5 Best B2B Data Enrichment Strategies to Help Your Business

5 Best B2B Data Enrichment Strategies to Help Your Business Grow

Overview

B2B data enrichment enables businesses to maximize their returns on sales and marketing efforts. Properly enriched lead data helps your clients devise better marketing strategies, find more actionable leads, and target them efficiently. It helps them narrow down the range of targets and reduces the amount of efforts wasted in chasing false leads.

Thus, as a data aggregator, B2B data enrichment is an area where you can’t afford to skimp on investment and resources. Because, the quality of the data you sell, determines the future of your business.

As a B2B data aggregator, you’d already know that. But if you are new to the business, or thinking of moving into aggregating and selling data to other businesses, you need to be keenly aware of the needs of business data enrichment, and adopt the best data enrichment strategies used in the industry.

Data enrichment is the process of aggregating and sorting structured and unstructured data captured from multiple sources, enriching the data further with additional data points, validating and verifying for accuracy, and standardizing and integrating it with the customers’ database.

But the scaling of operations or enhancing data quality becomes extremely challenging if the strategies used to enrich data have loopholes. More tools and more staff won’t solve the issues for you, unless you stick to sound strategies, and agile data enhancement and maintenance solutions.

This is why, in this article, we’ll be focusing on the five best data enrichment strategies you should focus on, to grow as a data aggregator.

Why data aggregators are obsess over data enrichment

The fundamental market need fulfilled by B2B data aggregators is supply of properly enriched data, and that’s what sets them apart from other data sellers. That’s why businesses buy data from them, or they’d have done it by themselves. And enrichment is not simple addition of verified data points.

Verification by itself is insufficient, because it just announces the consistent presence of some element in past and other available records. We all know of the huge number of ‘properly verified’ errors infesting the world of data.

B2B data enrichment takes the game of making data usable to a whole new level, beyond basic sorting and verification. The added data has to be contextually valid and factually authentic. It should offer a complete and correct picture of the customer, should be free of duplicates and outdated records, and should be as current as possible.

Data enrichment is a highly technical process, whose demands vindicate the need for specialized data aggregators. And that’s why data companies act like fanatics when it comes to data quality and enrichment. Anyone hits them there; it’s a hit below the belt.

For data aggregators data enrichment ensures:

B2B data aggregators need robust and agile data enrichment solution to manage their sellable databases. Efficient data enrichment ensures:

• Enhanced data quality

• Increased data relevance

• Arrest of data decay

• Access to readily usable data

• Deeper understanding of customers

B2B data getting dirty at the rate of 70.3% annually makes data cleansing an enrichment imperative for B2B data aggregators constantly struggling to maintain hygiene of their databases.

Every data aggregator is in itself aB2B business, and enrichment of data helps them from two perspectives. First, they get the same benefits their clients get as businesses powered by enriched data. And next, they get to raise the quality and salability of their core product, which is data.

Direct benefits of data enrichment to B2B data aggregators

· Higher credibility

· Improved customer satisfaction

· Higher client retention

· Better client acquisition

· Increased revenue, profit, and ROI

Top 5 B2B data enrichment strategies

1. Data acquisition using extensive research and credible sources

Identifying relevant customer touchpoints to capture data goes a long way in building an effective B2B database. There are multiple data sources like feedback on websites, customer interaction, CRM records, invoice, third-party external sources, data from market research agencies, news articles, social media, etc.

Data is available in abundance, but identifying what data to extract, how to extract, from where to extract, and when to extract is a challenge. Hire data cleansing experts who can identify credible sources for data acquisition.

Use a blend of automation and manual web research techniques to identify relevant records. Use web scrapers and crawlers for harvesting data at scale from all legally available sources. And use all credible data sources, online, offline, conventional, unconventional, structured, and unstructured to build a comprehensive B2B database. Gaining deep understanding of data acquisition and challenges associated to it requires domain experience and expertise.

2. Enrich data after thorough cleansing

Data acquisition alone will not give you a panoramic view of the customer profiles required by B2B data buyers. B2B companies look for engagement-ready data for personalized and strategic marketing. Enrich the profiles with extra information relevant to the business like demographic, technographic, firmographic, and other types of data to make them more usable.

As an aggregator, you need to ensure that the external data is accurate, relevant, and sufficient by validating, verifying, and cleansing the data. Identify probabilistic duplicates and do data matching and deduping. Make sure that redundant and outdated data are removed and replaced with accurate data. Make sure that the new data is processed to make it compliant with your client’s data standards. Data enrichment is extremely important for the accuracy and relevancy of acquired data.

3. Invest in semantic data enrichment

The online behavior of prospects, like what content they are engaging with, their reviews, and feedback, talks a lot about their chances to buy a product. This behavioral dataset indicates the interest of the business or individual in the product, opning up more opportunities for the sales team to segment and target prospects for better conversions.

Therefore, as a data seller, investment in intent data is important. Semantic data enrichment is a complex process where data collected from unstructured sources like social media, reviews, videos, Instagram images, reels, etc,, is sorted, analyzed and used for lead enrichment and segmentation.

4. Perform data segmentation

Segmented marketing campaigns always have an edge over non-segmented campaigns; a survey compared 18 million emails, found that segmented email campaigns saw 14.3% higher open rates, click-through rates doubled, bounce rates were 4.6% lower and the number of unsubscribes was 9.4% lower (1). It helps the sales team focus on important segments and better connect with prospects.

And therefore, well-segmented data sells like hot cakes. Invest in data segmentation techniques and market experts who can define the market and plan segmentation. Group customers into segments based on shared qualities and characteristics like similar needs, preferences, or buying behavior.

Factors such as age, income, demography, behavior, geography, and customer journey will help better define the market and help your buyers reach previously inaccessible customers. Market segmentation is vast and requires specialized skills and if done properly can drive business growth.

5. Monitor and update data constantly

Data enrichment is not a one-time process, it needs constant monitoring to handle data decay issues. Businesses need data that is accurate and updated and enriched all the time. Redundant, obsolete, and trivial (ROT) data requires to be flushed out from the database and replaced with current records constantly. Regular testing and monitoring will be required to maintain the quality of data.

Real-time data provides valuable context and actionable insights into clients’ business operations. So, invest in technology and automated data update solutions. A chatbot can extract information in the form of conversation that one might not receive through any other form of customer interaction. Rule-based macros track changes in current data and trigger alerts for an action. Use automation.

Conclusion

Companies look for huge volumes of data to create personalized marketing campaigns. The data goes beyond contact enrichment, covering data types like demographic, technographic, chronographic, etc. Semantic data is also gaining pace for catching the intent of customers and analyzing their online behavior.

Assessing the reliability of the content produced by social media users, judging the quality of answers, and distinguishing spammers have challenges that data aggregators need to address. Finding the right sources of data, pulling data from deep wells, judging the relevance, enriching it with additional data points, validating and verifying the data, formatting, standardizing, segmenting, organizing and monitoring requires a robust enrichment process.

Doing it in-house is an option if you have the infrastructure, technology, and resources in place, but may not prove cost-effective. Outsourcing can also be a good option that is used by many businesses.

Author

Snehal Joshi spearheads the business process management vertical at Habile Data, an integrated data and digital solutions company. Over the last 20 years, he has successfully built and managed a diverse portfolio spanning more than 40 solutions across data processing management, research and analysis and image intelligence. Snehal drives innovation and digitalization across functions, empowering organizations to unlock and unleash the hidden potential of their data.

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