Case Study Team: Silicon Hysteresis BITS Pilani Team members Puney Chawla Samyak Sahu Vinayak Khandelwal X X Novus, IIM Visakhapatnam You’ve joined as a PM at Apna. Please install the app from Play Store . It is highly recommended that you experience Apna as a new user. For this case study, please keep the Android app only in scope. Apna is a professional networking platform for blue and grey-collar workers. Apna.co is witnessing on average over 1 million jobs being posted on its platform, ranging from a beautician, carpenter to a graphic designer. It’s currently in 14 cities and expanding into four new cen tres in India per month to cater to a spike in demand from small businesses in non-metros. Read more at: https://bit.ly/3H22gQb As a PM, the task at hand is to improve DAU/MAU and Average Time Spent on the App for Apna. 1. Recommend 2 Product solutions, one each for DAU/MAU and Average Time Spent. a. Avoid UI tweaks or marketing solutions like notification, email, etc. Avoid suggesting UX revamp b. For each recommendation, provide your hypothesis as to why each solution will work. You may provide industry reference, numbers, theorems, etc. - anything to validate your recommendation c. Avoid wasting slides by giving generic intro to Apna. Keep your solution to 10 slides only in PDF format only d. Don’t give more than 2 solutions. Go deep rather than giving a list of features 2. Create neat wireframes for all of them (Balsamiq, Figma preferred) 3. Align the right success metrics. Avoid mentioning HEART metrics, AARRR just for the sake of it at the end. Align the specific metric for each feature. The Problem | The User | Prioritization | Feature Recommendation | Metrics | Risks The Problem Background Target Users P The job seeker-side platform for apna experiences active users during the course of finding a jobT P Users sign-up, search for jobs, fix interviews and drop- off as soon as getting a job.X P Due to this behaviour, user retention is being affectedT P Users are not able to leverage the community features adequately. P Blue-collar workforce living in major citieb P People with non-technical niche skills. Boost the general engagement & retention on apna’s Android application, by improving th® T DAU/MAU (DM) → stickiness T Average Time Spent (ATS) on app Primary Objective Problem Relevance P Because of the hesitant and technophobic tier-2/3 job and career growth market, engagement is as important as acquisition while bringing it online.X P The demographic that apna caters has little to no competition in the upskilling and networking solutionsT P Refining the mentioned metrics in the objective will increase the relevance of the platform for all stakeholders involved. Underlying Assumptions P O ptimizing for one metric will inevitably affect the other metric with a new feature. H ence, what improvement is correlated to the improvement of what metric can only truly be figured out with repeated experimentationT P Active user refers to a user who opens the app. similar corollary is taken for DAU and MA U ( MAU > DAU always, so the DAU/MAU <1 always * P Definition of AT S ( Average Time S pent ): Average [ (A - B) *N] ; where A = session start time, B = session end time, N = total number of sessions in the given day The Problem | | Prioritization | Feature Recommendation | Metrics | Risks The User The User User Personas Primary Stakeholders Job Seeker Employer Primary Stakeholders Job Seeker Employer Other Stakeholders n Freelanceri n Community Manageri n Mentors experienced in niche domaini n Govt. Exam preparing students The Problem | The User | | Feature Recommendation | Metrics | Risks Prioritization Pain points & Prioritization 18.4 70 1.5 70% 4 4.5 60 0.5 60% 4 42.7 80 2 80% 3 15 50 1.5 60% 3 24.5 70 1 70% 2 Resume and Visiting Card Reach Description Impact Confidence Effort Final Score (out of every 100 users on apna) (0.5 - 2 in steps of 0.5) (in %) (in terms of person- months) apnaGroups v2.0 Job-focussed learning Learning cards Referrals + + + - Features User can make a resume on the platform; uniformity of information; vernacular resume becomes possible Re g ular pokin g related to terminolo g ies of the current j ob or any speci fi c j obs / interests. A learning segment added to the offerings , containing bite- sized learning and re w ard based q uizzes N et w orkin g and upskillin g events , to be held w eekly in the G roups section on the app. A referral feature on selected j obs , through w hich users can suggest people w ho are appropriate for the role offered. Pain points Proposed Solutions & Prioritization Resumes for diverse vernacular not present on any platfor ~ The demo g raphy that apna caters is not open to e x plore secondary avenues for career g ro w th (such as upskillin gr Users w ant credible resources to upskill themselves and pro g ress in their career x O n platforms like Y ouTube , users face too many irrelevant choices Users aren ’ t incentivi z ed to make connections , w hich also comes in the w ay of apna as a social net w ork
