9 Types of Customer Data | Pros, Cons and How to collect

The 9 types of customer data mentioned in this blog are differentiated into two parts,

1. Common customer data types

2. Customer data types - based on how and where they are collected (Source), for example, from customers or the company.

Here are the 5 common customer data types,

Here are the 4 customer data types, differentiated based on how and where the data is collected,

Here’s how the 9 types of customer data are connected,

Identity data includes Zero party, first party, and second party data.

Behavioral data includes First party and the second party data.

Descriptive data includes Zero party, first party, and second party data.

Transactional data includes First party and the second party data.

Attitudinal data includes Zero party, first party, and second party data.

Here’s the table to understand the difference between the customer data types mentioned above,

Table of Contents

Identity Data - Name, Email address

Data about the basic personal information that helps businesses recognize and understand their customers.

Common types of identity data are:

Pros of Collecting Identity Data

Personalized communication: Identity data, such as a customer’s name, can be used to do personalized marketing.

For example, an online retailer could use a customer’s first name in their email communications, making the interaction more personal.

This simple personalization can make customers feel valued and appreciated, enhancing their overall experience with the brand.

Here’s one from my life, I always get notifications from Google during my birthday, and the birthday balloon effect over my Gmail profile image is cool; even though it doesn’t have any value to my life it still feels good.

Improved Customer Service: Fast access to identity data helps businesses provide quicker and more efficient customer service. By utilizing this data effectively, companies can also track Customer Success KPIs, for driving higher levels of retention and engagement.

Atelecom provider, for example, could use a customer’s caller ID to instantly access their account details, speeding up service and improving satisfaction.

Deeper Customer Insights: Collecting identity data allows businesses to build detailed profiles of their customers - Often called us buyer persona - which can inform better-targeted marketing strategies and product development.

Astreaming service like Spotify might analyze users’ age, location, and listening habits (Behavioral data) to personalize playlists and music recommendations for each person.

Cons of Collecting Identity Data

Privacy Concerns: Handling personal information can lead to privacy issues. Businesses must be strict with privacy laws like GDPR or CCPAto respect customer privacy and maintain trust.

For instance, a social media company using personal data for ads must manage the fine line between personalization and privacy invasion.

Security Risks: Protecting identity data is crucial, as breaches can lead to serious identity theft problems.

If a retailer’s database containing credit card information were hacked, it could result in significant financial losses for customers and damage to the retailer’s brand reputation.

Maintenance Costs: Keeping data up-to-date requires ongoing effort and resources. Inaccurate or outdated data leads to poor customer service and marketing mistakes—CMDB software can automate the process of tracking and updating IT assets, ensuring that data related to these assets is always current and reliable, reducing maintenance costs and improving overall efficiency.

For example, healthcare providers must continuously update patient records to ensure accurate diagnoses and treatments.

How to collect Identity data?

Online forms are a standard method of collecting identity data.

These forms can be sent to the audience and customers as links or embedded within a website to collect data from website traffic.

Pop-up forms on websites also gather data, mainly when users are about to sign up for products or webinar events or when they are downloading eBooks - often used as lead magnets - This data collection can also occur at the point of online purchase; remember giving your name, email address, and house address to Amazon?

Additionally, identity data can be gathered during direct interactions with customers.

For example, a customer service representative may ask for a customer’s name and account number to verify their identity during a call or live chat session.

Tool recommendation: GoZen Forms AI - An AI-powered and no-code online form builder platform.

Behavioral Data - Time spent on a blog, Most used product feature

Data about the actions and behaviors of the audience and customers as they interact with the brand.

For instance, with Google Analytics 4 (GA4), you can quickly see how much time people spend on each blog post.

The time spent here is the behavioral data.

Here are some types of behavioral data a business collects,

Website Navigation Paths - Sequence of pages a user visits within a website.

Purchase History - Records of what a customer has bought in the past.

App Usage - Tracking how users interact with an app, including the features they use most.

Email Interactions - Data on email open rates and click-through rates.

Social Media Engagement metrics - Including comments, shares on posts, and saves.

Content Viewing Habits - Pages and blogs people consumes the most.

Pros of collecting the behavioral data

Targeted Marketing: Understanding customer behavior allows you to execute data-driven marketing.

