Leadzen.ai’s Guide To Big Data Collection And Analysis In B2B

The advancement of big data technology has opened a wealth of information for organizations. Data is simply unavoidable, particularly for today's B2B market realm.

Leadzen.ai's Guide To Big Data Collection And Analysis In B2B

You've been told several times about the necessity of data collection and analysis. But did you know that according to Techjury,

"The big data analytics market will reach $103 billion by 2023."

It's enough to turn anyone's head.

'Big Data' indeed!

Every day, Internet users create around 2.5 quintillion bytes of data.

A large portion of this data is saved as a B2B database for subsequent examination for various objectives that benefit the specific company. So it is important to get a better understanding of big data and analytics in b2b.

What is Big Data in B2B?

To begin with, let's understand what exactly big data is.

According to Oracle,

"Big data is larger, more complex data sets, especially from new data sources.

These data sets are so voluminous that traditional data processing software can't manage them. But these massive volumes of data can be used to address business problems you wouldn't have been able to tackle before."

There are Four parameters (4V) characteristics:

Volume:this data is continually created
Velocity:you must handle it rapidly
Variety:a wide range of sources and data kinds are employed
Veracity:the data must be of high quality

B2B data is any pertinent information or B2B database about other businesses.

Its primary application is to drive marketing and new sales tactics or procedures. Companies use this high-quality data to increase business prospects, improve lead generation, and do better market research, among other things.

Need for Big Data Analysis in B2B.

By now, it is evident how B2B data is critical to the sales and marketing team's productivity, development, and success. Companies also use the data for market research, industry assessments, and power AI-based products.

It isn't easy to analyze all of this data. Still, Data analytics does not have to be complicated or time-consuming.

Big data analytics must process data in a variety of forms and kinds and make sense of it all.

Big data analytics systems use several modern technologies to combine data from numerous sources, extract critical insights, and discover noteworthy patterns. Among these technologies are:

Intelligent Automation (AI)
Machine Learning (ML)
Natural Language Processing (NLP)

Furthermore, to speak purely of B2B marketing and sales, the following are crucial factors that will help you illustrate the importance of big data analysis:

improved understanding of consumer preferences, purchasing behaviour, and attitude
more knowledge of market trends, goods, and rivals; and flexible supply chain operations that can respond swiftly to challenges and new business demands
Customer-focused recommendation engines
operational improvements such as lower costs and proactive equipment maintenance; and ensuring that data and analytics platforms can meet future business needs.

To sum up, if done correctly, the end outcomes include more successful marketing and advertising campaigns, enhanced company processes, etc. - all of which may contribute to superior financial results and a competitive edge over competitors.

Use Cases

Let's take a deeper look at firms' unique B2B database use cases.

Lead Generation

The most typical users of corporate data are sales and marketing teams. As a result, organizations use B2B data to improve their marketing and sales tactics, increase lead creation, and raise conversion rates.

Here are some B2B data lead generating sub-use cases:

Reaching your ICP
Lead nurturing
Cold outreach
Account-based marketing
Research and analytics

Personalization

This generation of marketing and sales can be called the battle for clients.

It's not about who gets the data but also who gets the data first. A sharper picture of the client experience is now more possible.

Big data allows you to collect information from social media, online visits, phone records, and other sources to optimize engagement experiences and maximize the value offered.

Business Analytics

B2B data can improve firms' research and analysis operations and boost marketing and sales by using Firmographics, technographic, and intent data.

There are several different analytical goals and procedures that firms might uncover by employing B2B data, such as :

Predictive maintenance
Risk assessment
Analyze the competition

Big Data Challenges

Companies' business aims and client demands alter as digital technology evolves.

The requirement for a tool that provides verified and real-time filtering of unnecessary data is at the heart of big data challenges.

The requirement to manage unstructured data is cited as a concern for 95% of enterprises.

You will need to think and deploy strategies to prioritize and separate massive data such that processing it takes as little time as possible, and each iteration delivers a meaningful outcome for the firm.

It takes a long time to clear data; only after that can it be used within software algorithms.

Even though there are many innovative ways for organizing and cleaning data in our time, your firm must choose the one that will provide the most efficient results.

Leadzen.ai's role in solving the challenges

Leadzen.ai is not just a lead generation tool but an intelligent database tool. It not only provides you with real-time and verified data but also enriches your existing database.

Essentially, lead generation enables you to locate consumers and clients, which is the ultimate aim of every organization.

However, developing a strong lead generation marketing plan requires time and work. You risk missing that connection and losing a customer if not done appropriately.

Furthermore, the AI paired with personalization and optimization can assist you in making the most of the data you collect. It enables you to be more strategic with your approaches, bringing you closer to your goals.

Let us look deeper into the features that help solve the above-mentioned big data challenges.

● Real-time data

Real-time marketing can be described as using behavioural analytics and automated marketing to present customers with the correct offer based on specific customer actions at the right time.

Using what you know about a certain client or group, you can perceive and respond to their requirements in the present.

Leadzen.ai provides real-time data that is valid up till the present day.

Let's say you are seeking recently funded companies. Our algorithm will give you all recently funded companies to that very day. Not only do you receive data on newly funded firms, but also on top investors, startup investors, angel investors, active investors, and much more.

This enables you to deliver relevant messaging, campaigning, and advertising that is targeted to your target audience's demands, interests, and needs. An active social media presence is one technique for marketers to adopt a real-time marketing plan.

● Verified Data

Data is continually deteriorating because people switch employers and email providers and even unsubscribe from emails they don't want to receive.

Data verification and database cleansing should be standard procedures in data-driven marketing, given the average rate of data degradation.

Marketers and businesses use Leadzen.ai's verified databases to discover erroneous contact information and emails unlikely to trigger spam filters.

For example, suppose you search for a certain individual by name. In that case, you will receive a whole digital profile of that person, including all his prior and current contact numbers and email addresses. It also includes the previous and present employers of the individual.

● Bulk Search

Big data cannot be sorted, separated, or evaluated manually. Big data necessitates large systems and tools that must be worked through to get the most of the data.

Leadzen.ai's bulk search options provide you with real-time and verified data and expand your existing database.

For example, if a pharmaceutical firm uploads the databases of all the individual physicians to whom they were delivering a certain drug, our system would first eliminate all leads that cannot be reconciled with its stored information and doctors who are no longer practising.

It will also provide fresh contact numbers, email addresses, and locations for any physicians if their information has changed or your database is missing.

This is a very time-efficient and accurate tool considering how much time working with big data can consume. All you have to do is, Upload your database and use a powerful bulk search to obtain complete and up-to-date prospect information.

Takeaway

Everyone's attention has switched from "if" to "when" enterprises will migrate to the cloud for computing, storage, and application administration.

To achieve big data analytics aspirations, cloud adoption is predicted to expand at more than six times the rate of conventional IT investment through 2020, as storage gets increasingly cheaper and computing approaches unlimited levels.

While this expansion may appear astonishing, it makes sense. Organizations that employ big data reported an 8 to 10% rise in profits and a 10% decrease in overall expenditures. Big data analytics has a bright future.