Amazon Shopping. We’ve all been there, just about to checkout…when all of a sudden a list of related products labeled as “People who bought this, ALSO bought this” appears. The next thing we know, you’ve just added two more products to your shopping cart.

THAT is a Recommendation System.

Recommendation Systems are responsible for dramatically increasing sales and revenue by using two very effective selling techniques; Up-selling and Cross-selling .

 According to a McKinsey Report, 35% of all Amazon’s transactions come from their Recommendation System. 

The good news is;

1.  Small Businesses: Small Businesses, Ecommerce and Enterprises can also benefit by incorporating this powerful AI (Artificial Intelligence) tool into their businesses.

1. Services: Recommendation Systems are not limited to the selling of just physical products, but are also excellent for selling “Services” such as Health and Wellness Services, Repair and Maintenance Services, Medical and Dental Services, Legal and Financial Services, Energy and Power Services, Beauty and Anti-Aging Services, and Restaurant and Hospitality Services, etc

Here at QAS (Quantum AI Strategy) our specialty is Recommendation Systems. Call today for your FREE Strategy Consultation (480-418-0033). 

The remainder of this article is technical deep-dive and will focus on the key motivations that make Deep Learning (Artificial Intelligence) a great tool for the recommendation of additional products and services for purchase.

The main topics of this article are:

• Recommendation Systems Introduction
• Recommendation Systems as a Machine Learning Issue
  • Content-Based Filtering
  • Collaborative Filtering
  • Hybrid Systems
  • Cold-start
• Why use Deep Learning for Recommendation Systems?

Recommendation Systems Introduction

A Recommendation System’s main objective is to filter the content that is delivered to the user, try to predict the “grade” or “preference” that the user would give to a content and then make a recommendation based on that preference. Once these recommendations are presented to the user, the likelihood of the user purchasing the recommended content is increased dramatically.

Recommendation systems are a machine learning subarea that aims to make new suggestions that may be of interest to you, based on your background and profile or on choices made by others with similar tastes. It is widely used as a marketing strategy by e-commerce companies, as recommending something in line with the user’s interest increases their chance of acquiring such a product.

The suggestions provided in the Recommendation Systems are intended to assist users in various decision-making processes, as well as which items to buy, which music to listen to, and which news to read.

Recommendation systems have proven valuable in helping users deal with information overload by filtering information and recommending what would be of interest to them.

This way, the consumer experience within the platform becomes more optimized and interesting.

Recommendation Systems can be used to recommend content in different domains, such as books, music, movies, retail, news, etc. 

Although there are specifics in each domain, two characters are key, the system users and the content offered by the platform. Thus the basis of Recommendation Systems is the content offered should be filtered in order to generate a personalized and relevant list for each user.

The examples are diverse: Amazon recommends books (or anything in the marketplace), Netflix recommends Movies, Spotify the songs.

In addition, for e-commerce, it has become one of the most powerful and popular tools by recommending products or services according to the habits of users.

You can make recommendations by comparing a user’s preferences with a group of other users. You can also make recommendations by looking for items with characteristics similar to those you have already shown interest in the past. User preferences can be harvested implicitly or explicitly. Implicitly, information is obtained through past purchase options, history of websites visited, clicked links, browser cookies or even geographic location. There is also an explicit way to check preferences using effective feedback, such as grades given for a particular item.

The benefits of a Recommendation Systems for the platform can be varied, such as: improving the user experience within the platform; keep the user logged in longer; increase sales of the products offered or simply all of them at the same time.

Of course, an effective recommendation system is quite complex, its development involves several factors, such as: User profiling and types of information gathering; Use of complementary data for comparison; Distinct filtering techniques; (Re) weight calibration and evaluations; Treatment of the fearsome Gray Sheep, which are characterized by newly discovered behaviors that do not follow the initial pattern; Performance and accuracy, among others.

What Applications Can Recommendation Systems Be Used On?

It is important to evaluate for which applications these systems are viable.

First, they must be based on the items being displayed or offered to users. In another case, the algorithm loses its meaning.

Another important point is that this mechanism is applicable only when there is a large amount of data involved. This is necessary to ensure that the methodology is efficient, since mathematical abstractions are made and the more data, the more accurate the abstraction function, and therefore the more correct the result.

Recommendation Systems and Machine Learning 

The system must learn the pattern of user content consumption from past experiences and predict what the user would like to consume in the future.

In general we can divide the different methods / algorithms into different categories of approaches:

Content-Based Filtering

Generates recommendations based on the similarity of content already consumed by the user. That is, it uses the content that the user has already consumed on the platform (read, bought, watched, listened, clicked ..) to generate a profile, so the system searches for similar content that has not yet been viewed by the user and then recommends.

The focus is on item properties. The similarity of a recommended item will be measured by the similarity to the properties of the item that the user has previously purchased or searched for.

The main advantage of this category is that it does not require much user feedback to start recommending something “useful”.

If the first NetFlix-watched movie was “Star Wars”, the system can already recommend the entire universe of movies, series, similar or related, without requiring a very long history of interactions.

There are two main disadvantages to using content-based recommendation:

1. The system places the user in a “preference bubble” where everything recommended is similar to what has already been consumed. This long-term preference bubble can lead to user disinterest in recommendations as it generates little diversity in the content presented. 

Another point is that depending on the context can be detrimental to the business too, as it does not present new products to the user and the profile may change more slowly than the user’s own preference.

1. This problem has more to do with solution modeling. Content-based filtering is performed when filtering similar content, now try to define what content similarity is.

Each context has its particularities and diversities regarding the features that can be used, such as categorical data, free text, numeric, images, audio. The point is that if the similarity function between Content A and Content B does not have well defined, will result in very homogeneous, repetitive or even nothing similar recommendations.

Collaborative Filtering

In the Recommendation Systems based on collaborative filtering the focus is on the relationship between users and items.

The similarity of items is determined by the similarity of their rating by users who have rated the same items, that is, if users have rated items with similar grades, they probably have similar tastes and accept recommendations based on this criterion.

This type of recommendation has positive results in practice, and avoids the problem of repetitive recommendations. One disadvantage is that it requires a large amount of information about the user and their surroundings to work precisely.

