In Part 2 of this blog series (Overview & Part I are here), the focus is on the criteria, components and considerations for your ideal KPI Dashboard.

When it comes to determining what you want from a Dashboard, keep repeating this mantra: less is more. Determine which metrics are useful to your success metrics and planning efforts and stick with them. Just because you may have access to an excessive wealth of data, it doesn’t necessarily mean it is a good use of time and resources.

Frankly, there are only a finite set of KPIs that are worth assessing. Once you determine what they are, the real value is looking at those KPIs through a different lens in order tell a more targeted and detailed story that can be applied to tangible and measurable efforts. When you design your Dashboard, consider these three discrete, yet highly interdependent elements:

A. KPI Definition – Which Metrics?

How do you define and structure metrics that address specific needs without getting lost in the sea of data from all customer interaction points or sucked down a rabbit hole focusing on the wrong metrics? As a starting point, metrics can be compartmentalized and aligned with the customer and marketing lifecycle to measure performance and impact across different aspects of marketing. For example, you could have the perfect segmentation and offer strategy in place, but using ineffective media placements or tactics to reach your intended audience. Or you may have the right media buy to reach the right segments, but applying the completely wrong messaging and offer strategies.

Defining and organizing metrics by categories allows you to be targeted and focused on the specific KPIs that address specific questions, such as:

  • Business metrics – what is the impact on acquisition, growth and retention?
  • Customer metrics – how did my customers and segments perform in terms of sales, profitability, lifetime value?
  • Marketing metrics– how effective are the different aspects of my marketing program lifecycle?
    • Delivery metrics – quality of list, media buy, list strategy, delivery rates, opt-outs, spam complaints?
    • Campaign performance metrics – media, messaging and offers?
    • Channel response metrics – response performance associated with segment, campaign and tactic?
    • Conversion / ROI metrics – buy-flow and conversion results associated with program?
  • Channel metrics – how effective are your interaction channels (e.g., website, POS, call center, social) as a whole? What was the multi-channel impact?
  • Operational metrics – how effective is your customer service, fulfillment, payment behaviors, etc.?

B. Views: how do you want to view the metrics?

Once you identify and prioritize the finite set of metrics that are truly worth your attention, the next level of report design is determining how those KPIs should be organized. There is a wide spectrum of views that can be applied to standard KPIs providing both a top down and bottom up view at different levels of value across the organization. These views range from aggregates, snapshot in time (as of date), time series (results during stated timeframe), volume, averages, variances, rankings / indexing, rates / ratio, etc. This is an important step to drive what “reporting universes” are needed to be developed to support final report designs.

C. Dimensions & Filtering Attributes: how do you want look at the metrics and views from a different lens to tell a different story?

Filtering is only as good as the data set will allow. This is why it is so important to fully comprehend your data dictionary, attributes, hierarchy and metadata to be able to apply filtering mechanisms to tell a different story, whether:

  • By Segments / Customers
  • By Category / Brands / Products
  • By Channels, By Geography
  • By Plan / Program / Campaigns / Deliveries
  • By Company / Location / Contact

Key Takeaway: These KPIs will be different for everyone, given your industry, business model, goals, marketing efforts – the permutations are endless. In many cases, companies are looking at the wrong metrics, don’t know how to organize the data in a practical and functional manner, or just don’t know where to start.

You can read the Overview and Part I here.