How to take customer data segmentation to the next level with custom AI solutions

Some tips and tricks for brands looking to capitalize on customer data based insights and turn them into solid BI

For many years, businesses across industries have been leveraging data and analytics to segment audiences to improve marketing campaigns and increase conversions. However, it's not exactly straightforward.

That's where artificial intelligence (AI) comes in. When you add AI into the mix, your targeting will become more accurate, dynamic and boost conversions. You can also do all this in real-time with various market segmentation strategies powered by smart algorithms.

As such, at the dawn of the fourth industrial revolution, paper-based surveys and general data analytics simply doesn’t cut it. It also doesn’t help deliver the experiences that your target customer base has got used to.

This article explores how MarTech and AdTech departments of companies across various domains can benefit from  AI-powered customer segmentation and what needs to be done to better understand your customer sentiments.

 

Contents

What is customer segmentation?

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.

What's the difference between personalization and segmentation?​

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.

AI-driven customer segmentation

When you add AI to data analytics, you can analyze customer data more thoroughly and generate in-depth results about the targeted segments. You can also use this information to automate personalized marketing campaigns for each group.

This approach will generate superior results compared to traditional analytics-driven marketing campaigns.  For example, custom AI solutions can eliminate human bias when analyzing data and identify hidden trends and patterns that you never thought of before.

Businesses also benefit from automatic segment updates that help accurately reflect changes in the marketplace. Other advantages include increased personalization in real-time and limitless scalability.

To benefit from AI-powered customer segmentation, businesses must first change their investments to understand customer sentiments. It starts with turning qualitative comments into quantitative data with specialized custom AI solutions.

This approach demands companies to bring together data from call center notes, customer relationship management systems, emails, reviews, social media engagements, chatbots, and more.

Once you have all this data in a centralized warehouse, custom AI models and tools will go to work and look beyond essential customer sentiments (positive or negative).

For example, to analyze customer experiences, you have to extract and map keywords related to the following:

  • Activities (ordering, service delivery, etc.)
  • Available resources (knowledge, product, skills, systems, and more)
  • Content (or situation that impact experiences (like time of day)
  • Customer role (neutral or provide suggestions)
  • Engagement (call center and chatbot interactions)

 

You can also break down customer emotion into categories such as love, joy, anger, surprise, and more. Cognitive responses are things like suggestions, complaints, and compliments. Your AI tool will transform all this information into predictive variables that can train AI models to pinpoint when customers are neutral, unsatisfied, satisfied, or have a complaint.

In addition, you can create segments by traffic source (for example, Google Ads, social media, and email campaigns), search behavior, time spent on the platform, content viewed, type of device (mobile or laptop), likes, and preferences.

There isn’t a “right” way to segment your audience. It really depends on your business, industry vertical, goals, customers, location, and so on. So, turn-key AI solutions can fall short as they didn’t build it specifically for your unique business (and audience).

Are you looking to build an AI-powered Customer Data Platform to take your MarTech/AdTech campaigns to the next level?

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, and proven methodologies.

Key benefits of AI-powered customer segmentation​

The advantages of using custom AI-based solutions to segment your target audience include the following:

AI highlights what’s missing

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.

Quickly identify the root cause

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.

Real-time emotional and cognitive responses

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.

Stop or Prevent Tumbling Sales

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:

  • Have a lot of accurate and complete data (the more and cleaner – the better).
  • Invest in developing the skills, technology, and knowledge base needed to work with data and segmentation strategies.

 

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.

Now let’s take a closer look at customer data maturity stages.

Customer data platform maturity stages

1. Unified customer profile (1st party data)

Identity resolution. Data/GDPR compliance. First-party data access.
 

2. Actionable customer insights (customer metrics)

Custom dashboards and KPIs. Data analytics tools, systems, and capabilities.

 

3. Outbound channel optimization (outbound media ROI)

Triggers and personalization. Channel context.

 

4. Digital media optimization (paid media ROI)

Custom audiences. Retargeting. Personalization. Media/bidding optimization.

 

5. Owned media optimization (CRO)

Real-time decisioning/personalization. Website + eCommerce conversion rate optimization. Testing.

 

6. Cross-channel orchestration (CX)

Customer cross-channel journeys. Online/offline integrations.

 

7. Advanced analytics (Personalization at scale)

AI/ML predictive models. Attribution.

In summary

The best part about all this is that the whole process can be codified and automated. It’ll also require little human intervention to manage it. Custom AI-based segmentation also provides all stakeholders with a comprehensive view of the entire customer journey to anticipate potential issues and respond accordingly.

Data helps businesses better understand their customers. Custom AI-powered data tools help enable more accurate customer segmentation, more meaningful engagements, and enhanced customer experiences—all of which have a direct impact on your bottom line.

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