Marketing Analytics Revisited

There are many articles and even books written on the topic of marketing operations and analytics.  I will share my perspective on marketing analytics from having worked with several clients, as well as having been a CMO for multiple public and private enterprise software/cloud companies.  The information I share below is equally valid for large and small companies.

I put the marketing analytics in three broad buckets:

• Basic analytics:  This helps me understand the big picture – how many MQLs, SQLs, SALs, as well as $$ value of pipeline generated this month/quarter at aggregate level and by sales region and trend charts.  I also want to understand nurture volume (i.e. what is on the stove and will be ready soon) and trend charts (how long does it take historically for leads to exit nurture and become SQLs) etc. Finally, I want to understand the top 10 campaigns running this month/quarter, planned spend and expected output from them. To keep things simple I recommend using marketing created leads as a criteria – otherwise marketing wastes too many hours arguing with sales operations and partner operations over attribution to make their contribution numbers look better.

• Optimization analytics: This helps me understand how the various channels and campaigns are performing, so I can continue to optimize my allocation of program dollars. I want to understand the performance of campaigns by early indicators (MQLs, SQLs, SALs) – especially for campaigns driving top-of-funnel, as well as by lagging indicators (e.g. pipeline generated, by forecasted deals, by deals closed).  The performance should compare planned vs actual performance, so even the performance of campaigns that you are ‘trying out’ and are allocating ‘risk dollars’ to are measured in context of expected results.  Without this level of visibility, it is challenging to shift money between channels and programs to maximize your performance, or to try new but risky programs.

• Alignment analytics:  This is a very important bucket that most marketing operations teams miss.  It helps me understand how well marketing and sales is aligned and where are the opportunities to improve this alignment.  For example, most sales organizations have target accounts for their sales reps.  What is the penetration of marketing database in those accounts (i.e. coverage in each of those target accounts by desired personas)?  I call it the Swiss Cheese analysis to assess coverage and holes. What is the pipeline breakdown by sales rep in each region – aggregate pipeline analytics may look good, but some sales reps may be starving, putting them at the risk of attrition. How well is the re-nurture program working (to wake up stale opportunities).  Segment analysis of the marketing pipeline generated to ensure targeting of marketing programs is aligned with go-to-market – i.e. is the mix of SQLs different than GTM in terms of products/segments/industries (where SQLs are coming from vs. your strategy).

If we can get marketing analytics right, we can build the right foundation for improving marketing planning and execution, as well as ensuring marketing spend is optimized for best results.