Personalization Has a Best Friend. Its Name is Machine Learning.
If words could hold weight, ‘personalization’ would be equivalent to a bag of bricks. From B2B, B2C and every business in between, personalization has widespread application and inescapable value. Whether it’s personalized content suggestions or predictive site search, all businesses are in a vicious pursuit for tailored experiences because of a simple reality: it’s critical to success. In this consumer-run world of commerce, it’s a must-have to meet customer expectations. So much so, in fact, that 52% of consumers are likely to switch brands if a company doesn’t make an effort to personalize communication.
The kind of personalization we’re talking about isn’t just blasted email campaigns with ‘$Name’ tokens in the subject line. True personalization understands customers at a deeper level – their real-time intent, purchasing history, preferences and complex shopping journeys. It then utilizes these insights to tailor congruent, 1:1 interactions across channels.
In today’s complex digital environment, this seems like an especially daunting hurdle. With no distinct beginning and end to the consumer journey, where should businesses start in their quest for omnichannel personalization?
The answer to this question can be found in machine learning. At this point, you’re probably familiar with these words – they’re usually heavily weaved into any article about personalization. And there’s a good reason for this. True personalization is not possible without the accompaniment of its trusty sidekick ‘machine learning.’
Machine learning capabilities
Machine learning essentially enables a computer to “figure it out for itself.” It can recognize patterns and automatically learn without being explicitly programmed. Technology that has this capability solves a multitude of ongoing internal hurdles for businesses and lays the necessary groundwork for achieving true personalization. Here’s how.
Track rich data
Today, many merchants struggle to not only gain a single, comprehensive view of their customers, but also to capture the deep, real-time data of each unique shopper that is critical for impactful personalization.
It’s not hard to see how this is a challenge. Consumers are complex and, in the digital age, so is their journey. The products that catch their attention more than others, the items they choose to research more critically, the size, color and style of items that they ultimately purchase – these are all aspects of how individual consumers behave and interact with brands. And they express these nuanced signals across digital and physical touchpoints.
Machine-learning solutions automatically and efficiently track incredible amounts of rich consumer data. For example, as customers browse through an ecommerce site, machine learning can track demographic information, purchase history, browsing, searching and engagement patterns. Machine learning can store this real-time data with other offline shopper data (CRM or POS data, for example), to create a comprehensive repository of deep, rich customer information. From this large body of data, machine learning can establish specific patterns and data relationships to piece together individual customer profiles (their personas, interests, attributes, preferences) and segment analytics.
Create a relevant experience
Yet, the data is only one piece of the puzzle. How can these insights transpire into a more enhanced customer experience? Even when businesses do have rich data about their customers, without a machine-learning solution, they don’t have the tools and automation to translate the intelligence into a better customer experience.
Machine learning solves for this, too. Because it has the capability to learn and take action without being explicitly programmed, it can dynamically create tailored experiences based on the signals of each unique shopper.
For example, using historical data and real-time online behavior, machine learning sifts through merchants entire catalog and identifies the options that are aligned with the specific needs, preferences and behavior of that individual customer. Whether it’s selecting the best products, categories, brands, colors, styles to recommend or shifting search results to help them find what they’re looking for more efficiently – it’s all driven and accomplished by machine learning. As shoppers keep clicking away online, signaling their current needs, machine learning continues to refine and improve the customers’ options, ensuring the recommendations are always extremely relevant to the user’s real-time needs.
Predict what’s to come
So, we know that machine learning provides the capabilities to personalize shoppers’ immediate experience. But even that is not enough for today’s consumers – they expect businesses to deliver relevance and personalization beyond their immediate needs.
Yet again, machine learning is to the rescue! Machine learning utilizes insights to establish parallels between what customers have bought and are likely to want in the future, creating opportunities for customers and businesses.
With predictive recommendations, for instance, customers are more likely to discover new items or products that they’re inclined to buy – nudging them towards more conversion and greater lifetime value.
On the other hand, merchants can seize valuable marketing and merchandising opportunities through predictive analytics. Machine learning can identify customer segments that are likely to engage with specific promotions, while also identifying how well certain products will do across different segments and customers. The result? Predictive insights that put the customer at the heart of the experience and that are organized to drive business decisions throughout the organization – AKA true personalization.
Ecommerce and beyond
Implementing a machine-learning system is a crucial first step to providing a personalized experience. As fast as these solutions can be implemented (usually within a few weeks), they can start making impact. For example, for our partner Lukie Games, 4-Tell’s product recommendations influenced a 2.9x increase in conversion, 15.2% increase in AOV (average-order-value), and a 3.4x increase in pages viewed across their ecommerce site.
Looking beyond the ecommerce experience, however, businesses can use a machine-learning solution to equip their workforce with the customer-specific knowledge and predictive analytics to further personalize the in-store and online experience – truly reinforcing an omnichannel experience that is 1:1.
4-Tell’s Smart Commerce ℠ Platform, for example, surfaces the rich data from our machine-learning system and organizes it into individual customer profiles, shopper segments and a unified product catalog. With this information, in-store sales associates can utilize our predictive recommendations and historical sales data to personalize the experience for every shopper that walks into a brick-and-mortar. To drive the impact of connected human interactions even further, 4-Tell’s Smart Commerce℠ Platform allows sales associates to manually curate product boards through 4-Tell’s ‘Your Store’, a microsite that lives on the native ecommerce platform and is dynamically built for every unique shopper.
Of course, this kind of organization-wide transformation can’t happen overnight. But with personalization relying on the efficient, scalable and intelligent qualities of a machine-learning relationship, bringing these two together at the start will ensure merchants are laying the groundwork for achieving exceptionally tailored 1:1 customer experiences that drive loyalty, increase revenue and improve overall employee engagement.