Example Reflection – Visualization

Challenge:

I am part of digital presales team and we got a problem statement from one of our prospect customers as to  why their mobile app was not used much and if we could recommend some outlier ideas for customer  retention.

So, our initial hypothesis had 2 users in mind with the below key challenges – First to find out why existing  users are not using the app much, why some users are logging in but not performing any action (like add to  cart, product reservation etc.) etc. Second to find out why new users are installing the app and not using the  features, why users are not creating their logins/user profile, where exactly their journey is breaking and why  etc.

With all these grey areas in our mind and initial thoughts we set off on solving this challenging problem  statement for our customer.

Selection:

Industry recognizes the importance of insights, but it falls short on driving decisions based on data. And many  companies don’t know how to harness the data they collect.

Hence we decided to use data visualization as a tool which would help us identify patterns in the usage  behavior, arrive at qualitative data points through which we could drive insights and suggest  recommendations based on that.

For instance, with the help of some online tools like appbot.com we first analyzed the sentiment data which  included the type of reviews like positive, negative, neutral and the category of issues like design, customer  support etc. This data helped us to identify the key area of concern. For example: Design had very poor  review ratings of 12%. So, we knew that users were not happy with the user experience of the app.

Similarly, we started gathering data around each aspect of the app and started drawing our inferences and  recommendations

Application:

In the discovery phase, we gathered data about the app from app stores which help us to identify the main  top 4 areas of concern like add to cart, product availability, customer support and user experience.

We also validated the hypothesis from our customer journey with the offering from their competitors (what  different they are doing and how different). This also helped us to explain them where exactly they stand and  why customers prefer another app compared to theirs.

We next gathered app usage data from their analytics tool to exactly understand where their journeys are  breaking, what percentage of users are not proceeding beyond a particular step in a section (for example  product portfolio, redemption) etc. Another instance what we observed is people are browsing products in  app but are finally making the purchase at the store. So, it was clear that the purchase experience is not  enticing to the customer in the app.

With this data we could broadly classify the areas where immediate attention is required like content,  behavior which if fixed can act as good sales enablers.

Insight:

With the gathered data, we now could draw insights and categorize our findings into 3 broad areas -

•  Voice of Consumer – Tapping into behavioral patterns of consumers to personalize journeys based  on data collected from app store

•  Voice of Data - Analyzing actual app performance data and driving insights to identify potential  journey opportunities with data collected from their analytics tool

•  Voice of Industry - Understanding the latest trends in the industry to identify and connect new  mediums based on competitor analysis done

Finally based on our inferences above we could come up with themes which helped us define potential  outlier ideas which would help them re-imagine their app by increasing the traffic and usage. One such  theme was product learning mechanism where in the user. For example: Engaging the user with monetary  benefits (coupon notifications) at critical touch points to push towards sales or enable location-based push  notifications and target the customer to purchase.

Approach:

I would use this tool whenever I get such problem statements from customers but might prioritize the need  of the customer and then dig deep into the tools/analytics to retrieve data. This will help us focus on main  crux of the problem.

Also, if the need is to cater to all the problems then a mind map also can be used to draw patterns and  provide recommendations based on that.


Example Reflection – Visualization Challenge : I am part of digital presales team and we got a problem statement from one of our prospect customers as to why their mobile app was not used much and if we could recommend some outlier ideas for customer retention . So , our initial hypothesis had 2 users in mind with the below key challenges – First to find out why existing users are not using the app much , why some users are logging in but not performing any action ( like add to cart , product reservation etc . ) etc . Second to find out why new users are installing the app and not using the features , why users are not creating their logins / user profile , where exactly their journey is breaking and why etc . With all these grey areas in our mind and initial thoughts we set off on solving this challenging problem statement for our customer . Selection : Industry recognizes the importance of insights , but it falls short on driving decisions based on data . And many companies do n’t know how to harness the data they collect . Hence we decided to use data visualization as a tool which would help us identify patterns in the usage behavior , arrive at qualitative data points through which we could drive insights and suggest recommendations based on that . For instance , with the help of some online tools like appbot.com we first analyzed the sentiment data which included the type of reviews like positive , negative , neutral and the category of issues like design , customer support etc . This data helped us to identify the key area of concern . For example : Design had very poor review ratings of 12 % . So , we knew that users were not happy with the user experience of the app . Similarly , we started gathering data around each aspect of the app and started drawing our inferences and recommendations Application : In the discovery phase , we gathered data about the app from app stores which help us to identify the main top 4 areas of concern like add to cart , product availability , customer support and user experience . We also validated the hypothesis from our customer journey with the offering from their competitors ( what different they are doing and how different ) . This also helped us to explain them where exactly they stand and why customers prefer another app compared to theirs . We next gathered app usage data from their analytics tool to exactly understand where their journeys are breaking , what percentage of users are not proceeding beyond a particular step in a section ( for example product portfolio , redemption ) etc . Another instance what we observed is people are browsing products in app but are finally making the purchase at the store . So , it was clear that the purchase experience is not enticing to the customer in the app . With this data we could broadly classify the areas where immediate attention is required like content , behavior which if fixed can act as good sales enablers . Insight : With the gathered data , we now could draw insights and categorize our findings into 3 broad areas - • Voice of Consumer – Tapping into behavioral patterns of consumers to personalize journeys based on data collected from app store • Voice of Data - Analyzing actual app performance data and driving insights to identify potential journey opportunities with data collected from their analytics tool • Voice of Industry - Understanding the latest trends in the industry to identify and connect new mediums based on competitor analysis done Finally based on our inferences above we could come up with themes which helped us define potential outlier ideas which would help them re - imagine their app by increasing the traffic and usage . One such theme was product learning mechanism where in the user . For example : Engaging the user with monetary benefits ( coupon notifications ) at critical touch points to push towards sales or enable location - based push notifications and target the customer to purchase . Approach : I would use this tool whenever I get such problem statements from customers but might prioritize the need of the customer and then dig deep into the tools / analytics to retrieve data . This will help us focus on main crux of the problem . Also , if the need is to cater to all the problems then a mind map also can be used to draw patterns and provide recommendations based on that .