In addition, the Collaborative Filtering approach ignores content characteristics and focuses on User X Content interaction. It assumes that the system does not need to know the characteristics of the content, but what content the user consumed to identify which other users had the same consuming behavior. 

This way you can “swap” recommendations among similar users when processing the collective.

These are recommendations very similar to “Users who watched Star Wars also watched Lord of the Rings.” This approach solves the two previous problems, takes the user out of the preference bubble, and does not need to define content similarity as features are ignored.

The special attention of this method is in relation to the modeling of the “interaction force” of the User x Content to represent the “How much the user liked the content”.

In practice, this value can be the user’s own rating of the content, the number of times he has accessed the content, how long he has been interacting with, or any other metrics that pass this idea of preference.

The most common way to solve the collaborative filtering problem with ML is to try to infer missing values in an interaction matrix, where input (i, j) describes the “interaction force” or score the user gave to the content.

If the system can ‘predict’ these values it is possible to generate recommendation by selecting the ones with the highest score and the user has not yet interacted. Array Factor Algorithms (ALS, SVD ..) are the most commonly used to ‘predict’ these values in classical approaches, but they are not the only ones.

Collaborative filtering methods also have disadvantages. One of the main ones is the scale of the solution itself, since processing a (collective) matrix of all users with all content is a computational challenge.

An even more damaging detail is that this array is extremely sparse, has many more values missing than filled in, usually less than 3% of the array is actually filled.

Another disadvantage of this approach is the need for a considerable amount of user input and feedback to start generating recommendation. That is, until a new user begins to have recommendations on what to consume, they have to interact with many more items than content-based filtering, which is a problem for platforms with little or no user history.

Explicit and Implicit Preferences

The system user can interact with content in a variety of ways, such as rating, favoriting, sharing, viewing and not interacting, playing…, etc.

The form of this interaction depends greatly on the recommendation domain, for example in the news domain, it could be how long he / she was reading, if the user viewed, shared the text on social networks, etc. The user would hardly rate the news or bookmark it, but in the movie domain such interactions would be more common to perform.

These preferences can be categorized as explicit and implicit. The explicit ones are when the user spontaneously indicates what is important to him and / or the degree of importance, an example would be the user to bookmark the content or to rate it. Implicit data is collected from user behavior within the system, this information may indicate preferences when it is not possible to explicitly retrieve them, an example could be stories viewed on a news site, reading time, percentage of watched video, search terms, etc.

Deciding which information to collect as a user’s preference will depend on the system itself and the domain of the recommendation. Importantly, regardless of the information that will be used as a preference, the goal is always to “model user interest in content”.

This categorization of preferences between explicit and implicit is important in the context of Recommendation Systems, since in many cases the model / architecture is preferably used in one of the categories.

Hybrid Systems

As its name implies, hybrid systems are those that have characteristics or combine the Collaborative and Content Based Filtering approaches. Overall, the results are better than both separately, which is evident when comparing the strengths and weaknesses of each category.

There are several approaches to leaving the hybrid system. The simplest is to unify recommendations generated by separate systems into a single list.

Other more complex approaches add content capability and information to collaborative filtering, or vice versa. They are indeed hybrid algorithms and bring better results.

Cold-start

This category seeks to optimize the recommendation for new users on the platform.

What to recommend when I don’t have user information?

All Recommendation Systems will have new users with little or no personal and consumption history information. A user named Cold-Start is precisely that user within the platform, and depending on the domain the number of cold-start users is larger and more important.

For example, news recommendation has a high rate of cold-start users, usually no one logs in to the platform and each session ends up being a different user, whereas on Amazon or Netflix the user needs to be logged in to buy and consume content.

The simplest way to recommend cold-start is to create generic lists, such as:

• Most accessed content in the last 24hs
• Geolocalized recommendation when it is possible to know the user’s location
• Diverse recommendation lists (best in each category)

Why use Deep Learning for Recommendation?

Just as computational vision and natural language processing were radically impacted by DL, we are currently experiencing the same in the area of Recommendation Systems.

It is not just the amount of academic papers that are growing and advancing state-of-the-art in the last 4 years. Large companies are moving from classic recommendation systems to DL approaches.

Key Points That Make Deep Learning a Great Alternative to Recommendation Systems

There are some features of Deep Learning architectures that make it deep in the Recommendation Systems wave and bringing breakthroughs in the state of the art. The main points are:

• Lots of data. Collaborative filtering techniques are limited by the size of the interaction matrix which are also extremely sparse. DL works very well with large volumes of data, can reduce dimensionality without losing the representativeness of the original information. Convolutional Networks and Autoencoders are widely used in this regard.
• Heterogeneous Data and Features. Depending on the domain, the data representing the content may vary widely, being categorical data, numeric, text, image, audio, etc. Being able to represent all this information in a unified way has a great impact on the similarity of content. DL can easily process and extract patterns in different domains by creating Embeddings, where it is possible to create a vector representation of whatever type of data, this facilitates semantic representation and similarity calculation.
• Dynamic behavior. Depending on the domain, the preferred behavior is very dynamic or short term. Classic algorithms have difficulty extracting patterns when this behavior changes too fast or is not present in the history. DL architectures such as Recurring Networks work very well with this dynamic and especially with the sequential behavior in content consumption.
• Better representation of User vs. Content relationship. The interaction force between these two actors is the basis of collaborative filtering, classical methods end up modeling this interaction in a linear way (ex. Matrix Factorization ..), which limits generalization. Neural networks are known for nonlinear representation of information and can best represent this interaction.

Conclusion

AI (Artificial Intelligence) is quickly changing the business landscape and providing tremendous benefits to those businesses that adopt this powerful technology early. Recommendation Systems have proven time-and-time again to provide a high-ROI and have been rated as one of the best investments a business can make. No more pushy sales tactics, No more embarrassing Up-sell attempts. Recommendation Systems allow you to “Set it and Forget it”. Let the system do the selling for you and see the tremendous increase in your businesses revenue and sales. 

Call today for your FREE Strategy Consultation.

QAS (Quantum AI Strategy) 

(480) 418-0033. 

