How Technological Advancement like Artificial Intelligence, Big Data is Reshaping Advertising
How the Technological advancement like Artificial Intelligence, Big Data is Reshaping Advertising
Internet of Things (IOT) is connecting more and more mediums to be marketing touchpoints, the foreseeable future will likely see the unification of programmatic ad campaigns across a variety of formats. TV, Radio, Out-of-Home, are already being used in programmatic, and VR, connected home appliances etc., are soon to follow. Voice assistants such as Alexa , Siri and Google Assistant) are becoming better every year, and holds marketplaces where advertisers should be present.
Programmatic technology is going to improve to deliver timed and targeted advertising messages to individuals, but a future where these ad campaigns are unified and delivered through Internet of Things (IOT) medium is well on the horizon. This in turn will reshape the landscape of programmatic, creating new opportunities to unify media buying.
Let us have a quick recap, how technology empowered the marketeers in the recent time :
Search
Remember during 2005, if you ‘searched’ an eCommerce store to find a product, you’d be unlikely to find the result you had in mind unless you knew it’s name or title exactly. Today’s ‘search’ is after much smarter, and it’s improved capacity not only help you find information on Google, but it helps you find the right products on Amazon or Target.com, the right movies on Netflix, and more.
Ten years ago, typing ‘men’s flip flops’ at Nike.com may not have yielded the results I was looking for. Today, it very much does.
Search in eCommerce space and marketing has improved due to the same underlying factors that have improved ‘search’ at large, including:
Technologies like Elasticsearch are now relatively mainstream, allowing any small eCommerce stores to have search that goes beyond simply matching keywords.
Data-as-a-Service companies (like Indix , among others) make it easier than ever to draw from search data from other larger sources, informing your own online product search without having to train your own search models from scratch.
Other improvements such as : Software to detect common misspellings is now more commonplace, and can calibrate for misspellings by context (i.e. : ‘Season cikets’ can be understood to mean ‘season tickets’, while ‘cikets’ alone might be more difficult to discern without context)
Google has done good work of simplifying and explaining some of their own search improvements and developments in their ‘Inside Search’ writings.
In the future, more and more eCommerce sites will follow in the footsteps of Google and others in implementing autosuggest, suggested corrections, ‘advanced’ search options, and other such improvements.
Recommendation Engines
Recommendation engines were a rarity in the world of digital marketing. Now this marketing technology is appreciated and loved by customers. Amazon’s book or product recommendations are excellent, Spotify knows your taste in music so well, this kind of ‘discovery aide’ amongst the millions of choices available makes them indispensable for companies with huge inventories (both digital and physical).
Today’s sophisticated online recommendations go vastly beyond a simple human-determined set of guidelines, or even a simple set of historical ratings from other users. A recommendation engine (you’ll also frequently hear the term ‘recommender system’) can pull from reams of nuanced data in order to draw conclusions from behaviors, actions, etc.
Netflix isn’t merely taking into account what movies a person has watched, or what ratings they give those movies – they’re also analyzing which movies are watched multiple times, rewound, fast-forwarded, etc. These myriad behaviors, when correlated and assessed over millions of other users, help to coax out the best recommendations.
Programmatic Advertising
Programmatic advertising has also drastically changed the way advertisement should be done , it is the automated process of buying and selling ad inventory through an exchange, connecting advertisers to publishers. This process uses artificial intelligence technologies… and real-time bidding for inventory across mobile, display, video and social channels – even making its way into television.
Artificial intelligence technologies have algorithms that analyze a visitor’s behavior allowing for real time campaign optimizations towards an audience more likely to convert. Programmatic companies have the ability to gather this audience data to then target more precisely, whether it’s from 1st party (their own) or from a 3rd party data provider.
Programmatic media buying includes the use of demand side platforms (DSPs), supply side platforms (SSPs) and data management platforms (DMPs). DSPs, which facilitate the process of buying ad inventory on the open market, provide the ability to reach your target audience due to the integration of DMPs. DMPs collect and analyze a substantial amount of cookie data to then allow the marketer to make more informed decisions of whom their target audience may be.
