Increased Mutual Matches | Driving Online Dating User Joy
Generated an 81% increase in mutual matches via design thinking, ideating and shipping an MVP, and measuring impact on KPIs.
Male users didn’t receive responses from their sent invitations so they didn’t experience the joy of dating which led to drop-offs. This reflected in key user metrics: low male retention, low order conversion, and low customer satisfaction (CSAT) scores.
We followed a design-thinking process and conducted in-depth qualitative user interviews. This unveiled an interesting finding: female users were serious, thoughtful, and deliberated a lot before tapping the "accept" button. Our question was how could we "casualize" their response behavior?
"Casualizing" took us to the dating space where a user can say yes (swipe right) and no (swipe left) to a profile. We envisioned an MVP, to be done only on the Android app (the maximum user engagement platform). We planned to use the existing APIs (using service-oriented architecture, SOA) and integrated them on a new UI with a Tinder-like swipe functionality (implemented via an existing component from the Android native library). I then sent it production within two sprints (ten working days) and achieved ground-breaking success.
• Saw an 81% lift in responses.
• Increased order conversion by 5.3%.
• Jumped 4.8% in CSAT score.
Increase the Number of Repeat Purchases for an eCommerce Store
Deployed human-centered design-thinking principles to increase repeat-purchases on an eCommerce store by 3.5%.
Repeat purchases constituted a small portion of eCommerce revenue, and we wanted to increase overall user retention and referrals.
1. Implemented a quantitative analysis of the cohorts of users buying products from us.
2. Obtainedualitative understanding of users—why/why not are they buying from us via in-depth interviews.
1. The primary reason people bought from us was the social component of our offerings where each product sold was supporting a certain cause the users cared about.
2. We didn't really "thank" them or made them feel proud about this, across the whole user journey
1. Mapped the E2E user journey (pre-purchase, during purchase, and post-purchase).
2. Began with the post-purchase user journey (thank you/order confirmation page and email).
3. Included inspiring content and videos to communicate the real-life stories of the end-beneficiaries, and the impact they made just by purchasing from us.
• Saw a +3.5% increase in repeat purchases on our eCommerce shop.
1. The hypothesis found by the human-centered design thinking approach was valid.
2. Replicate this "feeling of pride" across the other touchpoints of the user journey.
Increasing Monetization | Free to Premium Conversion
Oversaw a 5.4% increase in conversion from free to premium, without discounting or losing ARPO (average revenue per order).
To make free users realize what they were missing out on by not being a premium user.
1. One of the most useful premium features was the ability to chat with your matches.
2. Free users received a small notification (not very discoverable) on receiving an incoming chat.
1. If a free user receives a chat, we pop open a chat window in front of the user. And when they want to reply, they see a message to upgrade.
2. Essentially, what we did was to bring out one of our most useful premium features and place it within the user journey contextually.
1. Took an MVP approach and chose to start with a web platform because it was easier to implement, and also we could use the existing UI components from the library.
2. And we started with male receivers because females generally receive a lot of incoming attention, and it might feel overwhelming for them, and worsen their user experience.
3. We released it as an A/B test for new and existing users.
1. It led to a 5.4% increase in conversion for that cohort without discounting or losing ARPO (average revenue per order).
2. The next step was to extrapolate this to other cohorts and platforms.
Artificially Intelligent (AI) Predictive Dialer for the Telesales Team
Created a predictive dialer to automate manual, redundant, and monotonous tasks—causing a 92% increase in team productivity.
We were only reaching a 35% connectivity with consumers through telecalls. This was caused because of the disposal of nonconnected calls and scheduling them for later.
We implemented an AI predictive dialer that will direct only connected calls to the team. Working with the data science and engineering team, I also created a requirements document along with all the use cases. We then developed an algorithm that learned based on various user demographics and activity parameters.
• Ensure an agent is free before the call to ensure a good customer experience.
• A customer shouldn’t receive more than "x" number of calls in a day and should be called within the official time limits unless otherwise specified.
