Machine Learning: The Key to Modern Customer Profiling

A Forbes report states that 80-85% of enterprise businesses are investing in AI solutions at present.

AI is a machine’s capability to replicate intelligent human characteristics by taking a set of entries into account and processing that data to deliver preferred results.

With technological improvements in machine learning, organizations can build customer risk profiling models that identify potential customers who are at risk. 

In the marketing technology region, using AI in customer profiling results in unmatched personalization and perfect ad targeting.

The Base of Customer Profiling

The customer profiling process is all about the collection and inspection of data related to customers to develop deeper representations of customer segments within the industry. 

Traditional profiling concepts were dependent on transactional and demographic data, providing a partially stable view of customers.  

Furthermore, after some time, the digital age has brought abundant fresh data sources containing IoT device data, online behavior, and social media activities, notably nurturing customer profiles but also making the analytics process more complicated.

What is Customer Profiling in Simple Words?

Customer profiling is the collection and inspection of customers’ information to know what they need and who they are. 

In simple terms, it indicates website visitors’ characteristics that are expected to make product purchases. 

It is an outline of a customer’s group who share common behaviors, and customer profiles can help brands effortlessly identify, define, and segment their niche market.

Customer profiling in marketing allows brands to select suitable channels and messaging concepts in their marketing campaigns.

What is the Difference Between Customer Profiling and Customer Segmentation?

Customer segmentation and customer profiling are relevant concepts that serve clear intentions but are frequently used together to get a complete picture of a customer base.

Let’s highlight how the two differ from each other:

Scope

Customer profiling concentrates on small groups or people to create a detailed understanding of their needs. 

Customer segmentation looks at the entire customer base and wants to indicate familiar behaviors among customers to consolidate them into segments. 

Goal

The basic goal of customer profiling is to develop customized experiences for small groups or lone customers. It deeply meets the specific expectations of customers. 

The end objective of customer segmentation is to show off and target a particular category of customers with identical characteristics. 

Degree of detail

Customer profiling contains specific preferences of separate customers and their past purchasing history. 

Customer segmentation provides a complete view, focusing on regular behaviors among enormous customer segments.  

Use case

Customer profiling is valuable for customer service and sales teams to customize communications, enhance customer satisfaction, and develop solid bonds with single customers. 

Customer segmentation is always according to a specific set of goals. 

Representation

Customer profiling is usually represented as independent profiles, indicating complete data about small groups or particular customers. 

Customer segmentation is normally represented in the mode of clusters or segments, indicating the category of customers with matching characteristics. 

These two techniques are useful for organizations looking to know and engage with their customers fruitfully.

When integrated, they offer a substantial method for segmenting customers and understanding them, leading to enhanced organizational results.

What are the Benefits of Customer Profiling to a Business?

Increases profit

An advertiser or a marketing professional can create ads that are relevant to niche customers after identifying a customer’s profile. Best-performing ads can increase the possibility of product purchases. 

Customer profiling allows a marketing strategist to look for which products perform well among others and come up with advertising plans around such products. 

Engage fresh customers

Knowing details about a few customers lets advertisers develop greatly engaging advertisements for specific groups of people. 

Makes marketing transparent

Profiling customers lets marketers inspect the people who are interested in your services and customize particular advertisements for those people.

This makes marketing simpler, as you’re not wasting effort with meaningless marketing.  

Enhance the rate of response

Having knowledge of the customer’s purchasing behavior will allow you to select the suitable marketing method. Hence, you can see an increase in the ROI and response rate of customers.

Enhance the activity of acquiring customers

A customer profile can show the characteristics of perfect customers for an organization.

Hence, you can concentrate on customers who have these characteristics. So, the chances of converting these customers into possible customers. Therefore, your marketing approaches will bring higher returns.     

The Data Analytics Role

Data analytics is necessary for decoding the complicated and varied nature of the latest customer data.

By carrying out modern analytics techniques, organizations can fetch significant insights from bulk datasets, pointing out preferences or trends that describe numerous customer segments.  

Predictive analytics, a subpart of data analytics, can look at past data to predict customer behaviors for the future.

Companies use this predictive feature to customize offerings, foresee needs, and attract customers with suitable messages at the perfect time, notably bettering the customer experience.

What is Future Customer Profiling?