The classic example of programmatic advertising is SEM advertising on channels like Google (AdWords), Facebook, and Twitter. Companies like PredictiveBid and Israel-based Albert have decided to put a significant amount of their focus on programmatic advertising specifically.
Programmatic ads bring a tremendous amount of efficiency to bear on the ‘inventory’ of website and app viewers. Platforms like Google and Facebook have set the standard for both efficient and effective advertising – and it can be supposed that these systems will become more and more user-friendly in terms of allowing non-technical marketers to start, run, and measure campaigns on line (for all me
Marketing Forecasting
It might be referred to as ‘Insight from Marketing Data,’ a much broader topic. However, one of the most straight-forward marketing applications of business intelligence data lies in it’s ability to aide in predictions, a capability much enhanced by developments in AI.
We’ll likely compose another entire article (or indeed, an entire market report) on the domain of business intelligence. However, BI and marketing intersect so thoroughly that it would be disingenuous to refer to the rapid-fire advancements in marketing without referencing the underlying technology that helps companies make sense of that flood of marketing data and take action on it.
Companies like Rapidminer, Brist, Sisense, and others are aiming to become industry standards for business intelligence and predictions. Because of the generally high volume and quantifiable nature of marketing data (clicks, views, time-on-page, purchases, email responses, etc.), models can often be trained much more quickly on marketing data than on other information such as HR data, inventory data, etc.
Being able to predict the success of an email campaign or marketing initiative can help companies continuously improve marketing efforts (in display, text, video, or even direct mail).
Of the 2000+ marketing technology companies on the market at the time of this writing, a wide swath deal with data management and analysis. We can expect more of these companies in the future, and more refined marketing efforts from companies large and small as these technologies / assessment tools improve and become more mainstream.
Speech / Text Recognition (Conversational Commerce)
In 2014 and even in most of 2015, it might have been safe to say that while chatbots and speech recognition were an interesting use for artificial intelligence, it still hadn’t made a legitimate impact in marketing or advertising.
Beginning in 2016, a wave of legitimately viable speech and chat interfaces have crossed over into the marketing worlds – and some of them showed grand promise. Here are a few of the examples of traction today:
Amazon Echo – Echo has been a remarkable success in turning the internet of things into a reality, particularly in it’s ability for users to make purchases simply by speaking to the machine. You can order an Uber car or a Domino’s pizza with speech alone.
Facebook Messenger – Aiming to model the ‘online to offline’ strategy of chat-based purchases, Facebook Messenger allows users order flowers (and soon, much more) via chat alone.
Baidu’s Duer – Baidu’s new chatbot assistant is capable of ordering products within it’s interface
Others – Google and (reportedly) Apple are working on Echo competitors, and the competition over which brand will become the speech ‘hub’ of the smart home should prove to be an exciting one.
While chatbots and natural language processing haven’t made their way into the marketing departments of most of small businesses in countries like USA, the applications from the largest and hottest tech companies are certainly making waves, and making it clear that there’s a bigger trend ahead.
AI in Marketing / Advertising/ Public Influencing
When artificial intelligence is mentioned, you might think of robots and various other sci-fi- style inventions. However, AI doesn’t just mean robots that can walk and talk – there are plenty of less spectacular iterations of artificial intelligence that feature in our advertising industry in a more understated way.
While there are hundreds of potential applications of AI in marketing and advertising, I give below highlights some of the possibilities that can be considered to be exciting, and viable in the coming time:
Image recognition / machine vision: In the relatively near future, it may be possible for consumers on the web or on mobile to ‘search’ for products (or similar products) to images. This might be as simple as snapping a photo of a pair of shoes you want to buy, or using a web app to select a certain image that you found within a Google search. Companies like CamFind and their competitors and experimenting in this space already.
Customer segmentation: Companies such as AgilOne are allowing marketers to optimize email and website communications, continually learning from user behavior (eConsultancy).
Content generation: now a significant portion of sports and finance-related articles are written by machines, not by humans. Yeah – expect more in the coming time.
Companies like AutomatedInsights and NarativeScience have found ways to turn specific sets of information (domains like sports and finance are laden with time data and numbered data) into human-readable articles, sometimes indistinguishable from those written by human beings. In the future, companies may have product descriptions and entire product line-related articles composed entirely by machines, based on information about the products in question.
