PM Case Study: An app to conduct sessions

You are the PM responsible to reduce the turnaround time (TAT) taken by a creator after sign-up to reach their first transaction (from ~ 3 weeks to <7 days). Assume that all the traffic signing up is of the right user persona.

  • Evaluate the current flow by signing up here: https://app.com/
  • Identify the problem areas and create a list
  • Prioritize the problem(s) that you think will have the highest impact if solved and explain your thought process behind the prioritization
  • Pick the highest impact problem and come up with an end-to-end solution for it. – Prepare a PRD and any supporting documentation/ designs/ wireframes (can even be hand-drawn)

After evaluating the UX:

Pain Points (In the existing flow)

  1. The second step(after OTP verification): Fewer options to add in “What are you looking to sell?”
  2. The phone field in the Meeting place did not show a pop-up for adding a number even when the field is a required one.
  3. While adding time for the sessions:
  • Can add a checkbox that will make it easy for selecting the time “as above”. That will replicate the time in the other time fields this might reduce the time taken to check the boxes separately. It is confusing for the users basically as of now.
  • Can seek inspiration from Google calendars while setting up a meeting. It’s easy.
  1. After publishing the listing:
  • The option for sharing through Facebook is not working.
  • Limited social media icons are available. We should use the one people mostly use which is Instagram nowadays.

User Persona:

To reduce TAT:

  • Facebook sharing option to resolve
  • UI seems uneasy and confusing
  • Adding more visual clues to guide users to the next steps
  • Adding more social media platforms to share through like Instagram
  • Providing more blogs to different use cases that every user persona can relate to and apply to close the first transaction
  • Too much information available in an irregular manner
  • Can conduct weekly webinars on how to get your first transaction, and to keep growing

For prioritizing features to implement we can make use of the “Impact Vs Feasibility Matrix”.

After using the same, we decided to take up the pain points having a high Impact with easily feasible:

  • Providing more blogs to different use cases that every user persona can relate to and apply to close the first transaction — High Impact, low efforts, and low time taken to implement.
  • Can conduct weekly webinars on how to get your first transaction, and to keep growing — High Impact, high efforts, and more time to conduct.

So we will eventually go with providing blogs for every use case and the know-how. These blogs can also act in increasing the traffic, and views that will bolster our SEO. We should add a button to redirect them to the blogs.

Writing blogs can resolve the problem to get a sales rep to come in between that will also reduce efforts from our side that we can put to acquire more customers instead.

The flow after publishing their first listing:

  • Shared the listing through WA/Twitter.
  • Went to the blog suggestions to get their first transaction
  • Searched for the use case that resonated the best
  • Read through it to implement it
  • Implemented it

Other Important things: Extras

A cold outreach email to send to the targets(as per the User Persona created above)

Targeted Audience: Students/ working professionals mid-level

CTA: To get in touch & engage

50 Emails/day

Approach: Personal

Tools To Use:

  1. To extract emails: Skrapp.io, Snov.io, Waalaxy, leadleaper, Phantombuster, etc.
  2. To Send emails: Waalaxy, Mailchimp, Mailshake, sendinblue, Gmass, etc.

Platforms to extract emails from:

  1. LinkedIn
  2. Existing email list through newsletter and “engineering as marketing” way.
  3. Who visited our website?

Scenario:

An introductory email followed by one email to quickly follow up.

Clearly defined channels & their key actions to reach the targets

  1. Twitter
    1. Direct Messages
    2. Tweets under hashtags
    3. Engage via key actions
    4. Grow brand account
    5. Twitter Trend
    6. Influencer Marketing
  2. LinkedIn
    1. LinkedIn and then Email
    2. Messages
    3. Content through profiles
  3. Facebook
    1. Facebook Profile DMs
    2. Facebook Group Shares
    3. Facebook Community Partnerships
    4. Facebook Key Actions via Events, LIVE, etc.
    5. Facebook Community Owned or Acquired
  4. Web Presence
    1. Medium Articles
    2. Blogger Outreach
    3. Media Outreach
    4. Social Bookmarking Sites
    5. Listing on directories
    6. Podcast Outreach
    7. Newsletter Outreach
  5. ProductHunt or any alternatives
    1. Product of the Day / Week / Month
    2. ProductHunt Newsletter
  6. Social Listening
    1. Social Platforms
    2. Web
  7. Ads
    1. Ad networks
    2. Blogs / Websites
  8. Product-Led Growth
    1. Engineering as Marketing
    2. Viral Contests
  9. SEO
    1. Off-page
    2. On-page
    3. Technical

