Some tips and tricks for MarTech teams across multiple industries looking to achieve hyper-personalization and capitalize on customer data-based insights
The development of artificial intelligence (AI) and machine learning (ML) technologies has transformed many industries in the modern market. Enhancing client segmentation tactics can benefit significantly from the sophisticated tools that AI and ML provide for evaluating massive volumes of data and receiving insightful information.
This article explores how AI and ML can significantly boost customer segmentation and how companies from various industries can benefit from understanding their customers’ behavior trends and sentiments.
Customer segmentation describes the act of separating customers into groups based on shared characteristics.
Traditional customer segmentation protocols tend to be broad and divide the audience by age, sex, personal interests, socioeconomic background, geographical locations, etc.
They also divide the target audience further into categories like a first-time customer and a repeat customer. However, the level of segmentation often ends there.
To take it to the next level with custom AI tools, you must first determine if you have access to the correct data for the project. You should also have the ability to seamlessly connect data from disparate sources, scale on demand to provide targeted recommendations and leverage machine and deep learning (ML/DL) techniques.
The benefits of customer segmentation for organizations are numerous.
First of all, it allows businesses to engage in targeted marketing, tailoring their promotional materials and campaigns to various client segments’ unique requirements and tastes, which results in Increased conversion rates, consumer engagement, and brand loyalty.
Secondly, by understanding the distinctive traits and behaviors of various segments, consumer segmentation promotes personalized experiences. Due to the ability to customize services, content, and interactions, organizations may increase consumer happiness and brand loyalty.
Thirdly, customer segmentation offers valuable information for product development, assisting companies in producing goods and services that better satisfy their target market. Companies can concentrate on creating cutting-edge products that appeal to their target consumers by recognizing specific segments’ individual requirements and pain points.
By identifying price-sensitive segments and allowing businesses to customize pricing plans and discounts accordingly, segmentation also helps optimize pricing by optimizing revenue and profitability while attracting and maintaining customers. Additionally, by developing customized retention tactics that consider the traits and preferences of various segments, client retention and loyalty can be increased.
Successful customer segmentation gives organizations a competitive edge by enabling them to set themselves apart from rivals through distinctive value propositions, customized experiences, and focused marketing initiatives. This uniqueness improves consumer loyalty and brand perception, positioning the company as the market’s top pick.
77% of marketing ROI comes from segmented, targeted, and triggered campaigns
While segmenting your audience is key to achieving your marketing campaign goals, it doesn’t allow you to personalize your offering to build strong brand loyalty and boost conversions. But at the same time, personalization and segmentation aren’t too different.
Segmentation provides the foundation by grouping customers based on specific (general) characteristics. Personalization helps brands connect with them individually based on their unique needs, desires, and motivations. We can perceive segmentation as “macro-segmentation” and personalization as a “micro-segmentation.”
However, we only achieve true personalization when we deploy AI and ML algorithms into marketing campaigns. This is because these advanced solutions help marketing teams continuously capture and analyze each brand interaction. Upon completion, you can use these insights to target specific customers with personalized messages on a massive scale.
Segmentation alone can’t deliver insights with the same level of detail or sophistication. As such, you can’t target customers with unique messages on a large scale without AI-powered data analytics.
Segmentation patterns (even micro-segmentation) are not personalization. Only personalization can offer unique messages and value propositions that exactly match many individual customers’ needs, preferences, and desires. Brands using segmentation fail to tailor marketing to individual needs, which is critical in today’s competitive world. And with a technology solution that uses artificial intelligence and machine learning, marketers can create high-volume messages and offers that reflect quality, 1: 1 personalization.
Traditional customer segmentation techniques frequently oversimplify consumer behavior by categorizing people based only on simple transactional or demographic data. This strategy disregards the wide range of motivations, interests, and behaviors that affect customers’ choices.
The rising amount, pace, and variety of data generated in the modern digital era provide another restriction of traditional approaches. Customers now leave a digital footprint after using social media, mobile devices, and numerous online platforms, which can be used to segment them. Traditional approaches frequently need help extracting valuable insights from this excess of data.
Furthermore, the scalability of traditional segmentation techniques could be improved. These techniques could only work when the customer base expands and diversifies since they can’t keep up with the changing customer landscape. They could also overlook opportunities for organizations to adjust and outperform the competition by failing to recognize developing markets or changes in customer behavior patterns.
