the cloud instead of a black box. At all times marketers should be able to access their own data, be able to mix and match technologies and leverage their existing investments. A marketing cloud should be powerful enough to serve as a backbone across an organization, while maintaining ease of use and intuitiveness.
I look forward to seeing the giant marketing clouds evolve to offer this and more. In the meantime, some newcomers like my company, AgilOne, are taking the disruptive approach by evolving rich predictive analytics solutions into cohesive predictive marketing clouds, giving all marketers the advantage of machine learning.
How would you like to see marketing clouds and other marketing solutions evolve?
Share in the comments!
*Disclaimer, I am the founder and CEO of AgilOne.