reaches high-intent target audiences by understanding user behaviour has been constantly getting better at helping clients reach out to not only their target audiences but ‘high-intent’ target audiences or those who are most likely to convert into their customers. This is done by analyzing and understanding different datasets – Visitation Data, User Behaviours, Purchases, and Digital Footprints.


Visitation – As the name implies, this dataset gives access to which specific types of places are the people visiting. Are they going to automobile showrooms, theaters, apparel stores, salons, or beaches repetitively? How much time do they spend in these respective places and what kind of activities do they take up while there? Also, what time of the day are they visiting these places? In the case of branded products and services, their brand affinities also become a part of the datasets. Once we know the type of places they have been visiting, we are able to predict their behaviour on the basis of that. For example, in a case study around the coffee shops in Mumbai to determine the best places to open coffee shops here on the basis of the prosperity index, the number of people visiting these locations, and their visit timings. The map of Mumbai was broken into blocks of different colours, each indicating the prosperity index of the area – the darker the colour of the block the more prosperous the area is. All locations of all coffee stores in Mumbai were also plotted on it including Starbucks, CCD, Barista, etc and overlaid them on the prosperity index. The map also gives the revenue potential of these stores. It also indicates the number of people visiting that store in a time frame and which stores are clearly doing well, which are average, and which aren’t performing well. One could also figure out the number of people picking up coffee as a takeaway and those who were staying in the coffee shop to drink. The map also has circles indicating the colleges in these areas and the enrollment number of each of these colleges. In some of these colleges with a lot of enrollments, there is no coffee chain. This data could help those looking to set up stores or kiosks. Coffee chains can use all this data to determine the location of their next coffee shop.


Digital footprints – When people browse the internet in various ways and using various tools, they leave behind digital footprints which are immensely valuable to marketers and companies selling goods and services. The digital footprints provide insights into what people are searching online, and the kind of apps they download and use – gaming, shopping, luxury, grocery etc. All these provide insights into their online buying behaviours. Post-pandemic digital sales have skyrocketed and these insights help in shaping the consumer strategy and reaching out to the TA with the most intent of buying their wares. Additionally, one can also see how social media reflects their behaviour.


Purchases – The next dataset which is of big help is how the purchase behaviours of customers are like – Are they impulsive buyers or do they research and buy or do they shop for discounts before picking a product. What segments are they spending on? If they are spending on branded things or does branded retail not matter to them? Additionally, a lot of intel is also gathered about the customers by working with credit card companies & Point-of-Sale cos, as their spending patterns can be picked up from here – cash sales, credit cards, debit cards, e-wallets, etc.


User behaviors – Combining the purchase patterns of customers with other indicators like the place they live, their spending capacity, where they work, what are their travel routes every day and their mode of transport; if they own bikes or cars; what is the number of members in their family; the prosperity index of the place they live in – all these help arrive at a holistic view of the consumer.


All the above datasets helps optimize their marketing spending as they are able to cut out all the fluff while trying to reach out to people. They are now armed with the knowledge which helps them reach out to those with the intent to buy. These are also people who match their brand’s profile and are most likely to convert into customers.

Case Study: How Sherlock AI helped Tata Motors acquire consumers and reduce CAC

Following up on our previous post, Tata Motors, a USD 4 billion leading global auto company and an Infinite Analytics client, in its diverse portfolio, includes an extensive range of cars, SUVs, trucks, buses, defense vehicles etc.  They saw a whopping 75% reduction in Customer acquisition cost (CAC) upon using Sherlock AI. The client started using Sherlock AI a tool developed by Infinite Analytics with the mandate of addressing a few issues and challenges which were:

The client started using Sherlock AI with the mandate of addressing a few issues and challenges which were:

  • To find out people who have visited their showrooms or that of their competitors (a challenge faced by most automobile manufacturers).

  • Looking for consumers who are currently looking to buy a vehicle, and also finding the different consumer signals which help identify the intention of purchasing the said vehicle.

  • They were also looking for dataset acquisition. An algorithmic setup to blend data layers is a computationally intensive task and needs specialized and seasoned data scientists to execute it successfully. 

