B2B Brands Should Have Multiple Data Source

Posted by David Jones
8
Apr 29, 2021
164 Views
To strive in the competitive, data-driven B2B marketplace, your game needs to be on point for business tactics and for the other companies in the market. 'Time is' money' is not very true in the B2B sphere. It goes like this. 'Data is' money'! But of course, the data itself will not get you the money. However, you are responsible for using the value of the data you have to improve the business prospects. You also need to have a strong data-driven B2B content strategy for your business.

Multiple B2B data sources help the company to grow fundamentally more than anything else. And these are the reasons why B2B brands need to have multiple B2B data sources.

How data helps B2B brands
Make decisions
Data generation is not hard. Even small startups generate data. Any business that has an online presence and an electronic payment option of any kind, can generate data on customer behavior and habits, web traffic, demographics, and more. If you know how to use it, then all that data is full of potential.

Companies can use data to make decisions about the following issues:
  • Take over new customers
  • Increasing customer retention
  • Improving customer service
  • Better marketing management
  • Tracking social media interactions
  • Predict trends of sales

Generally, data leaders provide real-time data about their customers to make smart business decisions.

Solve problems
Suppose you ran a marketing campaign for lead generation, but it did not work as well as you expected. But then there is no return on the campaign. Is it a total waste?

NO! You've got the data from a lot of people. You can learn what works for your customers. Your distribution of achievements can be discovered to help you track. And with that, you can understand every step of the business and what needs to be determined in order to perform it properly.

Helps you understand performance
In simple words, data helps you measure performance. Sports teams are a great example of this. Sports teams collect data from previous matches of their opposing teams and try to analyze similar patterns or strategy used. They plan their game strategies to fit it. The data analysis in B2B improves the performance of the team through the collected data.

The collected data in sports and games will help you improve your performance.

Data helps improve the process
Since you have a lot of data to analyze thousands of different aspects of your competitors across different parameters, you can of course draw a simple map of the processes they practice. Compare that to your process, you can easily improve the process where needed and you can control different parameters of the process to change just a few parameters instead of the whole process.

Overall, the point is that a lot of data about helping you change and improve the process of your work, sure.

Understand the customers
You may not even know who your customers are without the database. B2B database helps your customers understand. Without a B2B database you would not know how much money to spend on marketing and whether the ROI is good. Without a B2B database, how would you know if your customers like the product or not and needs to change?

B2B database is the key to understanding the needs of your customers and the market. Another thing that is important is the B2B data quality.

Even if you have a lot of data that meets the B2B data quality standards, if you do not have the right tool to analyze it, it is absolutely no use. A useful data tool helps you access and interpret the B2B database to achieve higher sales.

Types of data
Different types of data are available in the market. B2B data sources have a database in more than thousands of parameters that you can imagine. And now the task here is to get the exact B2B database with the parameters you want to calculate and analyze for your business.

Predictive data
Prediction is referred to as an outcome of an algorithm that is provided with up-to-date or historical datasets and predicts the probability of a particular outcome based on new data. Now sure, the following question must have popped into your head that, what type of data is being released for predictive data analysis in B2B?

And the answer is historical data. Predictive analysis uses historical data to predict the likelihood of future events. The historical data are captured in a way that captures important trends in a particular mathematical model. Then, however, a predictive model is used to suggest actions or to take optimal results based on the current data.

Intended data
Due to significant success in Google's artificial intelligence and evolving algorithms, you will be randomly followed with everything and anything you search on Google. For example, has it ever occurred to you that you have a random search for a watch on Google and then right after that, whatever website you open, you will get ads showing watches?

Or you will see the advertisements of watches, even if you are looking for something related to them. That means Google learns your intent when you search for something. You can collect intended data that shows which leads are actively taking the research online, the account is shown to ‘Surge’ on those topics when the research on a particular topic picks up high in activity.

Sales and marketing team can then arrange the accounts in priority with the relevant topics over the qualified accounts that do not show the intention. B2B intent data encourages conversions and sales incredibly high, if used correctly.

How this intention-based search works in B2B is when buyers have problems or pain points, they visit various websites, download ebooks while they casually read articles and whitepapers. That leaves digital footprints when content over the internet is consumed.

You can reach the buyers very easily if you collect the online data and behavioral signals and use their digital footprints.

Now there are several ways to get and process raw data according to your needs. But more importantly, how you use this collected data.

Because of B2B intent data, initiating go-to marketing plans is effective for customers. Sales and marketing teams will get help with data segmentation and contact potential customers. Companies that do not use predictive intelligence limit their data responses from their own websites. However, their potential buyers would have been looking for weeks to resolve their pain points.

Here are some key use cases for sales and marketing teams with data intent in 2021:

Identifying the Importance of Early Buyers: Signal for purchase intentions helps you identify which companies are actively seeking the solution. This could be identified even before they fill out the form on your website or join your sales and marketing team.

Create a list of targeted accounts: Sales and marketing teams can easily filter the list of accounts that are actively interested in your product / services.

Personalization: Sales and marketing team can personalize the first outing with resources that match the potential matches you are already looking for.

Score leads and prioritize the account: Your lead score model should go through predictive purchasing. This will help you prioritize companies that show interest and have buying intentions; before your competitor comes into the picture.

 Analyze and retain customers: You can get real-time visibility into which customer is researching your topic and asking for solutions you provide. This kind of insight helps proactively sell your product or services. And also it identifies the pain points of the customers before you are blinded by customers who go to your competitor to renew or buy an offer that they did not know you had.

Customize data
The different ways of segmenting and scoring prospect accounts are all included in Fit Data. Demographics such as job level, job function, age, location, etc. Are included in it. And also includes the firmography of the company such as tech stack, size of the sector, generated revenue, industry and budget, etc.

Fit data looks more like a static, non-changing data. it may suspect whether an individual or an organization is a good fit. However, it does not talk about the context of period or time.

Opportunity data
Opportunity data helps in identifying favorable conditions for a company to perform when prospecting sales. Opportunity data basically gives people the opportunity to create new businesses, with the data like the promotions, mergers and acquisitions.
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