Application:Web Lead GenerationPersona:Sales Ops Manager, Digital Marketing Manager, Digital ABM teams (Account Based Marketing)Customer:Global Fortune 2000 Company
Customer Situation:Customer required insight on web visitors from a unique client web app where the only information on the visitor is email address and country. Customer needs to identify web visitors as key prospects, customers, partners or competitors to ensure proper follow up and sales routing. Required a fast and efficient way to drive insight on web traffic without changing web form itself or impact user experience.Solution:Client sends a monthly file to Profound with Email Address/Country. This can be automated with API if required. Profound appends company identity, address and DUNS#. Over 1 million records processed to-date.Results:Profound delivers a 50% match rate on client’s global input records allowing the client to identify customers and prospects from previously unknown visitors. This has significantly improved sales and customer engagements.
Application:Financial Services / SMB targetingPersona:VP Marketing, VP Analytics, SVP Lead GenCustomer:Global Financial Services
Customer Situation:Client has established customers in 25 counties outside of North America. There was difficulty in identifying reliable traditional firmographic data for segmentation, and new customer acquisition and campaign management in the SMB space. Payment history was not available or incomplete. Client needed to find data attributes to act as proxy where traditional data sources unavailable to support applying credit limits.Solution:Profound delivered a comprehensive database of all domains with DBI appended in each of the 25 countries identified. The client found significant value in building their scoring models by leveraging a number of DBI attributes. These included Unique Email User Count, DBI Density Score, Number of Devices, Network Growth, Number Links and Website Traffic, DomainRank™, Server Propensity Score, External/Backlinks, and domain date as proxies for firmographic data.Results:The client uncovered new SMB prospects that were previously unavailable. The data was primarily used for strategy and lead generation campaigns in the SMB space. In addition, new technographic insight assisted the client in assigning credit limits.
Lead & Customer Prioritization
Application:SMB Lead GenerationPersona:VP of Marketing/StrategyCustomer:Global Software Company
Customer Situation:Customer needed to position their new software to the SMB market for new and upsell opportunities and struggled with identifying SMB contacts vs. Consumer Contacts. The customer’s global database contained names, addresses and emails. Sales segmentation required SMB distinction for marketing messages and strategy.Solution:Profound processed all email contacts in the client’s database, and flagged all emails that originated from Free ISPs. Digital Business Intelligence (DBI) was appended to the balance of the list to allow for segmentation by digital footprint. Profound also appended history to track the network growth of each record.Results:Profound identified 20% of contacts that were classified as consumer originally by client as SMB. The client was able to build marketing campaigns to those identified contacts with specific messaging and products for the SMB space. As a result of proper identification and marketing, the client improved their conversion to sales rates and overall campaign ROI.
Application:Big Data & Lead PrioritizationPersona:VP of Mid Market Cloud SalesCustomer:Fortune 100 Company
Customer Situation:Our client was selling a cloud software solution and needed to identify prospects with a complex network. Their mid-market EMEA sales team had thousands of leads and limited resources to call on each lead. The client hired an outbound calling firm who could process 1,000 leads per week, with a total of 50,000 leads weekly. They needed to identify the top 1,000 leads each week to send to their Call Center.Solution:Profound appended DBI Density and Cloud Scores to identify the best prospects to target. The lead list was sorted by highest DBI Density Score and Cloud Score to create the list of 1,000 best prospects to place in call center each week.Results:Our client was able to focus on highly-qualified leads to maximize sales resources for the call center. The call center achieved their sales appointment objectives, enabling the sales team to exceed their goal.
Application:Sales Triggers for Cloud Persona:CAO, CDO, VP Analytics, VP Sales, VP Demand Gen/Marketing ServicesCustomer:Global IT Company
Customer Situation:Client wanted to create sales triggers to allow their sales force to target customers and prospects at the correct time in the cloud buying cycle and position the right product to align with the customer/white space data. Understanding where a customer/prospect is in the cloud transformation journey was critical to this client for sales positioning and product alignment.Solution:Profound matched domains to the client’s customer database with a 65% match rate. We appended DBI data each quarter including the Cloud Transformation Journey Score 0-4Results:Client was able to identify high-value prospects for acquisition campaigns. The sales team had key insights and knew when to engage prospects to their products, accelerating the sales cycle and ROI. The client was also able to eliminate prospects who were not interested in a cloud-based solutions.
