Marketing Analytics

What's Really Going On?
Marketing analytics should be factored in to all marketing plans.  Whether this is analytical activity to understand impact on revenue, or simply to determine and refine engagement at each stage, analytics is an essential aspect of all marketing activity.


Marketing analytics provides insight into performance, thus enabling refinement and improvement.

Campaign Analytics

Analysing campaign performance is necessary to maximise marketing spend.  End of campaign analytics helps determine what worked best and areas for improvement in future campaigns.  However, analytics can also be used to fine-tune activities during the campaign life-cycle.
To achieve the greatest value from campaign analytics, ensure your campaign metrics and key performance indicators (KPIs) map to specific objectives.  In addition, these measures should relate to aspects which can be modified.

Measure the right things, at the right time.  Consider these measurements against a preferred range for results.  You can then draw inference from these to inform meaningful changes in campaign activity.

Hard metrics, KPIs and the bottom line

There are a variety of different definitions of what constitute hard metrics.  However, we define them as metrics which have an attributable and consistent impact on the bottom line, or against specific business objectives.

Hard metrics can be measured to give a reliable indication of performance.  Similarly, key performance indicators (KPIs) are a subset of your hard metrics – more closely aligned with specific business objectives.

Good examples of hard metrics are leads generated from a campaign or clicks from an email campaign or advert.  These metrics can used to extrapolate, via projected conversion rates, impact against the bottom line.

Other hard metrics might include reach and engagement within a new market.  Whilst not directly attributable to bottom line, these are measurable and repeatable elements which constitute performance against SMART business goals.

Super-Charge Marketing Automation and Salesforce

Pipeline Analytics

Whether you are considering marketing automation, or progression of leads through the sales process, it is important to understand the progress of leads from one stage to the next.  If you encounter a long delay between two adjacent stages, this points to the potential for improvements in the escalation process.  Consequently, you should ask what can be done to compel leads to move on to the next stage.

We find that sometimes a process will work well for the majority of leads, but marketing analytics can alert managers to anomalies requiring extra nurturing or direct contact to address their unique needs.

Pipeline analytics informs the ability to forecast effectively; presenting the opportunity to take remedial action ahead of future projected dips, or peaks in demand.  We have experience in mapping the progress of leads from different sources through the sales and marketing pipeline and using this to inform future marketing spend, for maximum efficiency.

Attribution Modelling and the Rise of AI

When trying to attribute (financial) results to specific marketing tactics, or even specific marketing content, there’s a considerable challenge.  If your customer only interacted with one campaign touchpoint, this isn’t so hard to calculate, but typically the number is far higher (spoiler: there’s no magic number of touchpoints to achieve a sale!)  Salesforce claim that, on average, it takes between 6 to 8 touches to generate a viable sales lead.

The complexity of accurately modelling attribution has resulted in a multitude of approximated approaches.  There are arguments for and against approaches with different emphasis on first-touch attribution (high-value given to the first touchpoint), last-touch attribution (high-value given to the last touchpoint) and time-decay (the longer ago the interaction, the lower the value).

We have even experimented with, over a sufficiently large data-set, the use of algebraic notation and simultaneous equations, combined with first and last-touch attribution skewing!

The most important consideration is to keep your model consistent over time as variables are changed.  This provides a consistent framework for experimentation and refinement.

Our belief is that AI will soon take much of the strain out of attribution modelling.  With solutions such as Peltarion offering codeless access to AI computing power, it won’t be long before the vast data-sets available to marketing departments are pushed through ever-refining models to determine attribution with unprecedented accuracy.  We’re already looking at it.

Get In Touch

Share This