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Why B2B Brands Should Have Multiple Data Sources

 

To strive in the competitive, data-driven B2B market, your business tactics game should be on point and ahead of the other businesses in the market. 'Time is 'money' is not quite true in the B2B sphere. It goes like this. 'Data is 'money'! But obviously, the data itself is not going to get you the money. However, you are responsible for leveraging the value of the data that you have to enhance the business prospects. Also, you should have a strong data-driven B2B content strategy for your business.

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

How Data Helps B2B Brands

Making Decisions

Data generation is not hard. Even small startups generate data. Any business having an online presence and an electronic payment option of any sort, can generate data about customer behaviour and habits, traffic on web, demographics, and more. If you know how to use it, then all that data is full of potential.              

Businesses can harness data to make decisions about the following matters:

  • Acquiring new customers
  • Increasing the customer retention rate
  • Improving customer services
  • Better marketing management
  • Tracing social media interactions
  • Predicting sales trends

Overall, data provides leaders with real-time data about their customers to make smart business decisions.

Solving Problems

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

NO!

You did get the data of many people. You can get to know what works for your customers. Your performance breakdown can be uncovered to help you track. And that will let you understand each step of the business and what needs to be fixed for it to perform well.

Helps You Understand Performance

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

The collected data in sports and games helps improve your performance.

Data Helps Improving the Process

Of course, since you will have a lot of data to analyze thousands of different aspects of your competitors about different parameters, you can draw a simple map of the processes practiced by them. Comparing that with your process, you can easily improve the process where is needed and can check different parameters of the process to just to change a few parameters instead of the whole process.

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

Understand the Customers

You might not know at all who your customers are without the database. B2B database helps understand your customers. 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 whether your customers like the product or not and is anything needs to be change?

B2B database is the key to understand your customers' needs and the market. Another thing that matters is the B2B data quality.

Even if you have a lot of data which is up to the B2B data quality standards, if you don't have the right tool to analyse it then it is of absolutely no use. A useful data tool will help 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 could imagine. And now the task here is getting the exact B2B database with the parameters that you want to calculate and analyze for your business.

Predictive Data

Prediction is referred to as an outcome of an algorithm that has been provided with trained or historical datasets and forecast the likelihood of a particular outcome based on new data. Now certainly, the next question must have raised in your head that, which type of data is issued 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 is captured in a way that a certain mathematical model captures important trends. However, then a predictive model is used to suggest actions or to take optimal outcomes based on the current data.

Intent Data

Due to significant success in the artificial intelligence and the evolved algorithms of Google, you happen to get tracked with everything and anything you search on Google. For instance, has this ever happened to you that you did 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 get to see the ads of watches even if you search anything related to it. That does mean Google gets to know your intention when you search for something. You can collect intent datathat shows which leads are actively taking the research online the account is shown to 'Surge' on those topics when the research on some particular topic spikes high in activity.

Sales and marketing team can then arrange the accounts in priority with the surge related topics over the qualified accounts which doesn't show the intent. B2B intent data boosts conversions and sales uncannily high, when used correctly.

How this intent based search works in B2B is, when buyers have problems or pain points, they visit various websites, download ebooks or they happen to read articles and whitepapers. That leaves digital footprints as content across the internet is being consumed. 

You can reach the buyers very easily if you were to collect and use the online data and behavior signals from their digital footprints.

Now there are different ways to get raw data and process it as per your need. But what is more important is how you use that collected data.

Because of B2B intent data, initiating go-to market plans are effective for customers. Sales and marketing teams will get help with data segmenting and contacting potential customers. Companies who are not using predictive intelligence are limiting their data responses from their own websites. However, their potential buyers would have been looking for solving their pain points for weeks. 

Here are a few main use cases for sales and marketing teams using data intent in 2021:

Identification of Early Buyers Interest: Purchase intent signal helps you in identifying which companies are actively looking for the solution. This could be identified even before they fill out the form on your website or engage with your sales and marketing team.

