Resources > Blog > Understanding Your Level of Analytics Maturity

Howard Diamond

Senior Vice President, Digital Strategy

Understanding Your Level of Analytics Maturity

According to a study by Forbes Insights, 55% of U.S. marketing executives employ analytics to measure all or most of their marketing campaign returns, and that number is only growing. More than seven in 10 of those executives expect to increase the use of data and analytics in decision-making over the next few years.



As you start working toward your 2016 goals, it is a perfect time to evaluate how you will use data and analytics in the new year. The best way to get started? Pinpoint where your organization falls within the analytics maturity model.

 

The Analytics Maturity Model


First off, what is the analytics maturity model? This is something we’ve developed at Rise to serve as a basic guide for establishing how well your organization is able to organize and analyze data to drive business growth. Regardless of your level of analytics sophistication, it’s very helpful to create a common language among key stakeholders. By defining each competency level, you are able to create awareness and develop a clear path for moving forward toward higher value insights.

Understanding where you fall along the model will also enable you to best recognize your strengths and weaknesses within analytics, as well as guide your next steps. As you uncover your organization’s current level of analytics maturity, take an honest, cross departmental approach. We’ve seen instances where internal groups within organizations have assets or resources that others within the company don’t know about. For the most accurate view of your maturity, all stakeholders need to participate in this exercise. These groups likely include marketing, finance, merchandising, operations, analytics, IT, innovation, and other departments, based on your particular company.

It’s important to note that while you can move through the maturity model in the order I lay out below, it’s not necessarily a linear path. Additionally, not all stages of the model will be relevant for every organization. Use the following as a guide and adjust as necessary for your specific brand.

 

A Step-by-Step Approach
 


Discovery

 

The first level of the analytics maturity model is discovery. There are three components to this initial stage: define success, establish goals, and determine your needs. As Rise’s senior advisor, Jack Kraft, likes to say, “If you don't know where you are going, any road will take you there.”

When it comes to analytics, it helps to have a very specific set of goals that can be translated to a series of questions that stakeholders are trying to answer from your data set. These may include basic questions around KPIs to more advanced inquiries around customer profiles, buyer journeys, competitive insights, overall business goals, and more. For instance, how will you define success? How do your stakeholders rely on analytics?

The discovery phase builds the foundation for a well-thought-out analytics strategy, and ensures the subsequent levels of the maturity model are most impactful. Afterall, if you don’t know what questions you are trying to answer, it’s likely you won’t have the right infrastructure or skillsets to capture and analyze the right data.

 

Implementation

 

This brings us to the next phase of the maturity model: implementation, or what could also be called infrastructure. Analytics tools and data sources run the gamut, including web analytics, tag management, conversion rate optimization/testing, call tracking, attribution, customer relationship management tools, data management platforms, business intelligence/data visualization, and more. Take inventory of what you currently have and identify where you may need additional resources to reach your goals.

Be mindful that while you may already have some of these tools in place, it’s possible they aren’t configured in a way that answers the questions you identified in the discovery phase. Or, you may have made infrastructure changes or be using an outdated version of a platform that is causing data integrity issues. By frequently evaluating your needs and the questions you answered during the discovery phase, you can ensure you have the right tools configured in the right way.

 

Measurement and Analysis

 

Measurement and analysis is the next level of the model. If you’re in this stage, it means that you now have the right questions identified, have the right tools in place to capture and display data, and are in a position to start driving value from your data set. Likely, you will have internal analysts, an agency, or a combination of the two that are delivering both regular and ad-hoc insights across business, website, customer, and media metrics. We believe it is important to establish a formal cadence (perhaps weekly) to deliver these reports to key stakeholders, present the insights, and when appropriate, draw specific insights or hypotheses that can then be tested. This will help ensure you are continuously evaluating and learning from your analytics program.

 

Testing and Optimization

 

As your analytics discipline matures, you’ll be ready for the next phase, which is to engage in more advanced analytics such as testing and continuous optimization. Testing can take many forms, most commonly as A/B testing and multivariate analysis, media testing in terms of budget allocation, targeting and flighting, merchandising and product testing, and more. You may also want to start incorporating additional data points from voice of customer or panel data at this stage as well.

Start with a data-driven hypothesis to create a scalable testing program that will help you identify bottlenecks and points of friction in customers’ paths to conversion and/or engagement. These findings can then be used for spot fixes of critical areas, potential new engagement paths, or be leveraged for future website and content design.

 

Predictive and Attribution

 

The final stage of our analytics maturity model includes a host of advanced applications such as attribution, predictive modeling, cluster analysis, and more. In this stage, you are likely moving beyond canned reports and testing, and working toward answering more complex questions such as optimal media mix and customer modeling.

This is also the phase where you can begin to add in personalized targeting for your customers, as well as build out your audience segments using predictive analytics.

 

In summary, as you begin to act on your ambitious 2016 goals, keep in mind that having the right analytics infrastructure and insights in place can be the key to success. With clearly defined goals, the right tools, and the necessary skill sets, you can drive exceptional value from your analytics efforts.

To learn more about the analytics maturity model, for help identifying where you fall, or to talk about taking your analytics to the next level, reach out to Rise.

01/14/2016 at 12:00