Why Hoteliers and Travel Entrepreneurs Must Automate Dynamic Pricing

Dynamic PricingHoteliers 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. See what all different parameters Sherlock AI can help you in creating dynamic pricing and also with customer acquisition.

Dynamic Pricing and Hotels:

In the hotel industry, these pricing refers to the continual, real-time tweaking of room prices based on algorithms. These algorithms take into account the fluctuations in data of consumer demands, competitor pricing, seasonality, current occupancy, and other external factors to increase hotel revenue.

While such strategies have been a common practice in the travel and tourism business, it is now gaining momentum in the hotel industry for automating revenue management. When hoteliers switch to hotel revenue management software, dynamic pricing keeps a regular check on the market demand and supply of rooms and accordingly changes the pricing strategy in real-time to increase conversion rates overnight. 

Dynamic pricing strategies employ artificial intelligence to monitor every aspect including different segments of your target audience, their booking patterns, the length of their stay, their preferences in terms of rooms and amenities, and the segments of your hotel that attract maximum guests while keeping a constant watch on your competitor’s pricing structure. Dynamic pricing efficiently adapts your average room rates as per the changing preferences of customers, special occasions and seasonal peaks to attract maximum bookings and increase occupancy. With static room prices, hotels sell rooms at the same rates year round, and their revenue solely depends on the number of rooms occupied. This significantly increases the pressure to increase conversion rates during offseason.

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 a non-static 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.

Starbucks: 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.

Starbucks

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.  

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.

Want to use Sherlock AI in acquiring higher intent consumers? Drive up app installs, app engagements and user acquisition using Sherlock AI. See how it works hereWant to explore how Sherlock AI can help your business? Write to us contactus@infiniteanalytics.com

Subscribe to our newsletter for regular updates and interesting insights

Using Travel Customer’s Data To Help Extend Better Services

A travel customer’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. 

Travel, Social Media & Customer Engagement

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. Want to use Sherlock AI in acquiring higher intent consumers? Drive up app installs, app engagements and user acquisition using Sherlock AI. See how it works here

Travel

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 companies 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. The capacity for artificial intelligence to perform tasks that have traditionally required human cognitive function has made it especially useful for those in the travel industry, because deploying AI can save businesses time and money, while potentially eliminating human error and allowing tasks to be performed quickly, at any time of the day. 

Most hotels and resorts rely heavily on delivering excellent customer service to build their reputation and AI technology can assist with this in a wide variety of different ways. For example, artificial intelligence can be used to improve personalization, tailor recommendations and guarantee fast response times, even in the absence of staff. The key advantage of artificial intelligence in this particular field is its ability to sort through huge amounts of data quickly and accurately, where the equivalent for humans would take significantly more time and potentially contain more errors. 

The Dorchester Collection hotel, for instance, has used AI to sort through customer feedback from surveys, reviews and online polls, in order to build a clearer picture of current opinion, in real-time.

 

With such powerful tools at hand, the travel industry is poised to flourish. Want to explore how Sherlock AI can help your business? Write to us contactus@infiniteanalytics.com

Subscribe to our newsletter for regular updates and interesting insights

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.

How advertisers can get around Facebook Ads Aggregated Event Measurement

The Apple’s iOS 14 updates have made advertisers using events for conversion optimization or call-to-action such as the sales of tickets to a Coke Studio Live concert), to switch to the Aggregated Event Measurement with a caveat that one can have only eight events (such as a concert or the release date of a particular product like Vanilla Coke) per domain (a number which was previously unlimited). Events are used to create custom audiences on the basis of actions users have taken on an app or a website. These audiences can then be targeted with ads relevant to them when they visit Facebook. Let us see how we can continue to use Facebook ads well even after the iOS 14 privacy update.

Remove irrelevant events: If you have events which are unused in current ads when you see the Results or Optimization Events columns in Facebook you can choose to remove or skip these events from the new Aggregated Events Measurement setup. Doing so will save you a lot of time and optimize your ads.