Personalization of content will also be an important future trend Adobe and other companies are already competing on this feature as well.
Predicting Customer’s Action
FACEBOOK is offering to sell its ability to predict customer’s actions — and his loyalty — has new gravity in the wake of the 2016 US election, in which Trump’s digital team used Facebook targeting to historic effect. Facebook works regularly with political campaigns around the world and boasts of its ability to influence turnout — a Facebook ‘ success story ‘about its work with the Scottish National Party describes the collaboration as “triggering a landslide.” Since Zuckerberg infamously dismissed the claim that Facebook has the ability to influence elections, an ability Facebook itself has advertised , the company has been struggling to clean up its act. Until it reckons with its power to influence based on what it knows about people, should Facebook really be expanding into influencing people based on what it can predict about them?
Jonathan Albright, research director at Columbia University’s Tow Center for Digital Journalism, says that like any algorithm, especially from Facebook, AI targeting ‘can always be weaponized.’ Albright, who has become an outspoken critic of Facebook’s ability to channel political influence, worries how such techniques could be used around elections, by predicting which ‘people … might be dissuaded into not voting,’ for example.
Search terms
When we’re looking for a product online, we log on to a search engine or a specific shopping site and search for what we want without a moment’s hesitation. However, in the past, we had to be very specific about what we were searching: if you wanted a large black sweatshirt, for example, you had to type in exactly that. Today, you can search a few or even just one keyword and the search technology will provide the same results you’d get if you were as specific as you could possibly be.
A more recent development in the same theme is search technology that caters for spelling errors, too. Using the context of your search, certain spelling errors can be ‘ignored’, so to speak, and search engines can work out what it was we were actually trying to say. Accidentally searched for ‘London hoelts’ instead of ‘London hotels’? The inclusion of ‘London’ will enable the search engine to deliver results for your intended search, rather than the erroneous one.
Targeted advertising
Also referred to as ‘recommendation engines’, this type of artificial intelligence is another common tool for advertisers. On music streaming sites, we are recommended songs and artists we might like based on music we have listened to previously. For sites like Netflix, we are recommended films and shows based on not just what we watch, but what we watch over and over again, what we skip and what we search for.
This is something that happens to us every time we use such a platform. It’s very subtle, and we might take it for granted, but the technology is in place and very effective, so it shows that artificial intelligence is already firmly entrenched in the way we advertise and consume things.
Speech recognition
“Hey Siri”, “Ok Google”, “Alexa…” – these phrases, when written down, might look a little odd to say the least. However, they’re uttered millions of times around the world – at work, at home, on the bus, in the gym – our smartphones and ‘hubs’ can recognise our voices and our words and respond to our commands in the blink of an eye. If you need to know what restaurants are nearby when you’re on holiday, you can ask your phone’s AI assistant and it will pull through a list of the highest-rated restaurants in the area.
Listening to a song and want to hear music that sounds similar to it? You can ask your Amazon Echo “play me something like…” and it will do the rest. The latter, in particular, enables you to make purchases without lifting a finger. You can order Ubers or get a pizza delivered by talking to the small, unassuming cylinder-shaped speaker in your living room.
Machine Vision
This is currently offered by Google Lens on some of their smartphones, and it’s only a matter of time before it is introduced on a wider scale. One of the mooted innovations is image recognition or ‘machine vision’.
Theoretically, this means that we’ll be able to take a picture of something we want on our phone – a pair of trainers, a sofa or a table, for example – and our phones and computers will be able to use this image to search for that product or similar items.
Content Generation
We read articles in their millions every day, from opinion pieces to news articles and more. Currently, they’re mostly written by bloggers, journalists and writers, but there are also some pieces that are actually written by machines. They are written well enough, albeit with a lot of room for improvement, but for all intents and purposes may as well have been written by a person – but in fact, a computer has done it autonomously.
Artificial intelligence is becoming more and more sophisticated every day, with laboratories around the world striving to make the next major breakthrough, for both one-off showpiece creations and features that will seamlessly integrate into our everyday life. As these developments progress, the way we display and consume advertisements will change with it.
