The Most Important Marketing Campaign Analytics and Reporting

Highlights

  • Marketing campaign analytics and reporting connect data, insights, action and outcomes. When you establish these links, the reporting becomes less about measuring historical performance and more about improving future results.
  • Marketing campaign metrics are valuable in as much as they act as levers to grow revenues. When focused on the right metrics, even small improvements to actions are shown to deliver significant financial impact.
  • Smart marketers are defined by their ability to collect and curate the right data and apply analytics to make insights actionable at every campaign and customer touchpoint. It's a complex undertaking which is why those who succeed will achieve competitive advantage over those who do not.
Johnny Grow Revenue Growth Consulting

The 4 Most Important Campaign Analytics and Reporting Tools

Most marketers are data rich and information poor. It's an unfortunate paradox that degrades performance, revenue growth and marketing's contribution the company.

Fortunately, a short list of marketing campaign analytics and reporting tools can be assembled from your Marketing Automation Platform and CRM system. These tools convert data into actionable insights and significantly improve campaign performance results.

There are 4 types of analytics that are most influential in improving results. They are campaign dashboards, predictive analytics, industry benchmarks and Artificial Intelligence.

Here is how each type of analytic can significantly improve performance.

1

Marketing Campaign Dashboard

Clear campaign objectives set the vision and create the roadmap. But rigorous execution as measured by the right key performance indicators (KPIs) direct the path to campaign success and the realization of results.

A marketing campaign dashboard displays the most important conversion metrics in an easy to consume visual interface. This real-time information enables marketers to quickly identify trouble spots and intervene with timely course corrections.

Most campaign dashboards only display historical data. Better dashboards shift from lagging to leading indicators. And the best dashboards enable metrics to be interactive, so marketers can perform What-If modeling and scenario planning.

This type of pro forma modeling is a powerful lever to compare campaign assets and different campaigns to best allocate budget and scarce resources.

Below are some examples of Johnny Grow campaign dashboards.

Marketing Dashboards

The are many types of metrics that will improve campaign results. But if you are just starting out, skip the countless marketing activity and vanity metrics and begin with high-impact performance metrics (i.e., prospect engagement per flight and prospect conversions) and cost metrics (i.e., cost per lead (CPL), cost per MQL (Marketing Qualified Lead) and cost per SQL (Sales Qualified Lead).

No campaign lives in isolation. So, an aggregate view is needed to show how they rank among each other and perform together.  That's why Campaign Portfolio Dashboards are needed.

Marketing Dashboard Campaign Portfolio

A holistic view showing the marketing mix is helpful because programs often influence each other's performance.

Note that in the above campaign dashboard there are links for Alerts and proposed Actions. These are dynamically generated recommendations or links to Marketing Playbook Plays based upon conditions or threshold values for each KPI.

The point here is that marketing campaign analytics and reporting should start with  dashboards to get the right information to the right people at the right time – so they can adjust campaigns while in-flight to proactively improve results.

Dashboards are the top delivery tool to show what's working, what's not and where changes should be made.

2

Performance Benchmarks

Campaign benchmarks create additional marketing intelligence.

They show what good performance looks like. They provide a comparison to show where the company stands relative to industry peers and most needs to improve. They also support predictive analytics.

For example, lead management research shares the monthly lead database conversions. When filtered by industry and extrapolated, marketers can calculate how improvements to nurture marketing campaign conversions impact the quality and volume of leads sent to the salesforce.

Lead Conversion Benchmarks

Many marketers like to apply pro forma models to show how a 1 percent improvement in any metric impacts conversions and revenues.

Others with KPIs below an industry median may prefer to see the revenue impact by improving their campaign performance to the median level. Knowing the financial upside impact allows marketers to know how much they should invest to achieve that upside.

And rather than guesswork or estimated ROI figures with questionable assumptions, predictive analytics built on benchmarks use real data to provide confidence in calculating pro forma business outcomes and setting realistic goals. The knowledge of what has worked for similar marketing groups lowers risk and accelerates time to value.

3

Predictive Analytics

Marketers deliver the most valuable marketing campaign analytics and reporting when that information can engineer future financial outcomes. That's why predictive analytics are essential.

Predictive reporting analyzes historical and real-time data to show patterns, relationships and trends, and extend the trajectory of data using simulation and propensity models. The models can be exercised to understand how any campaign can be modified or improved to impact revenue growth.

