AI-Driven Personalization: Your Customer Engagement BFF
- AI-driven personalization. Essential in today’s engagement strategies.
- Consumer trust. Key to successful AI personalization.
- Organizational change. Needed for effective AI usage.
The public release in November 2022 of ChatGPT has resulted in enormous interest in generative artificial intelligence (AI). But AI is an umbrella term for several technologies, and AI has already been in wide-scale use in many industries and for many tasks. That includes AI-driven personalization efforts for customer and employee engagement.
In fact, the adoption of AI as a significant part of a customer and employee engagement strategies is now pretty much a given, at least at a basic level. The goal now for many organizations is to increase customer comfort with AI as it evolves and takes on new capabilities.
“While it is possible to provide personalization without AI or machine learning for the ‘Your Stuff’ category, most popular apps and products heavily rely on AI and ML for effective personalization,” explained Hans Sayyadi, head of engineering at Uber Eats, part of the national transportation company. Sayyadi is responsible for AI, machine learning (ML), search and personalization efforts at the firm.
“For the ‘Personalized Browsing/Discovery’ category, it’s challenging to imagine achieving truly unique and personalized results for each individual user without AI and ML-based solutions,” Sayyadi said.
Customers & AI-Driven Personalization: You Scratch My Back, I’ll Scratch Yours
In general, customers are receptive and delighted when engaging with properly personalized experiences. When organizations get AI-driven personalization right, the benefits can be significant.
But brands will suffer consequences for poor personalization efforts, cautions Bern Elliot, vice president and distinguished analyst at Gartner. Due to the weariness that some consumers have about artificial intelligence in general, organizations are often therefore faced with challenges in encouraging some consumers to enthusiastically embrace AI-driven personalization.
The key is to reward consumers for their willingness to engage with AI-driven features, by providing faster resolution to problems, quick answers to questions, and useful redirection to resources and human counterparts that can be of value. Monetary rewards or savings obviously help as well.
Basically, convincing customers to fully embrace AI-driven features is a quid-pro-quo relationship. Without those benefits, customers may be turned off to the experience, Elliot suggested.
“Consumers are open to sharing information with organizations and receiving personalized communications, but they have their limits,” Elliot stressed.
Related Article: How AI Can Help You Connect With Customers and Increase Engagement
Commitments to AI-Driven Personalization in Theory, but Not Always in Practice
Confirming these challenges is a recent report from Gartner Inc., The “Magic Quadrant for Personalization Engines,” published in July 2022. The report found, “Marketers, while committed to their investments in personalization tools, lack commitment to building the capabilities needed to use personalization tools to their full effect.”
As evidence, Elliot said 40% of customers responding to the 2021 Gartner Personalization Survey said they would stop doing business with a company if they found messages irrelevant and annoying. An even greater number — 55% — said they would stop doing business if they found the personal messages creepy or invasive.
Further, many products face challenges in delivering a holistic personalized experience, explained Sayyadi. “For instance, while algorithms might deliver highly personalized results, the lack of freshness over time can diminish their value,” Sayyadi said.
Diversity is another crucial factor often overlooked by AI tools, stated Sayyadi. He explained that algorithms have a tendency to overemphasize a user’s primary preference, which ultimately leads to a limited variety of output. Moreover, delivering AI-driven personalized presentations and explanations is equally important for a positive experience, which is often overlooked.
“The skilled resources in organizations vary and the ability of marketing to leverage these enterprise skills will vary,” explained Elliot. “In general, this is decided on a use case basis. In some cases, marketing planners use applications that have the AI functionality built in, and that offer the marketing person the integration tools they need to accomplish the objective.”
Helping Consumers Warm up to AI-Driven Features
There are some practices that organizations can use to bolster customer enthusiasm for AI-based personalization. Starting with simplicity is key.
“Organizations should focus on optimizing for engagement and conversion, rather than directly targeting retention or churn,” Sayyadi explained. “It’s important not to overload a machine learning model with all available data and expect it to learn everything on its own. Factors like diversity and novelty often require manual intervention in the post-processing step of ranking. Striking a balance between ‘Your Stuff’ and ‘Browsing/Discovery’ is crucial to avoid bias towards one category. Guardrails and manual intervention can ensure a more balanced and effective personalized experience.”
Despite all the public and corporate interest in AI presently, it’s not all good news. There are some tasks that AI struggles with when it comes to customer personalization.
“While AI can excel at almost any tasks given sufficient and meaningful data, it’s right now very challenging to capture long-term objectives or trade-offs between diversity, novelty, fairness, and short-term conversions,” Sayyadi explained. “It’s not impossible, but the investment required for achieving these aspects might not be justifiable for most companies and products.”
“There are several capabilities that remain challenging for AI-based personalization due to the need for more investment and data,” Sayyadi said. “Additionally, there are a few areas where AI has the potential to improve but currently falls short due to a lack of adequate data. These areas include emotional intelligence, contextual understanding, and ethical decision-making.”
Achieving these capabilities would require advancements in collecting and leveraging relevant data, as well as new techniques to enhance personalization algorithms and systems, Sayyadi said.
Finally, many emerging AI technologies require organizational changes be made in order to properly leverage the tools, Elliot said.
“These AI solutions also benefit from extensive access to marketing and sales data. These are major challenges that are not actually technical, but rather need to be addressed organizationally,” Elliot concluded.