Attending eMetrics 2017 in Chicago is a highlight of my career in 2017 – Both myself and my organization invested a lot to make the trip happen. I am glad it worked out well in the end.
I am very happy to come back to this topic, after started this blog by writing about Facebook metrics 6 months ago.
Just a little side note – I used to code a lot at 16 years old – never found much fun and gave up once I made it to college. Ironically, this time I had a lot of joy learning Python to connect to Facebook API for “insights” in the past few months.
The business case here is straightforward: We need to understand how we are doing in social media channel from the metrics perspective. As I addressed in my previous post, the data exporting feature of Facebook business manager is simply not good enough.
Using Python, I was able to directly call Facebook’s Graphic API, and retrieve the metrics – impressions, likes, shares, comments, link clicks etc – of each individual posts as far as 2 years ago, to a CSV file. Then I can port it with Microsoft PowerBI and do all kinds of analysis and visualization.
What I like this the most, is by having this Python code in place, I can bypass the traditional “Extract, Transform and Load” process of using the data warehouse. As a proud “full stack” analyst, I want my “data to insights” journey as simple as possible. Also, I can’t afford the typical 1 day of delay (of data warehousing), as a social media post can go viral and suddenly receive 100 “likes” in 4 hours.
Now let me explain how it works in a bit more geeky way…
As a self-claimed NTRP 4.0 tennis player, I am really proud of my passion for tennis. But I am writing this post not for sharing my passion and achievement of winning a tournament, but three pieces of learning I took away from it. Hopefully, they are helpful to my audience, no matter as a tennis fan or not.
In the last couple of years, I have been asked numerous times for a “dashboard”. In reality I was asked a chart showing “what is my project (‘s KPI) doing”. I usually performed a few data pukes and provided one PowerPoint slide, once the KPI is clearly defined, and the source data is in a “reasonable-messy” format. I was not impressed by the business reality of over-used term “dashboard”.
“What is a dashboard”
In my opinion, a true “dashboard” should serve beyond “what”, but also “why”.
I love new technology stuff. That is why I bought the Apple Watch 2 series 2 the day when it was launched. Mostly attracted by its waterproof feature for swim tracking, I was so pumped and even lined up in the retail store to get it on the first day.
Now I have been using it for almost 5 months and recorded 29 swimming workout. I’d conclude that it is a worthwhile purchase, as it helped me monitor and improve swim performance. Although the journey is not a smooth as I expected, let me share my experience.
Going over the game detail is not the intention of this blog post. (This link will guide you to the official game recap). I’d rather share what I have learned, or more specifically, what inspired me from this match.
The second half of 2016 is the fastest learning period for me professionally in a decade. Being put in the new job, I was forced to step out of my comfort zone, and learn to “swim” quickly in the deep water of digital analytics world. A “multi-channel” approach has been taken:
- participating interactive webinars (most of them are free as part of the solution-sell from vendors),
- formal on-line training(passed Google Analytics IQ certification),
- follow blogs and newsletters (started from Occam’s Razor),
- peer to peer learning (10+ face to face meeting with leading digital marketers, and analysts within my company),
- reading books
This approach is essential to build the competencies thru different angles, just like we need multiple “marketing channels” to promote products and services. You will always want to know which one is the best. For me personally, “reading books” is the channel that come on top without doubt.