Application:Sales Triggers for On Prem SolutionsPersona:CDO, CAO, VP Sales, VP Market IntelligenceCustomer:Global Hi-Tech Fortune 100 Company
Customer Situation:Client needed to identify white space for prospects that are investing in On-Prem solutions They also wanted to evaluate their current customer’s On-Prem products and services and assess their risk of moving to a cloud-based solution that would impact sales.Solution:Profound identified all companies in US and EMEA that have a complex network with continuing investment in On-Prem infrastructure vs. moving to the cloud. We leveraged key DBI attributes: DBI Density Score, network growth and Cloud Density Score to provide key insights. The dataset was updated quarterly to ensure fresh information to identify business trends for On-Prem and Cloud movement. Tracked customers network growth and their share of wallet vs. competitors.Results:Client was able to identify risk and the rate of customers migrating to the cloud with competitors. They were also able to prioritize prospects based on their network growth.
Application:Web Portal RegistrationPersona:IT Lead Digital PlatformCustomer:Fortune 100 Global High Tech Company
Customer Situation:Our client had a high-volume web portal form being filled out by customers, partners and prospects. They needed to build a seamless application to ensure each web form email address was not a Free ISP domain. The customer experience for form entry required little friction and had to be fast and secure.Solution:Profound’s DBI data has a Domain Classification field that identifies Free ISP domains (over globally 33K identified). Using our transactional API, our client was able to identify a prospect’s Free ISP email in milliseconds, triggering a request for a new email in the form entry process. This fast response met client requirements and secured the positive customer experience the client required.Results:A simple API integration to their website resulted in fast and secure response times. This met the client’s need to ensure portal web forms have a business, not Free ISP email. The client experienced a 25% increase in better-qualified web applications for proper internal routing and follow-up.
Application:Big Data & Lead Generation Persona:VP of Marketing/Strategy/AnalyticsCustomer:IT Distribution Company
Customer Situation:Client was building a Big Data repository with Hadoop platform. Their philosophy was that more data is better to see what is predictive. They struggled with building accurate IT models for targeting purposes. They were also utilizing IT survey data and were unable to access data across majority of customer base and prospects. They needed to identify web visitors, the applications they were using or were interested in, and prioritize the leads for sales teams.Solution:Profound generated a custom quarterly database of 7MM records. Key attributes we focused on were the percentage of network growth and device counts, URL, web server and cloud provider counts and related historical data.Results:Client was able to integrate the new data with Hadoop and Lattice platforms for predictive modeling, lead generation and Big Data area. Client found that Profound data was the most predictive 3rd party data source after their own sales/customer data.
Application:New Partner Acquisition ModelsPersona:VP Partner Services, VP Market IntelligenceCustomer:IT Distribution Company
Customer Situation:Our client needed to understand the current digital footprint of existing global Partners. They struggled with building a model designed to identify new partner acquisitions and needed a 3rd party data source to match to partner records.Solution:Profound matched their global partner database to domains with a 93% high-confidence match rate. We then appended Digital Business Intelligence and 3 years of historical data to the file.Results:Client was able to gain insight on their existing partner database and identified 6 DBI attributes critical to new partner acquisition model. Allowed client to better understand performance of existing partners and attributes in identifying new partners.
Application:Global Propensity to Buy Behavior ModelsPersona:CIO, CDO, VP Analytics, Director of MICustomer:Fortune 100 Global Hi Tech Company
Customer Situation:Our client was building a Big Data repository with Hadoop platform. They struggled with Propensity to Buy/Upsell existing global customers to cloud services. They needed to match 3rd party data to their customer data. Their goal was to perform analytics to prove the power of 3rd party data in predictive model.Solution:Profound mapped 70% of customer records to a domain and appended Digital Business Intelligence to 65% of customer database. We also provided 3 years of DBI historical data as well as current snapshot data that was used for analytical modeling. Cloud Transform and Velocity Scores were also appended to their customer file.Results:Profound data ranked #2 in predictive power, second only to client bookings data. Profound data was also the only 3rd party data source that provided this predictive power to client models in order to identify high-value look-a-like prospects.
Application:Unknown Web Visitor IdentificationPersona:CMO, CIO, VP Digital Marketing, IT director, Sales ops and Customer serviceCustomer:Global Fortune 100 Tech Company
Customer Situation:Our client had web traffic of 100MM+ IPs each month. Website traffic has a huge impact on Account Based Marketing Programs, Market Intelligence, Customer Service Routing, Partner and Customer identification and competitive company visits. Our client needed to understand what type of prospect was on the site so they could render differentiated content to visitors as well as block IP addresses of key competitors. Client had an IP log file from website visitors that needed to have intelligence to drive customer success.Solution:Profound created a Batch and Transactional API key for client. We mapped millions of IPs to business domains and appended over 80 DBI attributes. This enabled the client to understand all web site visitors and the specific web pages they were reading.Results:Our fast and reliable API processed reverse IP look-ups on each website visitor. This allowed the client to create big data applications to understand web visitors for Account Based Marketing Campaigns, Market Intelligence, circumvent competitive visits and optimize customer service routing. Differentiated content was rendered to specific visitors. These increased match rates improved results.
