Do you want to make your customers say \u201cShut up and take my money!\u201d right when they land on your store?<\/p>\n
You can always make that happen as long as you understand your customers and give them what they truly need and want. But how?<\/p>\n
Each of your customers has a different set of interests and habits. Knowing and understanding each of your customers is a hard task.<\/p>\n
What happens when brands spend some time treating their target audiences like people instead of treating them as numbers in an excel sheet? It could be hard and time-consuming but it\u2019s definitely worth. That\u2019s why predictive & personalized recommendation can be considered as a salespeople\u2019s best friend.<\/p>\n
So, what is Predictive Recommendation<\/em>?<\/h2>\nPredictive recommendation are tactics that allow online stores to sell and suggest relevant products, like Amazon — that is, to nurture visitors with highly personalized, useful content that helps convert them into first-time customers and eventually turns them into life-long customers. This type of recommendation typically generates significant new revenue for online stores, as it maximizes the average order value per customer for you.<\/p>\n
<\/p>\n
![\"Screen](\"https:\/\/lh5.googleusercontent.com\/rS6HZMhnTg7JgDM93OwIM5pwRIooTTpuQ0F5wadmwIBb-YqfaqNQNqIkUCe39Pi9Y371l_4TvG2Ts4NZAFzyJqkpOe2pFmwbAaU4ehc1XMPGTeSn_OGkf83lNhD8RUMcVM84L9M\")
<\/a><\/p>\nHow does Amazon\u2019s recommendation system work?<\/h2>\n
Normally online sellers can use up-sell and cross-sell to suggest products that they think their customers might like or need as a bundle. However, a predictive recommendation system (like Amazon\u2019s) doesn\u2019t assume, it uses precise data to run perfectly.<\/p>\n
First, it collects detailed information on each of your customers and of your product\u2019s performance, which you can hardly do with just 24 hours a day. \u00a0It covers your customer\u2019s purchasing history, type of products, colors, styles they like, how much they usually spend, when they finished their last order\u2026 and at the same time, it can also tell you how your products perform on the site, which product is more popular than others\u2026based on customer behaviors & interest.<\/p>\n
Second, with smart algorithms, it divides your customers into small groups based on their interests, behaviors, budgets\u2026 and then builds models for each of them. These models are then used to predict customer\u2019s behavior in the future.<\/p>\n
Last but not least, with the customer\u2019s models, it will determine relevant recommendations that have a higher probability of generating a sale and suggests those recommendations to the corresponding customers.<\/p>\n
![\"How](\"https:\/\/lh4.googleusercontent.com\/iQU2WLNIHKEAPwFHNmu0EZ6iPxRowcJFaMD7exdj5J1sNq6btgJEl21tEdNd1RT3rnyy15ifdEapphXOmRXXaKQjn0UW4u_WY4zIBPV9cNq8XsNL_Tyn99pOQp8EBBnrR_zPwH4\")
<\/a><\/p>\n <\/p>\n
For example, after collecting Ms. Jane\u2019s information, Amazon knows that she usually searches for bright color flat shoes on the site with price range of $39 to $59 and she usually buys new things on the weekend or during the holidays. With that information, the recommendation system will know that she would like a pair of trendy white flat shoes with the price of $45, and then suggest it to her or even offer her a small discount if she takes it home this Saturday.<\/p>\n
Predictive Recommendation can change the way you sell<\/h2>\n1. \u00a0\u00a0 Deliver relevant products\/content<\/h3>\n
By understanding your customer\u2019s behavior deeply and carefully analyzing his previous browsing history, a predictive recommendation system can suggest to him relevant products as he shops. The data is collected and analyzed continuously in real time so the predictive recommendation can react as customer\u2019s shopping habits change.<\/p>\n
<\/p>\n
![\"Screen](\"https:\/\/lh6.googleusercontent.com\/jiHJKS_sHev81PdaM_HZPbGXva22T4TvSHhDomefTDE_KrDNYVXkTqsi7WoHuR8jvdttBgXA7cYzjnAL5xgaMejbHkzXtD-LBXBmwJyjfMC6vFf8EW3IUYaYoSosNRTpIxHTIs8\")
<\/a><\/p>\n2. \u00a0\u00a0 Turn visitors into customers<\/h3>\n
Delivering relevant products makes the site more personal for the visitor and makes him feel like the site is made for him. Even if the customer doesn\u2019t have an interest in purchasing something at the moment he will be pulled to visit your site again to make a purchase when he\u2019s ready.<\/p>\n
3. \u00a0\u00a0 Engage customer<\/h3>\n
Predictive recommendation is not only a salesperson\u2019s best friend, it\u2019s also a customer guide. Customers are able to look more deeply into the product line without having to perform search after search. It saves the customer a ton of time and effort.<\/p>\n
4. \u00a0\u00a0 Increase your sales<\/h3>\n
