Openprise Data Orchestration Platform Delivers New Campaign Attribution Capabilities
Industry's First Solution to Address Challenges with Real-World, Dirty Data in Accurate Attribution
SAN MATEO, Calif., August 14, 2019 (Newswire.com) - Openprise, the leader in data orchestration solutions for marketing and sales, today announced the availability of new campaign attribution capabilities in the Openprise Data Orchestration Platform. Openprise is the first solution to also address the most common challenge around accurate attribution—the underlying dirty data in solutions like Salesforce, Microsoft Dynamics, Marketo, Eloqua, and Pardot.
The Openprise Data Orchestration solution provides all of the attribution capabilities marketers need to understand the effectiveness of their campaigns and their impact throughout the buyers’ journey. These include:
● First touch, last touch, and multi-touch attribution to reflect the dynamics of each business’s buyers’ journey
● Multiple attribution models that can be run simultaneously to identify the best fit
● Account-based marketing measurement to show marketing’s contribution at each stage of a sales cycle
● Custom attribution models that take into account the unique requirements of any company
● Analytics to graphically represent the impact of each campaign on an opportunity or a demand unit
Traditional marketing attribution solution vendors have focused on delivering increasingly complex attribution models and slick user interfaces, but haven’t addressed the reality that most companies have dirty CRM data. The adage “garbage-in, garbage-out” applies to attribution data, with poor quality CRM data leading to poor quality attribution results.
Openprise overcomes many common data challenges that hinder accurate attribution, including:
● Duplicate leads and contacts – When multiple leads and contacts exist that represent the same person, but an opportunity has a contact that doesn’t have all of the campaigns the person participated in, those campaigns aren’t properly credited. Openprise can automatically merge these leads and contacts to ensure more precise attribution.
● Opportunities with missing contacts – It’s very common in many companies to have few or no contacts associated with an opportunity, which makes it impossible to correctly attribute the first touch and last touch that resulted in the opportunity. Openprise can automatically associate additional leads and contacts with an opportunity based on activity data to dramatically improve attribution accuracy.
● Mass lead imports by salespeople – It’s not uncommon for new salespeople to mass update thousands of leads from their personal databases or previous employer when those leads have never expressed interest in a company. This leads to very inaccurate first touch attribution. Openprise can automatically exclude leads and contacts from attribution models based on a variety of criteria.
“As with any other sales and marketing process, attribution models are only as good as the underlying data foundation,” said Allen Pogorzelski, Vice President of Marketing at Openprise. “We’ve taken a fundamentally different approach than other technology companies in looking at the real-world problems that have hindered accurate attribution, and we’ve automated the dozens of underlying processes you have to get right first before you can show the shiny graphics. As a result, more and more companies are simplifying their martech stack by moving the attribution process to Openprise, as they’ve also done with lead routing, data cleansing, lead scoring, data enrichment, and other processes.”
“To be effective, every marketing organization must know what is really working to drive pipeline and revenue, but this has been almost impossible because the data has been either absent or wrong,” said Kerry Cunningham, Sr. Research Director, Marketing Operations and Demand Management, Forrester Research. “Companies like Openprise are applying advanced data technologies to finally allow marketing and demand gen organizations to truly understand and continually improve their performance.”
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Anne Marie McCallion
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