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Data Privacy and the New Secrets of Success | Part 2

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In Part 1 of Data Privacy and the New Secrets of Success, we discussed the critical importance of organizing your brand's digitalassets as they relate to data privacy and cyber security — but a secure dataset isn't just about compliance and liability. An organizeddataset is a treasure trove of insights waiting to be discovered, and presents a unique opportunity for businesses to optimize theirmarketing efforts and streamline spending by capitalizing on organized and secure datasets within a Customer Data Platform (CDP).
Data Organization: the Cornerstone of Actionable Marketing
A Customer Data Platform (CDP) is a unified customer database that builds rich customer profiles using data collected by multipleexternal and internal sources. It's not just first-party or third-party data, but an integration of everything, including the data entered intoyour CRM by your staff, the social media interactions
customers have with your brand online, even the data they're sharing from theirmobile devices. CDPs centralize this information, and enable businesses to derive actionable insights, intelligent audience segments,and personalized marketing messages and workflows. Once stored, a CDP cleans and combines the data to make individualizedcustomer profiles accessible to other company systems to drive efficiencies, improve customer experiences and support, and identifyadditional growth opportunities.
To make the most of a CDP, companies should adopt a systematic approach to data organization:
Unified Data Collection: Gather data from multiple touchpoints, including websites, mobile apps, social media, and offline interactions.This holistic approach ensures a comprehensive view of each customer.
Data Enrichment: Augment raw data with additional information to enhance customer profiles. This could include demographic details,purchase history, and behavioral patterns.

Segmentation: Divide the dataset into meaningful segments based on various attributes. Effective segmentation allows for targetedmarketing campaigns that resonate with specific customer groups.

Accessibility and Usability: Ensure that the organized dataset is easily accessible to marketing teams. User-friendly interfaces andintuitive dashboards empower marketers to extract insights and make informed decisions.

As important as all that is, it will become even more important in 2024, when Google is planning to "kill off" the third-party cookies thatare the foundation of so many of today's digital marketing strategies. Crucially, the right CDP can work without the use of third-partycookies. And that future readiness, combined with more meaningful, centralized insights into customer behavior, was exactly whatbeverage giant

AB InBev was after when they chose

Treasure Data as their CDP partner.AB InBev | CDP Case StudyTreasure Data is our center of marketing ... our key to how we’ll live in a cookie-less world, in an even more digital environment, andstill connect with our consumers.

– Luiz Gama | Senior Global Martech Manager, AB InBev"

With the multinational nature of AB InBev’s business — along with strict local regulations on both customer data and alcoholicbeverages — it was clear that managing AB InBev's consumer data worldwide required technology that could scale," reads TreasureData's case study. "It was also time to bust data silos company-wide, while still respecting increasingly stiff regulations on data privacy,security, and data export."

At AB InBev, the Treasure Data CDP integrated data from 70.1 million customers across more than 1,000 different sources andplatforms into a single, golden customer record. Acting as that single source of truth, the CDP eliminated internal silos that hadhampered marketing efforts in the past and enabled marketers to access more accurate and enriched customer data analytics, set upintelligent ad campaigns, and monitor every customer interaction from a single interface. "Smart attributes help with segmentation andpredicting customer behavior," the study continues.

And, while AB InBev presents a large-scale implementation, it's worth noting that smaller organizations like regional banks, hospitals,and auto dealer groups can also benefit from a CDP that's powered by organized and secure datasets. These have the potential totransform their marketing strategies and optimize spending by improving the following:

Targeted Campaigns: Leveraging well-segmented data, businesses can craft highly targeted marketing campaigns. These campaignsconnect with customers on a personal level, resulting in improved engagement and conversion rates.

Eliminating Duplicate Ad Spending: Duplicate ad spend can drain budgets and hinder campaign effectiveness. A well-organizeddataset helps identify instances where the same customer is targeted multiple times, enabling companies to optimize ad spend andachieve higher ROI.

Personalized Communication: With a comprehensive view of customer preferences, brands can tailor their communication toindividual needs. Personalization enhances the customer experience, fosters loyalty, and increases brand affinity.

Strategic Resource Allocation: Insights from a centralized CDP empower businesses to make informed decisions about resourceallocation. By identifying the most effective channels and customer segments, organizations can channel resources where they aremost impactful.

You can learn more about how a CDP can help your brand enhance your customers' data privacy, build greater trust, and find moreactionable insights in your existing customer data cloud in the short video, below, by Treasure Data CEO Kaz Ohta.

https://www.youtube.com/watch?v=0DvwkpCqQ7Q

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Why do most AI proofs of concept fail to scale?

Proof of concept projects are built to answer one question: can this be done? They're not designed to address the harder questions about integration, ownership, governance, and operational fit. When the proof of concept ends, organizations typically lack the infrastructure—unified data, real-time orchestration, clear ownership—needed to move to production. Without this foundation, scaling becomes prohibitively expensive and complex.

Why do most AI proofs of concept fail to scale?

Proof of concept projects are built to answer one question: can this be done? They're not designed to address the harder questions about integration, ownership, governance, and operational fit. When the proof of concept ends, organizations typically lack the infrastructure—unified data, real-time orchestration, clear ownership—needed to move to production. Without this foundation, scaling becomes prohibitively expensive and complex.

Why do most AI proofs of concept fail to scale?

Proof of concept projects are built to answer one question: can this be done? They're not designed to address the harder questions about integration, ownership, governance, and operational fit. When the proof of concept ends, organizations typically lack the infrastructure—unified data, real-time orchestration, clear ownership—needed to move to production. Without this foundation, scaling becomes prohibitively expensive and complex.

Why do most AI proofs of concept fail to scale?

Proof of concept projects are built to answer one question: can this be done? They're not designed to address the harder questions about integration, ownership, governance, and operational fit. When the proof of concept ends, organizations typically lack the infrastructure—unified data, real-time orchestration, clear ownership—needed to move to production. Without this foundation, scaling becomes prohibitively expensive and complex.

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