Combining events: When you don’t have more event spaces left try and combine events which are similar in nature. For example, two different product launches which would have the same call-to-action can be clubbed. Content events too, can be clubbed as one such as blog views, guide views, video views etc. You could also club events for seasonal actions in one place. While on your Aggregated Events, when you use custom conversions with the combined actions, only one event is created, instead of a lot of events with limited slots.

Using what is there: Though there are only eight spots for optimization and conversion tracking, one can still make any number of events. If the events you have set up are not in the top eight for Aggregated Measurement, you can create audiences to use for ad set targeting (in other words a group of customers shall be targeted for a particular event). When you create audiences you can choose from the eligible events to be used as audience source and create retargeting ad sets on the basis of particular event actions to try and get the user or your probable customer to the final conversion action.

Not all of these strategies may work in all events but some may work in certain situations. It will take a bit of trial and error before we arrive at what works best for us. What we would also like to say here is that one must make use of specific targeting options while they’re still available but also to prepare for what lies ahead. Broad targeting using Facebook’s ad delivery system helps find potential customers one would not have known about otherwise. Targeting expansion can help improve the advertiser’s campaign performance by allowing Facebook’s system to reach a wider spectrum or group of people than what is chosen by you in targeting selections. Both broad targeting and targeting expansion may be good strategies to begin with!

How Apple’s move to protect user data cost Facebook ads dearly

Unregulated data can be dangerous as brands get access to user information, but the tables were turned when with a clean sweep (or 14.5 update in 2021) Apple empowered its users to protect their privacy if they so wish. This is done using a simple opt-in prompt on every app and allows users to give or withhold consent to third-party sites such as Facebook to track their user data. What the feature essentially does is that it blocks tracking of their info resulting in less data in the hands of advertisers and thus less revenues. This also means that targeted advertisement (tracking who is the brand’s TA and targeting them instead of the entire spectrum) would take a massive hit. We have earlier delved into how AI helps brands in leveraging user data. But what followed Apple’s data privacy move was the inability of the advertiser to know if the Facebook users visited their site, made a transaction or purchase, and ultimately in measuring the quantum of effectiveness of the ad campaign they ran on the platform. As per Facebook the data protection features launched by Apple have impacted its ad revenues. Facebook even went on record saying the impact of this feature on ad investment has been much more than expected by advertisers and said it would now be harder to measure ad campaigns on the platform besides increasing the cost of achieving business outcome via Facebook ads.

Technicalities aside, how businesses got impacted is a grave tale. According to data from AppsFlyer’s Performance Index 14 comparing H2 2020 to H2 2021, a quarter of total budgets shifted from iOS to Android due to Apple’s move. The shift in average ad spend dip was between 10% to 15%. One look at the numbers shows how big the impact has been. As per analyst Michael Nathanson, Facebook is bound to generate USD 129 billion in ad revenue in 2022 implying an ad business growth of just 12 % this year, as agaonst 36 % in the previous year.

Facebook’s response to the situation to make things right for the advertisers is what it calls ‘aggregated event measurement’ till a better fix is found. While Facebook will still not be able to tell advertisers which individuals clicked on a link or downloaded an app upon seeing an ad, it can tell them what a larger group of users did. What Facebook does in future, remains to be seen.

AI is ringing in opportunities for semiconductor companies. Here’s how!

With machines being trained to mimic cognitive functions of the human brain, semiconductor companies have been put on a growth chart which they didn’t have access to in the past even with all the innovations in chip design and next-generation devices that are fabrication enabled. Most AI apps such as virtual assistants rely on hardware for various functions.

Semiconductor companies could get 40-50% of the technology stack

With the creation of advanced Machine Learning algorithms, AI allows us to process huge data sets, and also learn, and improve over a period of time. Deep learning, a kind of ML made a huge leap in 2010s when it enabled generation of quite accurate results with a much wider range of data and the least requirement of data preprocessing by humans.While improving training and references developers often face challenges in storage, memory, networking, and logic. If semiconductor companies provide next gen accelerator architectures, they could enhance computational efficiency.