Campaign portfolio optimization is a process, not an event. So, to visualize that process we use a proprietary model we call the Predictive Pyramid.

Revenue Growth Predictive Analytics

The Johnny Grow marketing campaign analytics and reporting shifts information from hindsight to foresight. It shows how changes in any campaign impact conversion and revenue goals.

And it's not just predictive, it's interactive to support dynamic modeling and What-If scenarios.

This type of model is needed because no promotion, asset, offer or artifact lives in isolation. Each has cascading effects that impact many areas and those impacts must be considered when making tradeoffs. This visualization is helpful in determining where to invest your limited time and budget to achieve the biggest financial uplift.

The data that drives the calculations is sourced from campaign history if its available or industry benchmarks if it's not. In working with clients to populate the predictive pyramid, we find that most believe they don't have the needed data. But quite often they have more data than they think, it's just disorganized and fragmented in data siloes.

And like the dashboards, this measurement system should induce action. Remember, if your marketing campaign analytics and reporting are not causing changes to your execution and portfolio mix, you're doing it wrong.

4

Artificial Intelligence

We know from marketing analytics research that 89 percent of the Best-in-Class marketers regularly use artificial intelligence (AI). That's 43 percent higher than their lower performing peers. And that's a significant gap that should not go unnoticed by marketers looking to make a bigger contribution.

89 percent of the Best-in-Class marketers regularly use AI. That's 43 percent higher than their lower performing peers, and a big opportunity to improve marketing campaigns.

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Many AI use cases are similar to predictive analytics. But instead of marketers exercising use cases, an AI algorithm performs the modeling continuously and either informs the marketer of improvement scenarios or implements the improvements to the campaign automatically. A combination of both actions is most often used depending on the magnitude of the change.

We know from speaking with clients that AI projects often underperform. We find that when they pursue a technology viability approach they often stall in perpetual pilots. We also know that AI results accelerate when the company pursues an outcomes-based approach and begins with high impact campaign use cases.

We've developed over a hundred different marketing campaign AI algorithms, mostly in Salesforce Einstein and Azure Machine Learning, to improve dozens of financial outcomes. Below are some examples to stimulate your thinking.

  • AI can design the most effective campaign component assemblies. The technology can apply multivariable propensity models to show the combination of offers, content, channels, and call to actions (CTA) for each customer type or target audience to optimize lead acquisitions, lead conversions and pipeline growth.
  • AI is especially effective at recommending next-best-actions. It can calculate the next-best-offer, flight, content artifact or channel based on account and contact attributes (i.e., ICP, account firmographics, contact demographics, customer segmentation) and online behaviors (i.e., digital footprints, buyer propensity).
  • We have increased lead acquisition rates by double digits when using AI with customer journey and buyer persona modeling. In these scenarios the models show what engagement (i.e., flight, offer, CTA, channel) to deliver at each state of the buyer journey and/or for each buyer persona. This level of specificity is required for precision marketing.
  • Marketing AI is effective at churning through large volumes of data for campaign portfolio mix modeling. It can also include or build a lead attribution model to surface the most cost effective combination of campaigns for specific goals (i.e., conversions per period, conversions per cost, total conversions, etc.)
  • AI is essential to show Lead to Revenue (L2R) modeling. This extends lead acquisition propensity models to include analysis for post SQL (Sales Qualified Lead) conversion rates and downstream sales and financial results.

AI is the marketing tool of choice to create, deliver and optimize contextual engagement across the entire customer life cycle. This technology provides the insights to meet buyers where reside, consistently engage throughout integrated campaigns, and deliver more relevant, personalized and contextual content at scale.

Many marketing leaders were early adopters of AI and gained both competitive advantages and remarkable ROI. However, AI in marketing is no longer in the early days. We crossed that chasm and any group not using AI today is clearly substituting labor for technology.

The question for marketing leaders standing on the sidelines is not if AI will impact their performance, but how and when.

Popular marketing automation platforms such as Adobe and Salesforce Marketing Cloud as well as market leading CRM systems such as Microsoft Dynamics 365 and Salesforce Sales Cloud have removed technical barriers with simpler AI tools. Solutions such as Azure Machine Learning and Salesforce Einstein put AI applications in the hands of non-technical marketers.

AI elevates data from an unused byproduct to an information asset. Data is the fuel, AI is the engine, and insights are the destination.