“Data is a natural resource.”
It is quite
common these days to find CEOs, CMOs and Analysts quote ‘data’ as the next big
thing in the industry. But the real question is how do we leverage this newly
discovered resource through a business model that is capable of assessing data
capabilities? The fact is that the power that we seek from data has in fact,
already found us.
CMOs and CTOs of
the world are right now thinking on how to use their own data and leverage its
power for business growth. As we speak - thousands of websites, digital
platforms, mobile applications are capturing and processing data in real time,
to mine meaningful consumer insights. To illustrate this phenomenon let’s take
a step back to see how digital marketing strategy is evolving.
When it comes to conversions, brands
today have turned to data, to give a highly targeted personalized experience
for customers through a host of analytics tools. But to truly realize the
potential, how can we optimize our processes from a business point of view? Can
we align organization verticals to reflect this data-driven approach to solving
problems? How can we collate data from multiple sources? More importantly, how
does a business assess their own standing (among competition) in terms of
maturity of their data collection and usage? What are the key access points to
evaluate data and analytics capabilities?
The answer is not a radical change,
rather a slight realignment of processes that look somewhat like this…
The
general purpose of this maturity model is to introduce a comprehensive
framework, which makes it easy to assess the most vital parts while setting up
a solid data and analytics foundation. It’s a road map that provides a
guideline for the necessary means to ensure that infrastructure and processes
enable frequent, regular optimization across media vehicles, digital, CRM and
brand measures on basis of the business objectives.
The
maturity model is designed to evaluate the mandatory requirements for a highly
actionable data and analytics foundation.
It can be used to evaluate the
ability to deliver advanced digital marketing techniques that are at par with
the evolving content consumption pattern. And in the process, assess the
underlying factors such as technology requirements, operating models, roles and
responsibilities to further augment capabilities. Keeping this in mind, we have
identified key areas that need to be appraised by organizations.
Strategy:
The key question to answer is, why are we capturing data and
what are we trying to achieve with it?
A clearly defined strategy
is the backbone of all advanced digital marketing activities and heavily
influences whether or not the change towards a data driven marketing culture is
successful. Usually the strategy is defined by the marketing executives, or at
least empowered by them and communicated throughout the organization. The
strategy provides the criteria to select business objectives and how to
leverage data in order to achieve the anticipated goals. The key points of
focus while framing strategy are Vision, Objectives and how are we going to use
the data for turning it to insights.
In short, the most
favorable situation for an organization would be to have a clearly defined and
well communicated strategy, which comes along with business objectives and
related KPIs.
Capability:
The key questions to answer is, how do we organize the
resource and skill sets for data maturity?
Another
major part regarding the overall maturity of data and analytics utilization, is
the expertise of the relevant stakeholders. Providing a well thought out
framework is essential for coordinating resources, empowering knowledge sharing
and planning for individual skill development. The first step towards this
framework is to analyze our organization’s operation model is.
Is
there a proper coordination between different departments? For example, do the
Social, CRM, E-commerce Marketing/Sales align together? Do they meet and plan
campaigns? Are the roles and responsibility for each stakeholder clearly
defined? How are they leveraging the collected data? Are they well aware of
their respective roles in marketing activities?
Thus,
the highest level of capability maturity is achieved, when there is complete
transparency of the individual capabilities, how they can be combined and what
the concrete steps are to increase them over time.
Technology
Key Question to answer is, what
is the role of technology and how does it enable our goals?
Advanced
digital marketing is largely dependent on the utilized tools and their
capabilities. Whereas the concrete technology stacks can differ from client to
client, they always are based on the same functionalities. For instance, you
can expect a CRM system to be in place, as well as a web tracking tool, to
capture user interactions and preferences but are these tools sharing the data
with each other and does our team use the data in effective way? In case if organizations
have vendors, is the optimal
value generated from vendor partnerships? What are our competitor’s doing? Do we get
training and support for our technology platform and tools used?
Thus
a close partnership with vendors, alongside a good understanding of their
future developments and clearly defined training and support requests form the
basis for a high level of maturity.
Process
Key Question to answer is, how
do we affect the culture so that data driven decisions are adopted?
Having talked about
capability and technology before, the next aspect is concerned about the needed
processes to make the most use out of both. Vital parts are the data collection
itself, how to request analytics services going beyond just mere Excel
spreadsheets and how to guarantee reliable data quality all the time. We need
to have a governance and operating model defined for our data collection.
A highly mature process
setup is built on automated, less error prone data collection and refinement,
accompanied by standardized ways to request and serve information requests.
Insights
Key Question to answer is how do we
ensure data is turned into insight and ultimately action?
Finally, the last aspect
of the maturity model is evaluating how data is turned into meaningful
information outputs, to what extent relevant insights are generated and how
those are operationalized. All the above mentioned are forming the most
tangible and directly visible outcome of a mature data and analytics
foundation. In order to
evaluate the organization maturity in this regard, it is investigated if there
are managed expectations regarding the insight generation, how frequently they
are derived and how valuable they are.
The insight maturity is expected to
be on a high level, when information is not created to solely document
performance, but is actively incorporated into day to day thinking. Recurring
information needs are served in a rather effortless and efficient way.
One can judge themselves on these aspects to know
where they stand compared to maturity Level!
