Some companies such as Velvet Palate, a web-based seller of hand-crafted wines, are using web and social analytics tools to customize offers for existing customers and prospects.
Velvet Palate uses data analysis to identify the websites that customers come from before visiting its website as well as the types of products they click on and abandon while on these sites, according to a recent New York Times article.
The company uses these insights to improve its offers and cater to customers’ tastes. This includes creating different offers for different types of customers, including regular customers; consumers who have purchased once but haven’t returned; and shoppers who have visited the company’s website but haven’t yet made any purchases.
For instance, to entice one-time buyers to return, the company will sometimes offer free shipping for a limited time.
Indeed, there are numerous ways that companies can use data analysis and data discovery to identify customers and prospects who are hovering around their websites. For instance, companies can share engaging content to attract high-value customers and prospects, which can often prompt them to spend more time on their websites.
Companies should understand that providing visitors with engaging content can have a multiplier effect. Other websites will start linking to their websites while visitors are also more likely to share appealing content with their friends and social connections. Each of these courses can result in higher website traffic as well as an increase in potential customers.
To help support content efforts, data analysis and data discovery can be used to identify the types of content that resonates with high-value customers (as measured by clicks or time on site, changes in unique visitors, etc.). Analytics can also be used to identify the characteristics and behaviors of high-value customers and match them with the types of prospects a company is trying to attract.
Companies can also use data analysis and data discovery to identify the transaction histories and other known information about repeat customers while they’re visiting a web page as well as similar characteristics displayed by new visitors to help craft customized offers on the fly. Available customer data can be analyzed by the retailer to suggest the next best action to be taken with a particular customer based on his purchase history and other variables.
For example, let’s say a customer who’s previously purchased a washer/dryer set from an online retailer is identified through his IP address when he’s visiting a web page the retailer has set up for dishwashers.
Depending on a number of variables, including the customer’s transaction history with the retailer, the brands a customer is viewing online, and the visitor’s estimated customer lifetime value, the retailer could provide a personalized pop-up offer for a particular brand with a discounted price. Or maybe the retailer could offer an extended warranty to persuade the customer to convert.
Additionally, data discovery and data modeling can help a retailer identify new visitors who match its high-value customer profile and develop relevant and timely offers for each of them.
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