Personalized Recommendations Turn Webshop Into An Intelligent Salesman
July 15, 2019
Hands down generating recommendations and offering personalization come off as generic terms and not something unheard of. Though, what made it so common that I could expect almost every reader reading this blog to experience it first hand? Pretty simple – Amazon!
More than 195 million people hit Amazon each month. That’s more than a 100 countries’ population combined!
It’s interesting to think that Amazon keeps millions of people engaged in its e-store and make them come back for more. For you, for me, for everyone, there are personalized recommendations such as ‘Inspired By Your Browsing History’, ‘Customers Who Bought This Also Bought’, ‘ Best Sellers In This Category’ that make one feel like this store knows my taste and provides me with really good sales assistance.
It definitely has succeeded in creating a unique retail ecosystem in real-time so that no one has to have hassles in a marketplace of over 1 million sellers and their +12 million products! Challenging enough, eh?
Personalization contributes amazingly to Amazon’s vision: ‘To be earth’s most customer-centric company’
So after implementing personalization technology, what did Amazon achieve?
Let’s take a look at the succeeding factors that implementation of personalized recommendation contributed to.
Numbers have a lot to tell. According to a McKinsey Report, Amazon’s recommendation engine accounts for up to 35% of its revenue. In 2018, Amazon reported $232.88 billion in sales, which grew by 31%. Personalized cross-selling and up-selling definitely helped to increase average order value.
Massive Conversions Driving To Sales
A Forrester analyst’s research states that Amazon’s conversion to sales of on-site recommendations could be as high as 60% in some cases based on the performance of other e-commerce sites.
Amazon reached a significant milestone – More than 100 million people pay for Amazon Prime, globally!
Amazon’s Founder Jeff Bezoz said: “The no.1 thing that has made us successful by far is an obsessive-compulsive focus on the customer as opposed to obsession over the competitor.” His focus on customers has led Amazon to create a benchmark when it comes to onsite personalization.
Customer Retention And Their Loyalty
Amazon Prime memberships have a 94% retention rate after the first year and 98% of those users stay on for the third year. What a benchmark Amazon has set for all the retailers!
These achievements are what each and every retailer hopes for their business and strives hard to achieve them. To get there, smart infusion of technology can definitely uplift the business, remove the guesswork and singlehandedly perform all the required tasks.
Let’s Take A Closer Look At Personalization
At A Store
You go to a shop and you look for top-wear, the salesman assists you in discovering all the brands the shop offers in top wear category and presents you the product varietal. You show interest in white shirts. Quickly reacting, the salesman brings you the wonderful white shirts, pairs up with jeans and boots for trial. You love it, and you buy the whole look! Technical term to this practice in personalization: Cross-Selling.
Had the salesman been suggestive of a superlative material/brand than what was being bought, he would be practicing: Up Selling.
“Photo by Min An from Pexels”
The brick and mortar store assigns a salesman to help the customers with discovering products and make purchase decisions. The more a salesman knows about the customer’s preferences and interests, the better the suggestions and assistance.
In the history of retail, this ‘personal touch’ has always been provided. It always helped with more sales and prompted customers to buy; in order to fulfill their wishes and needs.
This is personalized shopping: shopping assistance based on your interests and preferences.
At An Ecommerce Website
With the advent of online shopping, more and more retailers now consider catering the trend of shopping via the internet by establishing their e-retail businesses. But keeping your store static by just showcasing products won’t do much. Especially if your product count is high with lots of varieties and categories to offer. There are approximately 12-24 million e-commerce businesses but only a few are making a living out of it. Certainly, it’s a challenge to attract people on your store and once you bring in the traffic, the next challenge is to convert them into customers.
Undoubtedly, online retailers also want to serve customers the way offline shops do with even better offerings. So can retailers offer salesmanship on an online store? Is it possible to attend every customer? And individualize their shopping experience?
Considering you’ve shopped online on big retail giants *eyeing Amazon*, you’re already in possession of the much-treasured answer – Yes!
‘’Photo by Negative Space from Pexels’’
In order to implement personalization and recommendation engine on your e-store, you’ll require software that matches your business requirements.
Once implemented, it will track customers’ actions, their behavior on-site such as what they are browsing, wish-listing, adding to cart, their purchase history and generate recommendations accordingly in real-time. Advanced recommendation engines will allow you to choose products to be shown to your customers, let you cross-sell and up-sell your products and optimize your product page in a way that a customer gets everything they are looking for at the very same page.
Two Popular Recommendation Methods In Ecommerce
Recommendation engine collects data and filters it accordingly on the basis of how it has been programmed to produce recommendations. Currently, there are two popular methods for running the recommendation engine in the e-commerce industry.
CONTENT-BASED FILTERING AND COLLABORATIVE FILTERING
Collaborative Filtering:A buyer’s transaction history inspires personalization to the shopper whose product interests are similar to that of the buyer and then the shopper is recommended the top products that the buyers bought.
Here’s the intent of doing so: A’s shopping interests could be similar to C’s shopping contents and interests. There’s a high possibility that the things purchased by ‘C’ will be likable enough to be bought by A.
Content-Based Filtering: Recommends the top items most similar to the product viewed by the user. This filtering works by being focused on products rather than customers.
Browsing behavior, purchase history and such actions on-site are captured for generating similar items.
Top online retail giants use software that combines both of these methods to produce recommendations. Yet, we’re only halfway to the full evolvement of personalization. Think, if the current recommendation systems could yield such great revenues, what will the evolvement result in?
I’ll leave you with this questioned future and remind you of the current state: Customers today expect to experience personalization on every shopping website they visit. You as a customer do too!!
This being said, retailers should be keen on implementing recommendation software to enhance the shopping experience and not let the customers turn away.
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