How Sherlock AI rose to occasion and provided just the solutions that were needed 

We started by mapping the layout of all car showrooms and consumers visiting the dealerships were tracked as well. We then combined the mapping with IA’s proprietary analysis to help in understanding the locations where the ***device has shopped***, where the individual lives, and other metrics such as prosperity level (insert prosperity blog**), transactional data, automobile dealership visitation, search trends on digital platforms (Google, Car ggregators), etc. to create classifiers based on the person’s interest in purchasing a vehicle. After doing so, we ran precisely targeted campaigns to the people who were mostly to convert (as customers of Tata Motors) on Social media platforms such as Facebook, Instagram, and Google. These activities led to some amazing results for Tata Motors as they witnessed a whopping 75% reduction in Customer acquisition cost (CAC). 

These kinds of results have been made possible as we at Infinite Analytics are First-party data independent, 100% GDPR & CCPA Compliant, and track the most comprehensive real and digital signals from 40+ datasets and 350 million consumers. 

As a testimonial, Rajan Amba, VP, Sales & Marketing, Tata Motors PV, said, “Infinite Analytics has been a partner to Tata Motors, in the last one year. Their platform, Sherlock AI is helping us scale our network expansion, both in the passenger vehicle business. It also delivered what it promised – a reduction in CAC and a new perspective in customer acquisition that no one in the market provided. They are essential to our growth ambitions and we look forward to deepening our relationship with them.”

How TATA Motors used AI to reduce CAC by 75%

Did you know, players in the Automotive sector can use AI for improvised sales by connecting sales to marketing data sets which were used earlier? AI can be deployed to automate lead-management and related activities, in predicting which products are very likely to be sold to customers for now and in future. It can also be applied for dynamic pricing in the sales process.

Tata Motors Limited, India’s second largest car manufacturer, was looking to lower the cost of acquiring a customer (CAC). By CAC, they meant CAC in actuals and not the Cost Per Lead. The company wanted to lower the cost of acquiring a customer who would be purchasing the vehicle in their showroom. To achieve their goals, they began using Infinite Analytics’ AI platform, Sherlock AI in 2021.

Since last year, we, at Infinite Analytics, started working with Tata Motors with the objective of reducing the CAC, as well as work with their PV, EV and CV categories, in order to play a role in making them the largest manufacturer in India. 

In the course of this work, at the end of the third month, the CAC reduced by  75%, and Tata Motors acquired the capability to expand their dealer/showroom network in places of a potential high demand/growth. Earlier, each location for network expansion took over 5-6 months. Using Sherlock, they were able to reduce it to a mere few weeks. They were thus able to expand the network exponentially, using the platform’s algorithms (which algorithms).

Complementing our work, usage of social media also helped vastly. The company gained a new perspective in customer acquisition that no one in the market provided. The insights were crucial for their growth plans, the results for which have already started to show.

How AI Has Helped Food Delivery Cos Reach Greater Heights

Food delivery companies all over the world are using AI to reach greater heights. The same is true about apps such as Zomato, Dunzo, Borzo etc in India. If you are in India and remember the Swiggy app of yore, you may also remember how it was just another option when other apps didn’t work or delivery partners were not available. Today, Swiggy boasts of an order volume that has grown over 200%. Applying AI to its workings, the company generates terabytes of data week after week. The delights of using the app today are such that it has converted dedicated users of rival apps to being their own loyal customers. How 

did they do it? In their own words, this was achieved by real-time, micro-optimization of dynamic demand-supply, over and over many times during the day. With this they were able to provide the urban consumers with hitherto unexperienced levels of convenience.  

The How

Swiggy achieved its goals by creating a three-way hyper-local marketplace wherein the company matched the demands of the consumers with supply from vendors i.e. restaurants, cafes, and stores as well as deliver executives (mostly people looking for quick money and side jobs). They used AI across this marketplace to deliver a delightfully seamless customer experience to achieve unparalleled growth and also drive operational efficiency. Infact, they are now so dependent on AI for their growth that they consider it impossible to go back to the time wherein they relied just on human intelligence to achieve their goals.

Behaviour of the consumer

Collection of consumer data helps Swiggy get hold of the behavioral aspect of things. By knowing their customer’s behaviour through this data they can deliver personalized experiences. This is achieved using Catalog Intelligence with which ML models help enrich the Swiggy catalog with meta-data. For example Classifying foods on offer as vegetarian, egg, or non-vegetarian and even categorizing similar products under sections for example: salads, soups, main course, rice, breads, and dessert. Use of Customer Intelligence helps the company in customer segmentation on the basis of their affordability (derived using their past buying behaviour) and also log customer churn i.e. when a customer stopped using the app or service. Customized and relevant content is shown to the customers  using catalog intelligence and customer intelligence. You may notice the app showing you your previous order and prompting you to re-order the same. This happens as the app may have noticed you ordering the same food from a particular place again and again. This even includes showing you restaurants nearby you depending on your current location and not your usual home or office location. Their Live Order Tracking feature is one of the most popular features among customers. They even get to know of delays and when the delivery partner is at their doorstep.

