January 8, 2024

2024 IT Trends: Using AI to Optimize Your First-Party Data Strategy

By Derek Slager

2024 AI Optimization Trends

IT professionals are in for another challenging year thanks to advancements in artificial intelligence, ongoing labor shortages and changes to data privacy.

As we look ahead to 2024, there are a few notable challenges IT leaders need to consider. This includes the deprecation of third-party cookies, the imperative to scale customer data operations and the anticipation of addressing IT teams’ continued resource limitations.

Let’s look closer at a few of the major trends and changes we will likely see in 2024 and how IT teams can ensure success in the new year.

Rethink Your Data Strategy.

Unfortunately, the demise of cookies is here. Next year, Google will disable third-party cookies for 1% of Chrome users in Q1, with complete deprecation planned in the second half of the year.

To compensate for the loss of this data, IT professionals should focus on developing a robust and comprehensive first-party data strategy. Collected directly from interactions with customers, first-party data tends to be more accurate and relevant compared to data obtained from external sources.

However, while first-party data is an important asset for companies, it can be challenging to unify and organize it. To address this problem, many companies use customer data platforms (CDPs) as centralized hubs for collecting, organizing, and cleaning up customer data from disparate sources such as websites, mobile apps, CRM systems, social media accounts, and more.

And let’s face it, tidying up and combining data is no easy feat. It typically involves merging various sets of related but often inconsistent or fragmented data about the same customers, all with the goal of building a complete picture of them. Why? Because that complete picture is what really helps companies with their marketing campaigns.

With a first-party data strategy and a robust CDP, IT teams can gain valuable insights into customer preferences and engagement patterns. This knowledge aids teams in making informed business decisions and optimizing both acquisition and retention.

Make your customer data operations scalable. Here’s how.

Customer data is complex and will be even more difficult to leverage as third-party data phases out and companies shift their approach to rely on first-party insights instead. At the same time, ensuring data quality, accessibility and reliability to support different business functions is essential, especially those related to customer-centric activities.

Customer data operations are hard to scale due to the various data sources with numerous formats, the extreme volume of customer data, and the speed at which the data collected changes. On a practical level, there is a human element that presents headaches with scaling customer data. For example, people changing their names, entering false email addresses, or mistyping information can create dirty data that is hard to resolve.

On top of those issues, customer data needs to conform to stringent government regulations, such as the EU’s General Data Protection Regulation (GDPR), California’s CPRA, Virginia’s VCDPA, and many others.

Without having a proper strategy or the right tools in place, dirty data can lead teams to draw incorrect insights that impact companies’ marketing and business decisions. For instance, marketers may develop campaign strategies based on false information, rendering undesirable results.

IT teams should focus on scaling their customer data operations by investing in a robust, flexible infrastructure. For instance, organizations can benefit from cloud-based solutions that are capable of automatically scaling as data volumes increase and adapting to fluctuating schemas.

Building an adaptable system is critical to businesses’ success in 2024 and beyond, and IT teams must create plans to scale their customer data operations in the coming months.

Stop trying to build everything yourself.

Supporting customer data operations requires a lot of technical resources. However, technical personnel are in short supply, leaving most IT teams scrambling to complete daily tasks. With this shortage predicted to continue into 2024, how can IT teams make up for the skills gap happening in an organization? For certain tasks, AI can help.

AI supports enhanced employee performance and makes workers more effective at specific jobs, such as developing integrations and writing SQL queries. AI can also help collect, manage and analyze customer data.

To be successful at utilizing AI’s capabilities, IT teams will need to master the art and science of prompt engineering. Prompt engineering refers to the deliberate crafting and structuring of prompts or input queries in a way that elicits specific responses or behaviors from AI models.

This skill set involves understanding the capabilities and limitations of the AI tool and tailoring the input prompts to achieve very particular outcomes. It often involves experimenting with different phrasings, formats, or contexts to influence the generated content toward a specific goal or style. Prompt engineering will be an essential skill IT teams should be looking for as they make staffing decisions as AI becomes an integral business tool.

With a strong first-party data strategy and the right tools, IT professionals can mitigate unforeseen setbacks resulting from new initiatives that change the data landscape as well as ongoing staffing challenges. This also ensures companies can best meet their customers’ needs and business goals in the year ahead, paving the way for a successful 2024.

By Derek Slager

Derek Slager

Derek co-founded Amperity to create a tool that would give marketers and analysts access to accurate, consistent and comprehensive customer data. As CTO, he leads the company’s product, engineering, operations and information security teams to deliver on Amperity’s mission of helping people use data to serve customers. Prior to Amperity, Derek was on the founding team at Appature and held engineering leadership positions at various business and consumer-facing startups, focusing on large-scale distributed systems and security.
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