Marketing analytics is a developing discipline that has real potential to affect how companies perform over time. Technology and market research firm Forrester Research predicts that companies will increase their investments in data and analytic technologies during 2016 to gain better insights and improve decision making. However, only 29% of enterprise architects surveyed as part of this research thought that their companies currently did a good job utilizing their data.
The main reason behind low adoption and utilization of data is the information architecture. Information in its raw form has the potential be harmful, lead to incorrect decisions, or go completely unnoticed, costing businesses the opportunity of improving their tactics, execution and long term strategy. For information to take a positive direction towards organisational change and deliver business value, you need to architect it in the right form-factor.
In every company, information architects hold powerful roles. They are responsible for understanding organisational goals, and mapping those goals to leading indicators that guide the overall direction of a company’s investment and execution. In a marketing function, these information architects are often data analysts or marketing technology officers. These individuals are the ones who extract the signals from all the noise in the wild world of digital.
As part of this important mission, it’s critical for these information architects to understand how they can start information gathering, flow and architecture, and how to present that back to the business stakeholders and end users. To help these individuals design their analytics and decision-making processes more effectively, here are three essential principles. These principles are taken from an approach we call value-based design: a systematic approach for creating analytics and dashboards, with focus on roles, action points, key value indicators (KVIs) and KVI drivers.
- Principle 1 – Displaying the value. This involves looking at how an individual, team or organisation is doing in respect of their goals. For new projects, this is a clean slate and a chance to ask users what defines success for them, what information they want to receive, and how they want to receive that. For example, a VP of marketing may want to see trends over time for all programs, whereas a field marketing manager might want to see how her / his region is performing against corporate goals and as compared to other regions.
- Principle 2 – Diagnosing the cause. Following on from the first principle, it’s important to look at the reasons behind the performance. For example, whether the regional performance was due to promotional activities or had it to do with product fit for that specific area. Getting these answers helps the analytic team and users collaborate and look for more opportunities to make use of data. This cause and effect analysis helps set the strategy for moving forward and empowers users to decide what activities to prioritize over others.
- Principle 3 – Deciding to change. After these two steps, now comes the fun part. By looking at the previous analytic results and discovering their root cause, it’s now possible to see how future decisions can be improved. For example, a VP of marketing might initially look at how individual campaigns have driven increases in awareness over time. Gradually, this would lead to sharing best practices across different initiatives and eventually deciding to share the insights with broader teams of sales and service to align the whole company on activities that are directly linked to hard revenue or sales performance.
With these three principles, any marketing organisation should be able to create analytics and insights that matter. The ultimate goal of designing dashboards with a value-based design in mind is to deliver guided analytics for each individual or group of individuals that follow a simple Display > Diagnose > Decide information architecture.
Best practices for crafting a value-based design information architecture
A best practice for starting with the value-based design methodology is to begin with one to three KVIs. A KVI should be so important that the leader can get hired or fired based on the performance of those indicators. For example, a marketing KVI can be revenue or percentage of upsell.
Once those KVIs are identified, you then identify their drivers. Drivers are typically a handful of metrics that impact the performance of the KVI. It’s important to try and keep these drivers to less than five metrics. In this example, KVI drivers for revenue can include the number of marketing leads, conversion rates and sales volume.
The next step after identifying a KVI and its drivers is to come up with action points. Action points are given specific areas in the business where actions are required, based on the information presented to the individuals work in those teams. To follow on with this example, if your KVI is revenue, you should break it down by channels, regions, sales organization, inventory locations, or products.
Once these action points are identified, the next step is to tackle roles and map the action points to the way key decision makers will consume the information. For example, if your KVI is revenue, you should design Action Points for the sales leadership, marketing managers, account executives, and regional directors.
After exact roles and their action points have been identified, you should identify the most common decisions each user makes to improve the KVI and its drivers. For example, a campaign manager may need to look at the campaigns that are tied into later stages of a buyer’s journey and decide where to invest funding for promotional activities. This is where you provide drill-through information, so the campaign manager can see a list of running campaigns, top 10 campaigns in play, conversion rates of those campaigns by sales stages, and the influence of those campaigns on closing sales.
The last step is to tie in this information to the systems of record where the user can actually take an action. For example, you might link your analytics and dashboards for campaign managers to the exact campaigns in any marketing automation software. This brings the decision-making process full circle and ensures closed feedback loops are in place.
Value-based design, along with best practices for building it, creates the base for designing a set of analytics that deliver real business value for your organisations. Marketing often holds one of the largest discretionary budgets. Designing a value-based set of dashboards will help marketing teams make data-driven decisions, look ahead, and rationalize their creative and demand generation campaigns. Presenting data with the right information architecture helps marketers test their activities against company goals, iterate, and constantly work to provide a better return on investment.
The preceding article was originally published in MyCustomer on January 8, 2016.