100% satisfaction guarantee Immediately available after payment Both online and in PDF No strings attached
logo-home
Previously searched by you
Previously searched by you
logo
Summary Digital and Social Media Strategies exam preparations! CA$11.58   Add to cart
Quickly navigate to
Preview
  2.  
  3. Tilburg University (UVT)
  4.  
  5. Digital and Social Media Strategies (325222B6)

Summary

Summary Digital and Social Media Strategies exam preparations!

2 reviews
 124 views  9 purchases
  • Course
  • Digital and Social Media Strategies (325222B6)
  • Institution
  • Tilburg University (UVT)

Summary of the papers and lecture slides of Digital and Social Media Strategies (DSMS) of Lachlan Deer. Made for the exam of Passed this class with an 8 and thereby positioned myself in the top 10% of this class

Preview 3 out of 26  pages

Document information

  • December 12, 2022
  • 26
  • 2022/2023
  • Summary

Subjects

  • marketing analytics
  • marketing
  • minor
  • dsms
  • tilburg universiteit
  • digital and social media strategies
  • tilburg university

Written for

  • Tilburg University (UVT)
  • Marketing Analytics
  • Digital and Social Media Strategies (325222B6)
All documents for this subject (1)

2  reviews

review-writer-avatar

By: jorisdejong2003 • 1 month ago

review-writer-avatar

By: tielenssander • 1 year ago

Translated by Google

Too bad that some parts of the slides are skipped. If you add this one, it's fine.

reply-writer-avatar

By: twanvanderlinden • 1 year ago

Translated by Google

Dear sander, What a shame to see that you only give 3 stars to the document. Given your explanation, I would love to hear from you what pieces you missed so that others can use them as an addition to this summary. For me, however, the summary was sufficiently exhaustive.

Seller

avatar-seller
twanvanderlinden

Reviews received

Content preview

Week 1: Introduction
Marketing Thinking and Doing
marketers and their academic counterparts. Consider weaving as an analogy. Individual fibers
have value separately; when combined, they can produce useful materials or beautiful
tapestries. But this is not the case now, the move separately. The process of creating a better
weave is already underway, so the two parts (academic and practice) are moving towards
eachother.
Why wave?
Academic reasons: Better ideas, Better data, New tools, More effective teaching
Practitioner reasons: New lenses, Across-Industry knowledge, Going a mile deep, new tools,
benefit of the cold eye
What both can do? -> Find common problems, share a workbench, celebrate different types of
theory
What can academics do? -> Get out into the field, build a classroom lab, translate marketing
reality into ideas, create value beyond the paper, engage in healthy confrontation, embrace
research diversity
What can practitioners do? -> invite research experts, share your data, identify your theory
The marketing discipline: Inspire, connect, reward


Digital marketing: A framework, review and research agenda




A Thematic Exploration of Digital, Social Media, and Mobile Marketing: Research
Evolution from 2000 to 2015 and an Agenda for Future Inquiry
Digital, Social Media and Mobile(DSMM): (1)DSMM as a facilitator of individual
expression, (2) DSMM as a decision support tool, and (3) DSMM as a market intelligence
source

,Week 2: Marketing Meets Data
With Decision-Driven Data Analytics
The authors discuss the power of what they coin “decision driven analytics” and compare it to
a more traditional data-science approach to data driven analytics. They make many good
points, but I find the need for a new term a little too much. In the lecture, I’ll use “Gold
Standard” data analytics to mean “decision driven analytics”, because in my mind what they
are advocating is more along the lines of best practices in data driven decision making as
opposed to a whole paradigm shift.




Marketing and Data Science: Together the Future is Ours.
Pradeep and his co-authors (all of which are at the “top” of the field in marketing analytics)
discuss the evolution of marketing into a data driven field. They argue quite hard for
synergies between marketing as a science and computer science / data science. Its a good
article, and it highlights the transition that marketing has been going through over the past 20
or so years. Their push for computer/data science moves a little too much towards problems
of descriptive and predictive analytics for my taste (see for example Box 1) as opposed to a
causal toolkit but their over-arching point is full of merit, and is the current forefront of the
field.


Optimizing Digital Marketing with Data Science.
This piece is from a data scientist working in marketing and business analytics. Its included to
give you a sense of how a data scientist (as opposed to a more traditional marketer) thinks
about different domains in the digital marketing space.

, Week 3: Regression
Causal Inference: Econometric Models vs. A/B Testing.
This article talks about (i) the difference between observational and experimental data, and
(ii) estimating causal effects with and without experimental data. Its a good summary of
things you should know (or want to know) from a toolkit perspective. When reading, skip the
section(s) on Instrumental Variables because we won’t be needing that for this class.
“Unfortunately, a raw correlation between X and Y alone is NOT enough to help us establish
the causal relationships. The complicating factor here is a set of other features, called
Confounding Variables, that affect both X and Y. For example, factors, such as visitor
geolocation, gender, age and interest would affect both the use of the new feature and the
outcome of sales revenue. Therefore, we need to isolate the effect of the new web page design
(X) on the sales revenue (Y) while controlling for these confounding variables.”
In an observational study, we observe and collect the actual data (e.g., X and Y) relevant in
the study without randomly imposing any kind of treatment or restriction on a group.
In an experimental study, we randomly impose treatment to a group, while the other group
doesn’t receive the treatment, so that we can investigate the causal relationship between the
treatment and the outcome variable. Randomization design and intervention make
experimental studies different from observational studies.
Look through the slides again, about regression and interpretation


Look out for causality, instead use: ‘Is associated with’

The benefits of buying summaries with Stuvia:

Guaranteed quality through customer reviews

Guaranteed quality through customer reviews

Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.

Quick and easy check-out

Quick and easy check-out

You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.

Focus on what matters

Focus on what matters

Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!

Frequently asked questions

What do I get when I buy this document?

You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.

Satisfaction guarantee: how does it work?

Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.

Who am I buying these notes from?

Stuvia is a marketplace, so you are not buying this document from us, but from seller twanvanderlinden. Stuvia facilitates payment to the seller.

Will I be stuck with a subscription?

No, you only buy these notes for CA$11.58. You're not tied to anything after your purchase.

Can Stuvia be trusted?

4.6 stars on Google & Trustpilot (+1000 reviews)

73918 documents were sold in the last 30 days

Founded in 2010, the go-to place to buy study notes for 14 years now

Start selling
CA$11.58  9x  sold
  • (2)
  Add to cart