For example, a news website can suggest articles similar to those a user typically reads, improving their browsing experience.

Customer Retention: By analyzing behavioral data, you can keep customers engaged with personalized interactions.

For example, A fitness app might encourage a user who hasn’t worked out recently with a motivational message.

Increased Conversions: Behavioral data helps you make smarter marketing decisions that can lead to more sales.

For example, An e-commerce site might send a discount offer to a user who has viewed a product several times without purchasing.

Cons of collecting the behavioral data

Privacy Concerns: Customers might be uneasy about tracking, as excessive behavioral data collection can lead to privacy issues.

For instance, users of a health tracking app might react negatively if they begin to see targeted ads related to their health metrics, feeling their privacy has been invaded.

Regulatory and Ethical Issues: The collection of certain behavioral data poses a regulatory and ethical problem.

For instance, a company might face legal repercussions if it fails to comply with regulations like GDPR when collecting data from European users.

Limited Context: Behavioral data often provides limited context, which can affect the accuracy and applicability of the insights derived.

For instance, a high bounce rate on a website might initially suggest poor content quality, but it could also be due to technical issues like slow loading times.

How to collect behavioral data?

There are different type of behavioral data, each unique to the platform they are derived from,

Most online businesses collect their target audience’s and customers online behavior data from the below platforms,

Google Analytics 4 to collect website behavior data, like page viewed, time spent on blogs, bounce rate, downloads, Signups, etc. To manage user consent and comply with privacy regulations, consider integrating Google CMP, which provides tools for obtaining and managing user permissions efficiently.

E-commerce platforms like Shopify, Bigcommerce, Wix… these platforms gives behavioral insights into how many people purchased the product, added product to the cart, How many product page viewed, etc.

Social media analytics, for example, YouTube studio can show the viewers behavior with the video, like how many people watched, scrolled away, did they watched the video till the end, etc.

Mobile App Analytics tools can track app usage frequency, session duration, user navigation paths, features used, and in-app purchases.

Email Marketing Platforms monitor email open rates, link click-through rates, email forwarding rates, and unsubscribe rates.

Video Hosting Platforms, like YouTube, gather data on watched videos, viewing duration, user interactions (likes, shares, comments), and subscriber growth.

Search Analytics tools provide insights on search terms, search frequency, click-through rates on search results, and conversion rates.

Customer Support Software collects data on support request frequency, types of issues raised, resolution times, and customer satisfaction scores.

Descriptive Data - Marital Status, Income, Purchase history

Data about the characteristics, behavior and situations of a person’s life.

While identity data talks about fundamental information about the customer,

The descriptive data gets deeper, covering their personal life, unique characteristic and behavior of that person.

Here are the examples of descriptive data,

Pros of collecting the descriptive data

Insights into customers: This data gives a deep knowledge about the customer’s life, preferences, and behavior using it to change, refine and adjust the marketing activity, sales communication, and product or service development.

For example, An insurance company can use descriptive data to gain a better profile of their customers. They may know certain traits about its customers, such as their gender, age, and nationality,

Using the information an insurance company can personalize their policy recommendation to each person.

Targeted marketing strategies: By analyzing descriptive data, businesses can create marketing campaigns that target the unique preferences and behaviors of different customer segments.

For example, A car manufacturer could use descriptive data such as the age, income level, and family status of their customers to target marketing campaigns.

For instance, they might target young, single professionals with ads for sporty, high-performance vehicles, while targeting families with ads for spacious, safety-focused SUVs.

Competitive advantage: Collecting and analyzing descriptive data allows businesses to gain insights that gives a competitive advantage.

For example, A retail business could use descriptive data like customer demographics and purchasing habits to gain a competitive advantage.

For example, if they find that a significant portion of their customers are eco-conscious, they might decide to stock more environmentally friendly products.

This could give them an edge over competitors who do not cater to this customer preference.

Cons of collecting the descriptive data

Difficulty in collecting the data: Like Isaid, descriptive is not the kind of data your customer would give eyes closed,

They are real, personal and sensitive information people often neglect to give unless the situation and business type demands

For example, Some Recruiters or Banks might need some level of descriptive data - like house hold income, and number of family members - to hire and to give bank-related services.

