Enriching Your Product Data With AI and Automation to Boost Customer Experience
Thanks to new intelligent technologies such as AI, automation and conversational commerce, product data management is evolving at a rapid pace. According to Pimcore CEO, Dietmar Rietsch, with the intervention of these technologies, organisations can not only revolutionise the way they manage product information, but also reduce their time-to-market drastically and win customer loyalty.
How customers engage with brands and the way they shop is now a new ballgame altogether. Digital engagement with products is quickly replacing the traditional approaches; in fact, it has set new standards to keep up with customer expectations.
According to the Global Consumer Insights Survey 2019 by PWC, nearly a third of consumers buy products online weekly or more frequently, and this number has increased steadily by 5% points year on year.
Marketing and selling products with trusted and persuasive product data has a significant impact on improving customer experience and growing revenue. Brands are investing more and more in enhancing quality, reliability, and accessibility of product data to stay one step ahead in the digital business arms race.
Product Data Shapes Your Customer Experience
Customers can initiate their product research anywhere and analyse the prices/offers before they ever interact with the brand. They expect more from their purchase and are more than willing to shift spends if their expectations are not met.
To attract, serve, and retain customers, brands must provide personalised product information that augurs well for their persona, expectations, occasion, and channel across every step of the journey. That’s where enrichment, consistency, and transparency of product data becomes crucial for businesses.
Over the past few years, product information management software has turned out to be an integral part of operations teams that manage product data and are responsible for delivering seamless customer experience. It also streamlines workflow— where item uploads can be governed in real-time, product release to supply chain readiness can be faster, and truly personalized omnichannel customer experiences can be a reality.
Upcoming Trends in Product Information Management
Recently, the flux of product data and complexities of maintaining consistency in country-specific currency, language, localisation of products, and omnichannel delivery have dramatically increased the workload of operation teams, marketers, channel managers, product merchandisers and more. This compels operation teams to spend more time on administrative tasks rather than focusing on their core responsibilities.
At the end of the day, this can harm the discipline of powering product excellence. To make the life of product operation teams easier, three significant advancements are happening in the PIM software ecosystem, which include:
Automation Using AI: Optimise Product Information…Easily
With the volume and complexity of product information updated across the value chain in any organization, ensuring data reliability manually is a huge task. This is where automating the process of analyzing product data against the established unified data workflows and business governance rules can help.
Artificial Intelligence (AI) based automation can provide real-time analysis of structured data fed by a PIM system, thus improving your automation capabilities.
Product data optimised by AI helps eliminate human errors and enhances the quality and reliability of product data. Product information validation, normalization, gap analysis, improvement, translation, and proactive delivery across channels are just some of the processes that can be automated.
Intelligent Technology: Capture the Market…Rapidly
After a product has been released, marketers need to ensure the product gets maximum eyeballs from their target audience. Every single day when the product is not visible across channels and geography is a lost revenue opportunity. Marketers can amplify the exposure of products by automating the product content generation process using Natural Language Processing (NLP).
NLP deals with computers and human interactions. It enables product companies and marketers to quickly create unique product content in a natural human language using their master data as a source (e.g. relevant topics discovery, new product stories creation, and product content personalization).
With it, product teams can also simplify scaling, enable faster time-to-market, and minimise cost. This, in turn, can take product selling and customer experience to the next level across multiple commerce channels. Gartner predicts that a future use for NLG would be to construct sentences automatically, rather than using conventional product descriptions.
Conversational Commerce: Transform Customer Experience…Impeccably
The way customers discover products is transforming. Moving away from search-driven discovery, we are entering the phase of voice-based conversations. Google Assistant, Apple’s Siri, and Amazon Alexa are the live examples. According to Gartner, by 2020, 30% of our interactions with technology will be through “conversations” with smart machines.
Thus, it is essential to turn product information from keyword-based search to intent-based, natural human-like search for dynamic product discovery on virtual chatbots and voice-based interfaces no matter when, where and how customers discover products.
To make product information conversational commerce ready, Machine Learning (ML) can support in discovering meanings and relationships from product information like key phrases or product attributes extraction, content classification, identification of vital items based on customer queries, and serving results as per customer sentiments.
But here, trained product data is vital for ML algorithms, and trusted product information management makes it happen. Along with that, not only having a product information management (PIM) with a one-way interface pushing data out but using two-way interfaces for feeding behavioral customer data from commerce searches back to the PIM. This can be best achieved in a product information management (PIM), which can also manage customer data.
Furthermore, machine learning can also be applied to create a contextual knowledge-graph based on customer support incidents or enquires. In the near future, the multimodal search will take the front seat where voice and screen will converge to provide the holistic conversation experience to customers.
Conclusion
The confluence of automation, AI/ML, and conversational commerce is shaping new possibilities for businesses, and revolutionizing the way they manage and enrich product information. These PIM trends can change the paradigm the way brands optimize product information (management and enrichment), automate operations (accessibility and governance), accelerate time-to-market (omni-channel distribution), and win customer loyalty (brand experience).
Product data management is evolving at a rapid pace with the support of intelligent technologies, and the speed at which organisations can adapt to these advancements will determine how far they go.
The article orginally published on MULTICHANNEL MERCHANT.