Consumer Brands / Digital Economies - Article Analysis
consumer brands digital economy compete data analysis

What big consumer brands can do to compete in a digital economy

Another great example of how agile business models are essential in today's day and age. The rise of e-commerce, disruptive start-ups and innovators have left many traditional retailers in the dust. Changing lifestyles and increasing demands for more value have culminated in the giants of the FMCG world being left behind in the e-commerce era, with smaller, more agile businesses using the powers of data to better align with consumers’ values  and understand what they truly demand, and then customising their experience to match exactly. By running data analytics, firms can optimise their merchandise mix and marketing efforts, as well as inform future innovation, keeping them one step ahead of the pack.


Interested in this? We have helped numerous retailers to understand the power of their data. Check out our Retail Case Study which brings this concept to life or get in touch today.

For the original article Click Here.

Or looking for more insights and visualisations? Check them out here :-)

CommentaryJack Sloman
Money Attitudes - Article Analysis
investing by generation attitudes

This article explores trends around different generational attitudes towards money and breaks these discrepancies down into 7 major categories which include reactions to volatility and perceptions about how knowledgeable different groups believe themselves to be when considering finance. 

Investments Age Attitudes Millennial


[Image source: Raconteur]

Data like this is important because it informs us of characteristics about generations that drive decision making processes. By being able to better understand what motivates people in certain demographics, marketers can better target segments, in this case those willing or looking to invest money, by softening the friction areas and creating trust between them and their offering. Data analysis is about finding value within numbers, and by considering these trends, it becomes more simple to relate with target audiences and provide them value where value is in high demand.

For the original article Click Here.


Or looking for more insights and visualisations? Check them out here :-)

CommentaryJack Sloman
IBM Watson - Article Analysis
skin-cancer-detection-super-computer-IBM

Machine learning is a daunting concept for many people all over the world. The idea that machines can learn like humans delves into arguments which stress the importance of maintaining humanity and ensuring technology doesn’t have the capability to surpass us. IBM’s Watson is a very strategic segway for the company’s marketeers to broaden the trust level consumers have with these types of technology. By demonstrating its ability to solve a large social issue which many Australian’s relate to on a personal level, IBM is developing this network of trust in ways which truly help people, instilling confidence in those exposed to and willing to adopt this technology.

For the original article Click Here.

Or looking for more insights and visualisations? Check them out here :-)

CommentaryJack Sloman
Time-series modelling
Does one trend of sales predict another and if so, which way around and by how much?

This was the problem posed by one of our clients.

 
 

No matter what type of data project you work on, there are some fundamental steps:

  • Firstly, understanding the data available e.g. missing values, low volumes and outliers

  • We addressed missing values like so:

 

Trend with missing data points:

Trend with missing points addressed:

Missing values.JPG
 

We also wanted to remove the major spikes, as we’re more interested in the overall trend. Normalisation helps with this:

 

 

For this project, we used 2 years of data to compare trends (from a total of 5+ years)

  • We created artificial “lags” between the trends so that we could compare different time frames

  • We used polynomial models, which created smoother output trends for comparison

 

Polynomial trend for vehicle sales:

Polynomial trend for property sales

Polynomial_X.jpg
 
 

And we used linear regression models to understand the overall trends e.g. what is the overall direction, going up, down or remaining constant?

 

 

There are many methods for assessing time-series trends.

  • We are looked at the “dissimilarity measure” i.e. how similar/dissimilar are the trends?

 

Some similarities but not the same:

Very similar trends:

Polynomial trends - not the same.JPG
 

Do they increase and decrease at the same time?

Polynomial X rise & fall.JPG
 

 

Once we found our methodology, we then set about creating an automated procedure to process the tens of thousands of cases.

We could then derive the best look-a-like models with confidence indicators for further analysis.

 

Business use.JPG

From here, we then had the job of understanding what the different trends meant in real life terms.

  • Heat maps are great for finding interesting clusters of similar patterns.

  • The models can now be used to make predictions to test in real life.

 

To learn more about this project and others like it, start up a conversation with us.

Spotify - Article Analysis
music-data-optimisation-spotify

Spotify are a great example of creating unlikely relationships between two seemingly unrelated disciplines - music, traditionally for the rebels and creatives of the world, and data science, for the nerds. Historically the two may not have been considered complementary but Spotify have proven otherwise. Their three layered approach to understanding our music preferences and predicting what we might like moving forward has made us fall in love with the abilities of this app and is what makes data science and analysis so important. Locating value in new and untapped markets is what good data scientists do, and is something White Box is continually pushing to find with its clients.

For the original article and Spotify’s three step method Click Here.

Or looking for more insights and visualisations? Check them out here :-)

CommentaryJack Sloman