Privacy and ethics concern: It’s a sensitive and personal data, and the responsibility to keep it safe from internet theft or errors is immense.

Companies must ensure they are collecting, storing and using the data in the safest and the most ethical way.

For example, a healthcare provider must use encryption and secure methods to protect patient data.

If this data is leaked, it could lead to legal issues and a loss of trust, emphasizing the need for strong data security to maintain privacy and trust.

Resource Dryer: Collecting, storing, using, and maintaining a database of descriptive data need complex data management system and sharp expertise.

For example, For example, a small business like a restaurant might struggle with the technological and financial demands of implementing systems to analyze customer behavior through online orders.

How to collect descriptive data?

Descriptive data can be collected using the surveys, questionnaires, online registration forms, analytics tools like GA4, and in-built analytics tools of e-commerce hosting platform,

Tool recommended: GoZen Forms AI- Easily make beautiful online forms without any coding. Make filling out forms a breeze for your customers and collect more valuable data.

But to understand more in depth characteristic, situations, behavior, and preference of a person method like interviews, and focus group might be the best option, because you can directly talk with your customer in a real human way, Which makes people open up more compared to asking them to fill up the text fields online.

Attitudinal data - Customer reviews and feedback

Data about an individual’s opinions, preferences, motivations, and feelings towards a brand or its product or service.

This data type is essential to understanding how customers feel, perceive, and think about a company’s product, service, and even brand.

It includes qualitative data insights to help marketing, customer service, sales, and product development.

Further read: Qualitative and Quantitative research and methods.

Here’s the typical example of how attitudinal data is collected,

Suppose a company surveys to understand how customers feel about their new product.

The survey might have questions like,

1. On a scale of 1-10, how satisfied are you with the product?

2. Do you believe the product provides good value

for the price?

3. Would you recommend the product to a friend?

The responses to these questions are attitudinal data. Here’s a real-life store on attitudinal data collection,

Before we launch our products, we test them with a small group of people to get their reviews and feedback. This helps us make any final adjustments before releasing the product to the market.

Now, Guess this,

What customer data did we collect before launching the product?

Yes, it’s attitudinal data.

Pros of collecting attitudinal data

Retain customers: Attitudinal data like why a customer hates the customer service and what he expects after buying the product can all inform the customer service team and salespeople on how to keep customers satisfied, resulting in higher customer satisfaction and retention rates.

For example, A company selling fitness equipment might find through attitudinal data that customers are unhappy with the assembly instructions. The company can improve the instructions or offer assembly services, increasing customer satisfaction and retention.

Pricing strategy: Attitudinal data can reveal how customers perceive the value of products and services, which can give you the understanding to change or tweak the pricing strategy.

For example, A software company may discover through attitudinal data that customers perceive their product as high-value but overpriced.

The company could then adjust its pricing strategy by offering different pricing tiers or packages to better align with customer perceptions.

Sales optimization: The Sales team can use the attitudinal data to better understand the prospect’s motivation, preferences, and needs, which can result in better sales meetings and easier deal closing.

For example, A car dealership’s sales team uses attitudinal data and understands that safety is a top priority for their prospects.

They can then focus on promoting vehicles with advanced safety features during sales meetings, potentially leading to more successful deals.

Cons of collecting attitudinal data

Data Quality: The quality of attitudinal data can vary, and it won’t always be accurate and reliable, leading to incorrect insights or decisions.

For example, A restaurant chain conducting a survey on customer preferences might find the data collected unreliable because the respondents misunderstood the questions or didn’t take the study seriously, leading to potentially incorrect business decisions.

Subjectivity: Because people provide the attitudinal data, the variety of factors can influence the data, such as the way questions are asked, the time of day respondents are engaged, and their mood.

For example, A skincare company conducting a survey on product satisfaction might find that the responses are highly influenced by factors such as the respondent’s mood at the time of the study or the way the questions were phrased.

Temporary limitation: Attitudes, preferences, and motivations toward products and brands can change over time and become outdated quickly, thus requiring continuous data collection and analysis to keep the data and insights updated.

For example, a fashion retailer might collect attitudinal data indicating a strong preference for a particular style.

