Rising inflation and continued supply chain constraints make it challenging to adequately forecast and plan for demand. With the help of a connected data foundation and artificial intelligence (AI), companies can improve how they use customer data to make informed product and supply chain decisions, not just to be more efficient, but also to provide better customer experiences and improve return on investment.
A customer data platform (CDP) provides the ability to collect and organize all the data necessary to perform better inventory planning and optimization. A CDP connects customer-facing systems with data from product management, order management, and other supply chain management systems to ensure a complete view of how products and services are engaged with and sold.
With a centralized view of your product engagement and customer buying patterns, you can understand what’s being purchased, where it’s purchased, and how. And, you can use this understanding to improve your planning processes in a number of ways.
Demand forecasting is the process of predicting how much of a product or service consumers will want during a defined period. For example, how many televisions will consumers buy over the holiday period?
To adequately forecast the demand for a product, look at historical purchasing patterns. These patterns help you forecast when specific products will be in higher or lower demand. But it’s not only historical data you want to examine. You can even better predict demand by looking at customer intent and propensity to buy as well.
A CDP can tell you the historical purchasing patterns for your product. It also tracks customer activity across the entire buyer’s journey. Predictive analytics can take all this information and forcast how much demand you can expect for a product.
For example, say historical data shows that 500 televisions were sold between October and December last year, and based on past sales in prior years, you predict that number will increase by six percent this year. But there’s also lot of customer activity among existing television owners looking to upgrade to a newer system in a particular geographic location. The CDP predicts that based on historical sales and the uptick in activity among existing customers, that demand will increase by 10 percent, not 6 percent.
Planning Inventory by Channel Based on Demand
On a channel level, a CDP can help you plan your inventory across your e-commerce site and brick-and-mortar stores. This is critical because out-of-stock messages, whether online or in-store, are a serious customer experience issue.
According to the Adobe Digital Economy Index, consumers saw 60 billion out-of-stock messages between March 2020 and February 2022. Unfortunately, this trend is expected to continue, and consumers are not happy. A McKinsey study found that of 60 percent of U.S. consumers that experienced out-of-stock items during a three-month period, only 13 percent waited for the item to come back in stock; 39 percent switched brands or products, and 32 percent switched retailers.
These statistics are for online shopping. The story is similar in-store. Correctly forecasting demand at the channel level helps you plan inventory better, ensuring the right amount of products are available in the right locations.
Collecting Customer Feedback
Customer feedback helps organizations understand what is working and what isn’t. That leads to better decision-making around new product and service offerings, product offers, and the ending of existing product lines.
A CDP captures this customer feedback from a number of systems, including customer service and support, CRM, marketing automation, customer feedback surveys, review sites, and more. It can then combine all this feedback to provide marketing, sales and product teams with a close-up view of what customers think about products and services, and how they perceive the shopping experience.
By leveraging machine learning and predictive modeling in a CDP, you can also detect patterns in historical data and current customer activity. This information, combined with direct customer feedback, guides efforts to improve products and services, ensure the right offers are available, and identifies which products are underperforming.
Returns are a huge cost center. One report states that returns for online orders average 30 percent or higher than that of brick-and-mortar stores. That’s a lot of inventory that must be managed appropriately, resulting in high labor costs for restocking, logistical expenses, lost revenue, and increasing the chances of out-of-stock messages for other buyers. The same report states that the cost of processing returns is anywhere from 20-65 percent of the actual product itself.
It’s unlikely you’ll be able to eliminate returns, but by understanding past purchase history, retailers can make smarter decisions about mitigating returns. For example, suppose you have a customer that continually returns items purchased online. In that case, you could include that customer in more marketing for in-store sales where they can try the item before purchasing it, or helpful content that will help them find the right products they’re looking for.
Logistics and Supply Chain Management
By understanding what products are popular and where, brands can strategically stock inventory at warehouses to cut down on transport costs. A CDP can help you understand which physical store locations sell the most of a certain product or that a large percentage of customers who order a product online live in a specific city or state. You then use this information to determine how much product to carry at specific warehouses that feed physical stores or online orders.
Strategically stocking warehouses shortens delivery times to the end customer or store and reduces shipping expenses. It also cuts down on CO2 waste, a huge environmental benefit many customers appreciate and look for in a retailer.
Brick-and-Mortar Store Planning
Retailers have faced their share of challenges with brick-and-mortar store planning. In 2021, Coresight Research indicated 5,048 store openings and 4,975 closures across the U.S. and UK. In addition, UBS analysts predicted that 80,000 stores would close by 2026.
Retailers need to be more strategic with their physical store planning. By understanding historical customer purchases, returns, and current shopping activities, they can improve their planning of physical locations for new stores, pickup locations for online orders, or other in-store services.
This is also a critical process for DTC (direct-to-consumer) companies that want to open physical stores. A CDP can help them understand where they could stragetically open a store to meet the needs of existing and potential new customers.
Customer Data is Key to Making the Right Inventory Management Decisions
There are many ways retailers can leverage customer data to make informed product and supply chain decisions. By connecting data from the appropriate systems in a CDP, retailers can analyze historical purchase data, including what products were purchased, where, and how, and apply predictive modeling to forecast where demand will be greatest. These efforts create efficiencies in product development and supply chain management and improve the customer experience, which is top of mind for every retailer today.