As though the ever-increasing expectations of the c-suite from marketing teams to demonstrate the quantifiable impact were not enough and now we are in these unprecedented times, which have further highlighted the need for marketing to be more data-driven and efficient with their investments. Scrutiny around marketing budgets and the asks for returns from the marketing spend (ROI) have only increased in the current times. Across the board, we are seeing these are Top 2 marketing analytics priorities for B2B marketing teams.
With the onset of this pandemic, all marketing organizations had to adjust their annual plans and more importantly, double down on the “digital-first” strategies to account for the cancellation of the in-person events, which have been biggest driver of marketing pipeline and revenue.
The core competency that differentiates the winning organizations is their ability to objectively evaluate the investments that drive the highest engagement leading to pipeline growth. This is where
marketing teams are relying more extensively than ever before on marketing analytics to help guide the decision making around investments and execution.
Marketing performance insights and marketing analytics not only empower marketing leaders to have data-driven conversations with the c-suite, inspire/reward their teams based on performance but also optimize the execution of their strategy. Such modern marketers with the growth mindset incorporate analytics rigor as well as revenue mindset for every decision they make. Data-driven CMOs are increasingly relying on marketing analytics teams to lead the charge for demonstrating the quantifiable impact marketing organizations are making.
Latest (July 2020) DemandGen benchmark research around marketing measurement has once again highlighted that marketing analytics continues to be the top priority for B2B marketing organizations.
The research report has listed the top challenges that are preventing marketing teams from realizing the vision of data-driven marketing and how critical it is to build these competencies. This transformation to digital-first marketing has only accelerated the need for rigor around marketing analytics. According to this research,
82% of respondents believe enhancing the marketing analytics capabilities is a growing priority.
Even though many forward-looking marketing organizations have some kind of marketing analytics capabilities in place but 40% still believe their ability to measure and analyze marketing performance needs improvement.
With the experience of working with over 30 B2B marketing organizations for various marketing analytics projects, I have been working with the marketing teams when it comes to deploying these marketing analytics capabilities. Here is my approach when it comes to handling 2 of the highest priorities as are listed in this report.
The top priority (for 75% of the marketing teams, and not 100% – interestingly) is to show the impact on pipeline and revenue.
The importance of demonstrating how marketing is impacting the pipeline has grown manifolds in the recent times. There are numerous frameworks to track marketing’s impact but the 2 metrics that are my go-to when it comes to demonstrating the impact of marketing on pipeline and revenue are: Marketing Pipeline Acceleration/Influence and New Pipeline Sourcing. With these 2 metrics, organizations can quantify marketing-driven engagements across the accounts and relate them to the on pipeline growth. These pipeline correlations of the marketing activities provide valuable insights when it comes to understanding “when” the new opportunities were created in marketing engaged accounts and for the accounts with existing opportunities, how marketing was able to influence deal expansion and accelerate deal closures. These 2 KPIs are very influential when it comes to evaluating the market mix across marketing tactics and channels in different phases of the buying cycle.
The approach to designating an opportunity as marketing sourced vs marketing influenced is purely data-driven.
It is based on the timelines of the first marketing engagement in comparison with the sales engagements within an account. Given that the volumes of data are enormous now, the most efficient and automated approach to this analysis is by loading all the relevant data from marketing automation and CRM platforms in a SQL server (Redshift, Snowflake, MySQL, MS-SQL, BigQuery). Once the data is available, the logic described below can be easily implemented with the help of a SQL script and automated.
Identify all the engaged leads and contacts from each of the accounts with open or closed/won opportunities in a given time frame.
Find all the marketing engagements for each of the accounts through for the leads and contacts on those accounts using lead/contact IDs or/and domains.
Find all the activities (sales calls and outreach emails) for each of the accounts through all the leads and contacts.
Designate and combine all sales and marketing engagements data along with their respective dates and account level information.
Compare each marketing engagement and sales activity with the opportunity created date to find if the engagement happened before (how much before) or after the opportunity creation
Based on this data set, if the marketing engagement happened before the sales engagement, that marketing activity (or combination of the marketing activities via attribution) usually gets the credit for sourcing the opportunity.
Once completed, this analysis can be rolled-up to see the overall marketing sourced and pipeline number. This analysis can further be expanded across campaigns and account dimensions to track the performance of different campaigns, channels across different account segments and regions.
At a general level, this is the fundamental approach towards quantifying the impact of marketing on pipeline and revenue. Modern marketing analytics tools make it easy to deploy these frameworks. Depending on the business model, go-to-market strategy, and the marketing mix, I have been working with different organizations to deploy a version of this approach towards calculating the marketing sourced and influenced pipeline.
58% of the marketing teams consider tracking ROI from all marketing investments as the 2nd most important priority.
Marketing sourced and influenced data (as discussed above) enables tracking of Marketing ROI through marketing attribution. For every deal (especially in the enterprise space), countless marketing touches are involved from web engagement (visits, clicks) to email engagement, webinars, e-books, white papers, in-person events, and the list of all the touches goes on and on. While all such touches have a varying degree of influence on the deals, they all come at a cost from the limited marketing budget. It is not fair on part of marketing leaders to allocate their budgets to different tactics without having access to the insights around how these tactics perform. This is where multi-touch campaign attribution comes into play and it is the most valuable of all the KPIs as it powers the marketing investment decision making, which in turn drives the results for the marketing organization. Whether it is the First Touch, Last Touch, Multi-Touch, Equal Weight Multi-Touch, or Weighted approach to marketing attribution, when implemented correctly and validated, the insights provided are of immense value for the marketing organization.
To get to Marketing ROI, tracking marketing spend data is critical. Spreadsheets are the go-to tools for most of the marketing organizations when it comes to tracking and managing the program spend. It is cumbersome to track the spend on these numerous spreadsheets and more importantly relate them to the actual campaign data in CRM (usually Salesforce.com – SFDC) where all the pipeline data lives. Since all marketing and sales data is connected via campaigns (especially when it comes to tracking ROI), it makes sense to have the ability to track marketing spend at the campaign level within SFDC. To make this happen, I built a simple MRM (Marketing Resource Management) app within the campaign object in SFDC. It allows for the tracking of marketing spend over time for each of the campaigns and once rolled-up (at each campaign level), it shows the total marketing spend, itemized by date of spend at once place as shown here. Combining the attributed pipeline data with the marketing spend data is what brings Marketing ROI data to life. Here also, this data can be combined with other campaigns and accounts data to help the marketing teams understand what campaigns and tactics are bringing in higher ROI across different account regions and segments.
The set of these KPIs provides strategic insights to the marketing leaders and CMOs so that they can confidently demonstrate the impact that their organizations are making and help foster a strong working relationship for marketing within the c-suite, especially with the CFOs.
We are always on the lookout for inputs and examples from the marketing community to keep adding value for our customers. We welcome the inputs from other leaders and practitioners around what challenges they face when it comes to deploying marketing analytics capabilities? How are they defining the metrics for tracking marketing’s impact on pipeline and revenue?