At an estimated cost of £60 billion a year, returned goods are a big challenge for UK Retailers. The cost and time spent processing and repackaging your goods often means that the margins made for the sale are lowered or nullified completely. When you factor in the highly seasonal nature of the fashion industry, and the difficulty in selling off-season goods, returns can have a big impact on retailers’ bottom lines.
Customer centricity should be the goal for every marketing department. Retailers, more than ever, need to provide individual positive experiences during and after the sales process to encourage repeat business and loyal customers. Loyal customers are the most likely group to advocate your brand, it should cost less to market to them and, most importantly, they are the high spenders that generate the majority of your sales.
Retailers can measure customer loyalty with KPIs such as revenue, average transaction value, purchase frequency and spend per visit. But are your high-spenders really your most loyal customers, and how can you measure this?
Particularly in the fashion sector, customers will often buy multiple items with the intention of only keeping one or two items. Other times, customers may buy an item for an event and return the item when they no longer need it, as ‘intentional returns’. Returns can also highlight faulty product lines, unsatisfying customer experiences, and problems with e-Commerce channels. Across the UK, thousands of these returns go through the costly journey of processing to be resold every day.
To demonstrate this, take the example of Lucy Lee. Lucy spends £1000 with Retailer A every month, so Retailer A highlights Lucy as a high-value customer. However, Lucy also returns items to the value of £900 every month so her real value is only actually £100. Without a way of unifying all customer records, Retailer A may think, based on sales alone, that Lucy is one of their most loyal customers.
There is a solution to this challenge.
Data management systems take data from across an organisation’s various business systems to create a single, unified view of information from across every department. Data management and omni-channel insights can highlight where returns are most likely to come from, who, when, and why certain customers are returning goods.
These insights can identify the true value of customer interactions, high-performing products with customer segments and tailor marketing messages that are likely to appeal to the individual. With these questions answered, retailers can encourage a reduction in returns and proactively reduce the impact of returns to the bottom-line.
An omni-channel system enables retailers to get to know their customers on an individual level, with precision, to understand their buying habits, preferences and sizes. With accurate data and the ability to drill down to the individual, forecasting customer behaviour is simplified and logistics processes can be implemented to improve efficiencies.
With a data management system in place, retailers can send product information that will appeal to the individual, based on previous purchases, and tailor the products shown by customer measurements against current stock. Customers will only receive promotions that will directly appeal to them and should increase customer satisfaction and reduce the likelihood of returned stock.
A data management system can reveal the true cost of your biggest spenders and improve customer loyalty. By addressing customer centricity and targeting individual customers, a data management solution enables retailers with full customer insights, to implement efficient logistic processes and extract the most value from returned goods.