• Dialer will learn the answering patterns of users based on their demography and activity.
• Built reports and dashboards so that leads could monitor productivity and efficiency.
• Launched it as an A/B test with a few advisors and compared them against similar performing advisors in the other set.
• Increased telesales team productivity by 92%.
• Reduced the sales team size by half and added skills for cross-department usefulness.
Improving Visitor to Order Conversion
Increased the activation ratio by 3.7% via a new mode of payment; also led vendor identification and API integration.
The internet banking payment mode had a lower activation ratio. The primary reason was the bank’s site's UX. The bank login page loaded in a web view on mobile devices, rather than having a mobile-friendly UI. This reflected in key user metrics: low activation ratio and order conversion.
We studied user behavior focusing on why users used net banking rather than credit/debit cards. We researched best practices followed for net banking by other merchants.
1. Net banking was primarily used by parents who perceived a security risk with credit card usage.
2. Best practices indicated the use of a vendor which converts a bank’s login pages into mobile-friendly pages
3. Also, a small user segment was ready to use UPI (unified payments interface) as an alternative mode of payment to net banking, which had a higher activation ratio.
1. I spearheaded the integration of a new mode of payment: UPI with minimal handshakes and an intuitive UX.
2. I also led the integration of JUSPAY to make bank pages mobile-friendly and ensured database security.
• Increased activation ratio by 9.2%.
• Improved order conversion by 3.7%.
Increased the Conversion of Visitor to Donation
Increased online donations by 8%—using visitor-to-donation conversion— by removing friction via design thinking principles.
1. There were significant drop-offs across various stages of the donations flow.
2. Calculated the opportunity size to be a 15% increase (based on industry standards).
1. Identified the stages with maximum opportunity, and focussed on them.
2. Interviewed users in both buckets (successful and un-successful donations)
3. Followed design thinking approach of research, synthesis, ideation, convergence, prototyping, validation, execution & launch.
4. Identified the two largest drop-off points, and made the required UI changes.
1. The visitor-to-donation conversion increased by 8%.
2. The way forward was to replicate the same approach for the next two as well.
Machine Learning-based Discounting Algorithm
Increased revenue realization (top line) by 1.5% using differential discounting based on demography and activity parameters.
I conducted data and business analysis to arrive at the insight—users usually have varied reasons for using the platform. Hence, rather than offering flat rates/discounts, we could base discounts on user intent, i.e., a more serious user gets a lower discount and vice versa.
We needed to figure out the demographics and activity parameters that defined intent. For instance, an older female, living away from her home town who was also logging-in frequently would have higher intent (in the Indian context).
We created a machine learning algorithm along with the data science team considering numerous such parameters, which was then integrated with the existing discounting platform (keeping all existing logics uninterrupted). Finally, we tested it with a small cohort of users, which reduced and increased discounts for users based on the algorithm. We only tested it on new users, because existing ones already have a preconceived notion towards a price point, and this could bias our results.
We saw a 1.3% lift in revenue (a highly significant impact, which is generally achieved by multiple projects combined).
Business Pitch and Wireframe Creation | Online Wedding Planning Platform
Pitched the business plan to the executive team, including the value proposition, cost needed, and revenue projections.
While working with an online matchmaking platform, we wanted to diversify as a business into an online wedding planning platform. We were tasked to come up with a business model canvas.
It took a mix of primary and secondary research with consumers and vendors.
• Conducted secondary research around every category involved (apparel, venue, logistics, makeup, etc), with industry sizes and trends.
• Studied consumer behaviors and how the current wedding planning platforms were being used.
• Conducted in-depth two hour-long interviews by visiting consumers ar their homes, and understanding what matters to them, and the problems they were facing.
• Successfully launched an MVP to judge if users are interested to explore. Built initial wireframes to envision user flows.
We pitched our learnings to the executive team and secured the funding to launch a platform starting with one category at a time.