Redefining Personalization For Customers – AI Evolution In Retail (Part 2)

In recent years, the retail landscape has undergone a profound transformation fueled by advancements in artificial intelligence (AI). This technological revolution has not only reshaped how retailers operate but has also fundamentally altered the way consumers shop. In the first part of the series about AI, we introduced the concept and how the ways it impacts the retail industry. This part continues by focusing on personalization. What are the good, bad and ugly components of using the technology to build the best experience possible for consumers.

Personalization – Yesterday, Today and Tomorrow

Personalization has long been a key strategy for retailers looking to engage customers and drive sales. However, traditional approaches to personalization often fell short, delivering generic recommendations based on limited data. AI has changed this paradigm by enabling retailers to harness the power of big data and machine learning to deliver highly personalized experiences tailored to individual preferences and behaviors.

Predictive Analytics

One of the most significant ways AI is revolutionizing personalization in retail is through the use of predictive analytics. By analyzing vast amounts of data, including purchase history, browsing behavior, and demographic information, AI can anticipate customer needs and preferences with remarkable accuracy. This allows retailers to offer personalized product recommendations, promotions, and content that resonate with each individual customer, driving engagement and loyalty.

For example, according to a study by Salesforce, 62% of consumers expect companies to send personalized offers or discounts based on items they’ve already purchased while 57% of consumers say they’re willing to share personal data in exchange for personalized offers or discounts.

Personalization vs Data Sharing – 57% of consumers say they’re willing to share personal data in exchange for personalized offers or discounts. (Source: Saleforce)

AI enables retailers to meet these expectations by analyzing past purchase data to offer personalized discounts and promotions, increasing the likelihood of repeat purchases.

In-store Experiences

Another area where AI is redefining personalization is in-store experiences. Technologies such as facial recognition and computer vision are enabling retailers to deliver personalized experiences in real-time. For example, AI-powered mirrors can recommend clothing items based on a customer’s body type and style preferences, while smart shelves can display personalized offers based on a customer’s past purchases.

A prime example of this is Nike’s in-store experience, where customers can use AI-powered foot scanning technology to receive personalized shoe recommendations based on their unique foot shape and size. This level of personalization not only enhances the customer experience but also increases the likelihood of a purchase.

Nike’s foot scanning technology improve personalization by using AI to help customers determine their correct shoe size. This technology is available in store or at home (Source: YouTube)

AI is also playing a crucial role in improving customer service and support. Chatbots powered by AI can provide instant, personalized assistance to customers, answering questions, resolving issues, and even processing transactions. This not only enhances the overall shopping experience but also allows retailers to provide round-the-clock support without the need for human intervention.

What Is Hyper-Personalization?

Hyper-personalization refers to the practice of tailoring products, services, content, and marketing efforts to individual customers on a highly granular level. It goes beyond traditional personalization by leveraging advanced technologies, such as artificial intelligence and big data analytics, to create highly individualized experiences for each customer.

Hyper-personalization relies on collecting and analyzing vast amounts of data about customer behavior, preferences, and demographics to understand their unique needs and preferences. This data is then used to deliver personalized recommendations, offers, and experiences across multiple touch points, such as websites, mobile applications, emails, and in-store interactions.

Hyper-personalization has its pros and cons. Source: ThisIsEngineering at Pexels

For example, a retailer practicing hyper-personalization might use AI algorithms to analyze a customer’s past purchases, browsing behavior, and interactions with the brand to recommend products that are likely to be of interest to them.

They might also personalize the content and layout of their website based on the customer’s preferences and behavior, or send personalized emails with tailored offers and recommendations.

A big plus is that customers get a digital one-to-one connection with the brand while the big downside is that customers might feel like they are being monitored by big brother.

Personalization – The Bad Sides

Despite the significant advancements AI has brought to the retail industry, challenges remain. One of the biggest challenges is ensuring the ethical use of AI, particularly in areas such as data privacy and algorithmic bias. Retailers must be transparent about how they use customer data and ensure that their AI systems are designed and trained in a way that is fair and unbiased.

Another challenge is the integration of AI into existing retail systems and processes. Many retailers struggle to effectively implement AI due to a lack of expertise, resources, or a clear strategy. Overcoming these challenges will require a concerted effort from retailers to invest in AI talent, infrastructure, and training to fully realize the benefits of this transformative technology.

