With millions of online websites selling tons of products, why do you think they will be attracted to try yours?
Better product display?
A plethora of different products?
An amazing product follow-up?
Good return policies?
But is that enough?
People even buy from sites where one or many of these attributes are missing. Then what keeps them buying from that ecommerce store??
The reason is to showcase the right products at the right time as per the shopping affinities of the shoppers, which we call personalization in ecommerce.
As the term in itself talks much, personalization is almost like the Gettysburg Address. It should be for the people, by the people, and of the people. People = Shoppers, in this case. Shoppers, in all ways, have developed an affinity to the sites that recommend products based on their behavior. We all know the AI works wonders behind the screen, but a shopper who doesn’t care much about the backend is constantly amazed by ‘How does the site even know what I want?
To ensure you amaze your customers every time they log in to your site and make them keep coming back, LISTEN TO THEM.
Now, it wouldn’t happen in the old traditional ways of knowing your million customers by talking to them one-on-one or texting them to get to know them better. So, it is necessary to let technology do the talking.
With every industry, since AI has taken over almost everything, it is time to know your shoppers an inch more by reading their scattered data.
The data availability is extreme. The end consumers who shop online are numerous, their shopping affinities and behaviors are being studied, and a large pool of data points is obtained post-analysis. Now, if you assume for once with such an immense market presence by the customers, it is hard to segregate their accounts and activities, then that is true.
Enter Machine Learning!
Machine Learning Techniques are here to make a difference in ways where the customer is the hero and users form the biggest human pyramid. So let’s see what you need to do to engage customers!
Their online footprints need a lot of hand-garnering and nit-picking to deliver precisely what they want. However, this evolution has promised many ‘point results, and if tired of endless scrolling, that is exactly what companies should do.
Advanced ML techniques adopt a regressive approach to creating a platform about experience and engagement. In such a case, ML excels at pattern recognition, and AI concentrates more on creating recommendation engines.
We indeed do this for our ecommerce friends with such regression techniques by applying the Hybrid recommendation system.
With a Hybrid recommendation system, you can generate and provide suggestions by combining two or more recommendation strategies.
After applying such techniques, you need more consistency to maintain those data fragments and form actual data points. Problems like cold-start and data sparsity are solvable by providing optimized, automated solutions. With Machine Learning As A Solution (MLaaS), the market will soon make almost $7.6 Billion by 2023.
But how do you genuinely personalize the experience for your shoppers?
Imagine when you walk out of an online store after looking at the products and instantly you receive a mail with specially tailored discounts for you around those products. Traditional mail marketing is different, where organizations use the same email marketing templates to reuse and reuse until we exhaust the same pattern and gain no insights into it.
But what if those same mailers catered to every customer as if they were talking to you?
No one would want to waste their time juggling through a million products when all they want is a blacktop that they had already searched for the last time.
Based on the data study, if you feel the shopper has already searched for 'Black Nike Shoes,' then when they open the site window, recommend them Nike products or black products because both attributes specify their affinity to the color: black and the brand: Nike. This not only hooks the shopper's interest but also shortens the path to purchase.
Well, the maximum traction that you can avail from the shoppers is when you provide their last searched products, suggested products from their past search history, or recommended products based on both their behavior and the site behavior, on the HOMEPAGE: the first thing they see when they land on your site.
Many of our customers have tried and tested this with the Unbxd Personalized Recommendations engine seeing a whopping 20% increase in conversions on the landing page.
No! We are not talking about the diversion of traffic but the segregation of customers based on their shopping intensity and behavior. Segregate them if they are existing customers or non-returning ones.
Technically, in ideal situations, you should focus 60% on the ones that keep returning by offering them special coupons for being loyal customers or providing them with other loyalty points to create a trust base.
Focus the other 40% on the ones that may or may not return. Send them personalized emails where you talk to them about their leftover products in the cart or their search history so that they believe that you do care about them. Either way, it is a win-win!
A person in the US wouldn't want to know the prices of 'Nike Slippers' selling in the UK. They would care more about the products that are specific to their region. So tracking the right location and targeting them with specific marketing campaigns and pricing should be a no-brainer.
With these 5 tips, you will certainly level up the market in your favor. By shaping the right personalization strategy, importing the right product recommendations, and integrating all the methods in place, you are making it easier for you and your shoppers to make the right choice.
Unbxd deals with providing such amazing personalization experiences by integrating into your ecommerce site and letting your shoppers take a recliner seat while they buy their choicest products from your ecommerce store! To know more,Book a demo