Making 1:1 Personalization Possible With Artificial Intelligence

The way a company markets itself and its products or services can have a huge impact on its profits, brand strength and longevity. While a poorly planned and executed marketing campaign is unlikely to help a company greatly, using modern techniques such as personalization powered by artificial intelligence (AI) can push your campaigns to the next level.

Marketing Personalization Is A Necessity

While many brands have already started to personalize their messages using marketing automation tools, online marketing allows for much more specific targeting, almost on an individual basis. Potential customers leave breadcrumbs of their interests across the internet, through social media, shopping sites and search engines. This data can help you craft a better marketing campaign, one with tangible benefits.

Personalization at scale can lift revenue by 5 to 15 percent for companies in the retail, travel, entertainment, telecom and financial services sectors, according to McKinsey & Company.

There is no surprise to see that personalization is becoming a major consideration as McKinsey highlighted that more than 90 percent of retailers believe this is a top priority. However, only 15 percent of these companies are actually doing a good job at it.

Challenges With Marketing Automation Tools Today

One of the most important aspects of marketing is making sure you get your message to the right people. The more specific you can be with your target audience, the more likely you are to see a positive return on investment in your campaigns. There is little point telling a vegetarian about your new steakhouse, or advertising a luxury resort to backpackers.

A major limitation of many marketing automation tools today is the lack of available data. Whether it is because you are looking at a small data pool or that you cannot track a user over multiple devices, having holes in your data can make your personalization less accurate and therefore less effective.

For example, a user may access your site on their computer, phone and tablet. Many personalization tools don’t have the capacity to see this as one user and will instead report three different people looking at your site. This gives a disjointed view of the user and means you miss out on valuable information about the buyer’s journey.

Marketing automation tools do not always understand human actions or emotions either. They could fail to recognize that after someone has made a purchase, he or she will no longer want to buy the same item. This can often come down to a lack of data about a user’s behavior.

Another challenge businesses have is that they find it difficult to scale their marketing efforts and manage engagement across all their channels. One cause for this is the number of different tools they need to cover all the bases, and upgrading each of these can be a costly or impractical exercise. With different tools in place, there is often a lack of communication between what marketers are seeing, making it hard for them to act quickly and efficiently.

How Can AI Improve Personalized Marketing?

There are many ways that AI techniques can help brands improve their marketing campaigns. This is particularly true in terms of zooming in on one particular type of customers and scaling up for large projects.

Without any sort of AI, the information companies know about a visitor when they visit a website can be limited. However, the visitor may already have shown information about the type of person they are and what sort of product they are looking for. For instance, a consumer often purchases fashion goods from an ecommerce site A, but she also buys baby products from site B. Having this insight available for site A means the visitor can be exposed to relevant products and information to keep them engaged further.

AI can also help you engage potential customers once they leave your site. For instance, someone who has abandoned a cart on your site can receive a reminder about the product that they have left waiting, perhaps with personalized suggestions for other items if that one wasn’t quite right.

Another scenario could be knowing whom to send certain newsletters to. You may have a sale or promotion on products that might be of interest to some of your contacts. Sending one email campaign out to all your subscribers isn’t necessarily the best approach. So, knowing who has shown an interest and who would be likely to buy that product can help make that email marketing campaign more effective.

AI-powered personalized marketing is proving to be one of the most effective ways to maximize a marketing budget and it’s something that brands are increasingly making use of today.

Boost High-Quality App User Acquisition With Artificial Intelligence

In the early days of app marketing, brands defined success as the number of times their apps were downloaded and installed. With a deeper understanding of customer behavior in the app world has come the realization that the continued engagement and retention of customers is a much more valuable metric.

With one in five users abandoning apps after just one use, only the customers who continue to engage with the app will fulfil valuable in-app events such as subscription and purchase, which ensures higher return on investment and revenue, and lower churn rate. Brands have thus turned their attention to high-quality user acquisition (UA), throwing their money behind growing the customer lifetime value (CLV/LTV).

But it all starts with the app install. The key to optimizing in-app events is to drive quality installs by identifying users who will engage with your app prior to the initial download – at the right volumes and the right price.

