Empower Your Business With The Magic Of Machine Learning!
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Empower Your Business With The Magic Of Machine Learning!
Personalization in shopping is the process of providing personalized experiences on e-commerce sites and in-store, with dynamic display of product recommendations and personalized offers based on shopping history, loyalty history, e-shop browsing history and other data we have about the customer.
Personalization is increasingly important for businesses seeking not only to attract consumers, but to increase repeat purchases, sales and conversions.
It comes in many different forms:
Before the e-commerce boom, customers simply walked into stores and found a friendly employee who helped them find what they wanted.
Pretty simple, right?
Unfortunately, this kind of personal attention from customers remains extremely rare in the digital and in-store space today.
・ Businesses see an average increase of 20% in sales when using personalized experiences.
・ 80% of shoppers are more likely to buy from a company that offers personalized experiences. (Epsilon)
・ 44% of consumers report that they may become repeat buyers after a personalized shopping experience with a particular company.
・ 77% of consumers have chosen, recommended or paid more for a brand that provides a personalised service or experience. (Forrester)
・ However, in another survey of the research of the Forrester survey , 53% of Digital Marketing professionals said they don’t have the right technology to personalize customer experiences.
Product search in e-shop , the browsing data, the previous purchases , the product recommendations, the landing pages , the products put in the Wish List , the products put in the cart , the visits to the store , the products seen or not seen there and all other interaction points and should work collectively to form a complete picture to each customer during his/her shopping journey.
Advances in Artificial Intelligence and Machine Learning with mobiplus shopping Recommendation systems are an important factor in personalization.
Computers can perform fast data retrieval, allowing recommendations to be made real-time and scalability.
Companies can use their Customer Shopping Data and other data to recommend personalized products to each customer in real time and thus design an individual, user-oriented shopping experience.
To learn how the world’s top companies increase revenue up to 30% from existing customers using Recommendation Systems read the book below.
https://mobiplus.co/ebooks/customer-rediction/
You can almost accompany your visitors as they visit your e-shop, like a car salesman following a customer around the showroom.
All along the way, a good salesman finds out what kind of car suits his customers, what colour they want, what they can afford and how soon they want the vehicle.
Knowing the cars at his disposal , he can show them a car that meets their needs.
You can take a similar approach in e-commerce and in your store.
A key to making this work is to allow the computer to process all this information and match it to your stock in real time.
Machine Learning can not only help the business to guide customers at astonishing speeds, but it can also provide options that a human might have missed.
Identifies clusters with the customer’s past behavior or matches the customer with another group of customers who have purchased and have seen similar products to date.
The Machine Learning with mobiplus recommendation engineering can even identify completely new customer segments.
With the right personalization solutions, businesses can use Big Data to provide 1:1 personalization through search, browsing, layout and content.
You can return accurate results for each visitor, based on Artificial Intelligence , and ensure that the Recommendation Engine – Recommendation Engine – is constantly learning and improving.
You can provide accurate product recommendations based on customer purchases, search behavior and browsing history.
“You might like…” “Others who have also bought…” are common messages in e-shops that signal Recommendation Engine.
For example, if you have a client who looks at very high heels, then you can automatically recommend stiletto heels and shoes with four or five inch heels.
If a customer browses through various Louboutin high heel shoes, then Dynamic Categories may suggest Christian Louboutin Shoes or Christian Louboutin Evening Dresses and display products that match those categories.
The relative scarcity of personalized search is a huge missed opportunity, as visitors using search convert at 1.8 times the rate of the average visitor.
The search interface is the most important element in any e-shop.
Unfortunately, many of the search functions in e-shop today can hurt as much as help because they focus on keywords instead of the meaning of those words in the customer’s surrounding atmosphere
Spelling mistakes, the use of broad terms, differences in the way people describe the same product can make search results inaccurate.
Increase in revenue 30%
Reduction of rule writing time.
Automatic creation of product sorting order in the e-shop in the best way based on shopping and customer performance data.
Greater flexibility for your team to respond to customer needs.
As a result of this, the day-to-day life of marketing revolves much more around exploring customer behaviour and how to adapt to it!
Lartigiano increases revenue and basket using Artificial Intelligence!
See how he did it here!
Contact us now to see how the mobiplus shopping recommendation platform can give you the above features and increase your revenue. 30%.
mobiplus member Elevate Greece
Confirmed Innovation.
Test out the Recommendation Engine for up to 20.000 euros in revenue from recommendations. No credit card required. Just enter your company information and we’ll contact you with all the details.
