How Better Data Helps

Analytics

End the cycle of bad data by feeding your teams the most accurate, up-to-date, and comprehensive business data.

Data Quality

Feed your operations with the best data accuracy possible at the foundational level and watch your performance soar. The problem is when dirty data infiltrates your system, it can be difficult to decipher what is fact from fiction. Account data from the most reliable, up-to-date source, and integrated with flexible access via API, CRM, marketing automation, CSV, means that your workflow won’t change – it will just get smarter.

Customer Example:
For one of the largest global credit card and banking providers, they already knew their revenue data was highly accurate for their existing customers. Using that as a reference point, the organization conducted a free EverString Data Test which uncovered EverString’s quality matching and enrichment capabilities for not only their existing customers but net new accounts as well. Upon seeing the results, the team felt confident turning to EverString to serve various data needs for all four of their B2B divisions: SMB Banking, Large Enterprise Banking, SMB Credit Cards, and Large Enterprise Credit Cards. Not only did EverString’s data prove to be highly accurate and vastly superior to any other external data provider, but the depth of signals meant they could search, filter, segment and prioritize their target accounts using over 20,000 B2B data attributes, including nuanced information such as software installed, markets served, annual revenue for small private firms, and much more. read more
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Match & Enrich

Understand your target accounts more deeply and thoroughly. In addition to providing the most accurate NAICS and SIC classification, EverString also provides proprietary keywords for every business, providing 400 times more granular detail than 6-digit NAICS or 4-digit SIC codes. This enables you to drill down to reveal if a software company is a CRM business or an email service provider, as just one example.

Customer Example:
For one of America’s leading trucking carriers, Saia LTL Freight considers accurate location data mission-critical to their core business operations. Since EverString views a company’s address(es) like unique DNA signatures, Saia was able to access a comprehensive, detailed view of each variation in building or unit number. And since EverString matches at such a high rate, Saia can now cover even the less obvious locations like warehouses, stores, branch offices, and satellite locations with pinpoint accuracy. With EverString’s consultative approach, Saia determined a data strategy that carved out which fields to enrich externally and when to lean on internal data. Through this process, Saia struck the ideal balance between their own customer intelligence and the most reliable external account data from EverString. read more

Business Intelligence

When you work with data that includes unique, finely-tuned indicators and insights, your sales and marketing team can pinpoint accounts that are a good fit or are ready to purchase a product or service like yours.

Access an unrivaled business knowledge graph, which maps the similarities and interconnections between all companies. Through web crawling and Natural language processing (NLP), EverString’s knowledge graph understands companies at a level of detail 400 times more granular than 6-digit NAICS codes or 4-digit SIC codes. This enables you to quickly find similar companies (or look-alikes), and segment them at the most nuanced level possible, enabling the most accurate model building.

Customer Example:
DLL Finance is the nation’s leading equipment leasing institution, offering commercial finance, retail finance and used equipment financing to manufacturers, distributors and production-based businesses. Before working with EverString, DLL had a large international presence but hadn’t yet embarked on direct selling in the U.S. Since DLL serves a very broad range of different industry segments, if they filtered their target account list by industry classification (e.g.: agriculture), they would overlook many other new opportunities that fell outside of that classification yet could still use financing services (e.g.: NAICS 213111 – Drilling Oil and Gas Wells). Instead, DLL took a handful of key target accounts and leveraged EverString’s company similarity and keyword semantic mapping capabilities to hone in on clusters of companies from new industry segments that still were fit targets to prospect. This drove significant results for their ABM strategy and helped them establish a strong presence within the U.S. market. read more
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Modeling

Spend just minutes, not months, building data-driven models of one or multiple Ideal Customer Profiles with a high propensity to buy. Use these insights to help your sales and marketing teams be more efficient by focusing their effort where it’s most likely to pay off.

Customer Example:
Snowflake is a growing data warehousing platform with plenty of historical customer data past sales cycles and customer values. The challenge was to find more accounts like their ideal customer, because their best account wasn’t easily defined by just employee size or annual revenue. Instead, they needed a nuanced way to sort, filter and prioritize accounts, modeling with signals like job titles, employee size, industry classifications, and online activity. Snowflake took a phased approach to data modeling. First, they fed the model with accounts hand-selected based on prior knowledge of what success meant. They paired that with EverString’s modern b2b data, to find net-new accounts just like them. Next, they incorporated internal data points like time-to-close, deal size, and app downloads, and the EverString system was able to observe and document the nuances of all the combined data, including selected negative data sets (those to be excluded), all customized with the support of an EverString data scientist, dedicated to helping your team succeed. Snowflake paired their internal data with specific EverString’s account insights to develop and deploy 3 new models: Ideal Customer Profile (understanding target, high-fit audience), Deal Size (which accounts closed the larger deals), and Time-to-close (AKA ‘deal velocity’, or deals that would close fastest with least resources required). With EverString data, Snowflake could zoom in to a fine-tuned level necessary to identify and prioritize accounts at scale. Eventually, Snowflake’s own data science team took over, with EverString’s support every step of the transition. read more