Big Data Transforming Advertising
Big data is evidently transforming many industries. One such industry that has seen the maximum impact in terms of profitability is advertising. The communication between advertisers and consumers has changed drastically owing to the advancement in big data technologies. Big data helps advertisers to understand and optimise the demands of every single customer and convert them into prospective buyers.
Somewhere close to 30 years ago, advertising meant placing ads on TV, radio or the print media only. Now alongside, we have a proliferation of mobile, video, internet advertising which is considered the fastest growing formats in advertising today. With the shift to digital technologies, consumer habits have also evolved.
Through these evolved internet habits, advertisers are now enjoying more visibility and awareness of their brands which has provided for higher chances of revenue generation. The internet advertisers aim at conversions of viewers to buyers, for which companies are required to analyze trends and strategize on their campaigns.
Every click made on social media or browser is helping businesses understand the customer interests and behavior. A digital footprint means more than gold to these businesses as it helps them generate insights as to which products sparked an interest and which products were given a miss.
Harnessing Power of Customer Data
Today, our every internet move is tracked. We generate tons of data every second through our Facebook page visits, videos watched, likes, comments, tweets, search history and so on that is captured in the databases. This data is analogous to insights on our interests, current needs or responses to certain products already purchased. Enterprises seek such data for higher customer acquisition and retention by targeted delivery of promotional content for their products and services.
This means, now there is a higher demand for data processing and storage tools like Hadoop that is at the core of the big data technologies. The only challenge is that not many companies yet have the setup or trained professionals to mine and manage such data. These professionals should also be able to identify possible areas and audience for impact.
Enterprises need data to build a relationship with the customers- both current and prospective. The best companies can achieve this if they can anticipate or predict the customers’ needs on the basis of their past interactions with the ads or websites.
Also, email platforms are being exploited where personalized recommendations and notifications are sent. A lot of companies also resort to retargeting and dynamic creative optimisation (DCO) depending on the past interaction.
As customers get more savvy and informed, they can detect irrelevant marketing strategies easily and hence, the advertisers need to display their content only after understanding what they want to buy. This can be achieved if they can harness big data and analytics tools to produce the best possible customer insights.
Big Data for Marketing to Sales
Top players in the industry know the difference between big data and smart data. As data gets more ubiquitous, the data that may be relevant to your business strategies is something that needs to be spent understanding. Companies need to strategize so as to not create a negative user experience for their audience.
There are a lot of ways for further turning data into effective strategies e.g. content marketing is one such way through which companies can ensure higher customer engagement. Once the user starts interacting with the targeted page, one can start giving them more information on business and product. If someone has shown interest in a camera, the ad should direct him to some page where he can find further information about the company and the camera. This ensures better knowledge and user experience such that it motivates customers to eventually buy your product.
Future is Adtech Companies
The word adtech is an all-encompassing term that refers to software and tools that help agencies keep a track of and analyze their online advertising campaigns. It has been around in the market for a few years but it was not until the boom of big data that the companies started realizing the importance of adtech.
As companies strategize, the key is to deliver the content at the right time to the right people so that their advertising money is not wasted. Adtechs are here to do that and there are no signs of it slowing down.
Adtech provide an automated connection between buyers and sellers according to different criteria during which the buyers can see the ads. This is called programmatic advertising and most of the ads seen today use algorithms to automatically show up in front of a relevant audience after analyzing data in real-time. YouAppi is one such company that drives the acquisition and conversion of the most profitable mobile customers with machine learning and predictive algorithms.
Yes, the technology has empowered the advertisers but don’t be carried away by the term ‘ advertising/ marketing automation’. Creativity is the key of success. However, delivering targeted messages to custom prospects based on their recent interests in real-time is the new trendy opportunity. It would be a foolish not to capitalize on this. The secret to success around managing these new challenges is to anticipate them to the point that their inherent complexity is minimized or mitigated. Sounds simple, but it’s not. It means getting ahead of the curve and not just being a thought-leader, but a ‘do-er’. Having a bias to action is more critical now than ever. Those who move quickly will increase their chance of growth, success and survival – those who anticipate and are proactive will thrive.
