Technology has seeped into our lifestyles impacting how we live, shop, work and entertain ourselves. Thus, Artificial Intelligence (AI) and Machine Learning (ML) have become two buzzwords in technology that we hear all the time. However, AI and ML have been found to be used interchangeably more often than not. And that is where we confuse the two.
What is Artificial Intelligence (AI)?
To define in a layman’s term – “AI is the study of how to train the computers so that computers can do things which at present human can do better.” AI is not a system. It is something which is implemented in a system.
What is Machine Learning (ML)?
Machine Learning on the other hand, is an application of AI where we give the machine access to data and allow them to learn on its own without being programmed. It provides a system the capability to learn automatically and improve that with experience.
Hence, developers – typically – would first create an algorithm in the system to follow (AI), and then develop its machine learning capabilities to enable it to make its own rules from/with experience.
AI and eCommerce
Retail businesses worldwide have been using AI to wow their customers in variety of ways by:
- Improving customer recommendations by studying products bought earlier or bought as an alternative. Some machine learning engines like Perzonalization use cutting edge algorithms to learn prominent product features and user preferences to improve customer recommendations.
- Automating customer service with the help of chatbots
- Sending automated emails for cart abandonment, new offers and deals etc.
- Improving and simplifying the search functionality
- Learning more about customers through their online behavior (clickstream and search history)
- Applying data to automate warehouse operations
- Listening to online conversations of their customers and audience to improve marketing effectiveness.
According to a research, global revenue from AI will catapult from $643.7 million in 2016 to $36.8 billion by 2025.
When it comes to eCommerce, knowing and understanding the customer is paramount. But the target audience is hardly homogenous, and hence, a standardized marketing approach for one and all is hardly a solution these days. Thanks to techniques like micro-segmentation and AI, this has now become convenient for online merchants.
What is micro-segmentation?
Micro-segmentation is a step ahead of traditional segmentation where marketers used to segment their audience based on demographics, location, psychographics and behavioral data. With the era of big data, thanks to the myriad of new data types available, we are able to learn a great deal about our customers. Micro-segmentation will further break down the above broad categories into smaller and behavioral sub-categories to create a narrower target persona.
The data businesses now collect are much more granular and detailed. This data could be collected through social media channels, online interactions and one’s browsing history. With the help of this data, these micro segments of customers have become the target for tailored marketing and more personalized services. The narrower the segment, the better the scope for customized services, and better the returns. By offering customers with more of what they want, online businesses are more likely to make additional sales.
Benefits of micro-segmentation
- Fewer users result in better control
- Ease of implementing predictive analysis
- Better understanding of campaign effectiveness
- Reduction in planning time to finalize right marketing approach
- Cost effectiveness
- Increased brand loyalty
Getting started with micro-segmentation
To get started, the best way is to consolidate data from different silos (such as social media, website, mobile etc.) across your business into a single content repository. Your CMS should be the single source for all this data. Once that is done, you can use this holistic user data to better identify high-value micro-segments and personalize experiences for these segments with the help of AI tools.
There are different forms of data at your disposal as an eCommerce owner. It’s just a matter of choosing which one suits your current purpose. Some of these available types can be:
- Activity-based: You can tap into a number of resources to gather this kind of user information. Some of these are website traffic, call and mobile data, purchase history, and response to offers and incentives.
- Social Network Profile Data: This form of data can help you retrieve and look at your target audience to understand their profiles A few examples would be group memberships, and work history.
- Sentiment Data: Finding out what makes your target audience click is the first and the most important step to piquing their interest. Emotions are a big part of the customer’s experience. Look into their likes or follows on social media, comments, feedback and reviews.
Plenty of companies are using the method of real-time micro-segmentation for more efficient marketing and product promotions by catering to narrower and more specific audiences. Through big data and AI, segmentation produces a personalized product to just fit to the consumer’s needs. Online retailers can use real-time data to tweak their product offerings to loyal consumers, and make them more appealing for them.
What happens when AI meets micro-segmentation?
