How GE Digital Increased Accuracy on Marketing Influence Reporting by 195%

June 26, 2019 Jodie Lail

Marketing budgets vary by company, but any marketer will tell you, no matter how much is allocated, it is never enough! Marketers are often tasked to do more with less, wear several hats, work with limited resources, generate pipeline, and demonstrate that the marketing team’s efforts had an influence on revenue. This is no different for our marketing team at GE Digital. GE Digital, is a strategic business for GE with $1.2 billion annual revenue, long buying cycles of 18+ months, complex buying groups, and stakeholders across the globe. Marketing efforts are critical in moving our customers across the buyer's journey and a key contributor to the overall revenue goals of a sales driven organization. However, this impact wasn’t always an easy thing to prove.

The Problem

In the past, the GE Digital Marketing team would generate thousands of qualified leads for sales each month. However, without proper lead tracking, sales processes, and data management, it was difficult to track which opportunities resulted from marketing sourced leads or how they impacted revenue. Our marketing team was often left scrubbing data and preparing for meetings with sales and finance to no avail—we still lacked what we needed for alignment and as a result suffered from the perception of underrepresented influence.

Due to the long, complex buying cycles, my team had to collect data from every touch point on the buyer’s journey across multiple stages from awareness, consideration, solution generation, and deal acceleration. We tracked the Lead Source and last touch MQL campaign to try and connect the dots on how marketing campaigns helped close the opportunity, but we knew there was more and we were missing numerous other interactions. Without visibility into that data, we couldn’t measure the impact of our marketing efforts on each stage of the buyer's journey, making it difficult to show our impact on revenue and inform how to optimize our programs.

To be able to ensure we were reaching our customers with the right content, at the right time, to move them along in their journey we needed to know:

  • What campaigns were influencing deals?
  • Which tactic or channels were the most successful at each stage?
  • What sources were likely to convert the fastest?
  • What is the most successful touch point in pre-opportunity creation versus post?
  • And how much pipeline we, as the marketing team were contributing and influencing for the organization?

As head of Digital and Revenue Marketing Analytics at GE Digital, these were the questions I was tasked to answer.

Similar to other organizations, one of the greatest issues we faced was associating all engaged leads and contacts to opportunities, and ensuring all meaningful engagements captured as part of our marketing influence reporting. With our complex global sales organizations , we’d often see opportunities without any contacts or campaign associations, making it impossible to make that connection back to revenue.

I needed a tool that was ideally easy to implement (who has time!), solved my pain points, tied in all lead information, and provided me with flexible models that I, a non techie, could customize to meet our business needs.

The Solution: LeanData

We implemented LeanData in 2017 and have been a happy customer since. We started with lead assignment and routing solutions. I was then introduced to their attribution product in June 2018 and it provided the following must haves for our team:

  • Custom attribution settings which allowed us to define attribution models that make the most sense to our businesses
  • Versatile models out-of-the-box that I could immediately use, from first-touch, multi-touch, last touch, pre-op generated, and post-op accelerated models
  • Intuitive visuals of the buyer’s journey through opportunity touch timelines
  • Lead to account and opportunity mapping tying in every lead/contact to an account and their associated opportunities
  • Custom built marketing touch object, allowing our team to transfer data to a data warehouse for complex analysis and report on BI tools

What We Were Able to Achieve

We were able to increase the accuracy of Marketing influence reporting by 195%! Going from reporting on a part of the buyer’s journey, LeanData empowered us to track every Lead and Contact interaction with relation to the buyer’s journey.

As a result we had:

  • A complete view of the entire buyer’s journey and visibility on all marketing activities that touch an account or opportunity
  • View of every touch on an opportunity through multiple models
  • Opportunity timelines that show the first touch on an opportunity, to what created the deal, and the last touch that closed the opportunity

How it Works

LeanData captures all of our marketing touches through our marketing automation platform, Marketo, which then flow into a Salesforce Campaign. From within Salesforce, we show a summary on the campaign object and opportunity objects. Specifically, the buyer’s journey view on the opportunity allows our sales teams to have visibility on all of the personas within an account.

This empowers the sales team to personalize their discussions based on a few different data points including the last event the prospect visited or webinar viewed, where they are in the buying cycle. Delivering what customers expect today: a connected buying experience across teams, products, and platforms.

GE Digital is unique in that we use a customized attribution model specific to our marketing mix and our known critical points in the buying cycle. LeanData provided us with standard attribution models we began using immediately and we then customized by creating a weighted attribution model to attribute certain percentages of a deal to specific channels and interactions that we knew were meaningful to our buyer’s journey.

“Not every interaction in a buyer’s journey is equal.”

- Jodie Lail, Marketing Operations Manager, GE Digital 

For example, we value attendance of a GE Hosted event (e.g., lunch-and-learn) higher than a visit to a booth at a sponsored trade show, and even higher than clicking a link in an email. The ability to create multiple attribution models has helped us ensure that true meaningful interactions are receiving the appropriate values. To report on marketing influence and source reporting, we use the “weighted MT amount” field and combine this with formulas and queries created by our sales operations team to come to an agreed upon marketing influence metric.

“Having alignment and agreement with sales operations was critical in ensuring that our influenced reporting was accurate, trustworthy, and aligned with stories our marketing team shares in QBR’s and other reports within the organization.”

- Neenu Sharma, VP of Digital Marketing Analytics, GE Digital

Keys to Success for GE Digital

Involving sales operations early on in the process was critical in ensuring that we had buy-in. We had regular touch-bases from the implementation process through post-production and continued fine tuning our rules to produce attribution models that made sense with our buying journey.

I gathered feedback from sales ops to further adjust models and come to an agreed upon marketing influence and sourced numbers. It was an iterative process, which required repetition of definitions, models, data, and changes to the rules.

The Results

Prior to LeanData, the marketing team was severely underreporting influence due to the inconsistent process and lack of lead to opportunity tagging. After implementing LeanData, we are able to capture every touch point on the buyer's journey, assign specific opportunity amount to that touch based on a weighted attribution model and understanding what is working to drive conversion.

We summarized the values and integrated them to a query from our QMI datasets which then filtered down the number, taking into account various criteria by the organization, and we are able to report out on our final marketing influenced amount. With LeanData’s multi-touch attribution model, we are able to improve our accuracy on marketing influence reporting by 195% and truly show how marketing is impacting deals and generating pipeline.

The models provided by LeanData also help us optimize each stage of the buyer’s journey. We use various data points to tell the story of how customers interact with our product through visuals and story boards within Tableau. We capture all marketing touches and data points from LeanData, then using Tableau, we are able to plug in the data to various views, charts, and graphics that bring the customer engagements to life. Below are a few of the fields we use to power our stories:

  • FT (First Touch Attribution Amount)– what are the most successful tactic early on in the buying cycle
  • MT (Multi Touch Attribution Amount, aka even distribution across all interactions)– what are the most successful tactic throughout the buyer’s journey
  • Pre-Op (Before the Opportunity is created)– what was the most successful tactic in creating the opportunity
  • Post-Op (After the Opportunity is created) – what was the most successful tactic to accelerate the deals
  • Avg MT, FT, LT amounts per touch – what channel generates the most amount of revenue with the least number of touches

This combined with Marketo and Adobe Analytics data, helps us see anonymous behavior, lead creation and opportunity creation and get a complete view of the buyer’s journey. With the data and analytics provided by LeanData, we are able to optimize our marketing programs and increase engagement by 125% with the insights derived from the data and analytics provided by LeanData.

Most important of all, LeanData allowed us to show true marketing influence on pipeline and closed revenue that is trusted by both marketing and sales leadership.

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