Users cannot levera g e their informal net w orks e ff ectively. (analysis paralysis r Resume creation via apn Ó N et w orkin g events held on apna G roup x J ob-focussed learnin g solution x L earnin g card x Referral system for apna ’ s user s As per the RICE Scores, L ear ning C ar d s and R e f erra l s were implemented on their wireframes and their success metrics were calculated. The Problem | The User | Prioritization | | Metrics | Risks Feature Recommendation Feature 1: apnaGyaan Learning Cards Gyaan topic Gyaan Gyaan This week’s lucky prize!!! Is hafte ke challenges complete krein aur jeetein SONY wireless headphones Spoken English (3/125 completed) Daily English Practice (37/650 completed) Personal Finance (12/80 completed) Basic Mathematics (13/80 completed) Present streak Medals Earned 29 12 days topic Gyaan Gyaan 3/ 12 5 Spoken English Take up a C hallenge A shneer f rom Shahdara won P - tron porta b le car charging system in last week’s challenge Done Did not understand ? A sk all y our dou b ts on the apna groups New wor d Backlog ऐसा सं चित का|र्यजिसे अभी पू yर्णjकिया जाना है । Example: बै कलॉग कल काम पर आने से पहले मैं अपना बै कलॉग पू रा कर द ू ं गा ± W eek 3 (17/01 - 23/01 ¬ ± D ifficult y: M edium Spoken English C halleng e 3 o f 5 P ositi v el y to Negati v el y T omorrow to Yesterda y Your to T hei r Ev er y one to A ll “ Everyone has to bring a photocopy of their 2nd dose of COVID-19 vaccination certificate positively by tomorrow and submit it to your warehouse manager” W hat correction needs to b e made here ? Th e b o tt om ri bb on at t h e l anding page w i ll h a v e an additiona l l earning segment named ‘ Gyaan ’ Th e l earning segment w i ll b e structured l i k e a dec k o f cards , scro ll- a bl e 1 at a time A dai l y strea k counter and meda l s earned w i ll a l so b e disp l ayed Th e cards h a v e a de fi nition /v ideo / audio e x p l ainer o f 1 genera l topic Th e e x p l ainer w i ll b e f o ll o w ed b y a 2-5 q uestion q ui z, wh ic h w i ll test t h e l earning o f t h e person A learning segment added to the offerings, which is a culmination of existing medal based quizzes/challenges and new bite-sized content for users to upskill regularly at their convenience. The Problem | The User | Prioritization | | Metrics | Risks Feature Recommendation Feature 1: apnaGyaan Learning Cards topic Gyaan Gyaan Ashneer from Shahdara won P-tron portable car charging system in last week’s challenge Back to apnaGyaan Shabaash!!! card ko scratch krein aur apna reward paayein This week’s lucky prize!!! SONY Wireless Earphones Baaki Challenges dekhein Upon passing the quiz succesfully, the user will be entitled to various rewards Miscellaneous Pointers 6, The metric that is tar g e tt e d b y this f eat u re is * , The to p ics in a p na Gy aan are base d on the u ser ’ s skills an d j ob selection d one w hile re g isterin g on a pp , S ince the y are acti v e base d on cla p s , I n flu encers on the p la tf orm can be reache d o u t to make content f or car d0 , A s re w ar d s , u sers co u l d be g i v en o u t D ream 11 co up ons , W inzo balances , M X Pla y er s u bscri p tion etc These re w ar d s w ill be in line w ith the tar g et d emo g ra p h y , Users w ill also be n udg e d to ask their learnin g car d s relate d d o u bts in a p na G ro up0 , A banner o f the name o f the u ser w ho w on a p rize last w eek is sho w n , to f acilitate social p roo f. ( ) DAU/MAU -> Stickiness S ocial Proo f Theor y Feature Relevance D e v elo p ment & A ccom p lishment is the internal d ri v e o f makin g p ro g ress , d e v elo p in g skills , an d e v ent u all y o v ercomin g challen g es m Primer : The car d s look like a (f amiliar ) d eck o f p la y in g car d s to p rime the u sers f or a “p la y- to - learn ” e xp erience ( ] A fl ashcar d- like Q n A f ormat allo w s u s to im p lement p ro g ressi v e d isclos u re to re du ce co g niti v e loa d , s u ch as s p en d in g 5 min u tes to com p lete the a d a y’ s car d Z W hen u sers are acti v el y p artici p atin g in the p rocess o f ans w erin g qu estions , the y are more likel y to stick alon g as com p are d to d ail y p ost remin d ers ma d e on g ro up s W Ou r h yp othesis : lon g er the streak , more the u sers w ill sta y alon g, g reater the im p act on stickiness (Yu- kai C ho u’ s g ami fi cation b u il d Primin g an d the S cience behin d O nboar d in g - Min d the Pro du ct Users are more likel y to take action w hen the e ff ort is small S treaks tri gg er [ in v estment loo p s ] The Problem | The User | Prioritization | | Metrics | Risks Feature Recommendation Feature 2: Leverage your experience A referral feature on selected jobs, through which users can suggest people who are appropriate for the role offered. Refer Referral Enter details of candidate These will be directly sent to the HR Your relation to candidate Submit A ‘Refer’ button is placed on the existing jobs post page. The window will have a short form, where the user will have to enter