Guest Post by Chirag Deshpande, Head of Industry for High-Tech, Telco, and Media at Further
Leveraging AI to improve the speed and scale of content creation and customer interactions is a smart move, but innovation alone won’t produce the effect users are seeking. The advantages of developing custom AI models are plentiful, and companies in customer-facing industries are particularly well-positioned to benefit from the technology’s capabilities. Creative software companies and digital solution providers are now launching services that create proprietary, brand-specific generative AI — such as Adobe’s recent introduction of its AI Foundry — and interest in the options is surging.
While creative capabilities and digital advancements are essential, the true power of custom AI models stems from their connection to rich customer journey data. Without this link, AI operates in a bubble, deprived of the context and broader visibility that could enhance the technology’s capacity for personalization. Custom models that are hindered by these limitations fail to deliver outcomes that drive business growth. Without exception, connecting AI with analytics is crucial to customizing models that directly improve customer experience and boost ROI.
Incomplete Custom AI Models Will Struggle to Personalize Experiences
Developing a deep understanding of individual customers requires information on buyer behavior, preferences and historical interactions. An AI model alone cannot generate these insights. And without them, the content produced won’t be the right content for the right person at the right moment. Absent data-powered personalization, brands face a higher risk of customer disappointment, lower engagement and lost revenue. Over 75% of consumers get frustrated when personalization doesn’t happen, and 72% only engage with messages tailored to their interests.
To maximize their technology’s AI personalization potential, companies have to break down the silos that often separate creative and AI teams from data analytics teams. The key is to establish a continuous feedback loop where customer data informs the AI, and the model’s output generates new data to refine future interactions. The feedback loop typically works by:
- Informing the model: Use rich customer journey data — such as website traffic, purchase history, support tickets and engagement with past campaigns to inform the AI model’s creative output.
- Generating personalized experiences: Deploy the AI to create and deliver personalized content, offers or entire digital experiences tailored to individual customer profiles.
- Measuring responses: Analyze how customers respond to the personalized experiences, taking note of things like increased engagement or rising conversion rates.
- Refining continuously: Feed the new performance data back into the AI model, allowing it to learn, adapt and become progressively smarter and more efficient.
Converting AI-driven creative intent into measurable business outcomes is becoming a key differentiator for leading brands. It moves personalization from a theoretical goal to a practical strategy supported by metrics and tracked performance.
Connect High-Quality Customer Journey Data with AI for Maximum Impact
When AI models are fueled by comprehensive, customer-specific intelligence, the subsequent customized experiences are much more effective. Data-powered personalization can boost ROI by as much as eight times, and with 71% of consumers expecting tailored interactions, it’s becoming a standard requirement for meaningful engagement. Delivering relevant and timely content enhances the customer journey and fosters deeper engagement and loyalty, leading to higher conversion rates, increased retention and a stronger bottom line. This can’t be accomplished with just any data, though.
The caliber of the insights used to teach AI is just as important as the training itself. Collecting data at every touchpoint is the best way to understand the omnichannel journey and gauge which engagements resonate with various audiences. Responsible data collection and storage also improves AI model accuracy, enhances decision-making and optimizes business operations. Ensure data quality in AI by following best practices, such as:
- Follow data governance policies: Governance that outlines data quality standards, processes and roles creates a culture of data prioritization, which better aligns data management practices with organizational goals.
- Invest in data quality tools: Data quality tools handle cleansing, validation and monitoring automatically, giving AI models uninterrupted access to-quality data.
- Develop a data quality team: A team dedicated to maintaining data quality can help verify continuous monitoring and improvement of data-related processes. Teams can also train others and promote data quality awareness throughout an organization.
- Collaborate with data providers: Strong relationships with data providers reinforces their commitment to data quality and decreases the risk of receiving poor-quality data.
- Continuously monitor data quality metrics: Monitoring and measuring data quality metrics routinely helps companies find and resolve potential issues before they affect AI performance.
Custom AI models hold significant promise for enterprises looking to innovate and scale. However, realizing their full potential is nearly impossible without a constant stream of high-quality customer journey data. The pairing produces the synergy needed to ensure intelligent and effective personalization efforts.
Companies building or implementing custom AI models have to look beyond the creative outputs and take stock of their data strategy, then craft a plan to connect AI to the insights customers provide every day. Careful consideration of each component before moving to the next development phase is the key to turning AI investments into a tangible competitive advantage.
About the Author
Chirag Deshpande, Head of Industry for High-Tech, Telco, and Media at Further, is an accomplished leader with extensive experience in shaping business strategy, driving business development, fostering account growth, nurturing talent, and consistently delivering impactful outcomes. His expertise spans diverse industries, managing global portfolios and multi-million dollar initiatives in data and digital Strategy/transformation, consumer-centric marketing, behavioral and personalization programs, experimentation, cloud and AI programs, digital marketing, and partner strategies. Chirag specializes in crafting digital and data solutions strategies for Fortune 500 organizations and leading cross-functional teams focusing on growth and strategic navigation of the dynamic landscape of digital, data, and marketing technology.