However, fashion trends change quickly, and the data could become outdated within a few months, necessitating continuous data collection and analysis.

How to collect attitudinal data?

While questionnaires and surveys can be used to gather attitudinal data,

Most of the time, customers would hurry up the data entry with zero thoughts on giving true intentions and motivation,

However, more quality and intentional data can be collected with Interviews and Focus Groups. Then users’ attitudes towards products/services can be found through Online Reviews and Ratings on your website or third-party review sites like Capterra, Yelp, Foursquare, etc.

Opinions shared on social platforms can be collected using . Feedback Forms, used on websites or in physical stores, can also help you gather attitudinal data like their overall experience using the webpage, reading the content, or shopping experience inside stores.

Transactional Data - Chat history, Payment method used

Data about the records of interactions and financial transactions a customer has with a company.

For example,

Pros of collecting the transactional data

Better Business Decision: With a clear view of sales performance, revenue, and financial health, a business can make better decisions about hiring people or tweaking the value-delivery system from production to the hands of a customer.

For example, A retail store might use transactional data to identify that sales are highest on weekends.

This could lead to a decision to hire more staff for these days to better handle the increased customer volume.

Customer Insight - Data like the most popular product, frequently used payment methods, and most common price range they choose to buy can reveal a lot about the type of customers purchasing your product,

Which can then be used for landing page optimization, conversion optimization, and pricing decisions.

For example, an online bookstore might find that most of its customers prefer to pay via digital wallets and that the most common price range for purchases is between $10 and $20. This could inform decisions about payment options and pricing strategies.

Straightening the system: Data like the number of product returns can indicate something wrong with the operation part.

For example, A furniture manufacturer might find that many products are being returned due to defects. This could lead to investigating the production process to identify and rectify the issue.

Cons of collecting the transactional data

Data volume and complexity: As the business grows, the volume and complexity of transactional data increases.

Managing those large datasets requires advanced data management systems and can become overwhelming if additional tools and expertise are lacking.

For example, A multinational corporation might struggle to manage the vast transactional data generated from its operations across different countries. This could require investment in advanced data management systems and expertise. Databricks is one such tool widely used by large companies to handle complex data needs, often with the support of a Databricks consultant to ensure optimal setup and analysis.

Connecting challenges: It isn’t easy to connect data from various sources, such as online sales, in-store sales, and third-party vendors, into one unified system.

Mistakes happen when connecting these data, which can lead to wrong analyses and bad business decisions.

For example, an e-commerce platform with physical stores might have difficulties integrating online and in-store transactional data.

Mistakes in this integration can lead to errors like this, The in-store team might think a particular product is popular because people are buying it every day,

But in the online sales data, The popular product might be something else,

When there is strong integration between the online and in-store sales data, this misinterpretation can be solved, and the right product can be promoted in both the in-store and online stores.

Problem of historical data: Transactional data records customers’ past interactions or transactions with a business.

This data can reveal valuable insights into customer behavior and business performance.

However, if a business relies too heavily on this historical data, it might base its decisions on past trends, assuming that these trends will continue.

This approach can be problematic, especially in industries that are rapidly changing.

For example, consider a fashion retailer that uses transactional data from the past few years to decide what products to stock for the upcoming season.

If fashion trends change quickly, the historical data might not accurately predict what customers will want to buy in the future.

In other words, while historical data is useful, it’s also important for businesses to stay adaptable and consider other factors, such as market changes, technological advancements, and evolving consumer preferences.

So, a business should balance historical data with other data types and market research to make well-rounded decisions.

How to collect transactional data?

Businesses gather transaction data through various methods.

POS Systems monitor in-store purchases, helping with inventory and sales management. E-commerce Platforms collect data on online buying patterns to enhance sales.

Payment Processors like PayPal and Stripe manage online transactions, providing critical financial details and security alerts.

They track payment details and alert businesses to potential fraud, such as sudden transaction spikes or high-value purchases, helping prevent financial loss.

Here are the data types differentiated based on how and where it’s collected,

Zero-party data - Customer gives to the brand voluntarily

Data the customer intentionally shares with the brand.

