Enabling Digital Transformation to Create Data-driven CPG Enterprises - Manufacturing, Supply Chain & Brand Marketing
Personalization at Scale in Consumer Goods
Whether buying through retailers, or directly from brands, personalized experiences are proving to be a key ingredient of stronger consumer brand loyalty, and sustainable growth - whether that’s personalizing product recommendations, the products themselves or marketing content. With more consumers buying online, a trend that has greatly accelerated in the past 6 months, brands that can offer highly relevant, personalized experiences will be able to differentiate themselves and thrive.
According to Deloitte’s 11th Consumer Review, more than 22% of consumers are happy to share data for a more personalized product or service. Personalization not only helps increase customer engagement and brand loyalty, it is also helping brands optimize their manufacturing processes and supply chain, thereby reducing cost, and increasing efficiencies.
As increasing number of consumer experiences become digital, here are three ways that brands of all kinds stand to benefit from personalization:
Real-time individual personalized recommendations: With consumers interacting with brands across multiple channels, including various social media platforms, there has been an explosion in data related to consumer preferences. Being able to connect this data in a meaningful way and mine insights across touchpoints requires a data and machine learning strategy. With the right strategy in place, this can help brands shift their personalization strategy from a broad segment-based approach (i.e. factors such as demographics and general interests) to being truly individual and one-to-one.
Being able to combine real-time user activity with what is already known about the customer and products is crucial to delivering relevant experiences. Being able to capture in real time how a specific consumer is interacting with a product online, such as zooming in to see certain details, can allow brands to progressively tailor product recommendations to specific consumers with every interaction. Machine learning can enable brands to do this at scale across billions of interactions. It is important for brands to evaluate what are the different data sources that they have access to, and what are the gaps in data preventing from truly delivering the experience they want.
Personalized products and packaging: These insights about consumers not only help you bring the right products in front of right consumers, but also offer an opportunity to personalize the products themselves - whether that is bespoke or mass personalization. According to Deloitte’s research, for certain categories of products, more than 50% of consumers experienced interest in buying personalized products. And often digital-savvy consumers want to be involved in the personalization process. Understanding their preferences at scale can help brands identify what aspects of products consumers care about personalizing. This can result in a fundamental positive shift in the product manufacturing process and supply chain, and help reduce excess inventory.
Omni-channel personalized marketing and customer service: Brands are increasingly recognizing that personalization through the last mile of the consumer experience can build a flywheel. Most consumers interact with brands across multiple channels: mobile, web, social media, and across multiple devices. For example, understanding which products or services are driving engagement in a promotional email can subsequently help serve a more personalized online experience for a specific user.
Customizing your marketing communications simply based on broad personas is hardly sufficient for today’s brands. Every such engagement is an opportunity for the brand to deliver meaningful, hyper-relevant experiences across all points of interaction, be it in-app messages and notifications, interacting with a chat-bot, or a promotional email or text. Personalized communication means delivering tailored messages, product recommendations, offers, and discounts and more. And doing this at scale requires a long-term data and machine learning strategy
When building a machine learning-driven personalization strategy, brands need to ensure that they are taking a holistic look to bring together these various pieces of data in one place. This is true for both historical data, as well data getting generated from real-time interactions such as click-stream data. If a consumer is searching for a product on a website and then continues on to a mobile app, it is important to factor in the recent web activity to decide what the experience on the mobile app should be.
Delivering hyper-personalized experiences at scale requires a strategic focus. And this can have implications on the core processes for your brand’s business such as manufacturing, supply chain, marketing as well as customer service. Moreover, the use of data for building machine-learning based personalization models requires governance framework, including defining transparent and responsible use of data.