Data is the fuel that allows B2B enterprises’ revenue engines to function properly, and the finest B2B companies understand this very well. It is critical to the success of your brands. After all, business-to-business data is the foundation of your branding strategy. B2B brands are now dealing with the issue of low ROI or bad campaign results. While there are various causes for this, faulty data is at the top of the list.
If you don’t have good-quality data, nothing else matters. Everything builds on good-quality data.
Sales and marketing teams who want to create high-performing campaigns must understand the importance of reliable data. Until you have total faith and confidence in your data, all efforts to build new business will be greatly limited.
Did you know that? 75% of businesses that cut their investment in data quality saw a drop in sales or marketing performance. Furthermore, faulty prospect data costs sales teams 550 hours and $32,000 for every salesperson.
Having an accurate list of data is a significant difficulty since several issues can be linked to B2B data, such as data completeness, dependability, consistency, timeliness, relevancy, redundancy, and so on.
What is Human Verified Data?
Human-verified data, as the name implies, is data that an actual person has confirmed. The volume of human-verified data is frequently less than that of machine-processed data. However, you can rely on the human-verified data to be mainly accurate.
What is Machine-Processed Data?
A machine learning algorithm is used to gather (and verify) machine-processed data. Massive amounts of machine-processed data are collected. However, no one knows if the data is valid or even in the correct format. From a business standpoint, if you trust data that you have not verified, you should not trust any judgments made on that basis.
Value or Machine-Processed Data Over Human-Verified Data
Machine-processed data is based on a machine-learning algorithm that recognizes the pattern of the email format and then generates additional email addresses based on the pattern. This method does not re-verify whether or not the email address exists. As a result, severe gaps in data quality exist. As a result, your marketers and salespeople may wind up contacting a firm or utilizing an email address that does not exist.
On the other hand, human-verified data can provide more accuracy than machine-processed data. This is because the raw data obtained is subjected to human verification, in which a human contacts the individual to determine whether or not the data is correct. The additional layer of verification makes human-verified data more accurate and actionable than machine-processed data.
Machine-processed data is less trustworthy since there are gaps in the data quality and no cross-verification or human involvement. As a result, depending on machine-processed data for sales forecasting or marketing campaigns may not be the ideal option.
On the other hand, human-verified data is significantly more dependable because human verification is engaged in the process, as previously indicated. Human-verified data is most reliable for sales forecasting and making the most of marketing initiatives.
Machine-processed data has more scalability since automated machines process it. A machine can process millions of data points in less time using their technique. So, machine-processed data is a fantastic choice if you don’t care about quality and just want to reach as many people as possible.
Human-verified data cannot match the scalability of machine-processed data since it relies on human verification methods. On the other hand, human-verified data is a clear winner in terms of scalability with correct data.
Without verification, machine-processed data may suffocate the productivity of your sales and marketing teams. They risk pursuing incorrect prospects and leads. Furthermore, each negative interaction might lower the morale of your sales personnel, making them less interested in the data.
Furthermore, sales representatives that use human-verified mobile dials are 7x more likely to connect with decision-makers. Data accuracy ensures that your team is pursuing the proper leads. This saves them time, accelerates the sales process, assists them in meeting their objectives, and keeps them engaged.
If your ROI requirements depend on the number of contacts you receive for the cost you propose, machine-processed data is a solid choice. Inaccurate data, on the other hand, can cost your firm in the form of missed objectives, poor production, and bad morale. Thus, depending on machine-processed data will not yield outcomes in terms of revenue or conversions.
You are more likely to contact the correct prospects using human-verified data rather than machine-processed data. It improves your dial-to-connect ratio, giving you more opportunities to engage decision-makers, raising your conversion ratio.
Look for Top-Notch Data Quality and Coverage
Given the number we saw at the beginning of this article and the value human-verified data brings to your sales and marketing efforts, it makes sense to partner with a B2B data provider that provides high-quality data.
What distinguishes exceptional B2B data providers from competitors is their ability to give the best data coverage. After all, is it worth collaborating with a company that maintains high data quality but lacks the data you need? The following are some critical questions to ask a B2B data provider:
- How accurate is your data? (Leading providers maintain at least 95% data accuracy)
- Is your data human-verified or machine-processed? (Human-verified data tends to be more accurate and reliable than machine-processed data)
- How often do you reverify or update your database? (Look for a more frequent data verification program as data decays at a rate of 70% annually)
- What if I don’t find the data points I need? Do you provide customized data? (Look for specific or granular data filters to determine what you exactly want.)
- Do you offer data enrichment and cleansing services? (Look for both automatic and manual options)
With SalesIntel, you can reach over 14 million human-verified decision-makers. In that case, it is about quality and focus, 95% accurate SLAs. The other approach when it comes to signal-based or machine-based collections, there are two different types of methodologies. You have proprietary applications and permissions.
Book a personalized demo and see how SalesIntel’s human-verified data can help your team reach its goals.