Nowadays, shoppers do not differentiate experience by online, mobile, social, or store. Instead, they have a holistic perception of the brand and demand a smart, fast, and personalized experience whenever and wherever they interact.
To deliver personalized product recommendation is important for the brands to understand their customers. Transforming customer data into actionable insights is one of the key drivers in customer profiling. Consequently, answering to which customer a brand should offer a specific product is the vital success factor of customer loyalty.
New in-store customer experiences
In the fast-paced world of retail, businesses should automate key processes and use in-store technologies to take the experience to the next level. Powerful tools like product recommendation engines can help you connect with customers through targeted messages at exactly the right time while the shoppers are in-store.
Retailers can also make every interaction in-store memorable by investing in a clienteling app. The customer will be identified while entering the physical store by the micro proximity technology. The sales associate can have a unified customer profile by using the clienteling app to offer a personalized experience to the shopper.
Meet your customers’ expectations for personalized communication with advanced product recommendations.
Product recommendation campaigns work best when they are fully personalized and sent to a highly targeted segment of buyers. You need to send customers relevant products that might be of interest rather than sending everyone the same email. To make these marketing campaigns effective, you need to run all the below steps:
• collect all the valuable customer data across all touchpoints
• run method analysis and make product recommendations based on advanced technology.
Using that tools, you can create product recommendations far more likely to appeal to specific buyers.
For product recommendations to work in your favor in the e-commerce environment, e-retailers must ensure that the suggestions are relevant and data-driven to the consumer.
Based on the customer segment, the recommendation engine will surface recommendations of products that index well for the defined segment, using past purchases, behavioral data, or content-based filtering.
It is important for the brands to collect customer data from a physical store or online and to create unified customer profiles. In that case, the algorithm will know what your customers like and what triggers them to buy.
Technology makes it possible
Machine learning engines are revolutionizing the world of product recommendation. Search algorithms can now use add-to-cart, click, and purchase behavior in deciding which products should be suggested to the consumer.
These search-driven recommendations are available to Qivos Cloud and what makes them unique is that they require much fewer behavioral data to learn shopper intent than traditional recommender systems.
Providing a list of ‘recommended products’ that are like the product being looked at or searched for can – and will – lead to increased conversion rates and basket size.
We help our clients to deliver relevant and personalized recommendations to their shoppers. By creating targeted customer experiences, showing the right products at the right time, and in the right order and customer-oriented navigation, we manage to increase brands’ revenue and customer engagement.
Reach us out today to discuss with the customer loyalty experts, whether you have a support question, or a business inquiry. Click here to contact us!