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Analytical Trick to Boost Content Marketing2081

Member skappal private msg quote post Address this user
In the wake of ZMOT (Zero Moment of Truth) it becomes pivotal for any product company to choose the most appropriate advertisement channel for the promotion of their products. This not only helps the organizations to maximize their chances of creating the best first impression but will also help them to be discovered by today’s tech savvy consumers.

Today we will talk about a very interesting method of performing a statistical analysis to pick the most appropriate Ad channel to introduce your product or service to your potential customers. This analysis is called as Simple Correspondence Analysis which is very simple and straight forward.

This analysis can help to isolate specific Ad Channel corresponding the most to a particular product that was sold to a potential customer.

So let’s get started !!!

What do we need?

Sep 1: Get Data

Step 2: Start “The Data Crunching” (I used Minitab to perform this analysis)

Open Minitab
Go To Stat

In Stat select Multivariate
Within Multivariate Select Simple Correspondence Analysis

Once in the Simple Correspondence Analysis Console is up. Start putting in the stuff

Also Click on “Graphs” and check the box labeled “Symmetric plot showing rows and columns”

Once all the options are selected Minitab creates the following plot:

Looking at the plot it is pretty evident that majority of the products, Product 1,4,5 tend to associate pretty nicely with Social Media (positioned close to each other) and Product 3 & 2 are more closely related to Paper Ads and Television.

This gives the business a much better understanding of its potential sales strategy associated with a particular ad channel and pretty strongly position’s the organization to know how their consumer reacts to a particular touch-point.

In my next article I will talk about another statistical tool that can help the business to glean more insights out of the data with a real impact on the business’s profit margins.

A quick note for content writers and developer, even if you have data related to the tweets, likes, shares etc. on various social platforms for various services or products, this analysis can help to identify which social media platform is more favorable for your content promotion.

I hope you all would have enjoyed this approach !!!
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Top Contributor Steve private msg quote post Address this user
Interesting... I would have just looked at the numbers you have and gone... Paper Ads work for four out of five products... that's where I'm investing my time.
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Member skappal private msg quote post Address this user
@Steve, I am glad that you find this interesting. There are so many ways that we can uncover "Golden Nuggets" from the data that we have at our disposal.

Statistical procedures adds that extra intelligence to ensure that we as data consumers "make well educated decisions" and avoid "guesstimates"
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Top Contributor Steve private msg quote post Address this user
@skappal perhaps you can explain why social media works better than paper ads... when in four of the five examples paper ads out perform social media on leads generated... and only in one instance does social media out perform paper ads...

Just curious as to how it actually determines this... as logic to me... is to go with paper ads
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Member skappal private msg quote post Address this user
@Steve Thanks for asking this question !

To an extent you are right in saying that the paper ads have outperformed all the other mediums (Ad channels) if we go by sheer numbers and how they add up. However, the premise of this analysis is not to simply look at the aggregations but to explore the relationships of these Add Mediums with the Products.

This analysis explores the relationships in between the rows and columns. By transforming the data into “equivalent” space where the largest amount of variability in the data points is captured in the first dimension and the next largest amount of variability in the second dimension. If you look at the graph there are 4 quadrants with two dimensions showcasing which combination of Product and Medium falling close to the Row dimension of Product and Column dimension of Ad Channels). I know this can be confusing. That’s why it took me a little long to explain the logic behind this analysis.

In a nut shell data aggregation certainly helps but looking at the data from other dimensions in correspondence with other attributes can reveal more interesting patterns.

I hope this helps !!!
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