While it may not look like it on the outside, many businesses today rely on data analytics to gather business insight. Data helps a company strategize, target, and implement marketing and sales plans according to the insight gained.
As of 2021, there are over 11,000 SaaS companies the world over. It indicates the popularity and importance of data. However, data can also be ineffective in serving its designated purpose when left unmonitored or if a business database is not maintained correctly.
Read ahead to learn about the basics of B2B data, their sources, and the best ways to optimize B2B data.
What is B2B Data?
The backbone of a company’s sales and marketing revenue is B2B data. It is Machine-readable data, as opposed to human-readable. Machine-readable data is information that computer programs can process.
It comes from several public and private sources and can be applied to a broad variety of business functions.
Public sources of B2B data include websites, social media profiles, and news articles. These sources are freely accessible and completely available to the public.
Paywalled websites, financial and market information, and DaaS (data as a service) providers are popular private data sources. These are data sources hidden from the general public and can only be accessed through a subscription, authorization, or purchase.
Data Quality in B2B Data
There are five key parameters that determine data quality in B2B data services.
To guarantee that the data is relevant, the company must first determine its relevant contacts and accounts to target its sales and marketing activities. Focusing on the wrong data wastes the company’s marketing budget and the sales and marketing teams’ time when they pursue the wrong prospects.
It is critical to identify the target market to obtain high-quality B2B data. To determine who the potential purchasers are, the company must first identify the parameters of their Ideal Customer Profile (ICP). This data is organized into categories that describe the company’s accounts and clientele.
Once relevant buyers are determined, the subsequent step in getting high-quality B2B data is to ensure the accuracy of the data.
Inaccurate data can result from a wide range of issues, and the implications can be disastrous for any organization. A primary reason behind its occurrence is that certain steps during data checks are forgone.
Thereby negatively impacting marketing efforts by wrongly identifying a prospect, mistargeting accounts, or unintentionally missing prospects from campaigns. These issues can cause a marketing plan to go completely astray and have little impact.
While working with massive volumes of data and preparing it for a sales or marketing campaign, it is critical to evaluate its validity before using it.
Data validation guarantees that business’ data is formatted consistently, includes accurate contact information, and is free of mistakes and abnormalities.
Data validation is a precautionary measure that guarantees that data is being used efficiently and that a company can have the potential to generate more sales prospects.
As soon as B2B data is collected, it becomes stale. Data points are never static since information changes frequently. Working with outdated Data means losing time reaching out to the wrong people, as companies, email addresses, job titles, and even names might change.
The data source also determines the rate at which data decays. Email bounces are common in email marketing campaigns that use data that has not been verified. Sending an email to an incorrect or non-existent email address will result in a harder bounce.
These delivery failures will influence the domain’s health and the spam rating of subsequent emails sent by the company.
To have high-quality data, the company should create a database of rich profiles of numerous data points that allows for personalization and helps to derive deep insights from the data.
The breadth of the data available with a business adds another layer towards boosting the quality of the data. Without data breadth, companies will be unable to adjust marketing operations and view credible and effective insights from the data.
Examples of Bad Data
Here are some examples of bad data or data that adversely affects marketing through B2B email lists.
1. Incomplete Profiles
Incomplete data records can stem from a variety of reasons. Listed are some of the most common ones:
- Unavailability of data
- Multiple client profiles in various tools
- Lead forms are done through progressive profiling
- Form submissions are incomplete
- Filling up profiles manually
Obtaining data in its usable form can be time-consuming and labor-intensive, requiring manual checks to correct any misinformation.
2. Duplicate Data
The consequences of duplicate data might range from minor annoyances to consuming tremendous amounts of time and expensive cleanup procedures. Duplicate Data is a massive waste of time and resources on both ends of the spectrum.
3. Standardized Data
Unstandardized data can manifest itself in various ways, wreaking havoc on marketing operations and analytics. Data not provided in a standardized form might cause potential leads to fail when campaigns are planned, resulting in missed sales pipelines and revenue possibilities.
4. Multiple or Legacy Fields
Multiple fields or legacy fields that have not been appropriately discontinued might be quite confusing. More so if the company has inherited a CRM or marketing automation software.
It is far simpler to establish a new field, conceal the existing one, and then proceed rather than clean and deal with the existing data. However, suppose old fields are simply discarded instead of being properly integrated with the data. In that case, the database ends up with a CRM full of duplicate or even triplicate legacy fields reflecting the same item in multiple formats.
