How to Standardize Your B2B Database - A Step-by-Step Guide

B2B data is a key strategic business asset, given its pivotal contribution to driving successful marketing and customer engagement campaigns. Data aggregators spend a fortune on data acquisition activities, making huge efforts at tapping into a diverse range of structured and unstructured resources, online as well as offline. Every bit of data is critical for business growth and success, whether it is from the competitors or any other source.

It is not only about the contact list in your database, it is also equally important that the data is standardized and accurate. You just cannot afford data decay creeping into your system. Your marketing campaigns can fail if the data in your contact list turn out inaccurate and stale. The ROI of the campaign would be dismal, and your sales would show a huge downturn.

Data cleansing and standardization is a critical business need and a mandate for any data aggregator. Marketers need to address key data quality challenges to make sure they put in place a framework for ongoing database cleansing and standardization. As data sources get more diversified, unstructured, and voluminous, the need for data standardization becomes even more pressing. Whatever be your data source, be it machine-generated, emails, social media, etc. it will not be effective without data standardization requiring expertise, skills, and automation.

Challenges in standardizing a B2B database

With data flowing from multiple sources and diverse formats standardizing the data has its challenges. There are multiple issues like compliance and ever-changing data quality. Some of the challenges include:

• Dynamic nature of prospect information - When businesses close, merge, or relocate, the contact details change. If not updated regularly, the database ends up with invalid or inaccurate firmographic and other contact details.
• Reaching the right data standardization framework - Based on the different business requirements one needs to plan a data standardization strategy. It can’t be one plan fits all. B2B data is not only about collecting a name or a phone number; you would need firmographics, complete address, etc. To make sure that the data stays relevant in real-time constant monitoring and a set standardization system is required.
• Driving standardization vis-à-vis compliance - B2B data is sourced from across the globe but different countries have their compliance laws and rules to follow and you must abide by the law. The rules protect personal information and put conditions on how businesses can use the data. You must get complete information on the rules and laws of the country from where you intend to collect data.
• Aligning standardization process to ROI - The data you collect and standardize must align with the organization's goal. To be able to set a proper plan in place a complete understanding of the business need is required. The data should help draw actionable insights driving lead generation, conversion, and enhanced ROI.

How to standardize your B2B database

Here are 10 tips to standardize your B2B database

1. Employ a data enrichment framework

Build a data enrichment plan based on the business requirement. The existing data would require additional information for it to become actionable. Apart the appending the data with missing values, it would also need to be structured, cleansed, and standardized. Make an overall assessment and put an effective plan in place which would ensure consistent, accurate, and enriched data. B2B data should be a piece of complete information in a consistent format for correct business insights. Optimizing data leads to a better return on investment.

• Standardize data format
• Append incomplete records
• De-duplicate
• Remove obsolete records

2. Augment using quality data sources

Data needs to be relevant in the current context. To perform it needs enhancement in the context by adding additional information. Your role is not limited to only enhancing the raw data; you should be able to draw insights from the data. For instance, just having the name of the organization may not be enough to get results; you would also require the name of the contact person and his credentials.

• Use reputable sources
• Employ data enrichment tools

3. Enrich inbound data at all possible junctions

Inbound marketing is effective in the long run. Going by definition, inbound prospects present themselves as leads and are better sources for lead generation. Inbound lead generation funnel depends on getting the right traffic to your lead generation sites. Enhance inbound data at all possible junctions for better progressive profiling.

• Use company blog
• Infographics
• Webinar

4. Standardize data at the source

Checking data quality at the entry point is half the job done. Plan a system and monitor every data that enters your CRM. Once data is standardized at the entry it gets much simpler to later detect duplicates and remove them. Like there could be two similar data in different formats making duplicate identification difficult.

• Create a standard operating procedure (SOP)
• Develop standardization rules
• Use automation for accurate data entry into your CRM

5. Practice data stewardship

Data stewardship with a complete understanding of how the data flows and sources ensure the safety and usability of your data. Empower your users by providing trustworthy data.

• Understand and define your data
• Establish systems and processes to monitor data health
• Maintain data quality
• Ensure compliance and security of data

6. Create protocols for field values

Data traceability is critical; no point in having a huge dataset if data can’t be traced at the right time and used. Put a plan in place where data is structured and stored in a way leading to high traceability. Often a certain value can be entered in more ways than one and therefore create a system where values can be entered in different ways and remain consistent.

• Create drop-down menus
• Create protocols for field values
• Define your standards
• Have a set of rules for each field

7. Conduct multi-checks

Data standardization is a complex process and requires a structured and well-defined workflow in place to make the data consistent across all channels. Make sure the workflow has no loopholes because even a minor problem at a single point can make the entire system collapse.

• Deploy multiple checkpoints in your workflow
• Have a regular schedule in place to audit your process

8. Automate contact/list segmentation

Using automation to segment your contact list is a sure way of getting accurate results. Use advanced tools with rules and bots to streamline your contacts based on their location, profile, or online behavior. Manual marketing efforts are time-intensive and error-prone; use tools and technology for more goal-oriented marketing efforts.

• Work smart with automation
• Choose right tools

9. Continuously evolve your database management SOPs

An SOP for database management works well to keep data accurate and usable. But there is a need to keep revisiting the SOP making sure it doesn’t go obsolete. A well-defined SOP goes a long way in keeping data standardized. As business evolves the business goals also evolve. Always keep track of the end in mind and keep visiting your SOP at regular intervals.

• Identify your audience
• Define the scope
• Keep the end in mind
• Review, test, and edit

10. Conduct periodical data audits

Your data is useful so long it is clean. Data decays at a fast pace and therefore the need for constant audits. Data audits if conducted regularly works as an important compliance component that keeps track of your records and identifies any breach. It helps identify problem areas in your data and business operations.

• Check user access and authentication
• Use data auditing tools
• Partner with a B2B data standardization solution provider

Conclusion

B2b database is not just about contact details; the data has to be relevant and actionable generating strong leads and convert. Data aggregators need to deep dive into the huge amount of data they gather and set a plan to be able to present the data in line with the requirement of the business.

Data needs to be collected, cleaned, standardized, segmented, and audited regularly so that it gets actionable. Deploying macros, rules-driven scripts, custom bots, etc. helps improve data accuracy. A well-planned data standardization plan is the need of the hour.

Data decays at a very fast pace and so does business needs; so, a data aggregator's job is never done.