To differentiate themselves on social media, businesses are turning to artificial intelligence (AI). In this article, we will look at the ways in which AI can enhance social media marketing, taking you to the next level in revenue generation, reduced ad-spend and discovering new trends and markets.

Having a social media strategy is pivotal to the success of any business.. Social channels are part of everyday life and it would be fair to say that many consumers are addicted to them. If somebody isn’t connected socially, they are perceived as a minority in an exponentially growing digital era. According to Statista, 70% of the US population has at least one social media profile, whether it be Facebook, Instagram, LinkedIn, Twitter or any other.

Ultimately, connecting with your audience is far easier if you are visible on the channels that they use. However, that doesn’t mean you should blindly market to everybody on social media. The channels still require the same care with targeting and personalisation that has become so prevalent with email over the course of the last decade. That is easier said than done given that all your competitors are trying to do the same. But, are all your competitors using Artificial Intelligence? And the answer is NO; which gives you the upper hand by implementing AI.

What is AI?

While we won’t go into huge technical detail here, it is probably worth giving a brief overview of what we mean when referring to AI. Essentially, it is the science of getting machines to act and understand things like humans do. It is something we use every day from doing a Google Search to talking with Siri and shopping on Amazon, all of which use some form of AI. 

Many applications of AI in its current state are powered by what we call machine learning. This is using existing data to predict future behaviours and generate automated decisions. If we put that into a social media context, all the ad buying and content creation that digital teams do, could be automated using AI software and tools. By ingesting behaviour and purchase patterns amongst other data, machines can self-learn and continually improve their own performance without human intervention. Another amazing benefit of AI is that the information being analysed by AI does not have to be your data; it can be your competitor’s data. Providing you insight on what strategies are working for them and implementing those strategies yourself. 

Social media platforms themselves are filled with AI. Some examples that we see on social media include how Facebook tags photos. The integrated AI is now able to analyse a picture and recognise faces, automatically tagging them. Instagram now does the same. While we might take it for granted, the way that LinkedIn displays “People we may know” is a form of machine learning, using data to connect us to others. Twitter can show us relevant Tweets based on our past behaviour. 

Here are some of the ways that social media marketers can also make use of AI via the platforms. 

Social Media content created will laser-focus precision- targeting “Ready-to-Buy” audiences

A lot of time is spent by marketing teams in researching and creating content. There are many tools that can take this content and schedule it across various social media channels, but AI can take the next step. 

AI tools can scan your social media profiles (and your competitors’ social media profiles) to see what types of advertisements work best. For example, do videos get more shares than text or are value added offers better than financial reward posts? It does this by analysing historical data through machine learning and natural language processing. Artificial Intelligence allows you to “spy” on your competitors to see what has worked for them, and what is increasing revenue and profits; giving you a direct comparison to help you gain an advantage. 

Based on your chosen industry, previous posts and competitor knowledge, AI tools will be able to scour the web for recent news associated to the topics your brand will be interested in. The information it finds can be used to automatically generate new posts, fitting your brand voice. The more data that the AI tools have available, the more intelligent they become. This means your posts will continue to get better without any human intervention required. 

Using AI platforms to their full extent like this could cut content creation down by as much as 90%, getting hours of work completed in next to no time. As AI can extract data from millions of articles and blogs quickly, the likelihood of creating relevant and engaging posts is far greater than those created by humans. 

AI is not designed to replace human input in this instead but rather augment it. Marketing teams can focus on strategy and new technology rather than spending time on tedious and monotonous tasks such as research and writing content. 

Paid Advertisement (Ad spend): Never guess; only PREDICT with Artificial Intelligence

In a similar method to content, AI can predict and recommend the best types of ads to use for each specific target audience. 

AI tools exist that can help target your spend and targeting. For example, AI can process your spend and targeting data, review the results and then suggest what you should do to improve your revenue. AI also has the ability to “spy” on your competitors and identify how much they spend and where they spend. At scale, this can provide a lot of value when you have campaigns running across many different channels. 

AI has been shown to unlock channels that advertisers didn’t even think could provide a return. Where tools can process large amounts of data, they can present insights that might have been lost by the human eye. For example, an age demographic that responds to a certain type of campaign but got buried beneath mountains of other data. AI will be able to optimise all advertising. 

Some technology can take this further. New AI tools on the market can present ads to consumers on an individual basis. For example, based on how somebody behaves, what they buy, their demographics and many other attributes, AI can present different text and images that they are likely to respond and eventually purchase.

In fact, some are even smarter and can present ads based on consumer emotions. Some people prefer ads in a specific tone and that could even vary at different times of the day. How they respond can be used to gain an idea of their likely emotional reaction when presented with a new ad. 

Competitor Intelligence- the strategy that builds empires

AI systems can help businesses compile more granular competitor research than ever before. The days of browsing the social media posts of the competition are long gone as AI can quickly extract the text of Facebook posts, Tweets or Instagram images to see what is trending and what is performing. There are numerous benefits to this activity outside of the time and cost saving against the manual work of a human counterpart.

One example might be in tracking competitor promotions. If they are going to start a new offer, it is likely they will start doing warm up posts in advance. If you are using AI to monitor competitors in real-time, you can stay ahead of the game. 

It has become commonplace for customers to raise complaints on social media as they feel this gets a faster response, given it is in the public eye. Monitoring competitor complaints can provide valuable data on how your service is advantageous over theirs and what to avoid.

If you have competitors posting on social media, AI tracking tools can monitor the volume of likes and shares they receive for different types of campaign. As that information is open to the public, it is relatively easy to extract. Adding this to your own datasets helps to provide insight on what customers in your industry like seeing the most on social media and can drive your advertising decisions. 

New Trends, New Audiences, New Markets, New Revenue…More Profits

One of the more emerging fields of AI is in brand-image translation. If you are operating in a local or global industry, it is important to realise that different localisation and cultures have varying demands, desires and different methods of communicating. It is important to optimise and manipulate your advertising and brand-image so that it appeals to each audience, allowing them to connect with your brand. AI consulting teams such as Quantum AI Strategy creates multiple versions of a social media ads and target mixed cultures, mixed generations and mixed markets.