personal, experience and educational details for the person who is being referred. topic Gyaan Video Editor ₹ 26,000/- per month Seekify Technologies Role: Salary: Ba ck to a pn aJ o b s Sh abaa sh !!! A ap k e re f erral k i j o b la g g ayi Vin a y a k K h a ndel wa l P lace d at: Vinayak and Seekify thank you for your referral. Scratch the card below to recieve your reward. Th a nk yo u c a rd R eferr a l R efer more people U pon selection of the referred candidate, user will be rewarded a monetary or coupon reward. The Problem | The User | Prioritization | | Metrics | Risks Feature Recommendation Miscellaneous Pointers sn The metric that is targetted by this feature is en This feature will be initially released for users on the app with more experience, followed by anyone who gets a job from the app.b Nn Depending upon whether the referred person has a smartphone or not, they will be notified via text message/whatsapp message regarding their job application, followed by a CTA to download apnaV >n The employers will be given option while posting jobs to enable referrals. This would ensure the efficacy of referrals for only certain jobsV <n Employer or Apna will disburse rewards (both monetary and offers) on the basis of how niche the job profile is, how much is the salary etcV 9n The monetary reward will be a voucher, while other rewards will include coupons and discounts (similar to that of feature-1T 5n This process flow would be advertised/strengthened by sharing a bot generated post on relevant apnaGroups, and also by notifying it on the blue activity ribbon on apnaJobs page. Average Time Spentu Feature Relevance ÛV This feature provides a tech-based structure for the traditional referral practices in the unorganized marketV ̈V Apna would be able to enter into the feature-phone using population, which will rely on smartphone users to refer themV V This feature provides a lucrative way to leverage strong informal networks that many users have.b ÅV This will act as an effective way to increase Average Time Spent by the user, as they are spending thoughtful time filling out the referral form. It doubles as a good acquisition strategy for new users as wellV V In addition to the increase in time spent by user-1 to refer, the referred candidate also signs up and spends time to explore the app (random reward generating curiosity ªV Citing a research paper , hiring through referrals gives better results for employers in terms of work output and lower attrition ratesV V Advert for referrals on community groups sparks up an element of envy for some users, and it also acts as a healthy community- building activity. (Why to use referrals) Feature 2: Leverage your experience The Problem | The User | Prioritization | Feature Recommendation | | Risks Metrics Learning Cards Referrals Metrics Dashboard Stakeholders involved: Learners (job seekers), Content Creators Stakeholders involved: Referrer user , Invitee, hiring organization Target Metric: DAU/MAU -> Stickiness Daily Active Users x 100 Monthly Active Users Target Metric: Average Time Spent Total time spent on app x 100 Total # of active users ôõ Card Completions % :- 2. Click-thro u gh rate on apna Gy aan noti fi cations 3. Mean Streak length :- 4. F eat u re u sage % :- Driver Metrics ôõ Referral conversion rate :- 2. Total Referrals recieved per w eek 3. % of u sers completing referral form 5. F eat u re u sage % :- 4. Click-thro u gh Rate of ‘ Refer ’ b utt on Driver Metrics # of cards completed S um( S treak length) # of users opening apna G yaan # of users w ho refer # of jobs bagged through referrals in a day 3 0 day 3 0 day 3 0 day 3 0 day # of users w ho open apna G yaan # of users w ho use apna G yaan Total # of active users Total # of active users Total # of jobs bagged ( ( ( ( ( ) ) ) ) ) I t ’ s be tt er if u sers u se learning cards dail y for 5 - 10 min u tes instead of 1 - 2 ho u rs over a longer period ( w eek or month ) [ Toothbr u sh test ] The Problem | The User | Prioritization | Feature Recommendation | Metrics | Risks Learning Cards Referrals Risks \J The learning modules designed might take a lot of time to design, hence harming feasibility with respect to implementation timeJ 9J Explaining intricate concepts (e.g. derivatives in finance) could be difficult to do in bite-sized formatsJ 3J Users might resort to the pre-existing groups for gaining knowledge through external links, which decreases the usability of the feature (metric success could still be ensured!B /J Rewards offered could lead to expense-related problems. \J People might not find the rewards that they get lucrative enough to break their frictionJ 9J People might find it difficult to switch over to a new method of referrals, something which happens over traditional means in the present daysJ 3J Rewarding mechanism can make customer retention cost higher with monetization expenditureJ /J The feature could come in the way of acquiring new users, if the community benefits offered by apna are dwarfed by the end goal of people getting a job