Here are the examples,

Pros of collecting zero party data

Cost Efficiency in Marketing: Using direct insights from customers allows businesses to create more targeted and effective marketing campaigns that are less likely to waste resources on uninterested market segments.

For example, a clothing brand could use zero-party data to understand the specific styles and sizes customers prefer.

This would allow them to create targeted marketing campaigns for new arrivals, reducing the cost of marketing to customers who may not be interested in those items.

Higher Conversion Rates: Personalized experiences and communications based on zero-party data are more likely to resonate with customers, leading to improved conversion rates and sales.

For example, An online bookstore could use zero-party data to recommend books based on a customer’s favorite genres or authors, leading to higher conversion rates as customers are more likely to purchase books that align with their preferences.

Optimized Resource Allocation: Knowing what customers want allows businesses to allocate resources more effectively, whether marketing spending, research and development, or customer service efforts.

This optimization can result in cost savings and more efficient use of company resources, which would otherwise be spent elsewhere because the company doesn’t know what the customer wants.

For example, a tech company could use zero-party data from user surveys to understand which features users want most in their next product update.

This would allow the company to allocate development resources effectively to those features rather than spending time and money on less desired features.

Cons of collecting the zero party data

Data Bias: Zero-party data is voluntarily provided, and it can be biased based on the segment of the customer base that chooses to interact.

For example, a video game company might receive feature requests predominantly from the most engaged, hardcore gamers through their in-game survey, which might not represent the desires or needs of more casual players.

Customer participation problem: Zero-party data collection heavily depends on customer participation. The business may have incomplete datasets if customers do not engage or choose not to provide their data. (Which often happens)

For example, a beauty brand might set up a product preference survey,

However, only a tiny percentage of site visitors complete it. In that case, the data gathered will not be representative or extensive enough to inform broad marketing strategies.

Differences in wants and behavior: Zero-party data assumes that customers know what they want.

However, there can be a difference between what customers want and their actual behavior.

For example, customers might indicate a preference for healthy food options in a restaurant’s survey but predominantly order fast food items in practice.

How to collect zero party data?

Zero-party data collection involves directly engaging customers to voluntarily share their preferences, opinions, and personal information.

Effective methods include quizzes, surveys, polls, popups, onboarding forms, and interactive experiences like gamification and contests.

Post-purchase surveys, webinar event forms, account creation forms, and support interactions are valuable ways to collect zero-party data.

First-party data - Brands collect from customers

Data collected from the audience and customers through the business owned channels like websites, and app.

Examples of the first-party data,

Pros of collecting first party data,

Accurate: First-party data comes directly from the audience and customer interaction, so it is often precise and relevant.

Higher control: Businesses have complete control over collecting, storing, and using the first-party data.

Easier privacy compliance: First-party data is simple to manage in terms of privacy and compliance because it is collected from customer interaction or consent.

Cons of collecting first party data,

Incomplete Data: Collecting first-party data can often result in incomplete information.

This is because the data is typically gathered from direct interactions with customers, such as website visits, purchases, or form submissions.

If a customer doesn’t fully engage or provide all the requested information, the data collected will be incomplete.

This can limit the scope of insights derived from the data.

Scaling Challenges: As businesses grow, scaling first-party data collection and analysis produces two main problems,

Financial (you’ll need a more sophisticated analytics tool, which costs money) and expertise human resource (because when the analytics is complex, the person who handles it must be an expert, and finding the best experts is often hard).

Data silos: If the whole first-party data is not connected right, the data silos can happen between departments, leading to misinterpreted data analysis and getting a clear picture of a customer’s preference and behavior.

Imagine a large hospital system in which each department—such as radiology, cardiology, neurology, and emergency—uses a different system to store patient data, each with distinct practice management software features tailored to their specific needs.

For instance, the radiology department might have detailed imaging data, the cardiology department might have heart monitoring data, and the neurology department might have brain scan data.

Each department understands its data well and uses it to care for patients.

However, suppose a patient has a complex condition that involves multiple departments.

In that case, the lack of data integration can lead to problems.

For example, a patient might have a heart condition affecting their neurological health.

The cardiologists might be unaware of the neurologists’ notes and vice versa due to the data silos.

This could lead to an incomplete understanding of the patient’s health and potentially wrong treatment plans.