AI tools can not only help with creating context but also have the capability of using context to distinguish between complex linguistic differences. For example, one campaign ad will appeal to a baby boomer triggering the baby boomer to purchase the product/service. The exact same product/service will then be marketed to a Millennial, but with a different tone, jargon, terminology and context all together prompting the Millennial to purchase the same product.  We are getting close to what is known as AI-powered content localisation which would allow social media marketing to introduce businesses to consumers across the globe.

The AI available on social media is giving consumers the opportunity to fix errors. Direct translations in multiple languages are tricky meaning early tests of the AI are prone to error.  It will be hard for machines to automatically pick up how to translate English idioms into French for example meaning human intervention is integral to get it right. As users offer solutions, the AI platforms learn from mistakes and continue to improve.

Social Sentiment Analysis

AI applications like natural language processing (NLP) can be used to conduct a social sentiment analysis of your brand. This involves taking all posts and mentions to ascertain whether there is a positive or negative opinion of a campaign. 

For example, if customers were posting on Twitter that your app is slow and keeps crashing, sentiment analysis would recognise the terms “slow” and “crashing” as negative keywords, decreasing the brand score. A sentiment analysis adds a point for positive words and removes a point for negative words to give each post a score. This can be totalled to provide you with an overall benchmark.

Sentiment analysis is perfect for social media as it goes beyond tracks likes and shares. It tells you what the customers are saying and gives your business the capability to read between the lines. 

While teams could do this activity manually, AI and machine learning can extract masses of posts to perform a real-time sentiment analysis. This could be vital straight after launching a new product or doing an app update for example. 

Virtual Assistants (Chatbots)

According to a report from Hubspot, 47% of consumers would be willing to purchase a product directly from a chatbot. These are the pre-programmed messenger applications that can quickly answer customer queries without the need to speak with a human. The most popular example is Facebook Messenger so social media as so many consumers use the channel meaning it makes good sense for business applications.

Chatbots use an application of AI known as natural language processing (NLP) to understand what the user has said and return the most relevant response. Marketers can use this to their advantage and program the bot to say specific messages based on how the user is responding. Disney created a bot when they released the feature film Zootopia in 2016 which presented teaser trailers based on the user answers.

There are several benefits to deploying social media chatbots. 

• 24/7 response. Chatbots don’t need to sleep and can speak to your audience no matter what time they want to talk.
• In-app experience. If brands can incorporate a chatbot into social media, it keeps them within the platform. Consumers hate having to bounce between apps so if brands can create a single one app experience, it keeps everyone happy
• Real time journeys. Chatbots can reach out to customers in real-time. For example, a chatbot can push out an instant message if a customer doesn’t purchase. Email could never do this. 
• Conversation. Chatbots can get away with more pertinent questions than something like email as they are conversational by nature. This could be valuable in finding out more about your customers, adding data to fuel further AI technology. 

Chatbots have become a must have of any social media marketing strategy. 

Summary

AI tools could take us to a world where marketers don’t have to put in any manual effort to manage their social media accounts. Contents can be automatically created, translation tools help you connect with everyone, sentiment analysis provides real-time feedback and you can predict likely ROI of a campaign before it has even started. Marketing teams can focus on the strategic elements of their role rather than the research and admin of creating and managing campaigns. 

Of course, there are risks with automating too much. It could create a place where brands don’t even know what they are saying to consumers because AI does it for them. AI must be used as an amazing time and cost saving social media marketing tool that augments existing processes, rather than completely replacing them. 

However, the benefits of AI discussed in this article heavily outweigh the risks and the time to start embracing technology in social media marketing has come. 

Are you still using outdated SEO methods? Well, Google knows exactly which sites are using updated methods and rewarding them generously with Page 1 search ranking, Domain Authority and Trust.   In 2018, Google celebrated its 20^th birthday. It is quite amazing how a search engine we rely on so much is still incredibly young in the grand scheme of things. Right now, most of us couldn’t imagine life without Google and I’d be surprised if readers of this article don’t use the search engine every day. In 2019, statistics from StatCounter showed that Google have almost a 90% share of the search market. 

The reason that Google has become a mainstay in our lives is because it has fundamentally changed the way we search the internet. The philosophy of search is still about information retrieval at its core but the queries we use, the channels we’ve adopted and the devices available have all been through many evolutions. Who wouldn’t thought 10 years ago that you could talk to your phone, have it search Google and then tell you the right answer?

Google have shaped the internet and for marketing teams, this means they have also drastically changed how they carry out one of the core functions of their role in search engine optimisation (SEO). Google are rewriting the SEO landscape all the time, changing the way companies rank and therefore, how marketing teams need to advertise. 

The majority of changes we’ve seen have come from Google’s adoption of artificial intelligence (AI) which started in 2016. In that year, Google purchased the AI based company, DeepMind, as well as implementing their RankBrain technology which is founded on machine learning, an application of AI. In this article, we will look at how those AI and machine learning applications work and what they mean for marketers in 2019 as we move towards a new decade.

What is AI?

Before we look at the technology, let’s just remind ourselves what we mean by AI. Generally, AI is any intelligence shown by machines in contrast to natural intelligence that you would see in humans. AI systems will demonstrate some of the behaviours of human intelligence like problem solving, planning, designing and perception. To accomplish this, AI has different applications, one of which is called machine learning. 

Machine learning takes existing data to create patterns and predict future actions and decisions. Given the huge amount of information processed by Google every day, there are naturally massive data stores and fantastic potential to generate powerful insights.

Important ways Google have used machine learning

Since the start of 2018, Google have really stepped up their game when it comes to experimenting with AI. Before we start talking about RankBrain and what that means for SEO, here are some of the key things you should know.

• Beyond pure search, Google properties like Maps, Images and YouTube mean that total market share is well over 90%. These properties are just as important for SEO.
• Google updated their algorithms over 3,000 times during 2018. This means marketers have to keep on top of their SEO strategy and stay ahead of the game
• Google now uses neural matching on over 30% of queries. This means they connect words to concepts during searches to provide more accurate results. We won’t go into huge detail in this article, but marketers need to be aware of neural matching.
• It is thought that about 62% of Google traffic is via mobile or tablet search. The way people interact with those devices is very different and needs to be part of SEO strategy. 76% of keyword generate a different search result on mobile vs desktop
• Mobile searches for “where to buy” have grown by 85% in the last two years
• 15% of Google searches are ones that have never been encountered before

The statistics go on but the takeaway here is that the way we search is forever changing and SEO must follow suit for businesses to remain competitive. 