UA requires that marketers analyze data points to identify customer behavior trends and arrive at insights around which of them will demonstrate stickiness. With the sheer volume of data that marketers are dealing with today, manual analysis is not feasible, and they are turning to artificial intelligence (AI) solutions for help.

AI Learns From Historical Data to Optimize Future In-App Events

AI solutions offer marketers an in-depth understanding of customer behavior and a holistic view of CLV. They start with a wider audience and then use data to narrow down the field to high-performing lookalike audience profiles. This is achieved by studying historical data, identifying patterns and then using these to predict whether a specific campaign will attract the right audience and convert them. Historical data enables the AI tool to discover and target similar profiles most likely to respond to your message.

By using the deep learning method, for instance, Appier uses proprietary deep funnel optimization to improve campaign performance. The deep funnel approach allows the system to learn from similar campaigns and historical data, in order to make predictions as the campaign is running or even beforehand to ensure cost and time efficiency. It also helps in audience sampling through prediction, even if the traffic source is not from historical data. If trends show that the KPIs will not be met, deep funnel optimization will then recommend that campaigns be tweaked or stopped.

Additionally, the tool analyzes which users are engaging with the app through in-app purchases or signups, and then optimizes the ongoing campaign to find and target more such users. Essentially, these users are targeted based on data that predicts they are more likely to make in-app purchases. As a result, the solution drives highest quality traffic, which, in turn, leads to high-quality installs and well-performing in-app events.

Here is an example of how deep funnel actually works in practice: Indonesia’s leading ride-hailing app wanted to attract as many users as possible in a competitive market. It decided to use AI tools to recruit valuable users who were likely to make more bookings, and thus lower its customer acquisition cost (CAC). The company deployed Appier’s deep funnel predictor, which predicted and optimized future events in the conversion journey (such as retention and purchase) by analyzing early user patterns, such as clicks and installs. Together with the ad fraud predictor blocking suspicious traffic to ensure better campaign performance, the company was able to push up its install rate by 119 percent and booking rate by 63 percent, while reducing the CAC by 45 percent.

In this way, using AI tools allows marketers to efficiently manage their app-install advertising frequency spend, and improve UA.  

Reactivating Existing Users and Preventing Ad Fraud

A complete AI solution can also target your existing user base and boost in-app events by reactivating sleeping users through user segmentation and customized marketing campaigns.

In addition, AI tools can further optimize app install ad spend by detecting fraud. A recent global study estimates the share of fraudulent installs has accounted 11.5 percent of all marketing-driven installs over Q1 2018, costing marketers US$700-US$800 million around the world. AI’s machine learning capabilities can detect and prevent suspicious ad installs through multi-stage fraud detection, where the algorithm learns to identify new and evolving fraud patterns and develops new rules to respond.

As all app marketers know, an app install is not the end of the line – it’s just the beginning. The marketers’ challenge lies in understanding and predicting the customer journey, engaging customers and pushing them towards more valuable in-app events. AI can help you optimize your app-install advertising spend by facilitating high-quality UA, thus reducing trial and error, lowering cost per install, and enhancing revenue.

Pinpoint Your Ideal Audience With Machine Learning

Consumer behavior is increasingly fragmented, and that has resulted in the tsunami of data, which can be overwhelming for marketers. Thanks to the behavior-based model of today’s artificial intelligence (AI) tools, marketers can now leverage these tools to segment the audience beyond the traditional parameters to build a more accurate portrait of them as individuals.

By employing AI, you will be able to segment your audience into more granular tiers, and to see which are more valuable to your end goals. Considering that more than half of customers say they will walk away from brands that send messages they find irrelevant, it’s vital that you understand your customers’ current and future intentions as well as possible.

Individuals vs Categories: Looking Beyond the ‘Types’

Artificial intelligence can find hidden user patterns that have a positive or negative impact towards achieving the goal that marketers want to achieve. Hence it can help marketers reach a predetermined end goal, be it finding the more-likely-to-purchase audience segment to drive sales volume, or giving an existing audience a personalized product or content catering to their interest.

This is very useful for marketers. If a website has an audience of one million people, a marketer might want to target a certain segment of that audience, one which is more valuable to them (maybe they are more likely to buy their product, or read more of their articles).