Measuring Personalization Success

Implementing AI and personalization in retail can be measured using several key metrics to determine success. Here are the top eight most important ones used by the industry today:

Conversion Rate: Measure the percentage of website visitors or app users who make a purchase. AI-driven personalization should ideally lead to an increase in conversion rates as customers receive more relevant recommendations and offers.

Average Order Value (AOV): Track the average value of orders placed by customers. AI-driven personalization can help increase AOV by suggesting complementary products or encouraging upsells and cross-sells.

Customer Lifetime Value (CLV): Measure the total revenue a business can expect from a single customer over their lifetime. Personalization can help increase CLV by improving customer retention and encouraging repeat purchases.

Customer Engagement: Monitor metrics such as time spent on site, number of pages visited, and frequency of visits. AI-driven personalization should lead to higher levels of engagement as customers find the content and products more relevant to their interests.

Customer Satisfaction (CSAT) and Net Promoter Score (NPS): Measure customer satisfaction with the personalized experience and their likelihood to recommend the brand to others. Higher CSAT and NPS scores indicate that AI-driven personalization is resonating with customers.

Return on Investment (ROI): Calculate the ROI of implementing AI-driven personalization by comparing the cost of the technology and implementation with the increase in revenue or cost savings achieved. A positive ROI indicates that the implementation is successful.

Retention Rate: Measure the percentage of customers who continue to purchase from the brand over time. AI-driven personalization should help improve retention rates by creating more personalized and engaging experiences for customers.

Personalization Effectiveness: Track the performance of personalized recommendations and offers, such as click-through rates and conversion rates for personalized content. This can help refine personalization strategies to improve effectiveness over time.

By tracking some or all of these metrics, retailers can assess the impact of AI-driven personalization on their business and make informed decisions to optimize and improve their strategies.

Beyond metrics, retailers should take an opportunity to educate the public on how they use AI to drive personalization. They can then build and gauge comfort and trust level through feedback and provide additional awareness building, as required. The goal is to make people understand how the technology works for them.

Privacy Considerations

When implementing AI and personalization in retail, it is crucial to consider and comply with relevant privacy laws and regulations depending on where the operations are located.

Some of the key global and regional laws and regulations to consider include:

General Data Protection Regulation (GDPR): GDPR is a comprehensive data protection regulation that applies to businesses operating within the European Union (EU) and regulates the processing of personal data of individuals within the EU. GDPR imposes strict requirements on how personal data is collected, processed, stored, and shared, including requirements for obtaining consent, providing transparency, and ensuring data security.

California Consumer Privacy Act (CCPA): CCPA is a privacy law that applies to businesses operating in California and governs the collection, use, and sharing of personal information of California residents. CCPA grants consumers certain rights over their personal information, such as the right to access, delete, and opt-out of the sale of their personal information.

Personal Information Protection and Electronic Documents Act (PIPEDA): PIPEDA is a Canadian privacy law that regulates the collection, use, and disclosure of personal information by private sector organizations. PIPEDA requires organizations to obtain consent for the collection, use, and disclosure of personal information and imposes requirements for data security and breach notification.

Children’s Online Privacy Protection Act (COPPA): COPPA is a U.S. federal law that regulates the online collection of personal information from children under the age of 13. COPPA requires operators of websites and online services directed at children to obtain verifiable parental consent before collecting personal information from children.

California Privacy Rights Act (CPRA): CPRA is a privacy law that builds upon CCPA and further enhances privacy rights for California residents. CPRA introduces additional requirements for businesses, such as the establishment of a dedicated privacy enforcement agency and the implementation of data minimization and retention requirements.

Data Protection Directive 95/46/EC: Although superseded by GDPR, the Data Protection Directive (DPD) was the predecessor to GDPR and set out principles for the protection of personal data within the EU. While DPD is no longer in force, it may still be relevant for historical purposes or for organizations operating in countries outside the EU that have adopted similar data protection principles.

Bonus: Sector-specific regulations: Depending on the nature of the retail business and the data it collects, additional sector-specific regulations may apply (e.g., medical/pharmacy)

Looking Ahead

Looking ahead, the future of AI in retail is bright. As AI continues to evolve, we can expect to see even more innovative applications that further enhance personalization, customer experience, and operational efficiency. Retailers that embrace AI and harness its power effectively will be well-positioned to thrive in the ever-evolving retail landscape.

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