Analytics is the Key to Digital Transformation. And the power of what it can do has only become better with each passing year — we are just at the top of the iceberg as we will see an exponential rise in date. I have headed Analytics for a US Healthcare Firm and worked with multiple analytics teams/initiatives throughout different companies. There have been many mistakes and issues that I have faced. Here I have jam-packed several years of learning into these bite-sized snippets.

5 Common Mistakes in Analytics Initiatives

1. Rigid Company Structure & Placement of Analytics Function: Successful implementation of Insights needs cross-company collaboration. Sometimes functions work in silos & relevant information is not passed forward due to different priorities. Placement of where the Analytics is under is an important decision. It can be under tech, business, or as separate LOB-shared services. Each has its pros/cons, would be another article.

2. Data Availability & Quality: There have been times people have fought over the data that was presented in meetings because it showed something negatively or less desired than they expected. Before putting out any number, I always put the calculation methodology and data source. Analytics professionals must do due diligence on data cleaning, that piece of data would be used for decision making that would impact customers and business.

3. Not Investing in Quality Talent: Many companies don’t invest in Quality Talent. And the cost for this talent is also increasing. The requirements are more than the supply — data consumption has increased considerably post COVID-19 with more digital data. Many ex-colleagues and friends have been getting offers from US, UK & Germany.

4. Resilience: Checking the mountain to climb and finally climbing it is a different ball game. The same goes for business insights. The analytics team would need to work closely with the business teams to lead it to closure. That might take patience in case of long strategic wins. There was a huge endeavor to make one product & service line profitable. That took two years to achieve the same! Insights>Action>Outcomes.

5. Not Focusing on the Business Problem: Understanding the business context and the problem is crucial! Many times we would be focusing on the data more than the problem — I had done that earlier in my earlier phase as well. Now understanding the business and its problem is the first thing I focus upon.

There is a tremendous amount of data for hospitals, pharmaceuticals, mobile devices, healthcare apps, connected devices (IoT), past health records. There is data lying everywhere! These disparate data sets and other factors like lifestyle behaviors, socioeconomic, genomics & others would need to be combined and looked at holistically. These would help improve healthcare outcomes, reduce physician and administrative burdens and go to a value-based CARE system. In addition, data integrations would need to be created across multiple workflows, which are currently lying across different systems cohesively on a near real-time basis.

1. Predictive Healthcare: Predictive Analytics in healthcare can help detect early signs of patient deterioration, identify at-risk patients in their homes to prevent hospital readmissions, and prevent avoidable downtime of medical equipment. For example, it would help decrease fatalities in the neonatal ward where rapid monitoring is required.
2. Clinical Trials for Pharmaceuticals: Clinical Trials are a data-centric business where you speak about efficacy through delicately measured data sets at specified intervals. The scope for improvisation is always there. The requirements to bring more effective, less resource-intensive, and quicker drugs to market is urgent.
3. Historical Patient Data at Fingertip: Hospitals and Physicians would be enabled to provide better CARE for patients if they have all past healthcare data readily available in one place. For example, important data might get lost or forgotten in emergencies like allergies to a specific drug during a cardiac arrest.
4. IoT & Home Measurements: IOT & Health measurement devices will become so advanced that they will tell us whether or what kind of treatment is required post-checking our healthcare datasets and comparing it to previous ones.
5. Health Insurance & Claims — Use machine learning from the claims data sets and summarize patient files to use for new policy servicing especially in the medical underwriting stage. The current process majorly is a manually driven bureaucratic process. Underwriters manually scan through hundreds of pages of documents to come to a final decision. This could be done through NLP to make it a tech-driven human-supported decision where AI would highlight the case with a suggested course of action. The underwriter would take the case summary and give the final approval or modify it as required. This would help have better TAT for policy to the client, reduced claims, and decreased costs leading to better.

Customer Experience is the maker or breaker when the customer is spoilt for choice in many varied forms. It is the end-to-end experience starting from Account Opening to Doing Daily transactions through the Mobile App or Website. It’s all connected; any mistake could go viral where everyone is connected with even fewer degrees of separation.

You would have seen an emailer, SMS, or within the app where the company had asked you to rate how likely you are to recommend them out of 10. Something like below.

Net Promoter Score : 0–6 are termed as Detractors, 7–8 as Passives, and 9–10 as Promoters. NPS =% of Promoters — % of Detractors. The higher the score, the better it is.

The question is how to analyze these responses & the comments post getting them. These are 5 key steps — Each one of them could be one whole topic in itself.