The traditional way of doing this is by considering three dimensions: their demography (age, gender, social aspects); their behavior (how recently they visited the website, for example); and their interests. Using these, you can target people who put one item in their online shopping cart within the last seven days, for example. It can be quite accurate, but a lot of it is guesswork, which can also be biased or wrong. It would also only allow marketers to reach limited users with purchasing intent, whereas AI has the added value of finding all possible valuable-to-marketer-defined goal users.

AI also eliminates the guesswork. It considers many more factors to produce countless combinations. Let’s take a simple example. If we consider degree of interest (say 200), internet browser behavior such as how active each individual is in a period of defined time duration (1 million), site action (6), site frequency (100), site action duration (180), and multiply them all together, you would have an enormous number. Add in more age groups, users accessing the website on certain devices and another couple of factors, and you can see how quickly the possibilities escalate.

“AI gives you literally countless combinations,” says Magic Tu, VP of product management at Appier. “Usually people are phased by this level of complexity, and have limited time in which to interpret it, so they simplify the parameters. However, that loses the richness of the data.”

Following the Behavior Patterns

Not only does AI give you this incredibly granular view of your audience, it also tells you which factors will have the most influence over whether they convert into paying customers or not, and will spot those behavior patterns. It learns from the historical data and continuously adjusts the predictive result by incorporating whichever new data becomes available. All you have to do is to create the model and tell it your end goal. With AI doing all the heavy lifting, marketers are freed up to explore the potential opportunities hidden in the data.

By predicting what users are likely to do next, marketers can adjust their campaigns accordingly. For example, if a company used AI to find the customers most likely to buy a new product, they could segment these users and target them with personalized offers and/or messaging to help convert their intention into a sale.

Chicken, Cakes and Lookalikes: Getting to Know Your Customers Better

AI tools can also provide greater insight into your customers’ interests, and they can do so with an incredible level of specificity – instead of a subject like ‘food’, which is too broad to be of any use, AI tools will analyze the terms users use, such as ‘tonkotsu recipes’ or ‘chicken restaurants’.

The application of AI can make sense of social media posts too. By using natural language processing (NLP), it picks out the keywords and phrases (such as ‘I love this type of cake’) to gain a representative understanding of the post, and then to see its outcome (for example, whether someone who wrote that post went on to buy that type of cake). The more of these posts you feed it, the more accurately it’s able to analyze patterns of behavior, which it uses as a basis for building a predictive model.

“More structured data – like numbers – are of more use to AI,” says Tu. “By using NLP, we can turn articles, blog posts and social media posts into more structured data, which makes use of a huge repository of internet content.”

Using machine learning, ultimately we could segment audiences not only by their static attributes such as gender, age, interest and so on, but also predictive behavior in the near future which is the key marketers always want to know. Combining both static attributes and predictive behaviors together creates the most efficient audience segments that marketers could ever build to achieve their goals.

Changing the Conversation

Such innovations are changing how marketers reach out to customers. For instance, Appier’s Aixon solution recently integrated with Line, one of the biggest messaging platforms in Asia. By cross-mapping Line user IDs with Appier’s CrossX database of over two billion device profiles, it can segment them into keyword and interest areas. The customer organization then uses Line Business Connect to send personalized messages to the users most likely to respond positively.

If a Line user puts a product in their online shopping basket without completing the purchase, for example, the site owner can send them a personalized message through Line with a special offer to convince them to buy the item.

Thanks to AI tools, beauty brand Lancôme discovered that skincare shoppers and make-up shoppers are two very different segments. This lets it show a set of products, videos or articles for eye creams but not mascaras to the skincare shoppers, to avoid serving them with content they will find irrelevant. It also suggests foundation in shades closer to the user’s skin tone, instead of bombarding them with the full 185 shades, most of which will not be of interest. This has resulted in a conversion rate three times greater than previously.

By segmenting their audience using machine learning, marketers can build a more dynamic picture of who is using their service. By going beyond the static traits and targeting those customers who are more valuable, they can focus their marketing campaigns accordingly and meet their customers’ needs more effectively.