When a customer sees an option that interests him, he is more likely to add extra products to his cart. Each time he does that, both your average order value and the number of items per order rise which, of course, increases your total revenue. Moreover, it makes the customer purchase more<\/em> without feeling forced.<\/p>\nConclusion<\/h2>\n
As you already know, the volume required to make predictive recommendation for each customer is far too large to manage manually. Using predictive recommendation can help you save time and effort from collecting and analyzing customer\u2019s information, making your work less painful and effective.<\/p>\n
Moreover, a predictive recommendation system can provide you with up-to-date reports. As an automated marketing tool, it can support you and keep you on track, helping to measure your work and your site performance, so you can make changes easily if necessary.<\/p>\n
<\/p>\n
-By Katherine @Beeketing<\/a><\/p>\n
<\/a><\/p>\nHow does Amazon\u2019s recommendation system work?<\/h2>\n
Normally online sellers can use up-sell and cross-sell to suggest products that they think their customers might like or need as a bundle. However, a predictive recommendation system (like Amazon\u2019s) doesn\u2019t assume, it uses precise data to run perfectly.<\/p>\n
First, it collects detailed information on each of your customers and of your product\u2019s performance, which you can hardly do with just 24 hours a day. \u00a0It covers your customer\u2019s purchasing history, type of products, colors, styles they like, how much they usually spend, when they finished their last order\u2026 and at the same time, it can also tell you how your products perform on the site, which product is more popular than others\u2026based on customer behaviors & interest.<\/p>\n
Second, with smart algorithms, it divides your customers into small groups based on their interests, behaviors, budgets\u2026 and then builds models for each of them. These models are then used to predict customer\u2019s behavior in the future.<\/p>\n
Last but not least, with the customer\u2019s models, it will determine relevant recommendations that have a higher probability of generating a sale and suggests those recommendations to the corresponding customers.<\/p>\n
<\/p>\n For example, after collecting Ms. Jane\u2019s information, Amazon knows that she usually searches for bright color flat shoes on the site with price range of $39 to $59 and she usually buys new things on the weekend or during the holidays. With that information, the recommendation system will know that she would like a pair of trendy white flat shoes with the price of $45, and then suggest it to her or even offer her a small discount if she takes it home this Saturday.<\/p>\n By understanding your customer\u2019s behavior deeply and carefully analyzing his previous browsing history, a predictive recommendation system can suggest to him relevant products as he shops. The data is collected and analyzed continuously in real time so the predictive recommendation can react as customer\u2019s shopping habits change.<\/p>\n <\/p>\n Delivering relevant products makes the site more personal for the visitor and makes him feel like the site is made for him. Even if the customer doesn\u2019t have an interest in purchasing something at the moment he will be pulled to visit your site again to make a purchase when he\u2019s ready.<\/p>\n Predictive recommendation is not only a salesperson\u2019s best friend, it\u2019s also a customer guide. Customers are able to look more deeply into the product line without having to perform search after search. It saves the customer a ton of time and effort.<\/p>\n When a customer sees an option that interests him, he is more likely to add extra products to his cart. Each time he does that, both your average order value and the number of items per order rise which, of course, increases your total revenue. Moreover, it makes the customer purchase more<\/em> without feeling forced.<\/p>\n As you already know, the volume required to make predictive recommendation for each customer is far too large to manage manually. Using predictive recommendation can help you save time and effort from collecting and analyzing customer\u2019s information, making your work less painful and effective.<\/p>\n Moreover, a predictive recommendation system can provide you with up-to-date reports. As an automated marketing tool, it can support you and keep you on track, helping to measure your work and your site performance, so you can make changes easily if necessary.<\/p>\n <\/p>\n -By Katherine @Beeketing<\/a><\/p>\nPredictive Recommendation can change the way you sell<\/h2>\n
1. \u00a0\u00a0 Deliver relevant products\/content<\/h3>\n
2. \u00a0\u00a0 Turn visitors into customers<\/h3>\n
3. \u00a0\u00a0 Engage customer<\/h3>\n
4. \u00a0\u00a0 Increase your sales<\/h3>\n
Conclusion<\/h2>\n