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How Can You Nurture And Expand Your B2B Database

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02 Jan 2021


A lot of enterprise businesses are committing horrible mistakes for paying little attention to their databases. According to reliable sources, 7 out of 10 businesses around the continent do not pay much attention to their B2B databases. This is a worrying trend considering that data is a center of decision-making in any form of business. A good number of businesses that fall in this category always have a different notion about the suitability as well as the use of databases. One thing is clear, it’s a dream of every business to reach the right audience and therefore, the impression that they do this because they don’t want to reach the right group of people is unfounded.

Compelling evidence shows that there is one primary reason for this. That is, the numerous day-to-day tasks that are expected to be implemented have reduced the relevance of databases. The result of this is having a database that’s stagnating year in, year out. Ideally, it will be important if businesses will spare some time to nurture and expand their databases. This is important because the sales revenue chain to a greater extent depends on your data size as well as quality. The following are some techniques that businesses can deploy whenever they want to successfully nurture and expand their respective B2B databases.

How to nurture and expand B2B databases

1. Profiling and analysis

Your data will serve little purpose if it is viewed in what is commonly known as isolation. Apart from providing half the picture, this practice can destroy the credibility of your data and you know what this means to your business. To put your database into its rightful use, it’s important to analyze your database. This process among other things will ensure that you get acquainted with specific information that’s necessary for your business organization. For example, you’ll get to know not only who your best customers are but also where they’re coming from.

Similarly, you’ll be in a position to understand what they’ve in common and not to mention their behaviors. The definition of data profiling was well-outlined with Search Data Management where it’s described as a statistical analysis together with assessment of data values in a given data set. The sole purpose for this is to ensure that the current data is consistent, unique, and to say nothing of logical. In general terms, data profiling is a process that enables businesses to look for both common attributes and trends among your clients.

Profiling data will bring to primary advantage to your business. One, it will enable marketers to identify groups of customers that are fundamental to the success of your business. Secondly, with the right pieces of information, you can come up with mechanisms that will attract more prospects thereby boosting your sales.

2. Predictive modeling

This is one technique that relies on statistics to make predictions about your target audience. In other words, predictive modeling involves using statistical data to determine how your prospects are going to behave in the future. In order to this, you’ll need to come up with different hypotheses that’ll help you to come up with an informed decision. From the Data Matters’ point of view, your hypotheses should take the following format:

 How valuable a customer are they likely to be?

 How likely are they to respond to a campaign?

 How likely are they to cease being a customer?

With these hypotheses, you can project your customers’ growth that’ll enable you to put measures in place that will generate more leads translating to higher sales.

3. Look-a-like model

In look-a-like modeling, marketers are expected to identify people who not only look but also act just like your target audience. This concept will be necessary when you want to identify a new target group that will enjoy your goods and services. When using this strategy, it’s important to identify some specific characteristics of your seed audience and find prospects that are similar to your target. In doing this, you’ll be hoping that you’re going to get a new batch of customers that are going to click on your ads or even watch your video on YouTube channel. Look-alike modeling requires one deploy machine learning in identifying users that are most likely going to bring actions.

4. Direct Response model

The direct response model to a greater extent is similar to look-a-like-modeling. The direct response model that’s also known as the responder model allows marketers to identify contacts in your database based on responses in one of your marketing campaigns. The sole purpose of conducting direct response marketing is to prompt target customers to take a specific line of action after marketing campaigns.


Even as businesses continue neglecting the basic aspect of B2B data, it still remains integral for the success of a business. One area that B2B data has proved more reliable is when it comes to making a marketing decision. Equally, B2B data contains vital information that will improve the performance of your business both in the shortest and longest run. Obviously, crucial business decisions such as the marketing techniques plus the number of products to be produced will be made based on the content of your database. Nurturing and expanding your B2B data will have a direct impact on your business’s market share.

There are plenty of methods that you can use whenever you want to ensure that your B2B database expands progressively. A number of techniques that were seconded by Victoria Hilditchinclude profiling and analysis, predictive modeling, look-a-like model, and direct response model. After understanding how each model works, it’s advisable to implement it in bits to ensure that you keep your data in good shape. At the same time, you can hire the services of a B2B data provider in implementing some strategies. This will ensure that your database offers the best results together with saving you the energy and time that will be spent on the entire process.

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