Define metrics to be measured

Given the problem statement, the Top-level KPIs could be:

  1. Response rate (Direct Messaging, email campaigns).
  2. Impressions, reactions, and engagements over the blogs, and social media posts.
  3. Number of Website visitors
  4. To understand which channel is working the best and getting the most numbers, we can use bitly links to track.
  5. Number of people interacting or are active in the funnel

Tools To Record and Analyze:

  • CRM analytics
  • MixPanel

PM Case Study: A step Tracker App – Fitness

Let’s assume the app name to be SSG.

Intro & Goal

SSG is an application that counts every step you take and reward based on the number of steps walked in a day. The features included in this application are; Rewards, Goals, Saves stats, a Step tracker, and more!

It’s high time to show your achievements, members can also add their friends and have fun collecting more rewards. 

The goal is to build a new feature that will drive “Walk with a friend to double your rewards”. 

For the fan of walking, you might be wondering how to earn double the rewards. Well, at this point in time, there is no way to double the rewards but with the new feature of “Walking with a friend,” the users will have a chance to double the rewards.

What is Walking with a Friend?

Walking with a friend is very simple, once you go through this process, you will get more credits than before. 

The first thing that you have to do is log into your account and then click on the “My Rewards” option on your dashboard. 

Once you click on that option, it will show all of your rewards that are eligible for bonus points. You can see them and then click on any of them and it will show how many points you will get when you walk with one friend or two friends. You can also see how many points other people have earned from walking with their friends as well as from walking alone.

Once you find someone who has already walked enough steps for getting bonus points, just click on him/her and start walking together until your rewards get doubled.

Who’s it for?

  1. Product people – those building products that enjoy discovering, playing with, and learning from new, innovative products.  Also serves as a pulse on potential competing products
  2. Shopaholics – always sourcing new deals and seeking signals to curate.
  3. Everyday Tech Consumers – these are people that love to find new stuff.
  4. Everyone – Whoever likes to walk and fan of fitness. 

How it works:

1) Join the Step Set Go App on your iPhone or Android device and create an account by providing an email address and password.

2) Create a profile by adding your name, address, phone number, and other details about yourself.

3) Walk with your friends or family members in real life to earn double the rewards!

Why build it?

  1. It’s something we’d personally enjoy 
  2. Early, initial traction from this feature will help gain more eyeballs on SSG. 
  3. The community of walkers is on the rise.
  4. Monetization opportunities in advertising and/or data
  5. Tech-risk very low
  6. Helps in increasing DAU, and MAU. 
  7. Amplifies the vision of Step Set Go which is to Make fitness fun, social, and rewarding.

What is it? 

  • See which routes are most popular by seeing the number of times people have walked them already. This lets you know which paths are safe for beginners or experienced confident walkers alike!
  • Get notifications when someone takes a break from walking (or starts walking agStep Set Go is a simple app for walkers. It’s easy to use and works like other popular fitness apps like RunKeeper or Strava. You just start the app and let it track how far you walk each day, and when you’re done, it will show you how many steps you’ve taken that day.

The app should have a feature built in that help make it more fun:

  • Track friends. This will let you know if someone needs help or is taking too long on their journey to get back into shape!

User Types

  1. Non-Registered walkers – people that have not yet registered
  2. Registered walkers – people that have registered and can track step counts
  3. Contributors – registered users that can track, earn, & double rewards using this feature

Steps 

Each walk must contain:

  1. Name – Name of the user added as a friend or a request by a friend to walk. 
  2. Acceptance – Accept the request and start walking
  3. Finish line – users need to mark it off as a finished walk together. 
  4. Rewards– number of rewards to be distributed as per the step counts of both user and their friends. 
  5. Shop – Buy multiple products from the SSG store using the rewards. 