Conventional segmentation methods need to consider the interdependencies and connections among client categories. Customers frequently have overlapping traits and may simultaneously belong to different segments. Ignoring these intricate connections might result in poor marketing tactics and make it more challenging to cross-sell or upsell goods and services.
Customer segmentation is being transformed by AI and ML technologies by utilizing cutting-edge algorithms and data analysis methods. Due to this advanced technology, businesses can process massive amounts of data in real time, find hidden patterns, and make accurate forecasts.
There are many benefits to using AI and ML in client segmentation.
Customer segmentation accuracy is improved by AI and ML algorithms. These algorithms can scan detailed information and spot complex patterns that conventional segmentation techniques would miss. Businesses can generate more specific consumer segments. As a result, allowing them to conduct highly focused marketing campaigns and provide individualized experiences.
AI and ML provide real-time insights into client preferences and behaviors. Businesses may obtain up-to-date information on their clients by utilizing these technologies, enabling them to react quickly to shifting trends and make data-driven decisions on time. The real-time component of segmentation powered by AI and ML helps organizations to remain flexible and modify their strategy in response to the most recent customer insights.
Scalability in AI and ML technologies enables companies to handle vast and varied datasets effectively. These technologies allow firms to scale their customer segmentation efforts as their client base grows since they can process enormous amounts of data without sacrificing accuracy or performance. Businesses can retain high-quality segmentation even as they expand and gather more data because of the scalability of AI and ML-driven segmentation.
There are two primary categories of customer segmentation in machine learning: supervised and unsupervised.
Supervised machine learning involves the marketer establishing predefined rules, and machine learning organize the data according to those rules. For instance, customers can be sorted based on the number of products ordered, average profitability, and average cost.
On the other hand, unsupervised machine learning employs an algorithm to discover distinct “clusters” among customers based on similarities that may not be immediately apparent. These clusters are typically small, enabling marketers to identify specific customer groups more effectively, thereby facilitating the provision of personalized offerings and targeted marketing. For example, unsupervised machine learning can identify the most focused customer cluster, comprising individuals who have made the highest number of product orders, spent the most money, and never returned to the website.
rinf.tech is featured by Clutch among Top 15 AI development companies in Romania. We help global enterprises and SMEs jump fast on the AI tech bandwagon and capitalize on custom ML/DL models thanks to our R&D Center, access to Europe's largest pool of AI dev talent, tribal knowkedge, and proven methodologies.
Your customer segmentation becomes more precise, dynamic, and capable of increasing conversions when you combine AI and ML with data analytics. You may examine client data more completely and produce in-depth results regarding the targeted segments thanks to machine/deep (ML/DL) learning techniques. Planning and carrying out the implementation of AI and ML in consumer segmentation carefully is necessary. Here are some different actions and steps to think about.
Select the segmentation process that best fits your company’s goals and your target market’s characteristics. Demographic, behavioral, psychographic, and predictive segmentation are typical approaches. By choosing the appropriate method, you can be confident that your segmentation model will include the most essential elements of your consumer base.
Choose the essential parameters or attributes that will be incorporated into the AI and ML models. These aspects should be enlightening and relevant to client behavior and preferences. Consider past purchases, browsing habits, demographics, social media usage, and customer interactions.
Divide your dataset into training and testing sets for model training and evaluation. Utilizing the training data, develop your AI and ML models and refine the parameters and algorithms for precise segmentation. Use the testing set to validate the models, then compare their performance to predetermined metrics. Typical measurements include precision and recall, rand index, and silhouette scores.
Aim for models that are both interpretable and explicable. For effective decision-making and the development of strategies, it is essential to comprehend the rationale behind client segment assignments. Feature importance analysis, model visualization, and rule extraction provide insight into how the models decide which segments to include in your data.
Integrate the trained models into your company’s business processes or marketing platforms. Ensure the data between your data sources and the AI and ML models flows smoothly. To verify accuracy and applicability, deploy the models in a production setting and continually check their performance.
Responsible data handling and compliance with data protection laws are ethical considerations. Implement procedures to safeguard client privacy and ensure the safe processing and storage of data. Customer consent for data gathering and analysis is crucial, as is transparency in data utilization.
