Decisions are crucial, whether in personal or professional life. The success of any decision depends on the source data, which must be accurate and up-to-date. Outdated or accurate data can lead to good decisions. To solve this problem, one must be aware of data scrubbing, which is data cleansing. This is a technical process of extracting oddities and inconsistencies or correcting corrupt, inaccurate, and redundant data in a database.
Before making any crucial decision regarding products, offers, deliveries, performances, hiring, pricing, or anything else, you need to work on data accuracy. This post will help you discover the problem of corrupt or expired data and how to generate leads with clean datasets.
The Problem of Expiring Data
Expiring data refers to obsolete or offbeat records. A customer database consists of contact details, demographic information, and other significant records, along with their quality. Quality is a major concern because digital communication is evolving rapidly, and a constant shift in customer behavior is observed. These incidents make data obsolete quickly.
In this regard, Zoom info figured out that the decay rate of B2B data is 2-3% every month. It clearly indicates that a business could see up to 30% of data getting offbeat within a year.
All in all, expiring data attracts challenges.
- Inaccurate customer insights: Data contributes to insights, which help in personalizing marketing, sales, and other efforts. If the data is correct, it leads to correct decisions.
- Wasted resources: Information-driven decisions are vital to achieving success. Marketing campaigns driven by incorrect or outdated contact information may waste resources and definitely reduce returns.
- Poor decision-making: Decayed data can hamper the flow of leads since decisions and strategies are based on data. Eventually, this will result in a significant loss of opportunities.
- Relying on decayed: Data decays frequently, leading to inaccurate and unviable decisions. This practice invites negative consequences like an exponential drop in sales and, hence, revenues.
The Impact of Poor Quality Leads
Poor-quality leads or spammy inquiries are a matter of serious concern. This practice can generate fake inquiries. The reason is definitely incomplete, incorrect, and irrelevant information available within databases. A report by HubSpot confirms that 42% of companies believe that insufficient data breeds inaccurate leads, which eventually adds up to the cost of wasting resources and time on fake leads (inquiries).
Why is this quality a concern? There are multiple reasons, which you can learn below.
- Low conversion rates: The sales team invests exponential time and resources to attract leads that are unlikely to convert into customers.
- Wasted marketing efforts: Marketing campaigns are crucial because they target the wrong customers, who should be more interested in purchases or investments.
- Misaligned sales strategies: Spammy sales data will definitely impact sales strategies. You won’t be able to understand the specific concern that needs to be addressed.
If the data is clean, an inquiry list or contact details would be good. Businesses target interested customers, who are more likely to be onboard as consumers.
How Does Data Cleaning Work?
Data Cleaning involves a hierarchy of steps to introduce accuracy and reliability. Let’s check it out below:
1. Removing Duplicate Data
Duplicates are easily visible. They are the most appalling mistakes, which can confuse and breed inaccurate decisions. Identifying and removing duplicates can improve data quality, leading to viable choices.
2. Updating incomplete or incorrect data
Businesses often struggle with incomplete records, such as client contact details and demographic information. This process fills all gaps in data quality to ensure their accuracy and completeness.
3. Standardizing Data
Variable formats may interrupt cleansing, especially when the data inflows from multiple sources. Data hygiene ensures standardized data, which makes sense to use correctly.
4. Eliminating Inaccurate Data
Expired data, such as incorrect email addresses, phone numbers, or anything else, must be filtered out. This practice provides accurate and relevant information that works for businesses.
5. Validating Data Sources
Ensuring data comes from credible sources is essential. Data cleansing for any business growthinvolves checking the validity of the data so businesses aren’t working with irrelevant or unreliable information.
Data Cleaning for Business Growth
Data hygiene is about maintaining accurate databases and fostering accurate insights for business growth. Here is how it helps.
1. Enhanced customer insights
This data-scrubbing process helps businesses gain clean insights into details that resonate with an objective. Data specialists focus on removing outdated information and filling gaps. As businesses access quality insights, they can better tailor their offerings, customize marketing campaigns, build stronger relationships with their customers, or achieve whatever the goal.
2. Better Lead Generation and Conversion
Hygienic records carry high-quality contact details of customers. These are technically called leads that help marketing and sales teams focus on the right prospects. With this quality, businesses can qualify leads impressively and, hence, focus on those who are more likely to convert.
3. Improved Decision-Making
Accuracy helps in achieving groundbreaking strategies. Businesses might be intended to storm into a new market, launch a new product, adjust pricing, or anything. Clean data enables stakeholders to make better and more realistic decisions.
4. Cost Efficiency
However, data scrubbing differs from generating revenue. Clean data prevents businesses from making bad and impractical decisions that are costly to implement. According to IBM research, bad data results in over $3.1 trillion in losses for the U.S. economy.
This fact clearly suggests that businesses can avoid wasting marketing spend, inefficient sales efforts, and costs on in-house resources. Overall, data cleansing can help mitigate these costs while aligning all associated operations accurately.
5. Regulatory Compliance
With the advent of data protection regulations like GDPR, businesses are supposed to comply with legal requirements. This cleansing process ensures maintaining clean and compliant databases without corrupt and irrelevant information. Overall, non-compliance can impose penalties and reputational damage.
Conclusion
The data-centric business world struggles with poor-quality data, including business leads or inquiries. For any decision, organizations require high-quality insights from customers, marketing, or sales. Data Cleaning can provide them with a high conversion rate and overwhelming leads.