Google RankBrain

RankBrain was the foundation of Google’s move into AI during 2016. The function of the machine learning technology was to better understand the entities that people were searching for and to turn those into concepts. This meant that Google would be able to better understand the query and return results that were connected to the initial search. 

The algorithms of RankBrain use data to recognise synonyms which instructs other parts of the algorithms to create the appropriate search engine results pages (SERPs). A lot of the evolution of Google in the last two years has been in making RankBrain more efficient. It now takes the content of every search, converts them into what are known as “word vectors” and performs mathematical calculations that have a very accurate guess as to what the user needs. 

What does this all mean for SEO?

So, we can see that AI and machine learning have changed how Google operates. Whenever Google release a change to their algorithms, it will undoubtedly have a knock-on effect on SEO. Here are some of the ways that it is influencing what marketers do.

Voice Search

Comscore have said that 50% of all searches will be by voice in 2020. With everybody having access to Alexa, Siri, Google Home, Cortana and others, the trends have changed massively in the last five years. These forms of voice activated AI have changed the way people search. The way we speak is very different to the way we type. Voice searches will have more natural patterns than typed text and are usually more informal. 

In terms of SEO, this means you need to adapt content to be more conversational. Traditionally, blog posts written for SEO would have a lot of repeated content to score points in the Google algorithms. As it edges more towards voice, natural full sentences of high quality content, answering audience questions have become pivotal.

Tools such as Yoast and Moz have come to the market to help marketers optimise their keyword formation. They help to formulate sentences, organise content and meet the basics of page structure for SEO. The technology within these tools also reminds users to add images and videos which are just as important for search as text.

Personalisation

We are living in a world where consumers demand personalised products. With so many channels and communications sent each day, the challenge for brands is in standing out from the competition. Whilst we often think about this in terms of the emails we send or in the ads we create, the same applies to SEO. What if two users who search for the same thing could get different results? Digital marketing teams now have massive amounts of data available from websites, blogs, social media and many other channels that their users interact with.

Using this data, brands can tailor what different visitors see on their websites. For example, if you sell sports equipment, customers who love skiing might see different content to those who are fans of football. The machine learning algorithms have the ability to dynamically create the right content for their users.

Many experts see Amazon as a leader in the world of personalisation. Their method takes user data to suggest products based on their history. With that technique, they can bring products that could otherwise be forgotten all the way up to the surface. Linking this back to SEO, the lesson here comes in associating content to different states of intent and capitalising on an opportunity to cross-sell. For example, if a user clicks a specific link on your website, that could be a trigger that they will be interested in a service that they wouldn’t have considered without being prompted.

Content Discovery

For consumers, discovering content is no longer just limited to a search results page as the Amazon example goes to prove. Marketers need to understand that their customers can engage with them anywhere, at any time. Predictive analytics, a segment of machine learning, ties all this together. Using patterns to understand the likely intent of your visitors can help SEO marketers deliver content to meet their demands. 

For example, there may be triggers that show if your consumer is not ready to purchase yet so instead you offer them comparison products to try and retain engagement. On-page SEO might provide them with more information about the services.

Some ways that AI can compliment SEO for content discovery are:

• Using semantically specific pages that associate queries and intents
• Use AI to publish content at the right time to the right people. 
• Create content based on the stages of your customer journey 

Content Marketing

Marketing teams are making use of machine learning tools that can takes masses of online content and extract the core details required for their SEO strategy. A big part of SEO remains in being relevant and topical. For humans to research everything is tantamount to impossible with billions of websites currently online. New AI-based tools are able to take away the manual work and provide relevant content straight to teams in real-time.

A progression of this comes in what we know as natural language generation (NLG). This technology takes the researched content and builds its own headlines and even full content in natural human language. Whilst it is nowhere near perfect at this stage, a model algorithm known as GPT-2 which has been supported by Elon Musk is getting a lot closer. The Huffington Post as well as others are using this type of technology. 

In SEO terms, have topical content that is automatically generated in human language is a dream for digital marketing teams. It gives them more time to strategize on other areas of marketing rather than be too concerned about their content. 

Top quality content is even more important when it comes to off-page SEO which are the actions taken outside of your own site that impact page rankings. Traditionally, marketing teams would post links to their site in as many places as they could to generate traffic, but that art has virtually become dead. The aim now is to get natural links. These links come from other people who adds links to your site independently without your knowledge. 

Machine learning algorithms can automate your social media content with topical posts that are likely to generate natural links from subscribers, followers and ideally influencers. You can quickly analyse emerging trends, understand your consumer voice and be quick to post on subjects that will see you as an expert in your field. AI can genuinely turbo-charge social media presence and off-site SEO. 

Insights

AI can deliver business insights capable of supercharging your SEO strategy. This can include market trends, competitor benchmarking, SERP and pay-per-click management amongst others. AI will give teams data insight that they never would have been able to gather with the human eye alone, in a similar way to how we discussed content research earlier in this article. 

Outside of Google, Pinterest is using AI and deep learning techniques to understand the context behind images on the website. 

Data insights will help SEO marketers better understand elements of the customer journey they need to update, where they need more long-tailed content and where they have opportunities to deliver what the competition are missing.

The future of AI and SEO

While the tech giants like Google are adopting an AI-First approach, the technology is being designed to supplement human roles and remove the monotony of some tasks rather than make them obsolete. 

Search engines will continue to evolve and incrementally change their algorithms. As the digital is still growing, consumer tastes are always evolving and search follows suit. The challenge for marketing teams comes in keeping up with the change. You cannot simply write a single blog post with some keywords today. All content must be well thought out within a full strategy that compliments the needs of the business and their customers.

As search engines like Google use more AI, marketers need to build their own AI tools to combat that. The automation available in modern tools means marketing teams have the ability to accelerate their SEO strategies and influence page rankings. 

Who wants a Chatbot?

A survey by Oracle showed that 80% of businesses want to have a Chatbot in operation by 2020 as they look to reap all the benefits they bring. Many of us probably use Chatbots every day. The best ones are those where we don’t even realise it’s a Bot we’re talking to.

