Data-First Strategy for an AI-First Future (Part-One)

Data Analytics can be needed for multiple business reasons. However, the core reason is to make better business decisions. Types of data-analytics:

  1. Descriptive
  2. Diagnostics
  3. Predictive
  4. Prescriptive

Few more questions as a data engineer, that you must be aware of (source: AWS Cloud)

  1. How quickly do you need analytic results — in real-time, in seconds, or is an hour a more appropriate timeframe?
  2. How much value will these analytics provide your organization, and what budget constraints exist?
  3. How large is the data, and what is its growth rate?
  4. How is the data structured?
  5. What integration capabilities do the producers and consumers have?
  6. How much latency is acceptable between the producers and consumers
  7. What is the cost of downtime, or how available and durable does the solution need to be?
  8. Is the analytic workload consistent, or elastic?

The AWS Big Data Blog contains many different types of readings, from technical, business, big data, and data analytics. Highly recommend it

Now- Let’s understand the consumer side Data

Businesses that have enough data on their consumer tastes and preferences will be able to personalize their offerings and hence out win the competition that does not have a personalized marketing strategy in place. The marketing engine of successful companies will move from the target segment selling to personalize selling.

As billions of consumers use smartphones, search engines, social media, e-commerce, ride-hailing, and fintech platforms, they leave footprints of data. Companies can use the data-trail to create products that appeal to them and also offer a personalized marketing campaign


Best-In Class Personalization Models are Emerging Across Industries leading to GAME-CHANGING consumer experience

Social Feed on TikTok or Facebook- What you see on the social feed is not what is out there, what you see is what keeps you on the social platforms.

Personalized permission marketing in E-commerce: Amazon is great at personalized marketing. Try searching for a product on Amazon, and if you want to understand its promotion strategy, add it to your wish cart. Over the next few days, your browser, emails will have an innocent bombardment of marketing messages, promo on offers on the same or similar products from Amazon. Amazon built its entire Amazon Lending business on the merchant data, and Square Capital is also a classic example of leveraging POS (point of Sale). China e-commerce giants are leveraging consumer data to push the right deals to users and the partner merchants

Mobile Application for personalization (Restaurants/QSR ): Starbucks Rewards program gathers a vast amount of data on customer spending and preferences. Starbucks can personalize the experience for every customer based on their unique preferences and spending habits. The Starbucks app creates a user experience that is both engaging and innovative. Leveraging the geolocation feature, a user can see where the nearest Starbucks locations. The consumer can see the menu at each location and order an item that can be ready upon arrival. The digital engagement with coffee-lovers has paid tremendous dividends for the company, and according to numerous estimates, the total number of Starbucks app users has crossed the~22 million number as of 2020.

Customization of apparel, shoes, and fashion wear (FASHION-WEAR): Nike offers its consumers the opportunity to design their shoes through its websites through the campaign- “Nike by You.” Your personal Nike design arrives in 2–5 weeks. There are some major bag retailers too that let you design your luxury bags.

OTT and Music Streaming (ENTERTAINMENT) — Netflix and Spotify recommends movies and music based on the user watching, listening, and browsing habits

Wearables Data through the watch (HEALTHCARE DATA)- Apple’s iWatch has the daily habits data for over 50–70 million-plus users depending upon the number of watches sold and being used to date. The first I-watch was launched in 2015 and by 2020 it has FUNCTIONALITIES like ECG (heart rate), Blood Oxygen Monitoring, and exercise routines.

Personalized Beauty Care (PERSONAL CARE)- Sephora an LVMH owned brand, is famous for the personalization it offers to consumers across online and offline channels. It is known for its personalized emails, Beauty Insider loyalty program, and in-store technology, such as Color IQ, . The color IQ scans customers’ skin to help them determine the right foundations, concealers, and lipsticks for their skin color. Shoppers’ Beauty Insider profiles are well-INTEGRATED across, its mobile PLATFORM, and can be accessed in store too. This redefines and personalizes consumers’ shopping experiences, no matter which channel they use to enter into Sephora’s Ecosystem.

Big Retailers Personalization (RETAILER): Are you sending mail blasts or curated offers to your retail and loyalty program users? Target knows when a family is expecting a child and hence all the offers for the family Target ID owners become maternity-centric offers.

Personal View on Money (FINTECH): Payment Wallets and Challenger Banks are trying their best to present ‘ONE PERSONALIZED VIEW’ of money by leveraging OPEN BANKING tailwinds.

Personalized and Data-Based Premiums (Insuretech)- Healthy insurance buyers who have to pay the same premium as unhealthy users in the same age group know what pain point is being referred here. Motor insurance should also be based on the driver’s past records, rather than just the age of the car. This will ensure that value is distributed fairly.

ALL B2B and B2C MODEL will eventually become B2ME models too in order to stay RELEVANT

Future Transforma on Roadmap: Top Imperatives for the CXO’s

  1. Build an efficient marketing technology stack to support customer personalization and Omni-channel strategy
  2. Conduct a maturity audit of data analytics engines and team.
  3. How personalized are your marketing campaigns and customer interactions? Mobile apps improve user’s experience, engagement, and personalization.
  4. Integrating online and online experiences can make campaigns and user interactions more engaging.


Sephora, and Starbucks were all non-tech companies, but now they own world-class online platforms too

Key Questions:

  1. How do you Spot, Collect, and Sort Consumer, Partner, Operations Data?
  2. How do you Connect, and Activate Data pipelines within your organization?
  3. Do you have Data-Management Systems in place to give you ready to deploy insights for -Product and Marketing Strategy. How do you churn your data lake to draw actionable insights on a real- me basis?
  4. Do you have a PERSONALIZATION strategy in place? It varies by industry and the core offer
  5. Who OWNS the data across your consumer platforms and Marketing Channel MIX?

Sidhartha Sharma

Digital and Platform Strategist, Ecosystem Expert. 15+ years in consulting

views are personal



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Sidhartha Sharma- Future of Tech, Digital & Data

Sidhartha Sharma- Future of Tech, Digital & Data


~16yrs Consulting- McKinsey & BCG-Digital Strategy, Ecosystems & Ventures | Start-Up Mentor | Platforms | Digital-First | Author & TEDx Speaker. Views Personal