Risk & Compliance
Application:Business Insurance UnderwritingPersona:VP Marketing, VP UnderwritingCustomer:US Insurance Company
Customer Situation:Our client underwrites insurance for a company’s online presence. They required a global view of each business’s digital footprint to assess risk. Key insurance prospects needed to be identified based on their network configuration. They required an understanding of their existing customers’ risk profiles for underwriting assessment and rate setting.Solution:Profound first assigned domains to each customer record. From the domain, Profound appended DBI (Digital Business Intelligence) with an emphasis on DBI Density—the number of devices on the network, the strength of SSL certificates, email servers, e-commerce vendors, and more. By utilizing Domain Clustering we were also able to identify the number of domains associated with each customer to determine the full digital presence of the enterpriseResults:DBI allowed the customer to identify the most sophisticated networks, enabling them to realign underwriting rates and identify new customers to upsell. The client was also able to prioritize prospects based on identified installed technologies and improve rate settings.
Application:SMB Credit Loan Risk AssessmentPersona:CDO, VP Credit Risk, VP SalesCustomer:US Business Credit Risk Company
Customer Situation:Client needed to identify attributes that would assess and predict risk for companies applying for SMB loans. The attributes had to be actual, not modeled data. Client also required proxy data sources that backfill information gaps for incomplete or non-existent payment history.Solution:Profound appended Digital Business Intelligence with over 60 attributes to the SMB customer file and appended 1 year archive and current data to each matched customer record.Results:The customer’s analytics team identified 4 powerful, predictive attributes that positively predicted SMB loan risk. By tracking a company’s network configuration and installed IT over time, the client improved their credit decisioning.
Application:Current Portfolio Risk AssessmentPersona:SVP Sales, CMO, CDO, Market IntelligenceCustomer:Global Fortune 100 Hi-Tech Company
Customer Situation:Client has a large global portfolio of companies that purchase On-Prem hardware and services. They needed to identify customers who were moving to cloud applications to determine the risk associated with the existing revenue based on On-Prem networks.Solution:Profound appended Cloud Transformation Journey scores to each customer record. This resulted in a 75% match rate. Archive and current data was appended with quarterly updates to capture trends. This produced valuable insight for the client insight into cloud services adoption.Results:Client developed a sales strategy to better-understand customer cloud buying adoption Risk of Portfolio was established with greater insight into Cloud Migration. Targeted campaigns and customer engagement was designed to upsell and position accurate product solutions.
Application:White Space Sales TargetingPersona:CDO, CAO, VP Sales. CMO, Demand Gen, Business IntelligenceCustomer:Global Fortune 100 Hi-Tech Company
Customer Situation:Client needed to define white space opportunities for their cloud software products for all companies in Germany, UK and France that were using AWS or Azure Cloud applicationsSolution:Profound created, with empirical evidence, company profiles that had the presence of AWS and/or Azure Cloud identified on their public facing network.Results:Client was able to identify existing customers from the dataset to suppress from the white space campaign by using the domain as the key identifier. They were able to launch targeted campaigns to key prospects that better-positioned their solutions against AWS and Azure to achieve higher response rates. They also developed knowledge-based campaigns to differentiate their solution in the marketplace.
Application:Competitive Sales CampaignsPersona:SVP Sales, CMO, CDO, Competitive Intelligence, Marketing Services/Lead GenCustomer:Global Fortune 100 Hi-Tech Company
Customer Situation:Client needed to find all companies in North America that were using Citrix to develop a competitive sales campaign. They needed market intelligence to understand competitive install base to define a sales and coverage strategy.Solution:Profound created, with empirical evidence, a master database of companies using Citrix along with a snapshot of their current digital footprint. We also appended contact data to the list for sales campaign execution with a 3rd party partner.Results:With an enriched competitive database and improved sales coverage strategy, the client launched targeted campaigns that resulted in greater than expected sales lead conversion rates.
Application:Telecom Targeted CampaignPersona:SVP Sales, CMO, CDO, Competitive Intelligence, Marketing Services/Lead GenCustomer:Global Telecom Company
Customer Situation:Our client needed to identify all companies with an ecommerce enabled website. They required market intelligence to understand the full market and white space segmentation.Solution:Profound created a master database of companies with ecommerce enabled websites. We appended website traffic scores and ranking intelligence for detailed segmentation to meet client requirements for analysis.Results:Client was able to identify existing customer and white space opportunities that met their criteria. This improved their targeted campaign strategy and results.