Brainstormed Ideas

  1. Guide and Structure Comments – preface comments with structure (e.g. “this product is similar to…”, “this product is awesome because…”)  Facebook’s “I’m walking…”, “I’m eating…”
  2. Related Links – press, blog posts, etc
  3. Editors Picks – “starring” editorial picks
  4. Walking Groups – people can create groups, similar to subreddits

Competitors & Product Inspiration

  1. Strava
  2. RunKeeper

Mockups

  • Index View
  • Detail View

Tech Notes

Models

  • User
    • id
    • name
    • username
    • image
    • daily_email?
  • Walk
    • user_id
    • user_id2

Go to Market

  1. Engage/Recruit Influencers – make them feel part of the product’s success and design
    1. Ask for direct product feedback
    2. Feedback on the new feature
  2. Invite Contributors Before Public Launch – ensure content is populated

IMPACT Metrics

The success of SSG relies on effective step loops and re-engagement with the tracking of user’s steps. 

So the KPI of this feature should be the following: 

  • DAU
  • MAU
  • Number of “steps w/ friends” per user

Future Ideas

  1. Notification Feed – notifications of new social activity (comments, like, etc.)
  2. Tweet to Post – the ability to tweet at SSG to submit new posts
  3. Social Medias – Adding more social profiles to SSG profile. 

Product Management: YouTube Case Study

Problem Statement:

You are a Product Manager at YouTube. Based on market research, executives have identified that there’s a big opportunity in the educational space and they want you to lead this initiative and launch an educational product – YouTube for Kids. To kick start the entire project, you are expected to write a Product Note that describes the problem YouTube for Kids is looking to solve, outline a high-level solution strategy, and build a picture of the competitive landscape and other products that the team can draw inspiration from. If there are critical tech constraints in creating this solution, you are expected to call those out separately. The Product notes will be circulated to the following teams – business, tech, design, and investors to build consensus amongst key stakeholders and help shape a project plan on the solution.

Product Notes

Problem Statement

Youtube has been around for over 15 years and has grown to be one of the most popular video streaming platforms in the world. Youtube is not just a place for entertainment, but also an educational platform that can help children learn new skills and develop their creativity.

Based on the market research conducted by our team, we can see that we have identified a gap between the different age ranges, who consume media through our existing platform. We can see in the world of technology and digitalization, every age group seems to have access to the platform. But the challenge is that we can see that the content present over the internet is not at all safe for children under the age of 13, it’s generalized and that is the primary concern of the parents of the tots, and tweens.

This could prove to be an opportunity for us to create for kids, to make them learn through videos, and games as their playlists to tune into.

Aim

To create an educational platform for kids to utilize the educational content without getting interrupted by inappropriate advertisements, adult content, and harmful content.

Solution: Youtube for kids.

It is going to be a safe and secure platform for children to learn and explore. It should be collaborative and interactive, with a focus on digital learning.

The best way to educate your kids is to keep them entertained. That’s why we’ve come up with a product called YouTube for kids where every video is tailored to the age and learning needs of the child. With this product, parents can spend less time monitoring their children, and more time playing with the kids

Educational videos for kids. YouTube for children. We want parents to be confident that their kids are learning something valuable after watching videos on our platform. Our videos will be interactive and our content would be made with love and care to ensure kids feel comfortable and entertained while they learn. Unlike most kids’ channels that just show cartoons, we offer a broad range of topics, in many languages, so children can enjoy learning about the world around them.

Competitors


Product Specs

User Persona: Kalyani: A mother

Targeted Audience: Children under the age of 13.

Content theme: English, Language, Arts, Math, Science, Character, games, etc.

User Flow:

  • Sign Up or Log in through a parent’s profile.
  • Create profiles for their kids.
  • Add content and set privacy limits.
  • The main page with the content selected by the parent.

UI Requirements:

  • Easy UI & flow.
  • Visual Clues to embed more to guide kids to the next steps.
  • Must have 5 Tabs: Learn, read, play, watch, and explore.
  • Ease of play.

Functional Requirements:

  • If the search by parents is on, then Type search enable.
  • If the search by parents is on, then Voice search enable.
  • Interactive.

