The current macroeconomic trends of persistent high inflation, imminent recession, tighter monetary policies, and market volatility are driving a decline in overall consumer demand. This requires a renewed and innovative approach toward understanding consumer behavior and rewriting strategies for driving the overall demand across marketplaces in this new normal. We need to give a seamless and personalized brand experience across multiple channels & devices by understanding the consumer’s buying journey. Suppose Customer A buys 30% of its products online and Customer B buys 60% of its products online, in that case a company focusing more on the online channel will be able to target Customer B better based on their higher online frequency. So, this is imperative to build an agile model unified across the customer base and sales channels that provides an adaptive sequencing of product references based on each customer’s choices. The idea is to blur the line of differentiation of sales channel for customers and focus on creating immersive brand experience for them.
Millenials and Gen Z dominate the current customer base, and they want a more immersive experience with better brand engagement. The choices of these consumers can fluctuate quickly as they have accessibility to a plethora of mediums that can influence their consumption. Therefore, we must understand the complete usage pattern of the consumers across all the brand sites using programmed tags/triggers along with a data collection layer and AI-led analytics. These tools will help us to figure out the reasons for conversion or higher bounce rates and result in targeting these customers more accurately based on demographics. Similarly, we can understand the change in product buying patterns across retail stores using IoT devices and platforms with AI-enabled algorithms.
Integrating demand variations across these channels into a unified structure will fulfill the customer desire of brands to know about their choices in this highly changing, social media and Livestream shopping-driven digital-native world. This will bring in a personalized understanding of product impressions and provide suitable recommendations to customers. It will help us drive product sales in the current volatile market by improving customer loyalty based on personalized demand understanding, product suggestions, and faster fulfillment.
As per a McKinsey report, “More US consumers are switching brands and retailers now than in 2020 and 2021, and about 90 percent plan to continue doing so”.
Ecommerce tracking with the help of key information tagging, the creation of data layers, and analytics tools are the key enablers of driving increased product purchases. These tools improve the customer buying experience through a data analytics-based, faster approach. To understand this, we need to map the complete user journey and their behavior patterns. The first step is to track the various marketing campaigns/steps taken to land customers on your website. Measure the time they spend on your website and their browsing and check-out patterns as well as collect information around user behaviour, which social media/marketing handle enabled them to land on your page, which products were browsed based on the customer demographics, types of products browsed, check out patterns, product performance, the success of marketing campaigns, and overall sales performance. We need to analyze consumer behavior across all these steps, measure the effectiveness of our marketing initiatives and optimize them. Doing so ensures that we drive sales with optimized buying steps, personalized promotion triggers, lesser web traffic, faster check-outs, and a connected consumer experience. All of this will provide a better customer experience and optimize the spending to improve the overall margins.
Similarly, we must observe the same patterns in retail stores using IoT devices. We can place IoT devices on each shelf to understand the real-time buying patterns of the consumer. The advent of 5G, beacons, near field communications, geo-fencing, and other related technologies like BLE (Bluetooth Low Energy) further decreases the latency and improves the information exchange with the customer. This enables real-time edge analytics on their movement patterns within the store, allowing us to match it up with their historical buying data, provide them with custom offers on their preferred products, and guide them to the right products using AI. This indoor behavior tracking will not only reduce the buying time but also create a higher loyalty for customers by providing them with real-time discount offers, advertisements on related products, etc., on their smartphone apps. Therefore, it will not only improve product sales, customer loyalty, and the overall consumer experience, but will also optimize the overall inventory and operational costs with better demand forecasts.