Marketers continue to be under pressure to connect Marketing to business results. With the explosion of channels and data, it’s not surprising that marketers face serious challenges in creating meaningful Marketing dashboards. Scott Brinker, editor and publisher of Chiefmartec.com, tells us that the Marketing Technology (MarTech)  landscape continues to expand. The plethora of technology is both a blessing and curse. Keeping up with the pace of change is daunting.

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The convergence of data, analytics, and technology is driving both the demand for Marketing dashboards and enabling their evolution. When we are working with companies on their Marketing dashboard,  often the first question we’re asked is, “What technology should we use for our dashboard?” There are a variety of tools available, from simple to complex, from standalone to integrated into other platforms.  Eventually, you will need a tool when you are ready to automate your dashboard that will leverage the data in your other systems, such as your CRM, Marketing Automation Platform, website platform, etc.  Before this step, you first need to create the blueprint for your dashboard.

Structuring Your Marketing Dashboard

In developing your Marketing dashboard, you want it to be able to perform on three levels: strategic, analytical, operational, and informational.

1. Strategic. This level of a dashboard focuses on executive-level views of performance and forecasts, and gives decision makers a quick overview of the health and opportunities of the business. This level offers a top-level, long-term overview of key metrics.

2. Analytical. This level of a dashboard is used for analysis and often includes more history, context, and comparisons. It focuses more on allowing users to interact with the data, such as drilling down for details and trends.

3. Operational or Informational. Use this level of the dashboard for monitoring the operations of Marketing. The individual aspects of Marketing are dynamic and constantly changing. It is at this level that you want to be able to make immediate and near-term adjustments and course corrections.

The Elements of Dashboard Construction

As you design your dashboard, take these four essential design elements into consideration:

1. Presents relevant metrics and KPIs and their performance targets in a consistent, easy-to-consume “look and feel.”
2. Reflects the relationship(s) between the metrics and their impact on the outcome(s).  Your metrics chains provide the framework for these.
3. Accounts for performance thresholds and integrates alerts when appropriate
4. Provides directional guidance on what adjustments, if any, are needed

Dashboards typically are based on these types of information: data, measures, and metrics

  • Data: A fact or piece of information. A Marketing example might be an industry vertical or a customer segment.
  • Measures: A dimension or an amount. Your current conversion rate or inquiry rate are Marketing examples.
  • Metrics: A benchmark or a standard. Customer Lifetime Value or a Customer Relationship Index are examples of metrics relevant to Marketing.

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Six Categories for Your Dashboard

We’re often asked what should be on our Marketing dashboard. While every dashboard will vary, we believe that every Marketing dashboard should feature the six categories below, including both performance targets and actual results. These categories represent the essential job of Marketing: finding, keeping, and growing the value of customers.

1. Customer acquisition and retention: Number of customers, retention rate, acquisition rate, etc.
2. Customer value: Lifetime value, loyalty, share of wallet, etc.
3. Customer equity: Referral rate, propensity to repurchase, etc.
4. Product innovation and adoption: Rate of product adoption, revenue from new vs. existing products
5. Competitive market value: Category ownership, rate of growth compared to competitors, market share growth
6. The bottom line: Show me the money. What are we investing in, and what are we getting in return for that money?

The Future: Predictive Dashboards

Returning to our automotive analogy in a previous post, when cars were first invented, they had no fuel gauge. As a result, it wasn’t uncommon to carry a gas can and to see folks thumbing for a ride to the nearest gas station and back. In today’s cars, our control panel lets us know when the tank is getting low, and it can also tell us how many miles we can go before the tank runs dry.

Once you have your Marketing dashboard where it is operating with real-time information, you can begin to establish thresholds and alerts to prompt corrective action. And then you’re just a few steps away from adding this predictive capability. How likely will make or miss our target? Will the current investment be enough? This capability requires that marketers go beyond data management to the realm of building analytical models.

Ready to take your Marketing dashboard technology to the next level? A key first step is a Marketing dashboard development road map. Let’s talk about how we can assist you in creating your dashboard road map.

How About A Dashboard Review?

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FAQ:

(written by Penn of Sintra.ai)
Q1: Why are meaningful marketing dashboards so hard to build today?
A: Marketers are under pressure to connect Marketing to business results while the number of channels, data sources, and MarTech tools continues to explode. The convergence of data, analytics, and technology enables dashboards—but also increases complexity and integration demands.
Q2: What is the wrong first question to ask about dashboards—and what should come first instead?
A: “What technology should we use?” is usually premature. Before selecting tools or automating anything, you need the dashboard blueprint—the structure, metrics chain, targets, and decision use-cases the dashboard must support.
Q3: What three levels should a well-structured marketing dashboard support?
A:
  • Strategic: Executive-level performance and forecasts; quick view of business health and opportunity.
  • Analytical: Interactive analysis with history, context, comparisons, and drill-down for trends.
  • Operational/Informational: Near-term monitoring to enable immediate adjustments and course corrections.
Q4: What four essential design elements should be built into dashboard construction?
A:
  1. Relevant metrics/KPIs and performance targets in a consistent, easy-to-consume format.
  2. Visibility into relationships between metrics and outcomes (via metrics chains).
  3. Performance thresholds and alerts when appropriate.
  4. Directional guidance on what adjustments may be needed.
Q5: What is the difference between data, measures, and metrics in dashboard design?
A:
  • Data: A fact or piece of information (e.g., industry vertical, customer segment).
  • Measures: A dimension or amount (e.g., conversion rate, inquiry rate).
  • Metrics: A benchmark or standard tied to outcomes (e.g., Customer Lifetime Value, Customer Relationship Index).
Q6: What six categories should every marketing dashboard include (with targets and actuals)?
A:
  1. Customer acquisition and retention (customers, retention rate, acquisition rate).
  2. Customer value (lifetime value, loyalty, share of wallet).
  3. Customer equity (referrals, propensity to repurchase).
  4. Product innovation and adoption (adoption rate, revenue from new vs. existing products).
  5. Competitive market value (category ownership, growth vs. competitors, market share growth).
  6. Bottom line (investment and return—what we spend and what we get).
Q7: What does a “predictive dashboard” add beyond real-time reporting?
A: It moves from reporting what happened to estimating what is likely to happen: thresholds, alerts, and forward-looking questions such as “How likely are we to hit target?” and “Is current investment sufficient?” This requires analytical models, not just data management.
Q8: What is the practical next step to advance dashboard maturity?
A: Develop a Marketing dashboard road map—a staged plan that defines the blueprint, data integration requirements, governance, and the progression from operational reporting to strategic and predictive decision support.

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