Data is the most valuable asset that any organization owns and plays a central role in shaping the most important part of your job: how you interact with your customers. Data helps you understand your customers’ motivations, behaviors, and needs so that you can shape a more granular, personalized, and relevant brand experience.
The challenge is that this data tends to exist in silos throughout the organization. Customer success teams are collecting data on product usage and customer sentiment. Accounting is accruing data that could be used to inform upsell campaigns. Sales is pouring valuable information about customers into their CRM.
People can be siloed, but data cannot
Data silos aren’t just frustrating, they also come at a cost. Tracking down data takes time. When you’re seeking market insights, how do you know whether you’re working with an entire body of data or just the tip of the iceberg? Incomplete data can lead to incorrect decisions based on flawed or limited insights.
To create a complete picture of your customer you need to find ways to break down silos across platforms, departments, and systems. Combining data from different sources–marketing, customer success, sales, and others–leads to what we’re all seeking to achieve: a unified, cohesive, and effective experience that resonates with each individual customer.
AI lets you combine data sets to learn what is relevant to customers. These insights can help you improve brand experience.
Unlock data insights with AI
This is where companies should consider the place of artificial intelligence (AI) and machine learning-driven data analysis in their marketing technology (martech) stack. AI allows you to combine multiple data sets, so they can learn what is relevant to their customers. You can then use that insight to inform the delivery of a better brand experience.
AI does this so much better than humans. Let’s face it; most of us aren’t data analysts. Even with all the time in the world, you can only draw a finite number of insights and connections across your data. AI, however, can process and identify thousands of data points related to trends, behavior, and intent, in just minutes.
Using these insights, you can then deliver a brand experience inline with your new understanding of your customers. For instance, you can craft and test content (AI can even automatically generate content). This process can help ensure that your targeting is timelier and more relevant and hitting the right channels. All of this allows you minimize risk in your marketing programs.
But even AI can’t work across silos
Just as humans struggle with data silos, siloed data can be a barrier to deep learning. Data is, after all, the foundation of machine learning. The more data you put in, the deeper and more accurate the insights you’ll discover. Departments who sit on their data are blocking the benefits of machine learning to the entire organization.
So, even as AI is becoming a necessary part of the martech stack, don’t grab your corporate credit card and download the latest shiny AI martech tool just yet. Before you make any investment in AI, you need a way to effectively incorporate any solution into your existing stack and manage the entire stack effectively, so that walls start coming down and data can flow from one department and system to another.
For example, you may be able to realize synergies between different platforms owned by sales, marketing, and customer success by uncovering ways to integrate them via APIs for seamless data flow between teams. This requires a tech stack that is optimized and gives visibility into your entire software ecosystem and connects teams to the tools and data they need.
The more you can maximize integrations to reach across departments, divisions, business units and teams to unite your employees, systems, and data, the better positioned you’ll be to maximize the full potential of your AI investment. You can learn more about how to align your tech stack in our whitepaper: The 2019 Guide to Tech Stack Management.