Internet of Things (IOT) is connecting more and more mediums to be marketing touchpoints, the foreseeable future will likely see the unification of programmatic ad campaigns across a variety of formats. TV, Radio, Out-of-Home, are already being used in programmatic, and VR, connected home appliances etc., are soon to follow. Voice assistants such as Alexa , Siri and Google Assistant) are becoming better every year, and holds marketplaces where advertisers should be present.
Programmatic technology is going to improve to deliver timed and targeted advertising messages to individuals, but a future where these ad campaigns are unified and delivered through Internet of Things (IOT) medium is well on the horizon. This in turn will reshape the landscape of programmatic, creating new opportunities to unify media buying.
Let us have a quick recap, how technology empowered the marketeers in the recent time :
Search
Remember during 2005, if you ‘searched’ an eCommerce store to find a product, you’d be unlikely to find the result you had in mind unless you knew it’s name or title exactly. Today’s ‘search’ is after much smarter, and it’s improved capacity not only help you find information on Google, but it helps you find the right products on Amazon or Target.com, the right movies on Netflix, and more.
Ten years ago, typing ‘men’s flip flops’ at Nike.com may not have yielded the results I was looking for. Today, it very much does.
Search in eCommerce space and marketing has improved due to the same underlying factors that have improved ‘search’ at large, including:
Technologies like Elasticsearch are now relatively mainstream, allowing any small eCommerce stores to have search that goes beyond simply matching keywords.
Data-as-a-Service companies (like Indix , among others) make it easier than ever to draw from search data from other larger sources, informing your own online product search without having to train your own search models from scratch.
Other improvements such as : Software to detect common misspellings is now more commonplace, and can calibrate for misspellings by context (i.e. : ‘Season cikets’ can be understood to mean ‘season tickets’, while ‘cikets’ alone might be more difficult to discern without context)
Google has done good work of simplifying and explaining some of their own search improvements and developments in their ‘Inside Search’ writings.
In the future, more and more eCommerce sites will follow in the footsteps of Google and others in implementing autosuggest, suggested corrections, ‘advanced’ search options, and other such improvements.
Recommendation Engines
Recommendation engines were a rarity in the world of digital marketing. Now this marketing technology is appreciated and loved by customers. Amazon’s book or product recommendations are excellent, Spotify knows your taste in music so well, this kind of ‘discovery aide’ amongst the millions of choices available makes them indispensable for companies with huge inventories (both digital and physical).
Today’s sophisticated online recommendations go vastly beyond a simple human-determined set of guidelines, or even a simple set of historical ratings from other users. A recommendation engine (you’ll also frequently hear the term ‘recommender system’) can pull from reams of nuanced data in order to draw conclusions from behaviors, actions, etc.
Netflix isn’t merely taking into account what movies a person has watched, or what ratings they give those movies – they’re also analyzing which movies are watched multiple times, rewound, fast-forwarded, etc. These myriad behaviors, when correlated and assessed over millions of other users, help to coax out the best recommendations.
Programmatic Advertising
Programmatic advertising has also drastically changed the way advertisement should be done , it is the automated process of buying and selling ad inventory through an exchange, connecting advertisers to publishers. This process uses artificial intelligence technologies… and real-time bidding for inventory across mobile, display, video and social channels – even making its way into television.
Artificial intelligence technologies have algorithms that analyze a visitor’s behavior allowing for real time campaign optimizations towards an audience more likely to convert. Programmatic companies have the ability to gather this audience data to then target more precisely, whether it’s from 1st party (their own) or from a 3rd party data provider.
Programmatic media buying includes the use of demand side platforms (DSPs), supply side platforms (SSPs) and data management platforms (DMPs). DSPs, which facilitate the process of buying ad inventory on the open market, provide the ability to reach your target audience due to the integration of DMPs. DMPs collect and analyze a substantial amount of cookie data to then allow the marketer to make more informed decisions of whom their target audience may be.
The classic example of programmatic advertising is SEM advertising on channels like Google (AdWords), Facebook, and Twitter. Companies like PredictiveBid and Israel-based Albert have decided to put a significant amount of their focus on programmatic advertising specifically.