Is Artificial Intelligence the Remedy for Brand Safety Woes?

When it comes to programmatic advertising today, companies are focusing on brand safety as much as they are on impressions, click-throughs and revenue generation, but it is virtually impossible to monitor brand safety due to the scale and speed that programmatic offers.

However, with the increasing adoption of artificial intelligence (AI) in digital marketing, such technology will not only help marketers better target their ideal audience, it might just be the cure for protecting advertiser dollars.

Programmatic Could Compromise Your Brand Safety

Brand safety came under the spotlight following a number of advertising mishaps in early 2017. Alexi Mostrous from The Times revealed that many household brands were unwittingly supporting terrorism on YouTube by placing their ads on hate and Islamic state videos. Later the same year, ads from some of the world’s biggest brands were seen to be running alongside videos that sexually exploited children. This led to widespread panic, with many brands pulling programmatic spend until they could be assured by publishers like Google that measures were being taken to filter out such content.

In 2018, brand safety has broadened to cover any offensive, illegal or inappropriate content that appears next to a brand’s assets, thus threatening its reputation and image. This could include controversial news stories or opinion pieces, as well as fake news or content that is not aligned with a brand’s values – for example, a fast food company’s ad appearing next to an article about heart disease.  

In a recent survey, 72 percent of marketers stated that they were concerned about brand safety when it came to programmatic. Also, more than a quarter of respondents claimed that their ads had at some point been displayed alongside controversial content.

Why are brands running scared? Because this definitely has an impact on consumer psyche. Nearly half of consumers are unequivocal about boycotting products that advertise alongside offensive content, and an additional 38 percent report a loss of trust in such brands.

To be fair to brands, they are not choosing to support such content.

Traditional Techniques Come With Limitations

Brands understand the damage that offensive content could cause their image, but it is not feasible for them to implement customized brand safety measures across each ad placement.

Digital advertisers do not have direct relationships with publishers. Ad exchanges receive inventories from thousands of websites that are auctioned off within milliseconds, on the basis of demographics, domain and size of ad. Hence, there are no checks for context or appropriateness, only audience relevance.  

While the explosion of programmatic may be offering more opportunities to reach the right audience, the sheer volume also makes it difficult to monitor.   

Of course, there are some topic areas that no brand will advertise around – terrorism, pornography, violence, etc. And brands can stay away from content around these by using blacklists, whitelists and keyword searching. However, these have their own limitations.

A blacklist, for example, details individual words that a brand does not want to be associated with – but this ignores nuances and context, letting some ads slip through the net, or blocking placements that may, in fact, be safe.

Finally, safety is subjective. A washing machine brand will have nothing to lose by advertising next to content on prevention of tooth decay, but this could be problematic for a biscuit or chocolate brand.   

In the long term, such techniques that are nuance-agnostic cannot completely assure brand safety. Neither can manual methods and checks keep up with the volume, scale and speed that are characteristic of programmatic today.

AI Introduces Context to Content

In this context, artificial intelligence, which offers a solution through algorithms that can understand nuance and context, is fast becoming an answer to marketers’ brand safety woes.

Although AI solutions might not be able to eliminate false positives or avoid the damage entirely, such solutions, specifically those that use machine learning (ML), natural language processing (NLP) and semantic analysis, can offer the nuanced contextualisation that programmatic is lacking today.

ML can ‘learn’ how people approve or blacklist content and then use this to automatically deem content as appropriate or offensive. NLP and semantic analysis assess brand safety at a granular level by understanding the context of a page rather than only look at the keywords or domain name.

Using AI tools that can process large volumes of data at speed to analyze inappropriate placements, advertisers can benefit from the scale and targeting efficiency of programmatic, while avoiding potentially damaging ad placements. Simultaneously, AI can also unlock the potential that false positives undermine by recommending content that brands would otherwise be blind to.

Post the YouTube-debacle, Google confirmed that it was using AI to make YouTube content safe for brands, stating that using ML allowed it to flag offensive content more efficiently and faster than manual methods.

Also, when it comes to brand safety, post-campaign analysis will simply not cut it. Brands have to combine programmatic tools with AI to ensure that the ad placements they are bidding for do not contain content inappropriate to the brand message.