Vendor X Food Delivery

To tap in the right vendors Swiggy uses AI for time-series based demand prediction models which help the restaurants plan ahead to meet the demand of the customers. The Company also uses ML to cut financial losses by identifying and preventing abuse.

With such advanced uses that are being improvised upon as you read this blog, the food delivery landscape is bound to change even more in the times to come.

Online Grocery Retailers Ride The AI Wave for Customer Acquisition

As Machine Learning has become more accessible, more retailers are leaning towards adopting it for customer acquisition. The same is also true about grocery retailers who are trying to strengthen their relationship with customers using AI and ML. AI uses personalization and other tools to provide better experiences to customers.

Hyper personalization: Long and never-ending product lists are fast getting replaced by personalized offers to entice customers into buying more. Online retailers are bidding to meet customer expectations in unique ways by making the customer experience better. Not only products but product recommendations need to be in tandem with the requirements of the customers, even before they know they want a particular product. AI steps in here by offering experiences customized to individual consumers rather than a discount or offer available to all. This is done on the basis of what products an individual is most likely to buy. It is a win-win as the customer gets gratification, the retailer strengthens the bond with customers, and also records an increase in sales.

ML for personalization: For online retail AI relies on ML algorithms trained with behavioral data to understand customer requirements in a better way. E-commerce players can use this data for personalized product recommendations to customers and present customers with a user-oriented shopping experience. This goes over and above just selling a product.

Diderot effect for AI in online retail: Have you noticed how online e-commerce stores gently nudge you towards a container to go with the new pasta packet you just bought or cooking oil spray to go with the potatoes you just purchased? This is what is referred to as the Diderot effect which is defined as follows – obtaining a new possession often creates a spiral of consumption which leads you to acquire more new things. It is basically an impulse buy on the part of the customer. Though this kind of selling is well-known to marketers in the physical retail space, in e-commerce, such behaviour can be brought about by personalization which is done by analyzing clicks and purchase history or searches of the users. These are then used to make not only relevant but near-apt products which are almost certainly bought by them.

Besides other tools for personalization towards the goal of customer acquisition include email marketing customized for each customer, welcome texts which are personalized, e-shop navigation according to customer visit history, chatbots etc.

Why Hoteliers and Travel Entrepreneurs Must Automate Dynamic Pricing

Hoteliers typically resort to dynamic pricing (changing the price of rooms as per changing market conditions) twice or thrice a year. Hilton has been practising dynamic pricing since as far back as 2004. This was perhaps done manually. But with the advent of machine learning applications in the game, dynamic pricing uses predictive analytics to add variables (upcoming holidays, lifting of Covid-led lockdowns, mid-week strikes leading to office holidays etc.) in forecasting the best price.

Case in point is a predictive analytics tool developed by Starwood Hotels in 2015 which took into account a plethora of factors to arrive at the best price for a point in time. These variables ranged from weather conditions, competitive pricing data, occupancy data, booking patterns of users, and many other variables. This system can either be fully automated or help from human operators can be taken to adjust rates manually if required. When hotels get hold of the customer data as well as market data, they can get direct bookings and earn more profits than they would with third parties (such as booking portals) involved.

Among the first to adopt dynamic pricing, Hilton did it when access to technology was far less as it stands today. By using the correct revenue management software, the hotel made the shift to dynamic pricing in an absolute manner and also offered it as part of its loyalty program. The end product is flexibility, cost savings, and good revenue gains.

Matildas, a boutique hotel in Chile too is using a revenue management system with a price intelligence engine. They got better prices, more revenue, and savings on labour costs as a result of this implementation.

Hotelmize uses AI for their Room Mapping to track dynamic prices for a given room across multiple suppliers. Enter the gamechanger AI which will predict dynamic prices for a particular room, and they can now accurately know the approximate duration for which the price will remain lowest.

