LFT Android App IMPACT

To check out The Problem and The Solution, visit the earlier post by CLICKING HERE

Once again, quoting Richard Feynman from my earlier post,

Now, if you went through the earlier post, you must have realized the app’s importance, so, now let’s look at the results. I’m very pleased to tell you that the app has not only fulfilled its core motives but also shattered some records and has contributed in unprecedented growth rate at Facebook (the only online medium where LFT is very active).

To find the app’s impact, I did the analysis of LFT’s Facebook page by collecting its data for the last 4 years (since the data before that wasn’t available on Facebook) to see how effective was the App in connecting our new members with us. The results I’m sharing in the form of graphs to make the point more clear.

Case 1:
It shows the percentage of likes on our Facebook page (during ADAR campaign) that increased in a year wrt to its previous year.

new2_LFT Peri-Adar PhaseFig1: LFT Android App Impact (Case 1)

PerecentInc = { AvgFbLikeInc(yr) – AvgFbLikeInc(yr) } / AvgFbLikeInc(yr-1)
>AvgFbLikeInc = { Total likes (during ADAR Campaign) } / { no. of days (in ADAR Campaign) }
>yr = {2014,2013,2012}

And clearly this year has seen unprecedented growth by contributing with a humongous percentage increment of 378.71% (2014 vs 2013) over the meager 46.12% (2013 vs 2012) and 16.44% (2012 vs 2011).

NOTE (Shortcoming of Case1): Now, although the previous graph speaks volume about itself but it gives the entire credit to the app ONLY for the huge success, and discards the impact of the recent work in each year. eg: let’s say what if one gives an argument like in the previous years there wasn’t much users on our page, our page wasn’t so active, and now it is very much active and itself drawing huge crowd by itself. To put those arguments to rest, I further dig the data of over 4 years and made the calculations more complicated to try to reduce the impact of the app but the answer still favored the app’s impact, yet again smashing all records. So, let’s look at the Case 2 which gives a clearer view about the impact.

Case 2:
I took the avg. of daily incremented likes on FB Page in pre-ADAR phase and during ADAR phase for all the 4 years. Put them through different calculations and this is what I came up with. The app just increased the level of communication with our newly registered members and helped us in getting connected with them without much fuss.

new1_PreAndPeriFig2: LFT Android App Impact (Case 2)

  • Detailed Explanation of the methodology used:

As I stated earlier that Fig1 won’t highlight the exact impact. So, the second approach was taken, and thereby overcoming the shortcomings of the first approach.


  1. One month’s data was collected around ADAR-phase for each year (2011,12,13,14). After finding similarity in the pattern, the dataset was concentrated to :
    >last 4 weeks before ADAR, let’s say Dataset1
    >3 or 5 days during ADAR (duration of ADAR in each year), let’s say Dataset2

  2. For each of the datasets, the no. of daily likes on the Fb page was taken into account and their average was taken to get a better perspective and remove any unwanted fluctuations. So, two new variables were determined :
    Avg1(yr) = { Total likes (on Dataset1) } / { no. of days (in Dataset1) }
    Avg2(yr) = { Total likes (on Dataset2) } / { no. of days (in Dataset2) }
    where yr denotes the respective year eg: yr belongs to this set of years {2011,2012,2013,2014}
    Also, Avg2 > Avg1 for each year

  3. For each year, the difference betwen the two variables, namely Avg1(yr) and Avg2(yr) was noted, which gave us DiffAvgLikes(yr). So,
    DiffAvgLikes(yr) = { Avg2(yr) – Avg1(yr) }
    Thereby, taking into consideration the impact of most recent activities on Fb Page, eg: each year there were more followers on the page, there were more people who were interacting with the page during the period concerned and other parameters wrt to previous years

  4. Now, the relative percentage increment between consecutive years (RPI(x,y)) was found out, to give a final reflection on the impact made by the app. The formula used was:
    RPI(x,y) = { { DiffAvgLikes(x) – DiffAvgLikes(y) } / { DiffAvgLikes(y) } } x 100
    where x belongs to {2014,2013,2012} and y= x – 1
    The graph was plotted with the RPI(x,y) on y-axis and the pair of consecutive years on x-axis for better representation

NOTE: I’ve tried to keep the nomenclature for different variables simple for better understanding.

  • Result:

As we can clearly see from Fig2, there is drastic and humongous change for the year 2014 even after nullifying and taking into account the recent activities on Fb Page for each year. Thus, we can safely reach to the conclusion that, as expected, this simple appe helped in solving some of our major problems by not only establishing a better communication with our new members but also helping in retaining them. This app has indeed broken the barricades which stood between the new members we made during ADAR and then getting them connected with us.

Now, we have even a stronger community and member-base which we can polish and provide a helping hand not in their lives by enhancing their skills but also bring a much larger and better change in this world, provided we stick to these members and get them involved in various activities/campaigns.

We now have a much larger work-force and thus the responsibilities have also increased to look after all of them and utilize their talent properly and polish their talent as well.

They say, the solution to our big problems lies in our simple actions. 🙂


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