The eCommerce industry is growing by manifolds across the world. From what started as a few stores that enabled online shopping; today, the smallest of brands are able to take their products online and market them to a large consumer base. Call it the ease of technology and the ability to use data, almost every eCommerce store is able to capture a segment of the consumer market - despite the rising competition.
But how do these stores really get a competitive edge over the others in acquiring, engaging and retaining their customers? By personalizing their customer communication on a 1:1 basis, just like a brick and mortar store’s salesperson would.
The need for personalization
There are almost 75% of your potential customers that are likely to get frustrated with irrelevant suggestions or products being displayed to them. Even according to Accenture, almost 40% of online shoppers abandon an eCommerce store to buy from another site. The reason being, they felt way too overwhelmed with too many options.
That’s why you need personalization.
When you take care of your customers on a 1:1 basis, they are more likely to remain loyal to your online store - no matter how many others come into the same niche. A study by Evergage also confirms the same by stating that 96% of digital marketers agree that personalization improves customer relationships drastically.
How does eCommerce personalization really work?
In the eCommerce space, machine learning is slowly being used to learn what customers prefer and how they want to consume information before making a purchase. The technology tests and adapts data based on various variables, to refine the best way to reach customers.
You’ve already experienced most of it while shopping on eCommerce stores like Amazon. But here is an overview of some ways that data works to fuel eCommerce personalization:
1. Demographic data
The gender, age, geolocation and other demographic data from customers is a great way to create consumer segments. It doesn’t just help in your business plan, but also know how these demographics work to change the consumer behaviour.
2. Consumer intent
Tracking your customer’s on-site behaviour, tracking and comparing it with their past sessions can help you understand what kind of products they are interested in. Their browsing history and latest purchases can enable you to recommend products that are more suited for their interests.
3. Behaviour prediction
With aggregated data, you can make correlations between your previous, current and future consumers to draw out behavioural predictions. This enables you to personalize your online store experience based on what they are most likely to convert on.
4. Custom variables
Apart from the obvious data you can track about a customer, you can also manually set rules to fuel your on-site personalization. For example, you can set an algorithm that drives the right customers to your preferred product pages. By matching the consumer data with your merchandising goal, you can take your online store success to a new level.
While there are endless possibilities of using machine learning on an online store, here are some that are most definitely improving customer experience and boosting the sales for top grossing eCommerce businesses.
Shaping up eCommerce with machine learning
1. Intuitive search
One of the most exciting applications of machine learning, is its ability to search for the things you might need, based on your previous searches and purchases.
When you’re looking for a product, you need to head over to an online store and search for it using the words that you think describe it the best. As a business owner in this case, you need to keep your fingers crossed and hope that you have made use of these typical keywords on the right product pages. That’s where machine learning steps in to save the day.
With machine learning, you can support a broader set of synonyms for the usual consumer searches. This ensures that the store visitors are led to the right products and more of your ranges are discovered at the same time.
2. Smart display
A layer of machine learning on your online store’s search algorithm, can fix leaky funnels. With machine learning, your store has the capability to learn on-site metrics and conversions. This enables you to create an intuitive display of products that are more likely to be converted on.
For instance, you can display the products that are sold the most or have the highest rating, at the top of the search results. Even product recommendations fall under this category of use cases. This optimizes your store for higher click rates and greater conversions. You can also filter these search results based on stock availability. Just as you see on Amazon!
This ability to deliver a dynamic experience across the different pages of your online store, personalizes a customer’s journey to conversion.
3. Timely interactions
There are a lot of customers who will reach your store, browse through a few products, add some to their carts and then leave without completing the purchase. By not making a timely interaction, you’re at the risk of losing a sale to a competitor store.
With machine learning eCommerce, you can implement chatbots on your online store. From providing customers with basic information like size charts for a specific product, the colours available and the shipping options, you can even take the interactions to make them more contextual. For instance, you can use chatbots to recommend products to a customer based on his browsing data or purchase history.
These data driven timely interactions can fuel your upselling and cross selling campaigns, convert consumers even when they are not on your store and further leak proof your on-site funnel.
4. Product pricing
Machine learning maps the existing customer data and offers predictive analytics that help you plan your store’s future. This could include the type of products you should be offering more, the pricing range that gets converted on the most and how you can drive maximum profits from each of the sales you make. You can easily A/B test what works the best for you to optimize price using machine learning.
Businesses can use machine learning eCommerce to plan ahead of time for campaigns and product stocking. For example, to save their resources before a holiday season as they would be required to offer bigger discounts.
5. Recovering abandoned carts
The average cart abandonment rate is a whopping 69%. That’s literally more than half your online store’s sales getting lost at the last minute. With machine learning eCommerce and automation in place, you can understand where and why your customers are dropping off, and create a recovery strategy for the same.
This includes sending them push notifications through your eCommerce store mobile app, shooting them a cart recovery email with a special discount to complete the purchase or simply, retargeting them with a social media advertisement.
Getting started with machine learning eCommerce
The new eCommerce industry is completely data driven. It is redefining retail and sales like never before. While the current state of the consumer market is constantly evolving and being evaluated, predicting their market response is the hack to creating a lasting brand.
One of the first and foremost steps your online store needs to take, before using machine learning, is identify your goals and the challenges you’re facing in getting more sales from your target market. This will help you streamline where you want to start making use of data to fuel personalized campaigns.
Machine learning is a vast technology and every use cases makes use of a different segment of it. If you’re a beginner or are interested in seeing how each of these segments work to empower stores like Amazon, sign up for a quick course to understand the basics of machine learning. Experfy's courses on Retail Analytics are designed to help retail industry professionals and managers understand and implement various data-driven solutions geared towards reducing costs and increasing revenue.