In a nutshell, common data types used in big data lead generation include demographic, behavioral, transactional, social media, and search intent data.
Analyzing these data types is what keeps Ad platform owners and niche lead generation companies in business, facilitating their ongoing sale of ad space or lead services to you.
Today, I’m going to uncover the nature of these data types and more.
Use this guide to understand the influence of certain data types on your Ad campaigns and reduce your reliance on lead generating companies when creating marketing strategies. Explore!
Data Types Used in Big Data Lead Generation
Remember, you don’t need to collect all these data types to capture qualified leads. The main aim here is to understand what data types matter the most when it comes to big data lead generation.
It is by understanding the data types that big data has found effective that you’ll make optimal decisions when determining what data to collect to achieve your campaign objectives.
Moreover, note that you don’t need massive data volumes as those used in big data generation. You need audience-relevant or client-specific data that falls under either of these data types:
Demographic data leads are the most sought after across product-based businesses. Why? Analyzing demographic data helps you connect with a group of people with specific traits.
Rather than targeting everyone within the market, you only collect the data that describes your ideal customer.
For instance, you can target a group based on their gender, age, location, personal contacts, marital status, education level or income level.
With access to this data type, you focus on a smaller group that’s more likely to convert.
You get to understand your target audience and design products that resonate with your audience. This saves you money and time while increasing the chances of making a sale.
While demographic data lets you know who someone is, behavioral data lets you in on how they behave, mostly at the subconscious level.
To collect behavioral data, you set up systems to monitor people’s actions when they interact with your platform, services, or products.
Some actions to monitor include website activity, app usage, social media activity, or email engagement.
What web pages does a particular visitor interact with the most? Or what product do they click on the most? Aim to understand their subconscious patterns or preferences.
Understanding your leads’ subconscious patterns or preferences gives you the opportunity to offer personalized solutions to the leads or make smarter business decisions when innovating.
Collecting and analyzing transactional data helps you understand when people are willing to spend and on what.
From it, you can extract purchase patterns and identify trends, allowing you to get into a specific market strategically or upsell to current customers.
Data that falls under the transactional category includes purchase history, preferred payment methods, frequency of purchase, refund requests and subscription renewals.
Besides powering market research and upselling strategies, you can also use this data to retarget certain leads based on the periods they are known to shop frequently. For instance, customers that shop heavily during Black Friday.
Like social media connects families, friends, and random people, so does it present an opportunity for your business to connect with existing and potential customers (leads).
Even if you decide not to actively work on your social media presence, you have the option of scraping social media data from platforms like LinkedIn and Facebook. This data holds in depth insights because it combines demographic, behavioral, and even search intent data.
To have an account on a platform like Facebook, one shares their personal details, including marital status and location. Then, while posting or interacting with posts, they like, share, comment, and even tag specific people — shaping a distinct behavior pattern.
By analyzing the user’s data, you get to understand where their interests lie, enabling you to craft highly personalized campaigns to convert those interests into a sale.
Search is where most users begin their online journey, looking for information to achieve their goals, satisfy their needs, or solve specific problems.
They express intent through the phrases or keywords they input into various search engines like Google.
The three most studied types of intent include informational, navigational, and transactional.
While informational keywords reveal what knowledge someone is in search of, navigational keywords reveal what brand they are looking for. Lastly, transactional keywords can help tell when someone is ready to make a purchase or take a certain action.
Other than the keywords, search intent data also includes search volume, search trends, where searches are coming from, and how often people click certain search results.
Overall, obtaining and analyzing search intent data helps you understand why someone is searching for certain data at a specific time or point. And, if you correctly decipher their intent at that time, you have the opportunity to capture their attention with the right message and convert them into a high-quality lead or customer.
When aiming to attract B2B leads, firmographic data is your best bet.
Take this type of data as the business equivalent of demographic data. Rather than focusing on people, you focus on data around a particular business and narrow down to what the decision maker is likely to say ‘Yes’ to.
Most useful firmographic data includes company size, industry, company’s geographic location, technology stack, ownership structure, growth stage, and key decision-makers.
Analyzing such data in line with specific objectives is the start to understanding whether your product fits a specific organization. For instance, it is not optimal to target a Fortune 500 company with a small-business-centric product.
Aim to analyze organizations with a greater likelihood of converting based on factors like industry, location, and size.
What’s the Bottom Line?
Big data lead generation has proven that data is the backbone of understanding current and potential clients. By independently analyzing or combining the data types discussed in this piece, you increase the likelihood of capturing high-quality leads. Remember, to boost the effectiveness of these data types, set clear objectives and only work with or analyze the most relevant data.