Unless you have been hidden away for the last few years, you would’ve heard all the buzz around Chatbots. Experts say they are going to replace traditional forms of marketing, entire customer service teams, and make mobile apps irrelevant. As well as doing all of this, they will cut costs while increasing revenue. Better yet, they will do it on their own without you or any other people having to lift a finger. 

Chatbots can be used on a business websites, Texting platforms, Email platforms and Social Media Platforms such as Facebook. In this article, we will look at Facebook Messenger Chatbots and discuss how it has quickly become the favorite AI tool for millions of business due to its ability to literally change the game of Digital Marketing.

What is a Chatbot?

Before we continue, let’s quickly clarify what we mean by AI and Chatbots. Ultimately, a Chatbot is an artificially intelligent automated messaging software. It can converse with people by answering questions and completing tasks without them needing to open an App, call somebody or visit a webpage. The software is founded on applications of AI known as machine learning (ML) and natural language processing (NLP). While we won’t go into technical detail of the terms in this article, ML and NLP work behind the scenes of a Chatbot interface to turn language into data. This returns a conversational and contextual response that is human friendly. 

If you already have a well-staffed customer service department, you may think that building a Bot is a pointless endeavour but if your bot has been trained effectively, there is no reason why you cannot achieve all the benefits we outlined at the start of this article. In bot terms, training means loading it with the right data so that it’s experienced enough to answer customer queries. For example, if you get 100 phone calls per day asking about the price of a product, the bot can be programmed to handle the request, saving time and cost of a human agent doing it. 

Every time a conversation is held on the Chatbot, it learns from that experience. This means that while you might start by giving it 50 questions that it can answer, over time it will learn another 50, and another and so on until it can support your business as well as, or even more efficiently than a customer service team. 

The Facebook Messenger Bot has become the most popular platform for developing Chatbots, especially when it comes to dealing with consumers. 

What is a Facebook Messenger Bot?

A Facebook Messenger bot is a ChatBot that uses the Facebook Messenger platform. As of May 2019, around 1.3 billion people use Facebook Messenger as well as 40 million businesses being on it (source: expandedramblings.com).  This means that straight away, if your business is using Facebook for Marketing, there is instant access to developing the Messenger Bot. Your followers and subscribers won’t have to visit a different site or download anything new, it is right there in front of them to use. 

The Facebook Messenger Bot is built using Facebook’s Wit.ai Bot Engine. This powerful deployment can turn language into structured data (this goes to back what we mentioned around ML and NLP earlier in this article). In short, the Facebook Messenger Bot can both parse conversational language and learn from it at the same time. Every interaction that hits a Facebook Messenger Bot makes it smarter. It is working just like a human brain. 

You might be aware that most Bot engines work in this way and in the main, you are right. However, what sets Facebook apart from the competition is the ease of use and sheer reach of the platform. The fact that users can search for companies with the Facebook Messenger app, you can invest in Facebook Ads, create Facebook Pages and join Groups means that there is a clear journey for your customers. Other ChatBot apps cannot provide this and you must join the pieces together yourself. 

Facebook has made it easier for businesses to develop a ChatBot and therefore businesses are flocking to it. We are going to have a look at some of the ways as to how businesses can benefit from a Facebook Messenger Bot implementation. In fact, Facebook say you can build a basic bot flow within just 7 minutes!  

Lead Generation & Lead Nurturing

A Facebook Messenger Bot is perfect for lead generation and nurturing as it makes life easier for the customer. Whilst you can deploy the Bot on your own website for customers to interact with, the best revenue and lead generating stream tends to be via Facebook Marketing. Facebook Ads, combined with Facebook Messenger, enable direct “Call-to-Action” buttons where the customers get instant support and gratification, all in one place.  

If you consider that 90% of people on Facebook use mobile devices, having a journey that is easy and responsive is of the utmost importance. Messenger can create support without forcing customers to leave the page. In terms of generation, a customer might see a “Send Message”, “Call Now” or “Learn More” button that automatically triggers the Messenger Bot and gets them interacting on a conversational level straight away. 

If you are looking at Facebook Messenger Bots, you probably already do Marketing on the platform using Facebook Ads. For a customer, there aren’t many things more frustrating than clicking on a Facebook Ad and being redirected somewhere else to fill in a generic form. Given that businesses have spent a lot of money on their advertising, you don’t want those customers to slip through the funnel without converting. 

Imagine a customer clicks on your ad and in turn, the Bot is activated instantly. The Bot can then ask personalised questions about the specific responses that customer gives you. Upon completion, the Bot provides the perfect result for the customer ensuring they convert. The benefit here is that businesses can achieve quality leads who are really interested in their services. 

A more simplified method of generating leads could be through using Facebook Ads that force a customer opt-in via the Messenger Bot, collecting details like phone number of email addresses. In using the Bot, customers are willing to give a single piece of information which could have huge future marketing value and loyalty

Improving Customer Retention and Loyalty

Customers receive communications from businesses every day, trying to sell them products and services. In many cases, once they eventually decide to buy, they never hear from that businesses again. It is very easy for a business to focus a lot of attention in acquiring customers, forgetting that retaining them is equally or more important. If you scroll through the Facebook Ads on your mobile now, the vast majority will be new customer offers rather than any form of existing customer nurturing. 

After a customer has opted in to Facebook Messenger, it gives you the opportunity to create an automated lifecycle and truly nurture their loyalty. For example, you can send them a message on their birthday. Given that everything is connected via Facebook and then customer has authorised you to use their data, this sort of information is available. You can do similar messaging if they haven’t spoken to you or visited your page for a while. 

As the Messenger Bot learns, it can send personalised offers and promotions to customer at the precise time they are likely to want them. These offers could be sent via Facebook Ads to allow the customer to click directly through and talk to you about it. 

24 Hour Customer Service

Arguably the most poignant benefit of a Facebook Messenger Bot is the ability to offer customers a 24/7 service. Whilst every business would love to have endless streams of equity to hire staff who can work around the clock, it simply isn’t feasible. It can be hard to find volunteers for the 3am shift! A Facebook Messenger Bot is available 24/7 which is ideal for a business operating across the globe to be able to serve all. 