Last but not least, brands should take note that AI tools are only as good as the rules that drive them. Hence, brands must first understand safety within their own context, and what they deem appropriate or offensive. Brand safety rules need to be re-examined periodically as context evolves so they cannot completely do away with human intervention, but AI can help to deal with the sheer volume and scale at which brands operate today.

Are Data Scientists Evolving With the Rise of AI?

As developments in machine learning (ML) are expected to progress at a phenomenal pace, it is set to become one of the most powerful tools for businesses to enhance productivity and drive innovation. While ML, one of the most popular artificial intelligence (AI) applications, holds a lot of promise for businesses, is the role of data scientist today already evolving in order to keep up with the change?

What Is Next in AI

Continued advances in AI will see autonomous systems perceive, learn, decide, and act on their own, but to ensure the effectiveness of these systems, the machine will need to be able to explain their decisions and actions to humans. This is so called explainable AI.

“In the future, many AI systems are going to interact with people, especially those who will take responsibilities, hence the reason why AI needs to be explainable, meaning that the behavior of the system needs to be easily expected and interpreted by people,” said Min Sun, Chief AI Scientist at Appier.

Sun also pointed out that in the future, AI is going to be less supervised, which means that it will require less human inputs, and be more creative.

Data science was previously concerned with time-consuming ML tasks, such as data wrangling and feature engineering, which could take up 80 percent of data scientist’s time, but such tasks can be automated sooner or later, according to Deloitte’s Technology, Media and Telecommunications Predictions 2018 report.

Such advances in AI will give data scientists more time to execute more complex tasks. However, it brings up a problem: a majority of data scientists doesn’t possess the required advanced machine learning skills, such as deep learning (DL), a subfield of ML.

The Impact of Machine Learning on Businesses

Previously, companies might have spent a lot of time doing guesswork based on consumer data gathered online and offline, which is usually fragmented and siloed. With an AI-based approach, brands are able to unify data across different channels for a holistic view and analysis of the audience and their conversion journey.

Machine learning and deep learning allow a computer to take in huge sets of data and not only predict the outcome, but also understand what the desired output should be. It can be integrated into many aspects of digital marketing, such as predicting consumer behavior and campaign outcomes, marketing automation, sophisticated buyer segmentation and sales forecasting.

With these technologies, businesses have a more efficient and cost-effective way to build trustworthy AI systems to be used by professionals and/or to be naturally interacted with human users, according to Hsuan-Tien Lin, Appier’s Chief Data Scientist.

So, it’s no surprise to see that businesses are increasingly catching up on the adoption of AI technology. According to the International Data Corporation (IDC), AI continues to be a key spending area for companies in the near future, with worldwide spending on cognitive and AI systems increasing 54.2 percent in 2018 to US$19.1 billion. That number could go up to US$52.2 billion in 2021, IDC predicted.

Bridging the Machine Learning Skills Gap

As more businesses look to adopt AI techniques like machine learning and deep learning, data scientists are urged to upskill, in order to keep up with the current trends. Rudina Seseri, Founder and Managing Partner at Glasswing Ventures, wrote in Forbes, “Data scientists – at least the successful ones – will evolve from their current roles to becoming machine learning experts or some other new category of expertise, yet to be given a name”.

Leading tech companies such as Google and Microsoft have already been offering relevant courses aiming to help bridge the talent gap. For example, Google not only made its ‘Machine Learning Crash Course’ available to the general public earlier this year as part of the company’s ‘Learn With Google AI’ initiative, it has also launched a machine learning specialization on Coursera, an online learning platform.

Andrew Ng, one of the world’s best-known AI experts, also launched a set of courses on deep learning through Coursera in 2017, hoping to help more people get up to speed on key developments in AI.

While technical skills will be foundation of the role of data scientists, it’s crucial for them to master human-centric skills too. Data scientists will need to develop a better understanding of the overarching business strategy and business challenges in real-world scenarios, in order to create solutions that can solve real problems.