It might also result in redundant tests if one department is unaware that another has already conducted specific diagnostics.

How to collect first party data?

You can collect first-party data through various methods like:

Google Analytics 4: This tool helps track website behavior, such as pages viewed, downloads, purchases, and time spent on blogs.

Tracking Transactional Data: Collects data on purchase history, popular products, least-liked products, and product returns.

Tracking Email Engagement with a Tool like GoZen Growth: Collects email opens, links clicks, Unsubscription rate and numbers, and more.

Other Methods Include:

User Registration Forms: Collect basic customer information through account creation forms.

Progressive Profiling: Collect small amounts of customer data through different touchpoints over time.

Surveys, Polls, Customer Feedback, and Reviews: Collect valuable insights into customer preferences and experiences.

Rewarding Users for Sharing Data: Through loyalty programs and discounts.

Interactive Content: Quizzes, polls, and gamified experiences engage audience customers and collect data from them.

Webinar Registration Forms: Gathers data from participants interested in attending webinars and events.

These methods are some of the most popular and effective ways to collect first-party data from your audience and customers.

Second-Party Data - First-party data shared between companies

Data collected through partnership with other company. Second-party data is usually another company’s first-party data.

Because second-party data is other companies’ first-party data, Here are a few examples of second-party data shared between the companies,

An online shoe store partners with a clothing retailer to exchange customer data.

The clothing retailer shares data about customers interested in athletic wear, while the online shoe store shares data about customers who buy running shoes.

Here’s how they might use the collected data,

Targeted Email Campaigns: Using the clothing retailer’s data on customers interested in athletic wear, the shoe store can create targeted email campaigns promoting their latest running shoes and related accessories.

Social Media Advertising: The shoe store can use data on ‘customers who buy running shoes’ to create lookalike audiences on social media platforms like Facebook and Instagram.

By targeting ads to users who resemble athletic wear customers, the shoe store can reach a broader audience likely to be interested in their running shoes.

Pros of collecting the second party data

High relevance and quality: Second-party data is collected directly from another partnered company. This direct collection often results in high-quality and precise data.

Cost-effective: Second-party data can be a cost-effective solution, especially for companies that lack a lot of first-party data.

Faster access to data: Second-party data is often sold in private deals, exchanges, and partnerships.

This allows businesses to quickly access and utilize the data, enhancing the speed of their decision-making processes.

Cons of collecting the second party data

Data compatibility: Connecting second-party data with a company’s existing data can be difficult if the data formats and standards vary.

This leads to extra costs and time spent on data integration and normalization.

Data misuse: The data provider (Company 1) might not have permission from their customers to use specific data, which results in legal and ethical problems if the data is used in ways (by Company 2) the original customer didn’t think about.

Limited exclusivity: First-party data you collect is unique to your company, and no one has access to it,

However, the second-party data you get from another company may already be supplied to various other businesses, reducing its exclusivity and diminishing its superior value.

How to collect second party data?

To collect second-party data, establish a direct agreement or partnership with another organization to share valuable first-party data.

A data collaboration tool like Lotame’s Spherical platform can be used to have transparent and flexible data sharing between companies.

You can also reach out directly to non-competitive companies with relevant audiences and negotiate private terms for data acquisition.

Finally, consider leveraging a data marketplace to connect with potential second-party data providers, offering a broader range of curated data to get audience insights and improve your marketing strategies.

Third-party data - Collected from multiple source and sold to business

Data collected from multiple sources and sold to other businesses.

First-party data is collected directly from audience and customers, while second-party data is collected through a partnership between two companies,

Third-party data is collected from a variety of sources, such as openly available data like public records, and then sold to other businesses.

The best example of third-party data is this,

The above report is from Statista, a great example of third-party data. Statista collected the data from multiple sources and then presented it in a digestible and insightful canvas. Statista also sells its collected data.

Here’s how we collected third-party data and found our ICP,

To identify our target Ideal Customer Profile (ICP), we analyzed the top industries represented in the

reviews of our competitors’ products.

We got into the G2 Crowd and filtered the reviews by industry, such as Marketing and Advertising.

Then, we noted the job positions of the reviewers in the top 5 industries.

