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Today’s consumers have relentless expectations. They expect a personalized one-to-one experience, but many brands struggle with how to bring all their disparate data sources together to engage their customers.
Artificial intelligence (AI) can bring companies closer to truly understanding their customers so they can deliver personalized, contextual, and timely experiences. Data is the foundation of AI. And with AI, how good your data is determines how good your intelligence is.
AI can fine-tune marketing campaigns, serve up better recommendations, and perfect the customer experience. For an AI initiative to succeed, it’s critical to look beyond the insights that AI will provide and focus on what will feed the AI system — data. The speed of getting the insights you need depends on the completeness of your data set.
Consider, for a moment, all the different channels a company might use to interact with a customer — websites, emails, third-party vendors, call centers, retail locations, mobile devices, app and more. Each of these channels produces a wealth of data that can tell organizations about who their customers are and want they want. In the current IT world, however, the data produced by those channels is often siloed in channel-specific marketing teams, controlled by different departments, and in technology systems that can’t talk to each other.
As a result, a company can have a lot of data on customer behavior, but still have an incomplete picture of the preferences and actions of specific customers. It’s like the old story about five blind people touching an elephant. The person who touches the ears think it’s a fan. The person touching the leg thinks it’s a tree. The person touching the tail thinks it’s a rope. Because no one has a complete picture, each person has his or her own perspective.
When it comes to customer data, the problem of an incomplete picture is the elephant in your room. Companies need to focus on three key things to build the right foundation: data inventory, data integration and the infrastructure and technology to manage and process their data. Focusing on these pillars can help you build an effective AI program and enhance your data-driven marketing.
Understanding where all your data lives
A successful AI program depends on acknowledging and understanding the whole picture.
While data is crucial to using AI effectively, the maturity of the data — or the value that you can get from a data set — varies greatly from organization to organization. To advance data maturity, companies need to first take an inventory of their data.
Businesses are really evolving to become data-centric enterprises, which means they need to know where their data lives, and know the data that is necessary to train on it and to learn from it.
Part of the challenge is that the data is siloed across different lines of business — and even within a line of business it might be siloed across CRM systems and data warehouses — so doing an inventory of all your data is the first and most important step toward building your AI program.
Integrate your data on a single AI platform
Even after you’ve taken stock of all your data, integrating and unifying it is another challenge.
Many companies make the mistake of unifying their data before considering what data is available, and what process they should develop to add, remove, and change data. But unifying incomplete data sets gives you the same misleading and uneven view of your customers you’ve always had.
Bridging the gap between first and third-party data and managing this data velocity, whether it’s inside or outside your enterprise, requires a well-thought out data integration strategy and the right technology. We address these issues with the Adobe Cloud Platform (ACP), which unifies our various digital clouds into a single enterprise view. ACP handles a diverse and growing set of data — 186 trillion customer transactions and 41 trillion rich media requests annually. Well over half of the analytics transactions come from mobile devices. Over 150 billion emails were sent from the ACP in 2017. And it manages more than 2.5 billion optimization requests each day.
ACP includes an intelligence layer, which is Adobe Sensei. Adobe Sensei is Adobe’s AI and machine learning technology, which enables you to find insights and patterns in your data. The technology also allows you to work smarter and more efficiently, so that you can give customers a consistent experience between online and offline channels, deliver more personalization and automatically find anomalies in your data that previously would’ve taken hours and hours of manual work.
Many companies previously built their own data lakes to manage the deluge of information they received from various customer touch points. But what ACP does is take away the heavy lifting that’s required to build your own data lake. We cleanse, prepare and curate the data and making it easily accessible so that companies can apply their domain expertise and really focus on getting value out of that data.
ACP addresses the data integration issue with Experience Data Model (XDM), an open standard that describes the language of digital experiences for assets, content, context, audiences, channels, and metrics. XDM allows a company’s data platform to ingest data from various external systems, and then merge that data. As a result, XDM quickly powers the machine learning framework and intelligent services without continuous data transformations or mappings, allowing data scientists to spend less time preparing data and more time finding innovative ways to use that data to improve the customer experience.
The importance of the right infrastructure and technology
An AI program also must have a strong foundation of security and privacy compliance. This is especially critical for General Data Protection Regulation (GDPR), the EU’s new privacy law designed to harmonize and modernize data protection requirements, which goes into effect May 25, 2018. ACP enables data governance across everything that is within the platform, and provides features to ensure encryption and rules-based access of data in compliance with these regulations.
We’re committed to help companies responsibly unlock the power of their data. We recognize our customers have sensitive data. We have a long-standing practice of incorporating a proactive product development effort, also known as “privacy by design.” What we mean by this is our technology has the ability to obscure IP addresses and allow individual-level opt-outs.
Once an AI system is functioning correctly, sales and marketing teams will be sharing information and communicating more effectively, capitalizing on each other’s insights to ensure customers are receiving only the most relevant offers and advertisements. Information from other sources that may be isolated today, like a company’s loyalty program, also can enter the mix, providing even greater understanding of each customer and more precise targeting for a better experience.
Because data is so intricately tied to how companies function and how people do their jobs, an AI program can require long-term organizational and cultural shifts. Ultimately, this will result in a more highly functioning organization. A comprehensive data set can provide the informational Holy Grail that companies want in an era of heightened personalization — a 360-degree view of their customers. And that’s much better than having isolated teams that can’t tell an elephant’s trunk from its ear.
The original post appeared on Adobe Blogs.