How AI could drive a big chunk of semiconductor revenues for data centers

The demand for existing chips by semiconductor companies will witness a growth With hardware as the differentiator in AI, but they could also gain by developing workload specific AI accelerators, which are not existing yet. According to the McKinsey report, “AI-related semiconductors will see growth of about 18 percent annually over the next few years—five times greater than the rate for semiconductors used in non-AI applications. By 2025, AI-related semiconductors could account for almost 20 percent of all demand, which would translate into about $67 billion in revenue. Opportunities will emerge at both data centers and the edge. If this growth materializes as expected, semiconductor companies will be positioned to capture more value from the AI technology stack than they have obtained with previous innovations—about 40 to 50 % of the total”.

Data-Center Usage: Cloud-computing data centers use GPUs for almost all training applications. GPUs are poised to be more customized to level up to the demands of DL, especially with ASICs entering the market. CPUs will lose to ASICs as DL based apps come to the fore.

Edge applications: A major chunk of the current edge training happens on PCs and laptops, but more devices may be used for the same purpose in the future. As most edge devices kneel on CPUs or ASICs, by 2025, ASICs are expected to account for 70% of edge inference market while GPUs will account for 20%.

Memory: Memory, especially dynamic random access memory (DRAM)is needed to store data inputs as well as for other tasks during inference and training. AI will be the enabler of opportunities for the memory market as something as small as a model being trained to recognize the image of a flower needs to bank on memory while the model works on the algorithms. AI chip leaders such as Google and Nvidia have adopted high-bandwidth memory (HBM) as the preferred memory solution, although thrice as more than the traditional DRAM— but it shows that customers are willing to pay for expensive AI hardware if they get performance gains.

The McKinsey report states many opportunities but also concludes that ‘ To capture the value they deserve, they’ll need to focus on end to-end solutions for specific industries (also called microvertical solutions), ecosystem development, and innovation that goes far beyond improving compute, memory, and networking technologies.”

Inputs from

How beauty brands are leveraging AI for customer acquisition

The global cosmetics market size has been projected to reach $463.5 billion by 2027. How this fast growing industry is leveraging AI is something we all can learn from. Customer acquisition is a big part of revenue generation but even bigger perhaps, is customer retention. L’oreal got its head in the right place with its AR and AI-powered mobile app StyleMyHair. Besides its other functions, the app points the user to the nearest hair salons where users can get their hair styled immediately. L’Oréal’s skin care at-home assistant called Perso creates personalized skin care formulas using AI. The system has a Breezometer which uses geo-location data to arrive at localized environmental conditions that can affect the skin of the customer. This may include UV index, temperature, pollen, humidity etc. When used on a  regular basis, Perso’s AI platform can not only assess skin conditions but personalize with better precision.

Another success story worth sharing is skincare brand MAELOVE’s use of artificial intelligence to analyse three million plus online product reviews to understand the needs of their customers and to deliver accordingly. Founded by a team of MIT graduates, their success story rides on their use of research for making formula blueprints. Theirs is a “radically affordable” skin care product line wherein everything is priced under $30. The bestseller, though, is the  $28 priced Glow Maker which boasts of an ingredient list quite similar to the award winning product CE Ferulic Serum priced at $166. The success of The Glow Maker is AI-backed as millions of product reviews were analysed to arrive at ingredients which worked and those that didn’t. It is interesting to note then, that The Glow Maker has already had four sellouts and is ready for pre-orders for a fifth time.

Methods at a glance

Product tagging helps in better product discovery. Products that are frequently brought together are flashed to consumers on e-commerce portals, gently nudging them to buy (sometimes at a discounted price). The home page of various portals display top personalized pictures of the products on offer, as per the choices of the customer. Engagement emails are sent out by brands with personalized promotions using data of the customers. When customers abandon online shopping carts, emails are sent with promotion to encourage them to complete their purchases. These emails are often also used from cross promotions.