Flight fare forecasting: New mobile apps are helping customers find cheaper flights which they find using price forecasting applications. Bagging the best deal on flights and hotels has become that easy nowadays. Being automated, these tools scan the market and alert the users when the best deals are available. Websites such as Skyscanner and Hopper provide such services by helping customers to book cheap flights with the help of analytics. When travel agency websites add similar tools they can take a quantum leap in customer acquisition, making them book more trips, and rake in much needed revenue.

How Starbucks Is Winning Customers Using Big Data

Through its loyal and rewards program, Starbucks could gather a huge amount of data, which it then used to its advantage to drive more sales from existing customers. With its  30,000 plus stores globally, it rakes in above 100 million transactions each week. Jon Francis, Starbucks’ Senior Vice President of enterprise analytics, data science, research data, and analytics, along with his team of data scientists, set out to improve business performance by using data collected through its mobile apps which have over 17M members. There are three areas where the company was able to improvise using the data it collected.

Personalization of customer experiences as well as promotions

The launch of the Starbucks rewards program via the mobile app led to a massive upswing. It could get their recorded data and using it they could tap into what their customers think and how they behave. They could analyze the customers’ purchase patterns too. With the app, Starbucks could find which coffee their customers get, from which locations and at what times of the day or the week. They even used a cloud-based artificial intelligence engine, Digital flywheel program, which is a recommendation system. So when a customer visits a new Starbucks location, the store identifies the customer through their phone and gives the Starbucks person making their coffee in the preferred or most repeated order of that customer. On the basis of their past behaviour i.e. the purchase history, Starbucks could now even suggest newly launched products this customer would like. Additionally unique rewards and discounts could be offered too. Going a step ahead, Starbucks even collects data on a customer’s ordering behaviour vis-a-vis the weather. Offering a discount to a customer on a hot chocolate on a rainy day is how they offer highly personalized experiences.

Introducing new products

Starbucks keeps launching new products every now and then. Now it uses its collected data to decide what kind of products should be offered. A good example of this is when offering their product out of grocery stores, they knew that 43% of customers skip sugar in tea. So they introduced unsweetened iced tea to drive sales. Similarly if data shows people like decaf coffee they would introduce a product keeping that in mind.


Location is everything from a retail PoV. Starbucks uses location-based analytics powered by Atlas, a mapping and business intelligence tool developed by Esri, to arrive at the most strategic  locations for its new stores. From getting to know the customer demographics i.e. spending power, income, population, etc., Starbucks decides where to open their stores for maxim profitability. 

Using Travel Customer’s Data To Help Extend Better Services

A travel consumer’s journey begins from the time they dream of going on a holiday. Their dream is ‘augmented’ when the hospitality sector promotes their brands on social media with alluring photographs and great copy or accompanying text which makes one want to go on this holiday at all costs. The next stage would be the ‘planning’ stage whereby they sit down and chalk out details of their upcoming holiday. Here, to push out your brand you’d need an appealing website, content with long-tail keywords,  engaging content on your travel website, investment in pay-per-click advertisements etc. All these will tell the travel customer that you are the answer they are looking for. Booking their travel and stay would be the next step, and it is a no-brainer that all customers look for easy booking processes and not the prolonged or complicated ones. And lastly, the travel customer experiences the services you have offered.

As per Amadeus, the travel tech company, 90 % of the US travellers who have a smartphone share travel-related experiences and pictures on social media and even review the services they use. TripAdvisor has 390 million unique visitors and 435 million reviews. There is a lot of data on demographics and psychometrics of user behaviour collected by travel companies but what is needed is for them to analyze this wealth of data to their advantage to be able to target users effectively. The voluminous data gathered by travel com[panies can be easily assessed with the help of AI based software. A great example of this has been set by the Dorchester Collection hotel headquartered in London. They use AI to analyze information such as customer reviews, feedback surveys, and online polls using its AI platform. They were able to understand that there was a lack of customer loyalty towards them and on the basis of this data they could come up with better strategies to tackle customers. Sarovar Hotels & Resorts also use cloud-based QMS for data analysis of guest feedback on various operational areas.

Sentiment Analysis Through Social Media

Sentiment Analysis comes handy in identifying and resolving customer grievances. Social Media comes handy in analyzing the mood or sentiment of the customer. They often use it to express their happiness or complaints about their travel and related experiences. If a customer expresses dissatisfaction about a cancelled flight on Social Media sites like twitter, an AI based tool can help the concerned company to get their act together whether it is in the form of a better communication system, discounts, free accomodation before the next flight or however they choose to remedy the situation at hand for a better customer experience. Sentiment Analysis contains a number of steps including data retrieval, data extraction and selection, data pre-processing, feature extraction, topic detection, and data mining.