With the rapid development of machine learning and data analytics technologies, they play a critical role in the growth of customer profiling. 

Let’s illustrate how both ML and AI are converting the view of customer profiling:

Actual customer insights

AI-enabled tools are automating profiling and routine data analysis tasks, saving time for sales and marketing teams to shift their focus on creative strategies and new tasks that need human attention. 

Improved team collaboration

The insights obtained from AI and ML-based customer profiling ease smoother collaboration between various teams within an organization. 

Social media data integration

Social media offers a load of information on customer preferences, and this information will be used by businesses to understand their customers even better. 

Tools including Hootsuite and Brandwatch insights let businesses track conversations, mentions, and hashtags associated with their industry or brand across social media.

Also, customers’ opinions about services or products can be collected through social listening or sentiment analysis tools.

What is the Impact of Machine Learning?

Machine learning algorithms offer improved solutions to improve the inspection of data, human behavior, and data mining processes using machine learning models.

This feature makes machine learning specifically perfect for filtering customer profiles gradually. 

ML algorithms are used to segment customers actively, identify fraudulent behavior, and forecast life changes that might impact buying intentions.    

Natural language techniques can inspect customer reviews, interactions, and feedback in natural language to fetch insights about pain points, preferences, and customer sentiments.

Moreover, businesses rely on AI and ML solutions for customer profiling to manage their customer relationships and adapt new marketing methods to create focused communication.

What Data is Needed to Develop a Customer Profile?

Below are the various types of data you need to gather to develop a detailed customer profile:

1. Age
2. Location
3. Services or Products used 
4. Preferences
5. Interests
6. Buying habits
7. Income
8. Gender

All this information is essentially important for your support, marketing, and sales teams to understand possible customers and increase the number of existing ones.

What Role Does AI Play in Customer Profiling?

eCommerce companies primarily use AI to profile their customers.

For instance, Amazon’s recommendation engine inspects your past browsing records and purchases to recommend products, thereby increasing the user experience.

Let’s illustrate the role of artificial intelligence in customer profiling: 

Data collection and consolidation

AI-based tools can freely collect and combine data from numerous origins, including customer support communications, purchase history, app reviews, and website interactions. 

Automated data analysis

AI-based algorithms can examine huge volumes of data rapidly and perfectly. 

Intelligent segmentation

AI algorithms can automate the activity of pointing out and segmenting customers according to particular criteria, including engagement levels, purchasing habits, and demographics.  

Recommendation engines

AI-enabled recommendation engines can recommend suitable content or products to users according to their previous behaviors and preferences.

How can Express Analytics help?

Express Analytics’ expertise lies in data-based psychographic insights that can be used to improve customer profiling activities for global businesses.

We help clients integrate data from countless sources, ensuring valid customer information via productive data management.

Also, we help them point out definite customer segments, which lets them create personalized marketing strategies by keeping each segment in mind.

3 Common Customer Profile Mistakes

Below are the three major mistakes that B2B organizations frequently make when handling their customer profiles:

Poor research and data gathering

The majority of B2B companies miss a trick by not spending enough effort and time to conduct research and gather data about their niche audiences, which can result in poor or incorrect customer profiles. 

Outdated profiles 

Another mistake includes keeping outdated customer profiles. 

Why?

Ignoring this feature can result in lost opportunities and less-than-great marketing strategies.

So, ensure some processes are in progress for continuous data gathering and inspection to keep your customer profiles updated at all times. 

One-size-suitable for all mistakes

Many B2B companies imagine that all customers within a specific segment (or industry) share their preferences, characteristics, and needs. 

This frequently seen misconception can lead to mediocre methods of sales and marketing or failure to impress all various customer groups productively. 

The best strategy is to divide customer profiles into more targeted, smaller groups according to factors including purchasing stage, company size, or challenges. 

This method allows you to adjust your messages and offerings nearly to the particular needs of each segment.

Conclusion

Undoubtedly, customer profiling using machine learning is the best way to identify and nurture the leads you’re trying to convert into customers.

Also, don’t forget that customer profiles provide real data by conducting surveys and creating a fresh buyer persona for every special character feature that isn’t already placed within a current profile. 

The successful implementation of machine learning in customer profiling allows brands to conduct automated consumer profiling in offline and online channels to save time.