Unlock Immersive Personalization through Data Science
Shoppers desire a completely personalized shopping experience. In fact, as time goes on, consumers’ expectations for connected, individualized experiences based on their past behavior and preferences has continued to evolve and grow.
Fulfilling these expectations for individually tailored commerce interactions is no easy feat for businesses. However, through data science and machine learning, creating 1:1 personalized experiences that go beyond product recommendations becomes possible.
Data science and machine learning can revolutionize the shopper experience by creating a completely personalized online store for each shopper. Not only would this store help the shopper quickly and easily find the products that they want to purchase, but it would also drive customer loyalty by combining the dynamic variables of product and people relationships to achieve fully personalized customer experiences.
Product Recommendations vs. Market Segmentation: What’s missing?
Historically, businesses have used product recommendations and market segmentation to drive personalized experiences. While these solutions have fulfilled consumer expectations in the short-run, both of these systems have specific limitations that won’t allow businesses to reach the next generation of personalized experiences that is required of them.
To give some background, here’s how the two work to enhance consumers’ experience.
With product recommendations, for example, products are recommended from the initial product that consumers view. From the viewed product, other items are recommended that are either ultimately bought after viewing the source product, or bought with the source product.
These personalized experiences are all driven by the relationship between particular products, or to put simply, product-to-product relationships. These interactions help to expose more of the catalog to consumers, while increasing the likelihood that they’ll find items that align with their preferences sooner. Though this can drive overall conversion and average-order-value, it only takes one dynamic, that being the relationship between products, into account when enhancing the online experience.
Market segmentation, on the other hand, is concerned with identifying shoppers that are likely to buy from your store. This kind of solution uses customer data to establish relationships between specific groups of consumers and the store and then uses that information to predict specific segments of shoppers that are likely to interact with the business.
Market segmentation is great for driving engagement for email campaigns and advertisements – it ensures that specific promotions are getting in front of the correct audience. However, because it only deals with the relationship between the company and the customers, it doesn’t help shoppers find specific products that are aligned with their needs.
In the end, both of these solutions don’t properly connect data to establish relationships that drive truly personalized shopping experiences. One is only concerned with products, one only with people – it fails to use both relationships when driving an experience as well as failing to use other dynamic areas of product and people relationships to drive more engaging and personalized experiences.
To create a 1:1 experience that is tailored to each customer, businesses need a solution grounded in data science and machine learning that brings these two pieces together – a system that is driven by the relationship between people AND products and that utilizes the dynamic variables within these areas to adhere to shopper’s various contexts.
Enter the personalized store concept…
At 4-Tell, we have recognized these limitations within current solutions and looked for innovative ways to combine the power of data science and machine learning to build an experience that is truly personalized.
Our innovative solution has come to fruition as ‘Your Store’ – an ecommerce microsite that is dedicated to the individual preferences and unique shopping behavior of each individual shopper. This microsite is populated with product recommendations that are automatically supplied by our machine-learning algorithm and manually curated by your team.
Utilizing the power of real-time behavior, historical purchases, human connection and site search, our powerful solution balances people and product relationships to create an experience that is truly personalized to the individual and adheres to the various, immediate contexts of each shopper.
Supported by data science and machine-learning, 4-Tell utilizes the power of real-time behavior and historical information to automatically curate product suggestions that customers are most likely to prefer.
We call this our ‘Virtual Assistant’, and it’s one component of our ‘Your Store’ solution.
When creating these automatic suggestions, our algorithm tracks real-time behavior and uses the information about recent views as the most important component for generating recommendations. The reason is simple – it signals shoppers’ current intent. Therefore, our virtual assistant uses recently viewed items to suggest products that are ultimately bought with the items viewed, recommends products with the largest likelihood of purchase based on recently viewed products and suggests trending products in the categories that shoppers have been viewing.
Let’s say customers viewed an item that they want to engage with further – maybe by reading more product information or adding it to their cart. In addition to our algorithm predictively suggesting what shoppers are likely to buy next based on viewed items, shoppers can visually access their recently viewed products to browse by items they’ve engaged with previously.
Apart from consumers’ real-time behavior, historical purchases play a large part in helping dictate customers’ future behavior and targeting items that are specifically aligned with their preferences.
Taking this aspect into account, 4-Tell’s virtual assistant also recommends products with the largest likelihood of purchase based on the entirety of a customer’s purchasing history. These suggestions include replacement products for items that have been discontinued, and companion products with purchases that are still in stock. In addition, the virtual assistant suggests trending products in categories that the shopper has purchased.
Furthermore, through ‘Your Store’, shoppers can visually access their historically purchased products. This enables the shopper to always have their favorite products at their fingertips, making it simple to maintain or replace the products that they’ve previously purchased.
Curated by your team
The relationship between customers and products is only one side of the coin. How customers interact with sales associates – and how they invest loyalty through nuanced communication and interactions – is another part of customers’ relationship with a business and it therefore must be a part of personalized experiences.
4-Tell has baked this element into our solution as well. Through ‘Your Store’, 4-Tell gives businesses the capability to curate product boards for each unique customer. Sales agents can leverage our customer insights (comprehensive customer history, real-time behavior and predictive suggestions all surfaced through our Smart Commerce℠ Platform) and use their nuanced industry knowledge to create personalized suggestions for each customer – further personalizing the experience while building relationships with high-value segments on a 1:1 basis.
Finally, 4-Tell’s solution tracks the shopper’s interactions with the products that sales agents post, ensuring they can receive commission for their efforts.
Sometimes it doesn’t matter how compelling and insightful the product recommendations or manually curated suggestions are – customers are just looking for something different. For this purchasing scenario, 4-Tell’s ‘Your Store’ includes personalized search so shoppers can quickly find the products that align with their needs and preferences.
However, we don’t let our insights fall by the wayside. 4-Tell’s personalized search results are initially sorted by word match and trending sales, then further refined by the shopper’s recent views, cart, previous purchases and shopper’s category and brand preferences – ensuring that customers spend less time sifting through items to find just the products that they’re looking for, every time.
Create personalized customer experiences with 4-Tell and data science
This is the future of personalized interactions – it uses all the rich information of customer and products relationships to drive comprehensive personalized experiences that connect the online and offline worlds and adhere to shoppers immediate context.
This is not just a concept – it exists today and is actively being deployed within 4-Tell’s customer base.
Be on the cutting-edge of technology and personalization – schedule a demo to learn more about how we’re reinventing the customer experience through data science and machine learning.