After repeating this process for 5 industries, we identified the most common job positions within each industry.

Here’s the question: Guess the customer data type we’ve collected from the G2 crowd?

Yeah, it’s third-party data. Why? Because we don’t have a direct relationship with the user from whom the data is collected.

Pros of collecting third party data

Better customer understanding For a new business, third-party data can provide a complete view of its customers, including their demographics, needs, trends, preferences, devices, etc., Helping them better understand customers even before launching a product.

For example, a new online clothing store can use third-party data to understand the fashion preferences, age groups, and shopping habits of its potential customers. This can help them stock items that are more likely to sell and effectively target their marketing efforts.

Competitive advantage Companies that use third-party data to understand the market and stay ahead of the trends can enjoy the fruits of competence in the industry.

For example, A company in the smartphone industry can use third-party data to understand the latest trends in smartphone features and customer preferences. This can help them design a product that meets the current market demands, giving them a competitive edge.

Time and cost savings Unlike first-party data, Collecting third-party data is an easier and quickest way to gain insights.

For example, A startup looking to enter the food delivery market can use third-party data to understand the most popular cuisines, peak order times, and preferred payment methods in their target area. This can save them the time and cost of conducting primary research.

Cons of collecting third party data

Data quality Not every third party’s data is accurate and of good quality. Without proper updates, some data sources can become outdated and irrelevant.

For example, A company might purchase third-party data about consumer spending habits only to find that the data is outdated and no longer reflects current trends.

This could lead to ineffective marketing strategies and wasted resources.

Finding relevant data—Third-party data public usage makes it hard to find appropriate data specific to your business needs and situation.

For example, a niche business, such as a company selling handmade wooden toys, might struggle to find third-party data that is specific to its unique market. Most available data might be too broad or irrelevant to its business needs.

Overdependence on external sources Relying too heavily on external sources for data and insight is dangerous; what if the data provider changes his policies, increases price, and goes out of business suddenly.

For example, A company that relies heavily on a specific third-party data provider might face challenges if the provider suddenly goes out of business or changes their data collection policies. This could leave the company without crucial data and disrupt its operations.

How to collect third-party data?

Third-party data is typically collected from a variety of external sources.

These sources include data marketplaces, licensing agreements, and specialized data aggregators. Data marketplaces such as Lotame and Eyeota are widespread platforms where businesses can purchase third-party data.

Alternatively, businesses can opt for packaged data solutions from providers like Data Axle, Salesforce, and Acxiom.

When selecting a data provider, brands should evaluate them based on their compliance with data privacy regulations and the customization options they offer.

This ensures that the data collected is legal and relevant to the brand’s specific brand.

In addition to these sources, brands can also use Data Management Platforms (DMPs) to collect third-party data.

DMPs like Lotame and Adobe Audience Manager can collect third-party data from data brokers (sellers).

Conclusion

You can literally collect any customer data you want; there’s a ton out there,

But understanding why collecting the data and how to use it for practical decision-making is another case,

Check out these 8 steps before collecting any customer data type,

1. Define the Goal: Identify why you’re collecting data. This will guide your choice of methods and inform how you use the information you gather.

2. Understand the Data Type You Need: Identify the specific data you need to collect based on your goals.

For instance, if you aim to understand your customers’emotions towards your product, you might focus on collecting attitudinal data.

3. Identify Your Data Sources: Determine where you’ll obtain your data.

This could be directly from customers via online forms and surveys or from business-owned channels like your website, where you can analyze user behavior with tools like Google Analytics.

4. Decide on the Tools and Technology: Decide on the tools and technology you’ll need for data collection. Here are some suggestions for each data type:

5. Develop Standard Procedures: Establish specific rules or steps for consistent and accurate data collection and analysis.

6. Consider Data Storage: Plan how you’ll store the collected data. Your chosen solution will affect how long you can access the data and how well it’s protected from leaks or misuse.

7. Ensure Senior Leadership Approval: Before you start collecting customer data, secure approval and support from your senior leadership team.

8. Respect Privacy Laws and Regulations: Always inform your customers about the data you’re collecting and how you plan to use it.

Respecting privacy laws and regulations when collecting and storing customer data is crucial.