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Data

Analysis

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Today's agenda

Project recap

Problem

The Analytics team

Process

Insights

Summary

Today's agenda will be as follows:

1. We will recap the overall project to give a high level understanding of the business problem we're tackling and the specific requirements.

2. We will dive into the specific problem that we, the Data Analytics team, have been focusing on and will give some background as to why this is such a big problem.

3. After introducing the problem, I will go over the team responsible from our side in tackling this task.

4. I will then go over the high-level process that we followed to complete this task, so that you have complete clarity in how we tackle these kinds of tasks.

5. Finally, I will go over the all important results and I will present them as a series of insights and visualizatio's from our analysis.

To wrap up, I will summarize and open for any questions.

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Project Recap

Social Buzz is a fast growing technology unicorn that need to adapt quickly to it's global scale. Accenture has begun a 3 month POC focusing on these tasks:

An audit of Social Buzz's big data practice
Recommendations for a successful IPO
Analysis to find Social Buzz's top 5 most popular categories of content

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Problem

But how to capitalize on it when there is so much?

Over 100000 posts per day

36,500,000 pieces of content

per year!

Analysis to find Social Buzz's top 5 most popular categories of content

Focusing on the last point that I mentioned there, this is what the Data Analytics team has been specifically focused on.

Clearly with such grand scale, this comes with a lot of data and with such vast amounts of data comes challenges.

To give a background on how much data you've been creating:

- You told us that your platform receives over 100000 posts per day which amounts to 36 500 000 posts every year, of which, this is all unstructured data making it very hard to make sense of.

In this day and age, content is king. Just look at some of the biggest platforms in the world, for example YouTube, Facebook and Netflix... they are all content businesses...

But how to capitalize on it when there is so much?

It's not just all about harvesting as much content as possible... The real value is in understanding and crunching this content to gain a deeper understanding of your audience and to therefore provide a more personalized and enjoyable experience.

And this is where out data analytics expertise comes in, with the insights that we've uncovered from this task, we can show you exactly how to take analytics to production at scale.

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The Analytics team

Chief Technology Architect

ANDREW FLEMING

Senior Principal

MARCUS ROMPTON

Data Analyst

YOU

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Process

1

2

5

4

3

Data Modelling

Data Cleaning

Data Understanding

Data Analysis

Uncover Insights

So, how did we tackle this problem?

Well we approached it in 5 steps:

1. Data understanding - the key to success on any data project is to understand the data in detail. So we took the time to understand the data model and domain of your business.

2. Data cleaning - after understanding your business, we then cleaned the available datasets and thought about what an ideal dataset should look like for this problem.

3. Data modelling - After ensuring the data was clean for analysis, we needed to process and model this data into a dataset that can precisely answer the business questions and produce the results needed.

4. Data analysis - With our new dataset, we used our analytical expertise to uncover insights from this dataset and to produce visualizations to describe the insights.

5. And finally we used these insights to unlock business decisions and to make recommendations on next steps.

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Insights

UNIQUE

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Summary

Food is a common theme with the top 5 categories with "Healthy Eating" ranking the highest. This may give an indication to the audience within your user base. You could use this insight to create a campaign and work with healthy eating brands to boost user engagement.

INSIGHT

Animals and science are the two most popular categories of content, showing that people enjoy "real-life" and "factual" content the most.

ANALYSIS

This ad-hoc analysis is insightful, but it's time to take this analysis into large scale production for real-time understanding of your business. We can show you how to do this.

NEXT STEPS

So to summarize:

We tackled this task and found the top 5 most popular categories as asked, but we also went one step further.

- We found that animals and science are the two most popular categories, suggesting that users like "real-life" and "factual" content

- We also found that food was a common theme amongst popular content and the most popular food category was healthy eating. This could be a signal to show the types of people that are using your platform, and you could use this insight to boost engagement even further. For example, you could run a campaign with content focused on this category or work with healthy eating brands to promote content.

- As much as this analysis was insightful, we are ready to take it to the next stage and we have the expertise within Accenture to help you realize these kinds of insights in production across your organization and in real-time. We would love to help you with this.

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ANY QUESTIONS?

Thank you!