Programmatic ads bring a tremendous amount of efficiency to bear on the ‘inventory’ of website and app viewers. Platforms like Google and Facebook have set the standard for both efficient and effective advertising – and it can be supposed that these systems will become more and more user-friendly in terms of allowing non-technical marketers to start, run, and measure campaigns on line (for all me
Marketing Forecasting
It might be referred to as ‘Insight from Marketing Data,’ a much broader topic. However, one of the most straight-forward marketing applications of business intelligence data lies in it’s ability to aide in predictions, a capability much enhanced by developments in AI.
We’ll likely compose another entire article (or indeed, an entire market report) on the domain of business intelligence. However, BI and marketing intersect so thoroughly that it would be disingenuous to refer to the rapid-fire advancements in marketing without referencing the underlying technology that helps companies make sense of that flood of marketing data and take action on it.
Companies like Rapidminer, Brist, Sisense, and others are aiming to become industry standards for business intelligence and predictions. Because of the generally high volume and quantifiable nature of marketing data (clicks, views, time-on-page, purchases, email responses, etc.), models can often be trained much more quickly on marketing data than on other information such as HR data, inventory data, etc.
Being able to predict the success of an email campaign or marketing initiative can help companies continuously improve marketing efforts (in display, text, video, or even direct mail).
Of the 2000+ marketing technology companies on the market at the time of this writing, a wide swath deal with data management and analysis. We can expect more of these companies in the future, and more refined marketing efforts from companies large and small as these technologies / assessment tools improve and become more mainstream.
Speech / Text Recognition (Conversational Commerce)
In 2014 and even in most of 2015, it might have been safe to say that while chatbots and speech recognition were an interesting use for artificial intelligence, it still hadn’t made a legitimate impact in marketing or advertising.
Beginning in 2016, a wave of legitimately viable speech and chat interfaces have crossed over into the marketing worlds – and some of them showed grand promise. Here are a few of the examples of traction today:
Amazon Echo – Echo has been a remarkable success in turning the internet of things into a reality, particularly in it’s ability for users to make purchases simply by speaking to the machine. You can order an Uber car or a Domino’s pizza with speech alone.
Facebook Messenger – Aiming to model the ‘online to offline’ strategy of chat-based purchases, Facebook Messenger allows users order flowers (and soon, much more) via chat alone.
Baidu’s Duer – Baidu’s new chatbot assistant is capable of ordering products within it’s interface
Others – Google and (reportedly) Apple are working on Echo competitors, and the competition over which brand will become the speech ‘hub’ of the smart home should prove to be an exciting one.
While chatbots and natural language processing haven’t made their way into the marketing departments of most of small businesses in countries like USA, the applications from the largest and hottest tech companies are certainly making waves, and making it clear that there’s a bigger trend ahead.
AI in Marketing / Advertising/ Public Influencing
When artificial intelligence is mentioned, you might think of robots and various other sci-fi- style inventions. However, AI doesn’t just mean robots that can walk and talk – there are plenty of less spectacular iterations of artificial intelligence that feature in our advertising industry in a more understated way.
While there are hundreds of potential applications of AI in marketing and advertising, I give below highlights some of the possibilities that can be considered to be exciting, and viable in the coming time:
Image recognition / machine vision: In the relatively near future, it may be possible for consumers on the web or on mobile to ‘search’ for products (or similar products) to images. This might be as simple as snapping a photo of a pair of shoes you want to buy, or using a web app to select a certain image that you found within a Google search. Companies like CamFind and their competitors and experimenting in this space already.
Customer segmentation: Companies such as AgilOne are allowing marketers to optimize email and website communications, continually learning from user behavior (eConsultancy).
Content generation: now a significant portion of sports and finance-related articles are written by machines, not by humans. Yeah – expect more in the coming time.
Companies like AutomatedInsights and NarativeScience have found ways to turn specific sets of information (domains like sports and finance are laden with time data and numbered data) into human-readable articles, sometimes indistinguishable from those written by human beings. In the future, companies may have product descriptions and entire product line-related articles composed entirely by machines, based on information about the products in question.