9 Ways to Optimize Data Quality
A study suggests that 21% of marketing budgets were wasted due to bad data quality. Therefore, it is also essential to know the tactics to optimize data quality to target marketing campaigns effectively through B2B data.
Read ahead for the nine best ways to achieve favorable results using B2B data services.
1. Single Vetted Source of Data
Using a single B2B data supplier to acquire all of the data ensures that the B2B database is in a standardized format with consistent data quality. A data provider who carefully checks the data before delivering it will offer the highest quality data.
It is critical to confirm that the vendor is GDPR compliant and can deliver consistent results. That will ensure that the company has a consistent flow of accurate and relevant data for its sales and marketing teams to rely on.
2. Routine Database Checks
Routine data cleansing, refinement, and validation checks ensure that the data is free of duplicates and comprises accurate information.
Businesses can ensure that they have all of the information they need to target the appropriate people, adapt their messaging, and achieve excellent email deliverability by doing so.
3. Defining an Identity Resolution Strategy
Identity resolution is the basis of many data challenges, particularly those involving duplicate data. Duplicate data arises when the identity resolution strategy has flaws or is not implemented correctly.
Defining and adhering to an identity resolution strategy will allow a business to understand their existing B2B data records better and reduce duplicates to a large extent.
While identity resolution alone may not eliminate all of the existing duplicate data, it will help reduce errors when using B2B email lists.
4. Creating Data Source Groups
While many software solutions have made data merging quite straightforward, reversing the merger after making a mistake can be more complex.
To counteract scenarios such as improper data merging and, in turn, losing vital data, centralizing data while keeping it separate via a concept known as “grouping” is a wise idea.
Grouping allows a company to establish data holding chambers within a lead or account profile. Each group is associated with a distinct data source. A business, for example, could have a group for their CRM fields, a group for their data enrichment vendor fields, and a group for the chat platform fields.
The following step is to construct a “unified data” group that will allow the company to reveal the best data when it is available.
The integrity of the original data sources is preserved using data source grouping, but companies can apply specific rules to reveal their preferred data when it is accessible.
5. Having High Data Points
If the data is sourced from a B2B data provider, a company should ensure that they have a plethora of information for each prospective and existing client.
Furthermore, whether the company has another source of data or leveraging existing data, data enrichment services can provide extra information on the data. The more data points a company has, the more it can segment its data and adapt the message to each prospect’s needs.
By interacting with the prospects on a deeper level, a firm is significantly less likely to be disregarded and far more likely to enhance its conversion rates and key metrics.
6. Data Processing Tools
Data processing and transformation is a strategy used to modify or produce new data by applying rules to available data.
Data transformation is frequently used to standardize data. Data transformation with a Processor is an automatic procedure that searches for specific data scenarios and then modifies the data based on rules that an individual defines.
Data processing technologies enable companies to not only clean and standardize data once but also aid in keeping it up to date over time.
7. Ensure Data Freshness
Because data is never static but rather evolves over time, having stale data will cause a whole slew of issues. The data’s integrity is determined by how recently it was generated or validated. It is critical to use the data as soon as it is received to avoid problems such as misidentifying prospects or email delivery failure.
To obtain new data, a company can either update the webforms on their website to entice more people to fill them out or collaborate with a B2B data provider that will generate data on demand.
8. Third-Party Enrichment
The practice of linking publicly available, third-party data on a lead or customer with what a company already knows about them in their current lead or customer record is known as data enrichment.
Even after bringing all of the data into one location and creating a single customer view, the company may still have empty fields.
Data enrichment with a reputable provider may supplement client profiles with important information, allowing the company to fill in the gaps.
9. Whitelist Certain Segments
Bad data synchronized across all tools can be extremely difficult to handle without a robust data governance policy. For the sake of convenience, many B2B marketing teams send and synchronize all of their marketing automation contacts to their CRM and vice versa.
However, this technique assures that the other tools will do the same if one tool raises concern for poor data quality.
Create “whitelisted” contacts and account segments using a customer data platform. Contacts and accounts in these “whitelisted” sections are synced with other tools, while the remainder is not. This method enables businesses to fine-tune their data flows and synchronize only the records containing data that they trust.
This article has aimed to inform readers about B2B data and its providers. Moreover, it has also provided insight into the functioning of marketing using B2B data.
The most vital thread that runs through the article is the maintenance of this data. It requires de-cluttering, and the best way to keep it from frequently happening is to exercise caution concerning handling data.
Further, the article suggests several different ways to ensure that the data that remains after cleansing is advantageous to businesses.
Having these tips at hand should be good enough for businesses to procure the aid of B2B data to push their business and marketing strategies to the next stage.