Parent’s guide:

  • Educational Value: Yes
  • Ease of play: Yes
  • Sex, Romance, Nudity: None
  • Smoking & Drinking: None
  • Violence: None

Parental Controls:

  • Keeping the search on and off for kids
  • Ability to block or report the content
  • History of what the kids have watched
  • Analytics

Challenges

  • Parental Sing-up needed.
  • Parental Monitoring is needed to curate the content for their kids.
  • Unwanted & Harmful content could slip through ads or the wrong type of search.
  • Typing for search by kids might not be that efficient.
  • Voice search could go wrong.

End Notes

Learning can be fun! YouTube for kids should be a library of educational videos, games, and songs that make learning new things fun and easy. With our YouTube playlist for kids, children can go on virtual field trips to learn about animals in the rainforest or design their own rollercoaster. Our product is going to be free to use and suitable for all ages under 13.

References:

https://www.commonsensemedia.org/app-reviews/noggin-preschool-learning-app

https://www.commonsensemedia.org/lists/youtube-alternatives

https://www.commonsense.org/education/app/youtube-kids#:~:text=Cons%3A

Prediction Models: Data Science Theory

Predictive modelling is the process that uses data mining (the process of finding similar patterns in the data), and finds the probability of any occurrence to produce output. In simple words, forecasting based on various methods.


It uses statistics to predict the results. Basically predictions are used to predict the future outcomes, but it can be applied on unknown data and where occurrence of time doesn’t matter.

Models are chosen on the basis of Detection theory(ability to differentiate between informative pattern and random pattern from the data).

The model is selected on the basis of testing , validation using the Detection theory to find the probability of any output of given input.

Predictive Modeling Process:

Modelling processes involve many algorithms and models and this process is iterative in nature, using multiple models or algorithms on the same data gives the best fit model of the data.

Categories of Models:
1. Predictive model
In this model, it uses past predictions for analyses and uses them for future prediction.
2. Descriptive Model
It defines the relationship between various entities of the data used.
3. Decision Model
This is used for making decisions over particular conditions and this is a repeatable approach which can be used again and again.

Exponential Smoothing:

It is a part of a time series where older data has less weight( less priority) as compared to new data and new data is more relevant and has more weight.
Smoothing constant represented by α ( it represents the weight of observation).
This is used for short term forecasting and is used in Tableau as well.

Types of Exponential Smoothing:

  1. Simple exponential smoothing
  2. Double exponential smoothing
  3. Triple exponential smoothing

Simple exponential smoothing

Arima Model:


It stands for Auto-regresive Integrated Moving Average .
Arima model belongs to the class of statistical models for analyzing and forecasting time series.
There are two types of models used for forecasting time series are:
1. Seasonal
2. Non- seasonal
ARIMA model is used for time series forecasting.

A fellow mate helped me write this article. Thanks to her.

Thank you for the read 🙂

Tableau: Business Intelligence Tool

Tableau is a Business Intelligence tool used for beautiful visualizations and analyzing the data. This is tool is easy to operate once you get a hand of it and its all of the features including creating animation. Adding Tableau in your skills is a plus. Various companies add experience of any BI tool to the required skills for Business Analyst, Data Analyst, or Data Scientist jobs.

Tableau is used to analyze the data for businesses. To create stories using visualizations. Help in figuring out the trends. My favorite application of Tableau is its forecasting. It uses Exponential smoothing model to predict the future number of any data you are using by taking the average of the previous values. It is somewhat accurate but not fully obviously.

It lets you take the dimensions and measurement variables to plot.

It also lets you add a workbook, rename a workbook, save and delete workbooks along with sharing any working using the online tableau version for online sharing.

I am using Tableau desktop version and also online one. You can download it as a professional, or as a student for free for a year. Check the website here for more: Tableau.

This is what Tableau looks when open, in the vertical blue bar, you can add the data of the type you have to get started or can open an existing workbook.

You can also connect to server mentioned there or open the saved data sources again. For other help, look in the right “Discover” Panel.

After adding the data, it will open a window like this:

I’m adding a data downloaded from kaggle about the world_bank in csv format.

Let’s go to worksheet mentioned in the screenshot to do analysis and check its working.

Adding a country name(Dimension) in the Marks palette, it will automatically pick a plot from the side bar to represent the data chosen.

