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.

Now, our goal as a product company is not just to improve sales across different mediums but also to create a unified omnichannel experience of buying. We will bring strong emotional engagement through the amalgamation of information across these mediums. Our current technologies are omnipresent and their ability to direct customer connection has become essential. The smartphone, which help customers to purchase through apps, is also assisting the customers in retail stores using beacons, BLEs, auto-checkout, etc. Now, it’s the job of the brand to take care of all these behavioral product purchase data and use them to create a unified model for sales. It should inform an understanding of no longer having different customers across different channels, but one customer across all these sales mediums. The idea is to track the marketing efforts and costs across all these channels and understand how they impact overall sales for that target customer. This will create a more personalized understanding of that customer's buying preferences and their preferred buying medium, and based on that, we can trigger our online alerts and in-store advertisements. All this will provide an improved and incentivized sales experience with the help of our optimized product pricing and analytics-based agile operating model.

This model will not only improve the ordering experience but will also increase the speed of the fulfilment process and make the brand more competitive. Many manufacturers, based on customer buying preferences, proactively moved to a ship from store model, even though the product was ordered online. This was possible by creating a unified buying model for that target customer and proactively replenishing the stock in the nearest retail store based upon their buying patterns to allow fulfillment from the store. Even if a customer visits a store and is unable to find their suitable product, then this model will suggest a new buying channel for that product with incentives. This new method can be based around pay-in-store, get-online model. This enables the store owner to order that product through an online platform, and the product price will be automatically matched with the lowest price offered and then shipped directly to the consumer without any shipping cost.

These adaptabilities will lead to an increase in customer satisfaction and loyalty. This model along with feedback-based persona development, will run personalized advertisements with a precise reach and better engagement. By using an approach of target-based commercial sales execution through a unified model enables a seamless buying experience with lots of personalized recommendations and a backend shift in fulfillment strategy for faster delivery. All this ensures higher customer satisfaction, leading to more positive buying trends, improved customer loyalty, and higher product demand.

Rahul Sinha

Deal Execution Manager, Consumer goods & Life Sciences, Americas 1

Rahul is an MBA with over 9 years of IT industry work experience in Technology Consulting, Bid Management and Business Development for Customers across CPG, Life Sciences, Manufacturing, Energy and Utilities.