Non-targeted campaigns show a 50% lower CTR than segmented campaigns
The advantages of using custom AI-based solutions to segment your target audience include the following:
AI takes the guesswork out of the equation. You’ll know exactly what your customers want and the touchpoints that really matter to them. Whenever business expectations don’t match those of the target audience, AI will alert your marketing team and provide them with an opportunity to fix it.
If there’s a problem with a marketing campaign, customer service, or customer experience, smart algorithms will quickly alert you to it. AI tools will tell you what the problem is, where it is, and what’s causing it. This approach can help companies identify product defects quickly, problems with their marketing messages, and limit customer churn.
In real-time, AI also helps enterprises understand their customers’ feelings about a product or service (even discreet emotions). It’s crucial because your customers won’t remember these feelings at a later date when you target them with a survey. Knowing what they are feeling in real-time also allows businesses to intervene at the right moment to avert abandonment.
Whenever your sales numbers are starting to plummet, AI will provide an opportunity to adapt your campaigns to help stop it. For example, if a specific segment is likely to return or fail to pay in full, you can avoid targeting that segment or not offer credit.
These insights can also help companies hold on to their customers, reducing the costs associated with customer churn and new acquisitions.
As we can see, the key to efficient and accurate customer segmentation is:
Dividing the customer base into smaller groups can allow marketers to identify segments they are not yet reaching. While this is true, there are still differences between consumers in any segment, so targeting one person in that segment may differ from the best way to target another person in the same segment.
Companies need to know where and when customers buy their products and services to better shape their distribution strategies. Unfortunately, traditional customer segmentation is not enough to provide this information on an individual level.
If you have a robust customer view of pure data and detailed customer information in the company’s data repositories, then you are in good shape.
Some time ago, a leading European retail company turned to rinf.tech for help building an advanced CCTV analytics dashboard to better define customer clusters, understand customer behavior, and improve informed decision-making.
Using stream cameras and video analytics servers (including Facial Recognition, Queue Detector, People Counter, Activity Visualizer and heatmap processing), our software engineering team built and delivered a dashboard solution to map each customer cluster’s needs and address their most sophisticated requests right away.
Our team delivered the custom-built solution with the following main features:
As a result, our retail client enjoyed:
Check out full project case story here.
Now let’s take a closer look at customer data maturity stages.
Identity resolution. Data/GDPR compliance. First-party data access.
Development of custom dashboards and KPIs. Deployment and integration of data analytics tools, systems, and capabilities.
Development of triggers and personalization. Channel context.
Custom audiences. Retargeting. Media/bidding optimization with machine and deep learning (ML/DL) algorithms.
Real-time decisioning/personalization. Ai-based website + eCommerce conversion rate optimization. Testing and validation.
Building customer cross-channel journeys. Online/offline integrations.
Building AI/ML predictive models. Attribution.
The future of customer segmentation with AI and ML is poised to revolutionize how businesses understand and engage with their customers. Hyper-personalization, predictive segmentation, automated customer segmentation, integration of unstructured data, cross-channel segmentation, and real-time and dynamic segmentation will be key trends in this domain.
By leveraging AI and ML technologies, businesses can unlock the immense potential of data, deliver tailored experiences, anticipate customer needs, automate segmentation processes, gain insights from unstructured data, create a holistic view of customers across channels, and adapt to changing customer behaviors in real-time. These advancements will enable businesses to achieve deeper customer understanding, enhance engagement and satisfaction, and gain a competitive edge in the dynamic digital landscape.
With the ability to access insightful data and improve marketing and commercial strategies, AI and ML technologies have emerged as game-changers in consumer segmentation. Businesses can gain a competitive edge, offer individualized experiences, and strengthen client relationships by utilizing the power of these technologies.
Organizations must integrate AI and ML into their consumer segmentation strategy to be competitive in today’s changing market. By implementing these technologies, businesses can boost their customer segmentation efforts and build deep ties with their customers.
Embrace the power of AI and ML in customer segmentation and unlock the full potential of your business. Consider implementing these technologies to discover all the possibilities for your company.
Demystifying the concept of data modernization, laying out a comprehensive roadmap for businesses embarking on this transformative journey.
Delving into the multifaceted costs of GenAI projects, uncovering not just the obvious expenses but also hidden ones, alongside strategic considerations for businesses.
Examining AI’s diverse effects on finance, delving into its advantages and obstacles, aiming to understand its transformative influence and future impact on the sector.