Businesses are looking for a total solution, Sun pointed out. For instance, self-driving car manufacturers need a system consisting of perception, communication, decision-making and control. In the old days, each module was designed separately, but this has been transitioning to more jointly design since the fatal self-driving Uber crash, where the perception system identified the pedestrian, but the decision-making module failed to react.

The ability for scientists to design a complete system consisting of multiple ML modules will become more and more important,” he said. “In the future, data scientists will need to have the modeling and analysis skills at the system-level to provide business people with the right total solution to the market.”

Food for Thought: 10 AI Quotes You Should Read

Artificial Intelligence (AI) is no longer just the domain of sci-fi fans or tech nerds, as it’s becoming a key pillar of how we do business and live our future lives. While we still need time to see how far this prodigious technology will go, AI has always been a subject of interest to some of the brightest minds and well-known personalities. Here are some of their thoughts on AI.

Supercharge Your Remarketing Strategy With AI

Did you know that the average conversion rate on e-commerce sites globally is around 2.6 percent? That says a lot about the immense potential to grow online sales.

One common practice to achieve that growth is through remarketing. Now with advances in artificial intelligence (AI), you can take advantage of methods such as machine learning (ML) to ensure that your remarketing is more effective than ever.  

Mike browses on your site and adds a pair of shoes to the shopping cart. He then abandons his cart without actually purchasing anything. As Mike continues his online activity, he is shown an ad promoting the very shoes that he was evaluating, pushing him to go back to your website and complete the purchase. The ad may offer a discount as an incentive. This is classic remarketing.

Remarketing aims to reach interested prospects who have not converted, and retarget them with relevant marketing messages that will entice them to purchase from you. It offers higher return on investment (ROI) because it engages people who have already shown an interest in your product and are hence more likely to convert.

Advertising is not the sole remarketing channel. Brands can re-engage with shoppers through eDMs and in-app notifications as well. The more targeted and relevant your messaging, the more effective your remarketing strategy.

And this is where AI can help.  

Boost Retargeting Performance With AI-based Segmentation

Traditional segmentation marketing, on which remarketing is based, has its own drawbacks – it’s difficult to link the right person with the right product, especially after shoppers log out, and even more complex to sift through data on different interests and groups to discern true interest and needs. Campaigns rely heavily on experimentation to see what works, and this can be time-consuming and expensive.

In this context, AI can facilitate better user segmentation.

AI helps optimize remarketing strategies by using ML to interpret data around users’ purchase history and identify patterns that can help predict future purchase behavior.

For instance, an AI-powered audience buying platform like Appier’s CrossX Programmatic Platform uses deep learning algorithms to analyze dozens of user behaviors in real time and predict which users have a higher chance of converting. Marketers can then prioritize retargeting their most valuable users.

Trigger Conversions With Personalized Recommendations

ML can also combine user behavior analysis and product information to generate personalized recommendations, further improving chances of conversion.

Here are some benefits of optimizing remarketing strategy using AI:

  • Retarget real shoppers for better ROI

ML can distinguish between shopping – actual conversion – and searching (window shopping) behavior, allowing you to retarget users with a higher likelihood of converting. Would you rather remarket to Elle who spends a lot of time browsing online but rarely buys or Patricia who visits an online site when she wants something and buys it?

  • Capture fast-changing behaviors using deep learning models

ML can identify patterns and predict changes in shopping behavior to make accurate recommendations. For example, Daisy has been browsing coats for a few days, but a sudden change in weather means a warm weekend, and so she searches for a T-shirt on Friday night. AI will give you insights around the short-term change in Daisy’s shopping behavior and recommend T-shirts for her to buy, increasing the possibility of an immediate purchase.

  • Retarget shoppers with the items they actually want to buy

AI can help you distinguish between serious intent to purchase from a user’s longtime browsing pattern versus something that they just checked out online but are not really interested in.

Offer Value Across the Customer’s Entire Shopping Journey

Remarketing offers value through the customer’s entire purchase journey online. Take a user who books a ticket to Tokyo. Traditional remarketing would target them with flight promotions to different destinations. This offers low value at best and is inaccurate targeting at worst. Once the user has booked their trip to Tokyo, they will next shop around for hotels, tour bookings, etc. And this what remarketing should address.

