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Future-Proofing Your Customer Analytics & Personalization Strategy

Future-Proofing Your Customer Analytics & Personalization Strategy

While most companies understand the importance of customer analytics, some still struggle with effectively gathering customer data or harnessing it to drive personalization. Add in new trends and technologies like generative AI (GenAI) and the landscape becomes even more challenging to navigate. We sat down with Rafique Daruwalla, Director of Data Analytics Consulting, to discuss how organizations can keep up with these changes and build a future-proof customer analytics and personalization strategy. Here’s what he had to say…

How important is personalization to driving success and retaining customers?

Personalization can positively or negatively impact KPIs, and having a mature customer analytics program and platform can be a major factor in powering personalization use cases. According to Adobe research, “76% of consumers change their tastes every few months, and 40% see themselves as very different to how they were just six months ago. Most importantly, over three quarters say they are unimpressed with brands that can’t keep up.” It’s clear from these statistics that being able to effectively personalize at scale across all channels is key to success.

What emerging trends and technologies will significantly affect analytics and personalization strategies?

Organizations need to be prepared for the wave of customer privacy regulations, the imminent launch of Google’s Privacy Sandbox and the rise of GenAI. This impacts every company — whether they are have a leading customer analytics and personalization program or are just at the start of their personalization journey.

On the one hand, GenAI and large language models (LLMs) present great opportunity for enhanced personalization. With AI, customer service teams can more easily send personalized content to customers based on previous interactions and marketers can leverage data to tailor marketing campaigns, promotions and messages. Additionally, GenAI has the potential to simplify and scale existing personalization use cases. For example, it can replace the need for separate models for classification (e.g., classifying customers into groups) and content generation (e.g., responding to customer queries) through a simplified integration with LLMs that can handle multiple scenarios.

On the other hand, Privacy Sandbox and other privacy-respecting initiatives can limit the data needed to hyper-personalize the customer experience if organizations cannot integrate with them. Having a customer analytics and personalization strategy, as well as a fully integrated analytics platform that combines customer data (including behaviors across channels, customer analytics-based segments and insights) with a personalization engine, can leapfrog an organization’s ability to attract and retain customers by enabling trust and consent. This combination also enables companies to take advantage of the GenAI use cases we described above to drive superior experiences. With an end-to-end platform that integrates data across the enterprise, you can effectively tune GenAI models and ensure an uptick in KPIs, such as conversion rate, NPS and engagement.

How can brands build a powerful, effective, future-proof analytics platform and strategy?

For most organizations, the customer analytics and personalization stack is a combination of in-house solutions and products that support marketing and data. That’s why it’s important to design and ensure a consistent, standardized architecture blueprint that can be implemented and customized across brands and regions.
Brands should also perform a thorough build-versus-buy analysis of the components that power the customer analytics and personalization platform to ensure the entire stack is future-proof, extensible and flexible enough to integrate with innovative technologies like GenAI. Performing this analysis ensures you can cater to ever-increasing segmentation and activation requirements across campaigns, channels, regions and brands.

How does Adobe Analytics empower an organization’s analytics platform?

Even after organizations surmount the hurdle of having a clean web and app analytics implementation with standardized tagging and reporting, they still need to integrate web and app analytics with the wider enterprise or customer data platforms. Web and app analytics are the ultimate source of real-time digital interactions, so they must be integrated effectively.

To solve this challenge, brands should consider implementing a versatile solution, such as Adobe Analytics, which can expose and integrate data in numerous ways (through the data warehouse, data feeds, APIs or other areas). From there, you can easily integrate this critical data set, not only within a marketing technology stack, like Adobe Marketing Cloud, but also with custom data platforms, such as those built on Databricks and Snowflake solutions.
In addition to providing multiple ways to pull and push data, Adobe Analytics is one of the few web and app analytics products with a built-in connector that supports a wide range of customer data platforms, be it Adobe’s very own Adobe Experience Platform or any non-Adobe product, such as Tealium.


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