With such powerful tools at hand, the travel industry is poised to flourish.

Customer Acquisition in Gaming: How AI is Helpful

The gaming industry is all about the number of ‘active users’ without whom the companies would come crumbling down. To prevent a mobile game from being a flop show, players are needed. So how does one acquire players for the game? A well-thought user acquisition strategy is the key to success.

Customer acquisition for mobile gamings can be achieved through two ways: Paid or Organic. Paid customer acquisition simply means converting users with the help of paid ads on various social media platforms as well as advertisement networks. These typically involve app installation ads that are prompted on the screens of users to insta;l the game. This strategy is quite effective in mobile ames as backed by data it helps gaming companies reach high-quality users. This also makes your game reach a wider audience. Reliant on data, campaign management is increasingly becoming automated and more efficient. Organic customer acquisition on the other hand involves discoverability of the game so that users can install it without the help of paid advertising. To do this one needs app store optimization, posting regularly on your own social media to increase awareness and make a community around your game.

Use Automation

Automating the customer acquisition campaigns got a long way in defining the success of your game. The Automated App Ads (AAA) option of Facebook demystifies the entire process of creating ads, especially if you are not a pro at media buying.  Backed by machine learning, not much input is required in automated campaigns as compared to standard app install campaigns. They are also beneficial as they help in testing multiple creative combinations making it possible to reach out to more users.

Combining two strategies

A combination of paid and organic customer acquisition is ideal. When a company begins its ad campaign they opt for paid campaigns as this encourages more downloads and once that is achieved, the game shows up higher in search results, increasing the discoverability, post which the company opts for organic campaigns. Using a variety of channels helps your ad and thus reach out to more and more people.

Setting KPIs

Key Performance Indicators or KPIs are truly important in measuring the success of customer acquisition campaigns. The mobile game owner would definitely want to know the number of installs along with Cost Per Install (CPI), Cost per Acquisition (CPA), and Customer Acquisition Cost (CAC). In-app metrics too help in knowing a lot about the customers one has acquired. The end goal of customer acquisition is also acquiring quality customers who not only play the game on a regular basis but also spend on apps via purchases. 

How AI helps Travel Marketers in Customer Acquisition

AI enables personalized marketing tailored to the needs of individual customers. Helping marketers in the travel industry overcome their challenges of catering to personalized product recommendations as well as information that is tailored to their needs. There is a plethora of  opportunities which marketers can explore with the sheer amount of data they have such as  geo-location data, demographic data, behavioral data et al. The obstacles are seen in the form of high customer acquisition costs and low conversion rates, diminishing brand loyalty from the customer end, and high rate of booking abandonment, which can go as high as 80%.

Getting across the apt content to customers at the right time is quintessential. Travel marketers can use a variety of tools for customer acquisition such as:

AI-based personalization

AI backed customization engines predict future behaviour of customers on the basis of customer eyeball data and their behaviour. Customized recommendations at each and every step are helpful to the brands.

Homepage Reccomendations: On the basis of the customer’s search history, they are shown information that is most relevant to them when they are on the homepage of a travel website. Example: A customer searching for hotel stay in Milan will be shown the best deals on all Milan hotels. This increases their chances of booking and such personalized home page recommendations can get the company relatively higher CTRs.

Product Recommendations: Product Recommendations help on the basis of the interest of the customer in higher conversions, especially if the recommendations are contextual. Sightseeing packages, honeymoon packages, etc. can be shown.

Category page reordering: This is a simple case of showing the preferred products of the user first. If a customer has a tendency to look at home stays rather than hotels, then the travel marketeers can recommend home stays at top resulting in good conversion rates again.

Exit popups: Customers often browse a lot before picking the end product or service. If a customer tries to leave your website a customized exit popup may just help them stay or make them think og coming back to your website.

Besides, personalized emails through tailored recommendations as well as push notifications may lead to conversions. To add to these methodologies, rule-based personalization or in simple terms, designing a whole website experience on the basis of the customer’s location, type of device used etc. can help in greater conversions. The advances in AI and ML have brought in a stream of opportunities for travel marketeers. Using AI for customer acquisition is definitely the way forward.