The recent trend toward digital marketing has led to the need for data-driven solutions for B2-B sales. Since the start of the COVID-19 epidemic, 84 percent of business executives have observed a higher demand for data-driven decisions across the different departments of their businesses. A majority ( 49%) affirm that data analytics are the main reason for improved decisions.
However, keeping high-quality data can be an actual problem. Only 13 percent of the companies that have already implemented an approach to data have stated that their data was good enough to consider it an asset of strategic importance. Most companies are not using their data effectively and have little specific input. The data is not used fully, making the analysis more difficult.
To fully harness the power of the data-driven approach to B2-B businesses, they must enhance the quality of their sales data. To do this, it’s important to understand the quality of data and its impact on your company.
Table of Contents
What Is the Quality of the Data
The quality of data reflects the condition that your data is currently in and the extent to which it fulfills its purpose. Since it can be difficult to assess the quality of information gathered, There are a few general guidelines that all datasets must satisfy:
Accuracy: How well does your dataset represent reality?
Completeness: What is the quality of the data you collect?
Consistency: How exact is your information compared to other information sources?
Timeliness: Is your data up-to-date?
Validity: What do you interpret your data to reflect the things you want to be able to
Uniqueness: How unique is the data you collect?
In general, high-quality sales data meets all of these requirements and is well-integrated within the whole organization and not only in the sales, IT, or marketing departments.
What Is the Reason That Data Integrity Is So Dangerous in Marketing and Sales
In contrast to highly-regulated industries (such as banking, pharma, or) and business functions (such as finance or accounting) in which inaccurate or missing data could result in penalties, closures, or even penalties, the marketing and sales department doesn’t have to face the same scrutiny in terms of the quality of data.
The worst part is that the end-users responsible for creating and updating a lot of the data aren’t known to be obsessive about the integrity of data. The salesperson has been known to resist CRM adoption in the stress of closing a business, and marketing professionals have been known to miss crucial steps when they rush to launch their campaigns.
This causes all kinds of issues with data integrity which negatively affect revenue. We’ve already mentioned that, in the Salesforce and Data.com data report, the average company loses 30 percent of its annual revenue due to bad data quality.
How Can I Improve Data Quality in Sales and Marketing
If you’ve completed cleaning your data and creating the data model, Here are nine ways to ensure the quality of your data in marketing and sales.
Benefits of a Better Quality of Data
The improved quality of data gives you an accurate and complete image of your business and the customers you service, which allows you to make better decisions that will save you time and cost. Quality data can guide marketing campaigns that increase customer engagement, customer trust, and better ROI on marketing and sales strategies.
Access to reliable information takes the guesswork out of crucial business decisions, improving efficiency and reducing expenses if you follow the steps below to get your business set up to be successful with a plan to collect important data that can be used to make important business decisions.
Establish How Improved Data Quality Impacts Business Decisions
Find a clear connection between business processes and KPIs, key indicators of performance (KPIs), and the data asset. Create a list of problems with data quality that the business is experiencing and the impact they have on the revenue of the business and other KPIs.
After establishing a clear link between data as a resource and the improvements required, Data and analytics executives can construct a specific program to improve data quality, establish the scope, list of participants, and a high-level investment plan.
Define What Is a “Good Enough” Standard of Information
To improve the quality of data To improve data quality, it is first important to know the “best fit” for the business. The responsibility of defining what constitutes “good” lies with the business.
Analytics and data (D&A) executives must regularly discuss with the business’s stakeholders to understand their expectations. Different areas of business that utilize the same data, like master customer data, might have different standards, resulting in different program expectations to improve the data quality.
Make Your CRM More Robust by Adding Quality Information
Based on Experian’s research regarding the governance of data, creating uniformity across the entire organization is the biggest challenge for 42% of the respondents.
The most effective way to tackle this problem is to implement an integrated Customer Relationship Management ( CRM) system used across the entire business. However, the efficacy of your CRM is based solely on the quality of data you collect.
Making your CRM more efficient using clear data can allow your team to get information more quickly, target potential customers more precisely, and reduce the time to sell substantially.
Make Use of Data Profiling as Early as Possible and Frequently
Profiling data for quality is the method of reviewing the data coming from an existing source and then summarizing the information on the data.
It aids in determining the corrective steps that need to be taken and provides useful insights that can be passed on to the business to develop improvement plans. Data profiling helps determine which data quality problems need to be resolved at the source and which can be corrected later.
However, it is not a single-time event. The process of profiling data should be performed whenever possible, dependent on the availability of data resources, errors in data, or other errors.
Daily Cross-Check Systems to Confirm That Records Are Flowing and Properly Being Tagged
This is crucial when operating a high-speed engine with hundreds of new leads daily. Check your sales automation, marketing automation enablement, and CRM applications regularly to ensure leads aren’t held up because of malfunctioning workflows, API restrictions, weak integrations with third-party vendors, or incorrectly tagged records.
Consider this routine as a way to protect yourself from human error as well as software integration problems. We at scale matters use our “Daily Data Check” dashboard to identify data anomalies, so they can be addressed without spiraling into chaos.
Conclusion
In this guide, we’ve given some suggestions and ideas for your project on data quality. If you require more help, Acrotrend provides a comprehensive array of services for data quality to ease your data problems with customers!