We can add other dimensions, measures, and change the color however we want. I also like when it helps you choosing the plot in the side bar by telling you the required dimensions, measures to make a graph.

I suggest you should try experimenting with this to learn new and realize its ability to analyse and perform various interactive tasks efficiently.

Thank you for the read 🙂

Algorithms in Data Science: A must to know

Algorithms as we know are the basics to know to be in any computer science topic. Algorithms plays a part in making your foundation about the field stronger. In Data Science, it is important to have an idea about the algorithms that are being used for analysis, prediction, and other various tasks. There are many algorithms but let’s start with the following ones:

  1. Linear Regression
  2. Logistic Regression
  3. K-means clustering

Data science involves regression techniques, classification, and clustering, etc. These above mentioned algorithms are one of the types.

  1. Linear Regression:

In this, linear relationship is the one between two or more variables where we try to predict dependent variable value(y) based on the independent variable value(x). If we draw this on a two-dimensional space we will get a straight line.

Linear regression continuous value outputs. It uses two variable(2-D plane):

Independent variable: x

Dependent variable: y

The equation of the line formed in the graph is:

y = mx + c

where m is the slope of the line and c is the intercept. It gives us a straight line by considering the best fits in the plot.

2. Logistic Regression:

Logistic regression is a binary classification method. It uses a logistic function also known as sigmoid function which was created by statistician to predict and describe properties of a system. It’s an S-shaped curve that takes any real-valued number and fit it into a value between 0 and 1(binary values), but not exactly at the limits(0 or 1).

1 / (1 + e^-x)

where e is the base of the natural algorithms or Euler’s number and x is the numeric value that you input to transform.

y = e^(b0 + b1*x) / (1 + e^(b0 + b1*x))

This is the equation for logistic regression where y is the output, b0 is the bias or intercept term, and b1 is the coefficient for the input value(x).

3. K-Means Clustering:

It is an unsupervised learning algorithm. K-means clustering is an iterative algorithm that partitions a group of data containing n values into k subgroups is the real definition of k-mean clustering. This algorithm is used to reduce the distance between the points that has from the centroid of the cluster.

K-mean clustering problem can be solved by either Lloyd’s or Elkan’s algorithm.

This uses squared error function to minimize the distance:

Where J is the objective function of the centroid of the cluster, K are the number of clusters and n are the number of cases, C is the number of centroids and j is the number of clusters. X is the given data-point from which we have to determine the euclidian distance to the centroid as shown in the figure. 

Images are taken from the web just for reference.

Other algorithms to know:

Support Vector Machine

Principal Component Analysis

Artificial Neural Networks

Decision Trees

Thank you for the read 🙂

SciPy Operations: Linear Algebra and Polynomials

Scipy is a python library that extends the use of numpy and makes use of matplotlib and numpy both. It uses numpy arrays as its data structure. It is being used in scientific programming, integration, derivation, and Fourier transforms, etc. Today we will try to perform linear algebra and polynomial functions using scipy and numpy.

As you know the drill by now, we first need to download both of the libraries using pip if it’s installed or using python 3 (where pip comes in-built).

>>pip install scipy

>>pip install numpy

To import:

importing lib

For linear algebra, we need to import another package that can help us in performing various operations in it. It is known as linalg. 

To import linalg: 

imp_linalg

Next, have to define the arrays using the module to solve these linear equations.

scipy_arrays

The equations are:

2x + 3y = 7

3x + 4y = 10

you can try other operations as well.

Matrix computations: 

To find the determinant of the matrix:

scipy_determinant

The inverse of a matrix using inv():

scipy_invmatrix

Working with polynomials: 

To use polynomial functions, we need to import the poly1d module from numpy.

Defining the values in the form of a polynomial as ax + bx + c.

scipy_poly

So, in this snippet, the equation is x + 2x + 3.

In block number 18, the value of the polynomial at x = 7 is 66.

We can also perform and plot graphs using scipy along with other computations.

That’s it for today, I hope it was easy to understand. For more, you can contact me via the form on this website. I’d be happy to share some resources.

Thank you for the read 🙂 

 

Working with Matplotlib: Plotting various graphs.