Besides being available all the time, a Messenger Bot is faster and more accurate than a human counterpart. Let’s say a customer wants to ask questions about a product. A customer service representative on the other end of a phone call will have to look through documents or maybe ask somebody else to get the right answer. 

A Facebook Messenger Bot can be loaded with your frequently asked questions or product specifications. As soon as a customer asks a question, natural language processing matches the data to a response and provides an answer instantly. No time wasted on a phone call for the customer or for the business. 

Cost Savings from Messenger Bots

There are multiple ways that a Facebook Messenger Bot can help a business save money. We’ve already spoken about how ChatBots can deliver a 24/7 service, but it is the type of service they offer which helps reduce costs. 

Customer service teams spend countless hours helping their customers to track orders or change delivery times and addresses. If a customer can do all of this via a Facebook Messenger Bot, as well as being convenient, it saves the cost and time of a call centre agent handling the query. 

As well as requiring less resources, those that are employed tend to be highly skilled. As the bot is trained to answer lower level questions, employees become expert knowledge workers and are highly valued. Valued employees are more motivated and happier in their roles.  With this, we get lower employee attrition rates which is a huge win given the Human Resource Institute estimation of it taking $10k to $15k to replace one front line employee.

Autodesk, a global leader in 3D computer-aided design, saw a 99% improvement in response times, cutting query resolution down from 38 hour to just 5.4 minutes after implementing their Bot (AVA). To put this into perspective, instead of costing $15 to $200 per query for a human agent, a virtual agent came in at only $1. The virtual agent was able to handle over 30,000 support queries per month. 

Across all industries in 2019, the average cost per click on Facebook Ads is $1.72. A problem arises if you are spending that money but not converting customers. In linking Facebook Ads to a Messenger Bot, marketers can target their advertising better and ensure a lower cost per acquisition as conversion rates increase. This can be achieved through the direct calls to action that we discussed earlier in this article as well as the improved nurturing techniques and loyalty offerings. 

Increasing Sales & Revenue using a Messenger Bot

As well as reducing costs, a Facebook Messenger Bot helps improve sales and revenue. Firstly, as a ChatBot can assist customers while they are in a sales process, it acts as an aid to conversion. Where the majority of Messenger users are doing so via mobile, they need something quick and simple. Facebook Messenger Bots can answer this sense of urgency. 

As ChatBots learn about the customer, they can offer more tailored content. Given that a Facebook Messenger Bot is directly linked to the social media platform, they tend to do this better than other platforms. Personalisation is a must have in today’s digital world if you are expecting a customer to purchase from you. If you consider something like Netflix, 80% of shows their subscribers watch have been recommended to them. Consumers want to be told what they need and don’t want choice. A Facebook Messenger Bot can help you do exactly that.

The ChatBot could even be used as a recommender style platform. For example, if Customer A purchases some red shoes and you find that 90% of all other customers who bought red shoes also buy a black dress, your ChatBot can promote the black dress straight away. ChatBots can deliver messages based on how a person talks to them. It can talk about and recommend the things that every individual customer is interested in. 

Facebook Messenger Bot – use cases

Whole Foods

The Whole Foods Bot acts as a customer concierge service by helping them discover recipes based on a set of ingredients. Customers can narrow down their search by the type of dish they like or by specific dietary requirements. To keep up with 21^st century demand, users can even search by emoji. 

Swedbank

The bank has introduced a ChatBot called Nina, designed so agents can spend their time on only the most useful types of call. The bot is able to resolve 78% of queries during the first contact meaning representatives are able to help the customers who really need it. As 75% of Swebank customers prefer to use a mobile app, this has turned out to be the perfect solution for them. 

Bud Light

In 2017, Bud Light created a personalised Bot that could order and deliver a case of beer to those who opted in within an hour on NFL game days. It used geo-targeting to do this and became aware of customer locations. Furthermore, if customers did subscribe to the service, the Bot prompted them to order on future game days. Engagement rate was at 83% with the introduction of the Bot. 

Should I build a Facebook Messenger Bot?

As digital technology grows exponentially, customers are favouring ChatBots more and more over traditional communication methods. In 2019, the usage of messaging apps has outpaced that of social media platforms and we are continually seeing new use cases. With those new use cases comes new types of customer behaviour. A few years ago, WhatsApp didn’t even exist but has now become something of a standard for all generations. It is thought that where these apps can target key emotions and conversational topics, marketers can set up relevant content exactly when users need it, building stronger brand associations. For a business, encouraging emotional and transactional interactions is tantamount to success in the digital era. 

In 2018, the four largest mobile messaging apps (WhatsApp, Facebook Messenger, WeChat, Viber) held 4.1 billion combined users against 3.4 billion on the four largest social media apps. Whilst the 2019 stats have not been released yet, it is thought that the gap is continuing to grow as businesses take advantage of the immense benefits. A report from AdWeek (sponsored by Facebook) showed that 68% of consumers say messaging is the most convenient way for them to stay in touch with a business. 

Companies who fail to invest in some kind of ChatBot platform look set to fall behind the competition. The usage and reach of Facebook Messenger make it an incredibly desirable solution for businesses. It is important to understand where your customers are and the type of channels they prefer using before finalising your decision but a Facebook Messenger plan should be part of your business strategy. 

Here at QAS, we are focused on putting the right tools in the hands of our customers to help them increase net revenue, improve customer satisfaction and reduce operating costs to mention a few. Reach out to us for your FREE AI strategy consultation;

Quantum AI Strategy

(480) 418-0033

As of January 2019, there were over 1.94 billion websites on the internet. Over half the world’s population now have access to the internet and are using countless devices to do so. In 2019, the number using mobile has exceeded those on desktop with a small percentage utilising tablets as their preference. What does this mean? With so many options, businesses face an astounding amount of competition to gain customers. What worked traditionally will no longer be a market leading strategy as demands, trends and technology changes by the day. 

Small to medium sized businesses need to find ways to keep up with the competition that won’t break the budget. While artificial intelligence (AI), machine learning and analytics might sound like buzz and hype to these businesses, it is that technology which will help them to succeed. In this article, we will look at one of those fields known as predictive analytics. Business leaders no longer need to be highly technical or have a degree in mathematics to reap the benefits of their data. 

