Hi everyone! You're watching 365 Data Use Cases. A very cool initiative by 365 Data
Science. So, make sure you subscribe to this channel. I'm Tina, and today I'll be happy
to share with you one of my favorite data use cases. Also, feel free to check out my
YouTube channel. You'll be able to find a link for it in the description and in the
upper right hand corner of the video.
Now, my favourite data use case is [drumroll] product development.
Product development is a HUGE and COMPLEX set of processes that rests on many moving
parts, lots of hypotheses testing, and ultimately many many decisions. The ultimate goal is
to create and grow a product that users love.
Think about your favourite apps today: instagram, uber, facebook, youtube. Well, Instagram has
infinite scroll and uber has different options like uber xl and uber pool. We take it all
for granted now that these features exist, but you better believe that these products
are labours of love - results of countless decisions to get them right, many of which
were not obvious at first.
It’s because of the complexity of product development, with all its moving parts that
makes data immensely powerful here. It’s like a shining beacon of truth ….
If you have the right data, and know how to tease out those insights.
In product development, we do lots of opportunity sizing to decide which features to build and
what to invest in. This often involves analysing similar products to see how they performed.
The data here can be structured like 1- 5 star ratings or unstructured like social media
reviews.
We also do experiments to test out the features we build and see how users respond to them.
The data here can also be structured or unstructured. We also want lots of data, which gives us
more confidence in our results.
So we have the data, but how do we tease out those high impact insights? Well, remember,
it’s the problem that defines the tools we use and the rule of thumb is to start from
the simplest and increase in complexity.
So first off - traditional analysis. Traditional analysis involves lots of hypothesis testing
and statistics, which 365 has a wonderful series on as well (link). It also involves
lots of data heavy lifting and visualizations with SQL and python or R.
Now, lots of things can be done with traditional analysis but machine learning has a solid
place in product development. There are lots of ways we use machine learning - one of my
favourite’s is using supervised learning techniques such as random forest, SVM, and
XGBoost to discover which features are contributing the most to the success of a product. These
models are easy to implement and actually super helpful.
NLP is also used a lot to analyse comments and product reviews.
Finally, unsupervised clustering and more complex techniques such as deep learning also
have their place. All in the name of developing an amazing product!
Product development is my favourite data science use case for two reasons.
First, because of how powerful (almost magical) it is in driving decisions. Here, data really
gets to shine because it’s both the source of truth and the driver of insights. You’ll
be surprised by how much value there is in just a quick XGBoost model for example.
Finally, data driven product development meshes really well with my personal philosophy, commonly
known as the 80/20 rule, where 80% of results come from 20% of inputs. In product development,
80% of the success of a product is determined by 20% of its features. That’s exactly why
choosing the right feature to build is so important!
Thank you for staying until the very end of today's video. I was super happy to share
this data use case with you. For more videos with me, make sure to check out my YouTube
channel where I talk about data science and lots of career advice - the 80/20 way, or
how I like to put it: the minimize effort and maximize outcome way. and if you want
to learn about more interesting data use cases, make sure to subscribe to this channel.