Make a List of Targeted Accounts: Sales and marketing teams can easily filter the list of the accounts who are showing active interest in your product/ services.

Personalisation: Sales and marketing team can personalize the initial outreach with resources that matches the potential matches that you are already looking for.

Scoring the Leads and Prioritizing the Account: Your lead scoring model should go through predictive purchase. This will help you prioritize companies that show interest and have purchase intent; before your competitor comes in the picture.

 Analyze and Retain the Customers: You can get real-time visibility into which customer is researching your topic and asking for solutions that you provide. These sort of insights helps in up-selling your product or services, proactively. And also, it identifies the pain points of the customers before you get blindsided by customers who go to your competitor to renew or buy an offering that they didn't know that you had.

Fit Data

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

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

Opportunity Data

Opportunity data helps  identify favorable conditions for a company to act on while sales prospecting.  Opportunity data basically gives the opportunity to the people to create new businesses, with the data like the promotions, mergers and acquisitions.

Benefits of Multiple Data Resources

You know how nonlinear algorithm in machine learning requires large amount of data to obtain nearly correct results?

The same way if you could have a large number of data and multiple data resources then it will be easier for you to get hold of a large number of people and user behaviour will be clearer to you. Getting data from multiple B2B data sources will enhance the quality and the chances of you getting different trends. It may be a big task, but it is also helpful.

Although here are three more reasons why you shoud have the multiple data resources:

Improves the Data Quality

Having data from multiple B2B data sources offers variety in data and that contributes in the analyzing the trend and user behaviour.

Having multiple B2B data sources for data is directly propotional to having quality data output.

This also help in creating a data-driven content strategy and that also ultimately improves the data quality.

Boosts Engagement and Traffic

The more data you have, the more traffic and engagement you will enjoy. User engagement is the most wanted thing. If you can have data from multiple B2B data sources, then you are more likely to have good traffic and engagement on your account.

Speeds the Conversion Process

Now this is like that mathematical principle; if A equals B, and B equals C, then A equals C.

Likewise, if you have multiple B2B data sources, you would get good quality of analysed data, apparently you would have large number of enagagements and traffic, and so naturally the rate of conversion will also increase.

How Can You Use the Data from Multiple Data Resources?

Now this is on of the important question that you need to know and understand in order to be able to handle the the data from multiple B2B data sources. How can you make the use of the data from multiple data resources?

Here are the ways to use the data coming from multiple B2B data sources.

Prospecting

Data from the multiple B2B data sources must be provided with all the information as per the parameters that you would need for prospecting. You can filter by the parameters that are needed for the job to create lists from your contacts that your sales team Uses.

Cold Outreach

You can provide your sales team with up-to-date data including email addresses and phone numbers. With this, you can get in touch with people who could be great leads for your business.

Account Based Marketing

Successful ABM campaigns can be conducted with good B2B data. To qualify the prospect and engaging with them, you can use information that comes through B2B data.

Frequently Asked Questions

How B2B data is typically collected?

Harvesting data is primarily done through web scrapers, and it collects obscenely large amounts of data- often running into tens of many contacts. Generally, they work on pre-determined algorithms and parsing methods to gather everything that matches the given pattern.

What is B2B data?

B2B data is an abbreviation of Business to Business data which basically is information that can be used to support sales and marketing campaigns. B2B data is information that will help you to identify new leads and will help you get in touch with them.

Who use the B2B data and for what use cases?

Use cases where B2B data is required are :

  • Prospecting for qualified leads
  • Reinvigorating cold leads by discovering contacts within the organization
  • Building the list of targeted potentials for outbound campaigns and programs
  • Re-identifying sales opportunities which are already present in your sales database
  • Routing inbound leads correctly
  • Qualifying leads based on competitive platforms
  • Validating and cleansing your database
  • Enriching leads in real-time for better segmentation and message relevance

 

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