Personalization of content will also be an important future trend Adobe and other companies are already competing on this feature as well.
Predicting Customer’s Action
FACEBOOK is offering to sell its ability to predict customer’s actions — and his loyalty — has new gravity in the wake of the 2016 US election, in which Trump’s digital team used Facebook targeting to historic effect. Facebook works regularly with political campaigns around the world and boasts of its ability to influence turnout — a Facebook ‘ success story ‘about its work with the Scottish National Party describes the collaboration as “triggering a landslide.” Since Zuckerberg infamously dismissed the claim that Facebook has the ability to influence elections, an ability Facebook itself has advertised , the company has been struggling to clean up its act. Until it reckons with its power to influence based on what it knows about people, should Facebook really be expanding into influencing people based on what it can predict about them?
Jonathan Albright, research director at Columbia University’s Tow Center for Digital Journalism, says that like any algorithm, especially from Facebook, AI targeting ‘can always be weaponized.’ Albright, who has become an outspoken critic of Facebook’s ability to channel political influence, worries how such techniques could be used around elections, by predicting which ‘people … might be dissuaded into not voting,’ for example.
Search terms
When we’re looking for a product online, we log on to a search engine or a specific shopping site and search for what we want without a moment’s hesitation. However, in the past, we had to be very specific about what we were searching: if you wanted a large black sweatshirt, for example, you had to type in exactly that. Today, you can search a few or even just one keyword and the search technology will provide the same results you’d get if you were as specific as you could possibly be.
A more recent development in the same theme is search technology that caters for spelling errors, too. Using the context of your search, certain spelling errors can be ‘ignored’, so to speak, and search engines can work out what it was we were actually trying to say. Accidentally searched for ‘London hoelts’ instead of ‘London hotels’? The inclusion of ‘London’ will enable the search engine to deliver results for your intended search, rather than the erroneous one.
Targeted advertising
Also referred to as ‘recommendation engines’, this type of artificial intelligence is another common tool for advertisers. On music streaming sites, we are recommended songs and artists we might like based on music we have listened to previously. For sites like Netflix, we are recommended films and shows based on not just what we watch, but what we watch over and over again, what we skip and what we search for.
This is something that happens to us every time we use such a platform. It’s very subtle, and we might take it for granted, but the technology is in place and very effective, so it shows that artificial intelligence is already firmly entrenched in the way we advertise and consume things.
Speech recognition
“Hey Siri”, “Ok Google”, “Alexa…” – these phrases, when written down, might look a little odd to say the least. However, they’re uttered millions of times around the world – at work, at home, on the bus, in the gym – our smartphones and ‘hubs’ can recognise our voices and our words and respond to our commands in the blink of an eye. If you need to know what restaurants are nearby when you’re on holiday, you can ask your phone’s AI assistant and it will pull through a list of the highest-rated restaurants in the area.
Listening to a song and want to hear music that sounds similar to it? You can ask your Amazon Echo “play me something like…” and it will do the rest. The latter, in particular, enables you to make purchases without lifting a finger. You can order Ubers or get a pizza delivered by talking to the small, unassuming cylinder-shaped speaker in your living room.
Machine Vision
This is currently offered by Google Lens on some of their smartphones, and it’s only a matter of time before it is introduced on a wider scale. One of the mooted innovations is image recognition or ‘machine vision’.
Theoretically, this means that we’ll be able to take a picture of something we want on our phone – a pair of trainers, a sofa or a table, for example – and our phones and computers will be able to use this image to search for that product or similar items.
Content Generation
We read articles in their millions every day, from opinion pieces to news articles and more. Currently, they’re mostly written by bloggers, journalists and writers, but there are also some pieces that are actually written by machines. They are written well enough, albeit with a lot of room for improvement, but for all intents and purposes may as well have been written by a person – but in fact, a computer has done it autonomously.
Artificial intelligence is becoming more and more sophisticated every day, with laboratories around the world striving to make the next major breakthrough, for both one-off showpiece creations and features that will seamlessly integrate into our everyday life. As these developments progress, the way we display and consume advertisements will change with it.