1. Categorization: Categorize the open-ended responses into specific line items for your company like Stability, Reliability, Usability, Support, and so on. Like, say 30% of your customers didn’t get a proper response from the contact center.
2. Dashboards: Create Meaningful dashboards from both the responses against demographics, profile age with the company, and much more whatever it is possible Tableau, Power BI, Data Studio — whichever suite you are using. Like, say Gen Z from Smaller Towns is not able to identify with your new branding campaign.
3. RCA: Share results with your internal team and do the RCA together- map the responses and brainstorm with your internal teams to find out the RCA. It could be something like a communication gap between the agent and the customer.
4. Use AI/ML for sure — Use AI/ML to do all the above easily from the rich text coming in like prioritizing, categorizing the comments, and much more. This is a way to do it automatically, consistently, and in a scalable manner.
5. Trend Analysis — Measuring the trends across time and keeping the spotlight on. If you don’t measure and track, then you never get better.

Every generation has its own set of unique customer needs and user behavior, which is a reflection of global events, social ethos and macro-economic events of the time they grew up in. Gen Z, or people born between 1995–2012, are similarly very distinct and remarkable. Globally, as well as in India, this is a generation which grew up around the internet, had access to mobile phones early in their lives and had a lot more choices than the previous generations. In the Indian context, this is the generation which truly grew up in a post 1990 liberalization, privatization and globalization era. The Indian Gen-Zs are a hyper-connected, mobile-first lot. More than half of India’s population or 600 million people are in this age category- companies across the board will have to adapt themselves to this set of customers or risk perishing in obscurity. This is the crowd that would make or break brands in the coming years and has already made several startups unicorns — case in point Tik-Tok that took the world by storm.

So, what do Gen-Zs want and expect from products and services? In our conversations with young adults, we have understood that there are three basic motivations for this set of customers- Community, Convenience and an ever-elusive “wow” factor. These customers have large personal digital networks, share their opinion on products with their friends and strangers alike and have potential to become very vocal advocates.

While Indian Financial Services space has always been competitive, Google Pay seems to have gotten a lot of things on the wishlist of Gen-Z right.

Meet Namish Singh (24 years old), a Credit Risk Analyst from Mumbai. On being asked about the financial products frequently used by him and his friends ,” Mostly use Google Pay and BHIM UPI like my friends. When asked about why Google Pay is preferred with his friends — he stated that GPAY has a good user friendly interface with rewards coupled with convenience.

Same for Utsab(25 years), a brand strategist from Kolkata who stated that it’s so simple that even his dad likes it.

The recall for Gpay is extremely high amongst Gen-Z. Ronak(22 years) an interior designer from Surat, Gujarat thought of Gpay when asked about the first financial product that comes to mind.

It appears that Google Pay hit the bullseye with the Gen Z generation with the quick rewards, campaigns like Diwali stamps, which keeps their customers engaged — every possible transaction is a chance to either win a scratch card or collect stamp. It is this anticipation of a possible reward, which led to India becoming the fastest growing market for Google Pay in the world. Google Pay went from processing 17 Million UPI Transactions per month to 918 million UPI Transactions per month within a 2 year time frame from August 17–19 , an astonishing 54 fold increase.

While companies can get everything right while designing their products, one factor which is often overlooked is communication. Indians Gen-Zs spend a lot of time online sharing videos, experiences, or just making new friends. We are seeing a whole new generation of Instagram and YouTube influencers, who passionately pitch products to their followers. In fact, the social media spend in India is growing at a CAGR of 6% and is expected to reach INR 8050 Cr (USD 1.1Bn). While some brands like Cred, Angel Broking (Sep, 2020 IPO) and even traditional banks like HDFC and SBI have active social media presence- the pressure on these players is mounting to “interact” with this set of customers who live their lives online, spending an average of 4 hours everyday.

The fight for the share of mind and wallet of Indian Gen-Z is going to be intense and will definitely push the sector forward for positive change . The world is watching with bated breath on India and it’s fin-tech warriors and players.

Manpreet Singh is an ISB Alumnus with Big4 consulting experience in the Financial Technology & Services sector and Himen Doley is an IIT-IIM alumnus leading Digital Initiatives for Angel Broking. Both are millennials from emerging Tier 2 cities.

Battle for Indian Gen-Z| The fin-tech Story (Part 1) was originally published in samosafinance on Medium, where people are continuing the conversation by highlighting and responding to this story.