The future of marketing involves a lot of data and the need for marketing analysts to mine insights and direct business strategy. If you are curious about marketing analytics, but not quite sure if you understand what it involves check out this link!

1. ANALYTICS
A FOCUS ON STRATEGY
KEY COMPETENCIES
AN OVERVIEW OF
MARKETING
Analytics helps to
create a competitive
advantage by helping
businesses make
better decisions
and by focusing
strategy on key
competencies
In 2016, 57% of
respondents from the
MIT Sloan Management
Review research study
believed that "analytics
provides competitive
advantage"
The insights gained
through analytics
must result in
action in order to
make an impact on
the business
In order to be effective an analyst needs a variety of skills
which include :
Sources: 1. Ransbotham, S., & Kiron, D. (2017). Analytics as a source of business innovation. MIT Sloan
Management Review, 58(3)
2. Ling, D. (n.d.). Countdown to success with analytics. Retrieved October 06, 2017, from
https://www.sas.com/en_ca/insights/articles/analytics/countdown-to-success-analytics.html
Created by Jordan Swedak
57%
43%
57%
Companies need to use a variety of
different data sources not just within
the company, but also from external
parties. It is important to establish a
culture that supports data sharing.
Automation of repetitive low skill
tasks can free up analysts time to
focus their attention on more complex
and valuable projects.
Analytics can provide valuable real-time
insights about customer behaviour and
help implement personalized customer
engagement strategies.
Requires a combination of efforts such
as complying to rules and regulation
about data sharing and scrubbing the
data to ensure it is accurate.
Have a clear understanding of key business processes and the business
industry.
Be able to scrub the data, find relationships in the data, and know what data is
needed in order to develop valuable insights for the organization.
Use story telling to translate complex data into easy to understand and
actionable insights.
Data
Sharing
Machine
Learning
Customer
Behaviour
Data
Governance
(Ling , n.d.)
(Ransbotham & Kiron, 2017)
(Ransbotham & Kiron, 2017) (Ransbotham & Kiron, 2017) (Ling , n.d.)