There are two predominant technology categories able to accurately report on revenue attribution; Marketing Revenue Intelligence (MRI) and Business Intelligence (BI). Both solutions mine big data and are able to help get past the limitations presented by CRMs (e.g. Salesforce) and Marketing Automation Systems (e.g. Marketo, Oracle Eloqua, Pardot).
Velocity and conversion rates of leads across time are critical factors in quantifying the B2B buyers’ journey, and something that Intelligence technologies can help solve. While Salesforce can show you how many net new leads and opportunities were acquired in a given month, it cannot report on lead progression through the funnel. Because of the way that Salesforce tracks data—not recording snapshots of lead data over time—it does not report on lead progress, or show marketers which touches influenced stage-by-stage conversions.
For example, suppose a webinar generated 250 leads in January 2015 and of those, 150 were MQLs. When analyzing this campaign in January, it would accurately report the 150 MQLs you had. In the months that follow, let’s say that 80 of the 150 MQLs become SQLs. Now, when you run January’s report on the same webinar, it would list 70 influenced MQLs, neglecting the 80 that moved forward.
Because of these limitations, storing historical snapshots of lead status across all stages is critical. Without this historical information, attribution becomes impossible; undoubtedly why so few technology companies are attempting to solve for it.
Intro to Business Intelligence Platform Attribution
Business Intelligence or BI describes broad category of applications and tools, designed to transform raw data into useful information. Early BI systems emerged in the 1960s and their development and adoption grew exponentially until the 1980s. Today there are dozens of solutions on the market, ranging from large, in-house enterprise installations, to lightweight cloud-based tools. Modern tools boast sexier UIs and better visualizations, but their underlying architectures have changed very little in the past thirty years.
BI technologies are capable of processing large amounts of data and the goal of BI is to allow for the easy interpretation of these large volumes. BI can be used to support a wide range of business decisions ranging from operational to strategic, and is most effectively used cross-departmentally.
When used correctly, a BI tool pulls all critical company metrics into a data warehouse (or DWH). The DWH is a literally a store that contains all line items, large and small, from across the organization. This data is crunched and analyzed to report on every aspect of business health.
WHAT IS A DATA WAREHOUSE? A data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis. DWs are central repositories of integrated data from one or more disparate sources.
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BI tools integrate with finance, ERP, CRM, marketing automation, help desk tools and literally any business critical silo. Most solutions use an ETL layer to extract the data. ETL (Extract, Transform and Load) is a series of processes that pulls the data in from each source, standardizes it and pushes it into the data warehouse.
Once the data is in the DWH, creating reports is a manual and highly technical process. While vendors typically provide some templates to get you started, due to the cumbersome, IT-heavy nature of traditional BI platforms, building out revenue attribution typically requires an analyst to create custom dashboards. Most companies hire dedicated IT employees and data analysts to oversee BI modeling, and reporting.
Today, a new school of BI tools has emerged, characterized by elegant UIs that are making the tools increasingly accessible. While it’s certainly possible for organizations to derive function-specific insights with modern BI, this level of custom modeling still generally needs to be built from the ground up.
Benefits of Business Intelligence for Attribution
If you work for an enterprise looking to solve a bigger business analytics problem, using BI for your metrics may make sense. For example, if you are a large manufacturing company with a complex supply chain, inventory controls, an RMA department and thin margins, BI is critical to pulling data out of silos and making it actionable.
This may also be the case if you already have a BI solution in place and your only additional cost is that of customizing and maintaining that solution to maintain your needs as a marketer. If the data warehouse is in place already, you have the ability to rely on “one source of the truth” that everyone in your company can align around.
When paired with the right dedicated team and modeling, BI software can provide data-savvy enterprises with a competitive market advantage and long-term stability.
Drawbacks of Business Intelligence for Attribution
Using BI for revenue attribution is a long way from being turn-key. BI implementations can take months or even years. BI systems require heavy overhead which tends to be overkill for marketers who already have CRM and marketing automation in place.
If your organization is already using BI, you will still need to dedicate a resource to building your reports. If you do have someone in house to do it, chances are slim that they are marketing experts, and it’s unlikely that they’ll be able to provide guidance on how to approach marketing attribution. Alternately, consultants and agencies exist that specialize in this, but engagements are pricey and often start with painful data-cleansing initiatives.
While revenue attribution is possible through BI, we recommend against applying a one-size-fits-all-business-needs patch. Instead, it’s imperative that marketers seek a solution tailored to the unique needs of our pain points.
Have you had success using BI for multi-touch attribution? Let us know in the comments.