Data is a natural resource that can help enterprises extract maximum insights to strengthen marketing efforts. But, sadly, enterprises fail to rank themselves effectively in their data/analytics maturity level.
It is quite common to find CEOs, CMOs, and analysts stating ‘data’ as the next big thing in the industry that enterprises need to encash on. But the actual question is how one can leverage this newly-discovered resource leveraging a business model capable of assessing the actual data capabilities. The fact is that the power that people seek from data has, in fact, already found them.
CMOs and CTOs globally are today planning on leveraging their data and use its power for business growth. For customer conversions, brands predominantly turn to data to give a well-targeted personalized customer experience, using a host of analytics tools. But to truly realize the potential, companies can optimize their processes from a business point of view, aligning organizational verticals to reflect this data-driven approach to solve underlying issues.
The answer is not some radical change, only a slight realignment and reimaging of data management processes as the need of the hour.
Firms to Develop an Analytics Maturity Model
The general purpose of any maturity model is to introduce a comprehensive framework to simplify assessment of the most vital parts, while building a solid data and analytics foundation. It is a road map that provides a well-defined guideline for the necessary means. This roadmap needs to ensure that infrastructure and processes enable regular, frequent optimization across media vehicles, CRM, digital, and brand measures aligned with overall business objectives.
Sample score for the Analytics Maturity model
After developing a maturity model designed to evaluate the standard requirements for a highly actionable analytics and data foundation, sample scoring is important.
It can be utilized to evaluate the ability to deliver advanced digital marketing techniques at par with the constantly evolving content consumption pattern. And in such a process, assessing the underlying factors such as the operating models, technical requirements, roles and responsibilities, is critical to further augmenting marketing capabilities. Considering this, brands need to consider the below identified key areas that all organizations need to appraise.
Enterprises need to answer why they are capturing data and what are they trying to achieve with it.
A clearly defined strategy forms the backbone of all advanced digital marketing activities as they heavily influence the success of a data-driven marketing culture. In short, the most favorable situation for any organization would be to have a well-communicated and clearly defined strategy, which comes aligned with business objectives and related KPIs.
Enterprises need to understand how to organize the resource and skillsets for achieving data maturity. The highest expected level of maturity is achieved only when there is true transparency of the individual capabilities and how they can be combined to take concrete steps for increasing them over time.
It is critical to understand the role of technology in enabling the overall marketing goals. A close partnership with vendors, alongside a reliable understanding of their future developments and clearly defined training and support requests, creates the basis to establish a higher level of maturity.
Enterprises need to also question the impact of data-driven decisions on the culture. A highly mature process setup is based on an automated, less error-prone data collection and refinement process, accompanied by standardized ways to serve various information requests.
Also, the ruling point is the efficacy of the analytics process- that turns data into insight and the process of applying these insights to drive business decisions. Finally, the last aspect of any maturity model evaluates how data can be turned into meaningful information outputs and to what extent relevant insights can be generated and operationalized.
To evaluate firms’ maturity level in this regard, it is investigated if expectations regarding the insight generation are well-managed, how frequently they are derived, and how valuable they are.
The insight maturity is always expected to be on a high level, while information is not created to solely document performance but is also actively incorporated into everyday thinking. Recurring information needs are served in an actual effortless and efficient way.