Matplotlib is a python library used for making graphs and plotting visualizations using pandas and numpy for datasets. It helps us in creating bar graphs, scatter plots, subplots, histograms, etc. It also lets us add axes properties, font styles, and line styles to the graphs.

To download, use this command in your terminal:

pip install matplotlib

To import this library to run:

imp_plt

Let’s start with the basic plotting of graphs.

plot() method to plot numbers on a graph:

plt.plot

we can also add two parameters with defining the color as follows:

You can add different colors by visiting this site: matplotlib colors

plt.color

Defining the x-axes and y-axes in a graph using xlabel() and ylabel():

plt.xlabelplt.ylabel

Now to draw bar graphs, scatter plots we have to define the values along with the figure size we want to use:

plt.declare

Bar Graph: To plot a bar graph using the above information, we can simply use the bar() method.

plt.bar Scatter plots: For scatter plots, use scatter().

plt.scatter

We can also create subplots but we have to be very careful while making one because it needs a numeric value to create a subplot within a range otherwise it will throw a value error.

To plot the 2-dimensional plot by using the plot() method.

plt.plt1

I hope it was easy to understand. I will try to add more technical use of these libraries later. Stay tuned.

Thank you for the read 🙂

 

Working with Pandas: Python Library for Data Analysis

Pandas is a python library that is used in manipulating and analyzing the data. Its main data structure is a DataFrame. It can read files of CSV, XLSX, and TXT formats. We can also use Pandas with Numpy and matplotlib to make it more applicable. As mentioned above, it helps in data analysis, and it involved other processes well.

Today, we will learn to create data frames, indexing of df, slicing of df, and how to read and manipulate CSV files using pandas.

First to import pandas type: 

imp_pd

Creating a DataFrame: 

pd_dfcreating

Indexing of a dataframe: Though the index will start from 0 itself, we can manipulate and alter the however we want.

pd_indexing

For indexing a column:

pd-foracol

Slicing of a dataframe: We can slice the desired data from a dataframe the way we do slicing in python.

pd_slicing

To Read CSV files: I’m using a dataset of world bank which I used in my previous projects. You can also download datasets for free through the UCI machine learning repository or on Kaggle.

Command:

file = pandas.read_csv(‘filename.csv’)

readcsv

To get the number of columns

 filename.shape[1]

To get the number of rows:

filename.shape[0]

You can also get the column, row names, can do slicing among them, and can find datatype being used in the dataset for a particular row and column.

head() function will give you the first five rows of the data and we can insert a parameter in the head() to choose a number to display. tail() function will give the rows from the bottom of the data, here also you can choose the number to display.

That’s it for today, I hope it helped you in knowing how to use pandas and take the first step towards your learnings. All the best.

 

Thank you for the read 🙂

 

 

 

 

 

Working with NumPy: Array Operations

As explained in the last post about Numpy that it is a widely used python library used to perform high-level numeric computations using arrays. It has applications in Machine learning and data analysis.

So today I’m going to perform some basic arithmetic operations on the arrays such as add, sub, multiply, and divide, and also will make use of vstack, hstack, dstack functions to joining and stacking the arrays.

Addition can be done using the add function or by using the operator as shown in the code below, just make sure to keep the dimensions the same otherwise it will throw a value error for not being of the same values.

np_add

Subtraction can also be done by following the same as we did with the add function and operator:

np_sub

For Multiply:

multiply() function is being used.

np_mul

For Division: 

divide() function is being used.

np_divide

To get the remainder and mod of division, we can do so by simply using their functions which are remainder() and mod():

np_remnp_mod

Power() function: 

np_pow

Hence, these were the basic arithmetic operations to get started with the NumPy package of python. But it also includes Joining and stacking of similar arrays which make Numpy even more useful.

JOINING

Using concatenate() function.

np_joining

Append() function, to define the axis of your array:

np_vstack

STACKING

For stacking arrays, we use three functions which are:

vstack() for vertically stacking in the arrays along the rows.

np_vstack1

hstack() for horizontally stacking in the arrays along with the columns.

np_hstack

dstack() for in-depth stacking by the new third axis:

np_dstack

That’s it for now, it was more of a code than explanation. But I hope it helps because of its simplicity.

I might add my Jupyter notebook in pdf form to the resources in the upcoming posts.

Thank you for the read.