Big Data Transforming Advertising
Big data is evidently transforming many industries. One such industry that has seen the maximum impact in terms of profitability is advertising. The communication between advertisers and consumers has changed drastically owing to the advancement in big data technologies. Big data helps advertisers to understand and optimise the demands of every single customer and convert them into prospective buyers.
Somewhere close to 30 years ago, advertising meant placing ads on TV, radio or the print media only. Now alongside, we have a proliferation of mobile, video, internet advertising which is considered the fastest growing formats in advertising today. With the shift to digital technologies, consumer habits have also evolved.
Through these evolved internet habits, advertisers are now enjoying more visibility and awareness of their brands which has provided for higher chances of revenue generation. The internet advertisers aim at conversions of viewers to buyers, for which companies are required to analyze trends and strategize on their campaigns.
Every click made on social media or browser is helping businesses understand the customer interests and behavior. A digital footprint means more than gold to these businesses as it helps them generate insights as to which products sparked an interest and which products were given a miss.
Harnessing Power of Customer Data
Today, our every internet move is tracked. We generate tons of data every second through our Facebook page visits, videos watched, likes, comments, tweets, search history and so on that is captured in the databases. This data is analogous to insights on our interests, current needs or responses to certain products already purchased. Enterprises seek such data for higher customer acquisition and retention by targeted delivery of promotional content for their products and services.
This means, now there is a higher demand for data processing and storage tools like Hadoop that is at the core of the big data technologies. The only challenge is that not many companies yet have the setup or trained professionals to mine and manage such data. These professionals should also be able to identify possible areas and audience for impact.
Enterprises need data to build a relationship with the customers- both current and prospective. The best companies can achieve this if they can anticipate or predict the customers’ needs on the basis of their past interactions with the ads or websites.
Also, email platforms are being exploited where personalized recommendations and notifications are sent. A lot of companies also resort to retargeting and dynamic creative optimisation (DCO) depending on the past interaction.
As customers get more savvy and informed, they can detect irrelevant marketing strategies easily and hence, the advertisers need to display their content only after understanding what they want to buy. This can be achieved if they can harness big data and analytics tools to produce the best possible customer insights.
Big Data for Marketing to Sales
Top players in the industry know the difference between big data and smart data. As data gets more ubiquitous, the data that may be relevant to your business strategies is something that needs to be spent understanding. Companies need to strategize so as to not create a negative user experience for their audience.
There are a lot of ways for further turning data into effective strategies e.g. content marketing is one such way through which companies can ensure higher customer engagement. Once the user starts interacting with the targeted page, one can start giving them more information on business and product. If someone has shown interest in a camera, the ad should direct him to some page where he can find further information about the company and the camera. This ensures better knowledge and user experience such that it motivates customers to eventually buy your product.
Future is Adtech Companies
The word adtech is an all-encompassing term that refers to software and tools that help agencies keep a track of and analyze their online advertising campaigns. It has been around in the market for a few years but it was not until the boom of big data that the companies started realizing the importance of adtech.
As companies strategize, the key is to deliver the content at the right time to the right people so that their advertising money is not wasted. Adtechs are here to do that and there are no signs of it slowing down.
Adtech provide an automated connection between buyers and sellers according to different criteria during which the buyers can see the ads. This is called programmatic advertising and most of the ads seen today use algorithms to automatically show up in front of a relevant audience after analyzing data in real-time. YouAppi is one such company that drives the acquisition and conversion of the most profitable mobile customers with machine learning and predictive algorithms.
Yes, the technology has empowered the advertisers but don’t be carried away by the term ‘ advertising/ marketing automation’. Creativity is the key of success. However, delivering targeted messages to custom prospects based on their recent interests in real-time is the new trendy opportunity. It would be a foolish not to capitalize on this. The secret to success around managing these new challenges is to anticipate them to the point that their inherent complexity is minimized or mitigated. Sounds simple, but it’s not. It means getting ahead of the curve and not just being a thought-leader, but a ‘do-er’. Having a bias to action is more critical now than ever. Those who move quickly will increase their chance of growth, success and survival – those who anticipate and are proactive will thrive.
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