The future of marketing is accurate micro-segmentation because true digital experiences that will resonate with your customers will only come from having a clear picture of their needs, what they value and how they behave. Micro-segmentation uses cross-channel behavioral insights and large amounts of data from different internal and external sources, enabling you to deliver highly personalized online shopping experiences. However, it is important to note here that digital marketing alone is not equipped for managing this intense level of analysis that includes the complexity of layering hundreds or thousands of data points to implement a micro-segment. And that is where AI comes into play. Artificial Intelligence does the heavy lifting of all this complex data and helps uncover micro-segments with ease, making micro-segmentation and personalization possible.
Big data applications powered with AI and micro-segmentation sucessfully collect, store, and organize data from multiple sources such as CRMs, social media platforms and websites. They then drill this massive data to small actionable chunks essential to creating a customer-centric marketing approach.
AI powered predictive technologies and micro-segmentation
AI tools are being used extensively for micro-segmentation and the possibilities are endless. With AI powered predictive technologies like the ones mentioned below, eCommerce businesses can excel in micro-segmentation marketing:
- Purchase intention prediction: With the help of predictive personalization technology, businesses can understand the context of a customer’s visit to their website. This intent, whether it is window shopping, or a real visit to purchase helps in target marketing.
- Personalized product recommendations: Based on each shopper’s unique preferences and needs, these AI tools help deliver personalized product recommendations. Personalized recommendations can also be sent out via emails, but AI can further enhance how consumers are targeted or segmented with products chosen for them. For example, Wayblazer makes personalized recommendations about travel and lodging based on location and the customers’ needs.
Morhipo uses a similar predictive technology to recommend products to their customers. They also have a live support to assist their customers 24/7.
- Personalized emails: Automated personalized emails can help in increased click-through rates, are real time, customizable and increase customer loyalty by optimizing customer retention. These emails can be about anything – from sending personalized recommendations, intimating customers about new products to addressing cart abandonment issues.
- Real-time performance tracking: AI tools coupled with micro-segmentation can help you track conversion rates for all pages, widgets and triggers in real time.
- Chatbots: Chatbots can be programmed to handle client operations such as resolving product issues, returns, processing warranties, and help customers in navigating across big and complex websites easily. Using Chatbots can also ensure that customers who visit websites at odd hours will continue to get personalized services and prompt support. One of the other AI and micro-segmentation best practices has been adopted by North Face where they’ve identified a big pain area where customers were finding it difficult to navigate through their huge product catalog in trying to find something they liked. They fixed that by using AI to develop a new tool that walks customers through the process and picks out the perfect products for them.
All the consumers do here is answering a series of questions covering aspects that may influence their buying decision – such as which style would they prefer:
And what features would they like:
Once all the information is gathered, their chatbot automatically picks out a list of personalized product suggestions:
Given below is an example of data-driven, dynamic behavioral messaging. This can also be done through incentivized overlays and notifications, exit intent overlays (such as: Leaving so soon? Sign up for our newsletter and get a 10% discount on future purchases), retargeted display ads, emails and much more. The most important thing here is to be able to depend on data and use that to design and trigger highly targeted, personalized messages for the micro-segment you have built.
Every online business must understand that the buyer journey is no longer linear, which is why things like mindset, location, recent activities, current environment and other contextual factors need to be taken into consideration in order to give the customers the best personalized experience. Micro-segmentation offers you the best way to identify who your buyers are, what matters to them, and how they behave. Gathering this information enables you to create the most compelling and relevant experiences for them.
AI helps these e-commerce businesses to analyze millions of day-to-day interactions, and micro-segmentation enables them in targeting down the offers to a single customer. The integration of e-commerce and AI is not only transforming millions of online transactions every day, but is also impacting in-store purchase behaviors.
Artificial intelligence isn’t a craze that’s going away anytime soon. A huge number of eCommerce brands (large and small) globally are testing AI tools in some form or another on their websites. If you’re able to embrace AI, you can get ahead of the curve and wow your customers too. Start your trial with Perzonalization today and discover how AI powered micro-segmentation can help your eCommerce business.