AI is enabling smarter marketing by making relevant recommendations based on previous purchasing behavior and data across ecommerce sites, expanding the number of products the consumer is exposed to. Marketers can use valuable insights on users’ intentions to retarget them with products that are similar in function, design, etc.

When AI is used to remarket cross screens, this proposition is further strengthened. Cross-screen remarketing allows you to reach your user on every screen they own and shorten the time between initial visit and purchase. And AI-powered platforms like CrossX help reduce the complexity of cross-screen remarketing by giving you insights on who to reach, on what screen, with what message and when.

Is AI Breathing New Life Into Email Marketing?

As digital channels such as social media continue to be a vital tool for customer engagement and product promotion, it’s easy to overlook “old school” tools like email. However, recent improvements in email marketing – underpinned by artificial intelligence (AI) – are turning email marketing into a viable marketing tool.

Compared to the possible instant response on social channels, email marketing tends to be less effective due to its limitations, including difficulty in finding and retaining subscribers, and low open and click-through rates.

Conventional wisdom would have you change certain things about your email content to improve its performance, such as offering more discounts, crafting a better subject line, or sending messages with a different frequency. But these suggestions are based on the notion that, as a human, you can guess why readers are or aren’t connecting with your content. While true to a certain extent, this assumption requires a high degree of trial and error to arrive at the desired response from recipients.  

Now, help from AI makes it possible for businesses to discover new lookalike customers, better understand and segment existing customers, predict topics of interest, and anticipate customer behaviors. These actions can help you solve some of the most vexing challenges with email marketing.

Increase Open Rates With AI-Powered Segmentation

There is a reason why your readers aren’t connecting to your content. By taking an AI-based approach, you can see a highly accurate analysis of the problem – as well as your audience’s needs – putting you considerably further along than if you only employ guesswork.

While only 21 percent of marketers in Asia Pacific delivered personalized email beyond just name in 2017, 76 percent of them indicated that they were keen to do better personalization in email marketing, according to a Econsultancy report.

The report also pointed out that using a data point in addition to the recipient’s name is twice as likely to trigger them to open the email. Imagining if AI could write a short novel that almost won a literary award, it can also analyze all the user data including the content consumed by users across screens, and then extract the most frequently used keywords to identify topics that your audience is most interested in, and create predictive segmentations.

Once you gain such actionable insights, you can then develop content or create offers that correspond closely with their preferences and needs. As AI is capable of identifying as many keywords as possible, you will have multiple touchpoints to engage with your audience.

You can even predict who will respond to your new campaign based on their responses to past campaigns, and customize mailing features that make it easier for them to do so.

For instance, a major online and print publisher in Taiwan used to send the same emails to all readers, resulting in low open and click-through rates. Content and headlines weren’t relevant or sufficiently attractive to trigger recipients’ interest.

By adopting an AI-based approach, the publisher used deep learning to link reader profiles with their online behaviors to establish segmented profiles based on key attributes, such as age and interests. This process allowed the publisher to tailor mailing lists to the right group with appropriate marketing content. As a result, its open rates increased by 42 percent, and click-through rates increased by as much as 107 percent.

Grow Your User Base With Lookalike Audiences

The right AI models can also analyze data gathered from users’ online activity to find those who “look like” your current customers, helping you develop targeted ads and other outreach efforts. This process starts with the breakdown of demographic data about your current customers with as much granularity as you choose. It can include data from your website, campaigns, apps, customer relationship management software, application programming interface integration, and more.

An AI-enabled platform then maps that information with additional sources to find close potential customer matches. Using this valuable data set, email outreach becomes less of a guessing game and more of a precise targeting tool.

Retain Subscribers with AI Prediction

Based on behavior patterns, the AI-enabled platform can help you identify subscribers who are likely to leave your service. Certain actions indicate their readiness to move on, but you can prevent this migration if you give them reasons to stay. Once you have identified this subset of your subscribers, you can plan and implement your re-engagement strategies, such as:

  • Creating emails targeted solely to this group of “potential unsubscribers”, and segmenting further into interest groups.
  • Offering surprises, deals or rewards specific to those groups.
  • Using formatting and links to make it easy for readers to take action.

