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.

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

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.

How brands are leveraging AI for customer acquisition?

As the fashion industry rapidly adapts to new technology, brands are leveraging AI and ML to reach out to existing customers and attract new customers. Leading fashion brand Tommy Hilfiger turned to AI to improve its designs when it tied up with IBM and Fashion Institute of Technology (FIT) in 2019. Their project Reimagine Retail, was directed towards using AI to map out future industry trends and also in improving the design process. Popular sports fashion brand Nike uses AI to keep the customer happy by personalizing customer experiences and improving engagement. As per Nike 60 % people wear the wrong shoe size and to fix this problem, the folks at Nike created the Nike Fit tool and integrated it with the Nike app where customers will be able to find their right size and even see how the product looks on their feet. This is done using a combination of technologies including Computer vision, Data science, Augmented reality, Recommendation Engines, and Machine Learning. 

Nike also has access to a wide range of customer data from its supply chain, enterprise data, and the app ecosystem. In 2017, the company had announced Nike Direct, a direct-to-consumer sales channel. To attract customers using product recommendations, the company has acquired four data science and analytics firms since 2018. Each of these acquisitions contribute to the ultimate goal of taking a step towards better customer experience. While ‘Invertex’ brought powerful 3d scanning technology that creates accurate models of one’s anatomy to Nike, ‘Zodiac’ projects revenue streams at the individual-customer level as it applies predictive behavioral models and customer analytics to target data, and Celect helps in optimizing inventory by predicting demand for the future by applying ML to the current data. So, by integrating varied tech, Nike uses the huge amount of data at its disposal to create customized recommendations and create demand. If financial figures are anything to go by, Nike Direct sales have shot up from USD 11.7 billion in 2019 to USD 16.3 billion in 2021.

Moving on to the luxury segment, Dior too, has used AI to launch its chatbot or beauty assistant called Dior insider. It chats with customers on the Facebook messengers and helps the customer with what they are looking for.

A new study from Juniper Research found that the global spending by retailers on AI services will reach $12 billion by 2023, up from an estimated $3.6 billion in 2019. Juniper expects over 325,000 retailers to adopt AI technology over the period. The future of the fashion industry clubbed with AI looks promising for sure

Role of AI in OTT platforms

That 98% match to your preferred movie types or TV serial genres on your favourite OTT platform is the handiwork of AI. With on-demand streaming fetching more and more users globally, it is only natural for media companies to look to AI to enhance customer experience. Let us look at how they are doing it.

AI in recommendations

AI-backed recommendation engines gather, collate, and extract user data before filtering it out in the form of recommendations.OTT platforms are some of the biggest users of such engines to push out the best content to their users. The more personalized the content is the more likely it is for the customer to remain loyal to the platform by watching more and more. Netflix Recommendation Engine (NRE) has great accuracy as it  filters content on the basis of an individual’s user profile using a mix of algorithms filtering over 3,000 titles at a go using 1,300 recommendation clusters. It is no wonder then that the market size of the recommendation engines is poised to reach $12.03 billion by 2025.

Role of ads and Metadata

As users of OTT platforms you may have noticed videos on these sites have information about the visuals, the emotions, a synopsis, and the genre of the show or film such as horror, romance, thriller etc. This is the metadata on which AI can assess the scenes in the show or movie to generate teasers automatically. Meanwhile, using the same metadata, advertisers find the ideal spots where they can place the products.

Demand forecasting

User behaviour can also be predicted by OTTs using demand forecasting. They can analyze which genres will resonate well with particular audiences. Demand forecasting can thus help OTT platforms find genres for fresh content, determine what is a good time/month/period of the season to release new content, the preferred languages and so on. Some of the most watched shows on Netflix for example are from other languages such as Squid Game in Korean, Money Heist in Spanish and so on.

As the entertainment industry moves towards wider use of AI, it is the early adapters that benefit the most. Intense competition also warrants constant innovation of AI tech to boost efficiency and keep up with the trend of upward growth.

If you are interested in the metrics of OTT and how AI is driving this craze, write to us at contactus@infiniteanalytics.com and do sign up for our weekly newsletter

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Meta Releases New Self-Supervised Algorithm data2vec

Meta AI, known earlier as Facebook AI, has launched what it calls as the “first high-performance self-supervised [machine learning] algorithm” called data2vec. data2vec is aimed at achieving self-supervised learning beyond specific use cases. Hitherto, self-supervised models were such that they could solve only a specific problem. A self-supervised language model could not solve a visual problem and a self-supervised visual model could not solve an audio problem. How data2vec will be different is that it will use the same algorithm to solve distinct problems , and move a step forward towards generalized artificial intelligence. A single model can now see, read, and listen, and comprehend rules across all these inputs. According to Meta AI, ‘through self-supervised learning, machines are able to learn about the world just by observing it and then figuring out the structure of images, speech or text.’ This approach is more effective for machines as they can now complete tasks of greater complexity like understanding the text for more and more spoken languages. With data2vec, Meta AI claims to be getting ‘closer to building machines that learn about different aspects of the world around them without having to rely on labeled data.’ We are nearing a future where AI could be able to use videos, audio recording, and articles to learn about even complicated subjects such as a game of chess or soccer, thus making AI more adaptable. Meta AI also claims that data2vec ‘outperformed the previous best single-purpose algorithms for computer vision and speech and it is competitive on NLP tasks’. The main idea behind data2vec is to enable machines to perform unfamiliar tasks as well. This will also bring computers a step closer to a world wherein computers will rely on less and less labeled data to complete their tasks.

The new algorithm works on a teacher network and a student network. The teacher network computes tasks from text, audio, or images and then the same is masked to repeat the process for a student network, which is entasked to predict representations of the full input data, while being given just a part of it. The prediction comes from internal representations of the input data, hence removing the dependence on a single modality.

One can access the open source code here.

If you are looking forward to machines with less reliability on labeled data or want to talk about data2vec contact us at contactus@infinteanalytics.com and subscribe to our newsletter

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