Should You Re-Envision the Role of Product Data in Hyper Personalization World?

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Today’s business landscape is driven by one ultimate factor: heightened customer experience. It can make or break a brand’s image. However, hyper-personalization empowers businesses to provide the best products and services that elevate customer experiences and thrive in a competitive environment. But what is hyper-personalization? Simply put, it is a customized and individualized approach where products, services, and content are tailored to customers’ unique needs and preferences.

According to a report, 77% of companies that personalized customer experiences observed increased market share. From e-commerce and education to streaming services, hyper-personalization is applied across industries. For example, in e-commerce and streaming services, it helps recommend products and content that meet the customer’s preferences the best. Here, product data is an essential, or rather, the ‘key’ enabler in this process. However, successfully managing product data is an arduous endeavor. So, let us discuss the various challenges in product data management and why businesses must re-envision their role to deliver value in the hyper-personalization world.

Challenges of product data management for hyper-personalization

Managing product data effectively is paramount for ensuring that businesses easily achieve hyper-personalization. However, this approach is not without its challenges. Some of them are:

Complexities in data sources

Various types and sources of product data play a crucial role in hyper-personalization. They include product attributes, features, benefits, reviews, and ratings, among several others, each having challenges. For example, maintaining consistency in product attribute data across dynamic catalogs can be difficult. Ensuring the accuracy of product features and descriptions across product listings in a multi-vendor setup can be a resource-intensive undertaking. Similarly, dealing with product reviews across platforms can be complex – from large volumes and spam to review manipulation, deriving meaningful insights is complicated. Additionally, while product ratings provide a general view of customer satisfaction, they are not absolute. Brands cannot rely wholly on these ratings for decision-making as they do not capture the subtleties of customer experiences and preferences.

Difficulties in real-time data management

From collection and integration to analysis to updation, managing product data from multiple channels and touchpoints in real-time can be marred by several obstacles. For example, if the quality and accuracy of data collected from various platforms are poor, it can affect the data’s validity and the reliability of insights derived from it. If data from multiple sources is fragmented and siloed, owing to storage in different structures, formats, and systems, it can prevent smooth multi-channel data integration. Real-time processing of product data can also render it vulnerable to threats such as thefts and cyberattacks. This can compromise data security and privacy.

Risks arising from deficient product data

Using product data that is inaccurate, incomplete, or outdated for hyper-personalization can result in the loss of customer trust, relevance, and loyalty. How? Fundamentally, the function of personalization is to provide customer-specific recommendations. Let us say the data about a product’s inventory is not up-to-date, and a customer is notified about its availability. While attempting to purchase the product, if the customer learns it is not in stock, their trust in the brand is lost. Similarly, if faulty data is used to recommend products or services that misalign with a customer’s preferences, it leads to losing relevance and interest in the brand. Finally, bad product data-led suggestions – both incorrect and inconsistent – can diminish the customer’s loyalty toward the brand.

Best practices of product data management for hyper-personalization

While managing product data is challenging, adhering to certain innovative strategies can boost the effectiveness of the process in the pursuit of hyper-personalization and transform the role of product management. Let us look at some of the best practices to achieve this.

Re-envisioning product PIM system

Reimagining product information management (PIM) systems for enhancing product data management across different platforms and devices is vital for hyper-personalization. A revamped PIM consolidates product data from various sources better and creates a single source of truth for product-related information, thereby eliminating data silos. By improving the consistency of data formats, rules, and standards, a redesigned PIM standardizes product data. This ensures the maintenance of data quality, accuracy, and uniformity. Additionally, a remodelled PIM optimizes product data using supplementary information such as product features, technical specifications, and customer reviews. Thus, product data is enriched. Also, re-envisioning product PIM streamlines the distribution of product data across touchpoints. Consequently, data integration across channels and other systems, such as content management systems (CMS), is enhanced.

Automating product data management with AI and ML

Technologies such as artificial intelligence (AI) and machine learning (ML) can help automate crucial aspects of product data management. From extraction to integration, AI/ML tools such as natural language processing (NLP) can automate the processing of multi-format data from various sources. This minimizes manual efforts, reduces errors, and maintains data quality. Trained AI/ML models and tools such as computer visions can perform real-time product data validation. They can accomplish this by studying historical data patterns, detecting anomalies such as duplication, and verifying against predetermined conditions. Hence, data accuracy is guaranteed. AI/ML solutions such as predictive analytics and natural language generation (NLG) can automate product data analysis, personalization, and distribution, enabling data optimization. Thus, data-driven decision-making and customization are facilitated.

Unifying data for holistic customer insights

A comprehensive view of individual customers and their preferences can be created by integrating product data with different forms of customer data. For example, combining product data with behavioral data such as product views, website visits, and interaction with promotional material, such as ads, can help ascertain the types of products a customer is interested in and make recommendations accordingly. Similarly, product data can be merged with contextual data such as location, time, and device type, among others. This can provide an in-depth understanding of how external factors influence purchasing decisions. Blending product data with transactional data purchase history can also offer individual insights. Such an integration allows determining a customer’s preferred brands, products, and purchase frequency, enabling forecasting future purchases.

Enhancing customer experiences with product data

Delivery of value-added and engaging experiences across the customer journey can enhance three key outcomes: customer satisfaction, retention, and loyalty. Product data can be optimally utilized for this. Based on purchasing habits, customers can be segmented appropriately and targeted with tailored product marketing campaigns. Product data can also aid in improved inventory management, which ensures product availability. Using data on product feedback, brands can engage in superior quality assurance and maintenance. Access to comprehensive product information can aid customer assistance and lead to quicker resolution of queries and concerns. Reliable product information can help educate customers better by providing a holistic understanding of the product. This empowers insight-driven decision-making and simplifies the purchasing process.

Increasing business performance

Holistic product data can augment the customer base and their lifetime value, increasing sales, conversions, revenue, and market share. Well-organized product data improves product discovery, raising the possibility of sales. Next, a thorough analysis of product data enables the formulation of competitive pricing strategies, which can appeal to cost-conscious customers, potentially expanding market share. Product data also facilitates preference-based customer segmentation, allowing for the designing of effective marketing strategies. This can attract new customers and increase conversion rates among old ones. Brands can also leverage product data for identifying cross-selling and upselling opportunities, thereby creating avenues for increasing average transaction values and revenue per customer. Effective product data utilization ultimately results in heightened customer experience, reducing churn rates and increasing customer retention.

Re-invent to resonate with customers

Personalization can drive a 5% to 25% uplift in revenue, with the rise in value directly attributable to the digital nativity of the business and its data-backed approach. A report found that when a company offers hyper-personalized experiences, 80% of customers are more likely to purchase from it. Therefore, it is safe to say that individualizing customer experience is more than a tool for financial success – hyper-personalization is an organizational model that brands must adapt to differentiate themselves from their competitors.

But to effectively enable hyper-personalization, it is essential to re-envision how product data is managed and used. As illustrated above, using emerging technologies can improve the efficiency of product data management through automation. Also, adopting inventive approaches, such as the unification of product data with other ancillary data and transforming PIM systems, can help brands maximize the utility of their product data.

Rajneesh Kumar
Rajneesh Kumar is Director, Marketing at Pimcore GmbH. He is developing the marketing plans. Operationally he is responsible for the marketing program and driving business outcomes. He builds data-driven decision-making frameworks – customer segmentation, Go to Market strategy, targeting, cross-sell/up–sell. Pimcore is an open-source platform for product information management (PIM/MDM), digital asset management (DAM), content management system (CMS), and eCommerce.

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