AI is the most promising tool that is driving personalization in email marketing like never before. The AI-based approach makes it possible to identify the behaviors and interests that should trigger customer engagement in email marketing, and determine how the content delivered should be customized to produce the desired outcome. The benefits mentioned above are testament to how AI can make this old marketing method thrive again.

 

What It Means to Be a Data Scientist Today

By Yao-Nan Chen, Machine Learning Scientist, Appier

Unless you have been hibernating under a rock for a few years now, you already know that explosive growth in the volume of available data is disrupting business as we know it. This data can be a goldmine for businesses that know how to capture, analyze and use it to power artificial intelligence (AI) technology. And that’s where data science and my role come in.

IBM has predicted that demand for data scientists will increase by 28 percent by 2020. The Harvard Business Review, way back in 2012, said that being a data scientist is the sexiest job of the 21st century.

I have been working in data science since 2013 and I still come into work at Appier each day eager to solve new problems.

What Data Scientists Actually Do

Simply put, data science involves using data to generate solutions that solve practical, real-world problems. In the business world, examples revolve around AI-powered solutions, such as pushing recommendations for users based on their demographic or usage pattern, or analyzing why sales of a particular product is dropping.

Data scientists set out on solving such problems by first extracting and consolidating data, which we then analyze for patterns and trends. We use this to build predictive models, derive insights, and implement proof of concepts to test the proposed solution to the problem at hand. The problems that we work on are very specific and often have no one standard solution. Hence, data scientists are tasked with thinking out of the box to come up with a variety of possible solutions.

The impact of our solutions is known only when they are implemented; so often, if the solution fails to meet the desired outcome, we have to go back to the drawing board and start over. But this just adds to the challenge and the excitement of trying to pin down that elusive solution and make it work.

What Makes for a Good Data Scientist

Of course, every job has some less lovable bits and the burden of the data scientist is data cleaning! In most cases, the data we gather is ‘dirty’, with errors and discrepancies in it. For example, data showing that sales of a product have dropped dramatically may simply mean that malfunctioning machines have failed to capture the data accurately.

Most data scientists will agree that data cleaning is the most boring part of this job. Our inside joke is that data science is 80 percent cleaning of data and 20 percent complaining about it!

But jokes aside, data cleaning is painstaking but important work. If not done right, it can have a huge impact on the accuracy and reliability of insights.

Aside from this kind of assiduity and attention to detail, a good data scientist, no matter how good they are technically, must also have a thorough understanding of business domain and the organization’s business goals. Our solutions have to be creative, but also useful and practical.

Keeping Up with the Latest Research

In this context, keeping up with the latest research in the area of machine learning can help us stay on top of trends and monitor breakthroughs to specific problems. We don’t need to reinvent the wheel – if a particular problem has been solved before, we can always work off that.

I regularly read papers on advances in machine learning, as well as in the specific domains that I am interested in.

It’s equally important to engage in discussions with peers, keep track of their recent research and poll their opinions on machine learning trends. This will help you keep abreast of all that is happening in this area.

Growing Demand for AI Expertise

Unfortunately, there is a gap between the growing demand for data scientists and the supply of talent in the area. AI is a new track and there is a shortage of people with the required expertise. What widens the gap is that not every data scientist is a good business person. They may be stellar at solving problems in an academic or research-based environment, but often fall short when it comes to real-world business problems.

Data scientists today must constantly evolve in terms of skill set. As the adoption of AI and deep learning grows, we are automating lower-level tasks and moving onto more complex problems. We already have some mature tools that can be used to build simple models for many business cases, and these are becoming simpler to use.

In the near future, data scientists will be required to know how to leverage and use problem-specific information. As AI becomes more complex, data scientists will need to work on more abstract problems and leave simple processing and analyses to automation software.


About the author:

Yao-Nan Chen is Machine Learning Scientist at Appier. He has more than five years of experience in Machine Learning, Data Science and Data Engineering and three years of experience in practical E-commerce recommendation system. Prior to joining Appier, he worked at Yahoo Taiwan on E-commerce Recommendation System, App notification recommendation system, Model tuning for sales volume prediction, etc.