How beauty brands are leveraging AI for customer acquisition

The global cosmetics market size has been projected to reach $463.5 billion by 2027. How this fast growing industry is leveraging AI is something we all can learn from. Customer acquisition is a big part of revenue generation but even bigger perhaps, is customer retention. L’oreal got its head in the right place with its AR and AI-powered mobile app StyleMyHair. Besides its other functions, the app points the user to the nearest hair salons where users can get their hair styled immediately. L’Oréal’s skin care at-home assistant called Perso creates personalized skin care formulas using AI. The system has a Breezometer which uses geo-location data to arrive at localized environmental conditions that can affect the skin of the customer. This may include UV index, temperature, pollen, humidity etc. When used on a  regular basis, Perso’s AI platform can not only assess skin conditions but personalize with better precision.

Another success story worth sharing is skincare brand MAELOVE’s use of artificial intelligence to analyse three million plus online product reviews to understand the needs of their customers and to deliver accordingly. Founded by a team of MIT graduates, their success story rides on their use of research for making formula blueprints. Theirs is a “radically affordable” skin care product line wherein everything is priced under $30. The bestseller, though, is the  $28 priced Glow Maker which boasts of an ingredient list quite similar to the award winning product CE Ferulic Serum priced at $166. The success of The Glow Maker is AI-backed as millions of product reviews were analysed to arrive at ingredients which worked and those that didn’t. It is interesting to note then, that The Glow Maker has already had four sellouts and is ready for pre-orders for a fifth time.

Methods at a glance

Product tagging helps in better product discovery. Products that are frequently brought together are flashed to consumers on e-commerce portals, gently nudging them to buy (sometimes at a discounted price). The home page of various portals display top personalized pictures of the products on offer, as per the choices of the customer. Engagement emails are sent out by brands with personalized promotions using data of the customers. When customers abandon online shopping carts, emails are sent with promotion to encourage them to complete their purchases. These emails are often also used from cross promotions.

How Leveraging AI Could Make Art Businesses Grow

Acquiring new customers is always a challenge for art dealers and gallerists. Even the Art Basel Report for 2019 indicated the same. What helps overcome this challenge, is knowing the demographics of your audience. With the art market no longer confined to a particular state or city or country, and newly introduced digitization of the trade, the art buyers of yore i.e. males have paved the way for younger enthusiasts who are seen investing in art. Millennials are, in fact, increasingly becoming art buyers and collectors as according to the 2019 Art Basel report they comprise 46% of the high net worth collectors surveyed in Singapore and 39% of the total share in Hong Kong. 69% of millennials purchased fine art and 77% purchased decorative art between 2016 and 2018.

Know thy customer

While customer demographics help in segmenting customers on a general level, psychographics help develop personas by telling customer needs and buying behaviour. Thus psychographics help in building their online personas and accurately predict what makes them convert. A combination of demographics, psychographics, as well as behavioural data for arriving at target groups would best help art sellers.

Make a move!

Another possible approach for gallerists and art sellers could be the use of precision targeting to help reach out to the right target audience among the customer segment that actually converts. To the customer, Precision Targeting gives a feeling that the marketer has crafted a personalized experience for them by reaching out with the right message at the right time. AI data points help in studying the buying habits of the customer for a particular product or service over a period of time. For example, gallerists may hold exhibitions at particular months of the year when customers are more likely to buy art or they may send newsletters announcing new pieces on particular days of the month.

What else to display?

ML zooms in into an artwork for its salient features and compares it with other artworks to find similarities and arrive at artworks which buyers would prefer. Advisors and dealers can know about their clients’ tastes and arrive at specific pieces which might be picked by buyers. Likewise, they can also determine which more artists can they add to their art line-ups.

Authentication and Validation

Besides the conventional analysis of material, authenticators, dealers, and auction houses can use AI-based software to detect the authenticity of an artwork. ML studies the artworks of various artists to know their aesthetic style such as the movement director of their medium (brush, pen etc.), the kind of pressure they exert on their canvas, and the previous works of the same artists to arrive at the authenticity criteria. These software can be deployed by sellers to encourage first-time buyers who otherwise may prefer to buy from particular galleries due to authenticity concerns.

With all these new techniques made available by AI, art sellers are poised to see their customer acquisition go up and have a better run in the market.