We will help you understand what predictive analytics is, why businesses need it, who uses it today and how it works.

What is predictive analytics?

There is a stigma that AI is only available to the tech giants like Google, Amazon and Facebook. However, the truth is that most tools for Big Data and cloud technology are now freely accessible to anyone. With a well-structured strategy, businesses can find cost savings in upgrading their infrastructure while being able to benefit from the insights and analytics in ways they never have before. Data that they previously couldn’t tap into suddenly becomes available in real-time with the potential for immense processing power to make business decisions through predictive analytics.

Predictive analytics is about using data, statistical algorithms and machine learning frameworks to identify the likelihood of future outcomes based on historical data. Whereas business intelligence teams have always worked towards telling you what has happened in the past, predictive analytics is focused on telling you what is likely to happen in the future. 

For businesses without big resource and technology budgets, there are plenty of tools out there to deliver predictive analytics. That said, many applications of predictive analytics are designed to improve efficiencies, reduce costs and generate income. Even with some initial cost, firms can find ways to get a return on investment (ROI) from their predictive analytics strategy. This will become clearer when we look at use cases later in this article.

Why is predictive analytics important now?

The theories of predictive analytics have been around for a long time. Just think how many years you’ve been watching weather forecasts on television for as an early example. It has become more important now as everybody has access to it. In using predictive analytics, businesses can gain a significant competitive advantage in areas such as income generation, reduction of operational costs and business efficiency. There are four key reasons why this has become even more important in the last decade.

1. Exponentially increasing volumes of data through omni-channel customer experiences. Businesses now have enough information to be confident in their predictions. For example, they will be making decisions based on millions of records and not hundreds as was the case during the AI Winters of the 1970s.
2. The cost of computers is now a lot cheaper than it ever has been before. The same applies to storage structures like the cloud which as very scalable
3. The tools associated with predictive analytics have become much easier to use. By that, they are not just easier for experts in the field, but even non-technical people can pick up the necessary skills.
4. With so much online traffic, the economic conditions are tough, and businesses must find ways to differentiate themselves simply to keep in profit. Not changing isn’t really an option. 

Applications of predictive analytics have shown the impact it can have. We’ll come onto those later in the article. 

How does predictive analytics work?

We won’t go into huge technical detail, but predictive analytics uses and application of AI known as machine learning. Machine learning builds statistical models and algorithms that teach computer systems to perform tasks without explicit instructions. It does this using existing and historic data to train the models. As more data is added, the models can continue to learn without human intervention. 

There are a few commonly used methods and two core types of models known as classification and regression. Classification is generally used to determine the likelihood of an event happening, usually tagging it with a 1 or a 0. For example, you might have an algorithm built to work out if it is going to rain tomorrow. The model will process all your existing data and label the next day with a 1 if it is going to rain or a 0 if it is not. 

Regression techniques move away from labelling and predict actual values. For example, it could forecast how much a customer is going to spend in their lifetime based on the data in their profile. It does this by estimating the relationships between several variables to reach a conclusion. 

There are a huge number of statistical techniques within classification and regression methods. Some you may have heard of are decision trees, k-means clustering, linear and logistic regression, gradient boosting and time series mining. The choice of algorithm depends on what you are trying to achieve through predictive analytics. Data Scientists will often spend their time testing several different methods to attain the best level of accuracy. There are systems that can automate that process as well such as Data Robot. 

Business benefits of predictive analytics

There are cases for using predictive analytics in virtually every industry. The main benefits can be split into five categories.

• Business productivity can be improved by using predictive analytics for proactive responses. For example, data from sensors in factories can collect information about the performance of machinery. Analysts can use this data to receive alerts as to when a machine is likely to need maintenance rather than waiting for it to break down. Staff don’t waste time fixing items unnecessarily and there is no machine downtime. 

• Marketing campaigns can be far more targeted using predictive analytics. Traditionally, marketing teams might bulk send the same email to every customer. With predictive analytics, they can gain insight from their communications and understand which customers are likely to engage with them. For example, those who prefer offers get an email with an offer and those who always like free delivery get a shipping discount. Targeted campaigns improve conversion and revenue while improving the customer experience.

• Social media is a prime example of predictive analytics. How many times have you seen a Facebook Ad and thought how much of a coincidence it is that you are interested in it? The ad you see is no coincidence and is based on data gathered from your profile and cookies, depending on what you have authorised. Facebook Ads can be used by all sizes of businesses and when targeted well, is very cost effective. 
• Recommender systems are becoming more commonplace in retail and entertainment in particular. Predictive analytics can match customer data to a cluster. A cluster is a group of people who appear to share similar characteristics e.g. age, salary, location. If other customers in that cluster have bought a specific product, the same item can be pushed out to new customers in the segment. Amazon would be using this kind of algorithm when recommending products for example. 

• Predictive analytics can help reduce business costs. An example might be in a call centre environment. Data can be used to forecast the number of calls a business will receive each day and ensure only the requisite number of staff are on shift. Other examples might be predicting system failures to proactively stop them or analysing patterns to negate cybercrime. 

• Predictive analytics can give you a competitive advantage. Only you have access to your own data and it can tap into patterns about why consumers choose your brand. In knowing what they like and how they behave, you can get the right products to the right people at the right time.
• In some industries, predictive analytics is vital for reducing risk or detecting fraud. A good example is in lending whereby data can give an indication into the potential risk of a consumer wanting a loan.  

How are predictive analytics used in practice?

Predictive analytics are being used in just about every industry. Below are some of the key examples to be aware of. 

Financial Services

Predictive analytics is used to detect fraud and understand the potential risk of credit. As an example, Commonwealth Bank completes a fraud check on customers within 40 milliseconds of a transaction being initiated. Trading is a prime candidate for predictive analytics to forecast the best stocks and shares to invest in at any given time based on their likely future. Insurance companies use predictive analytics to decide how likely a customer is to claim and rate premiums accordingly. 

Retail

In retail, predictive analytics is used to work out the best offers for different customers, the channel they like to use and the price point they are comfortable with. AutoTrader use data from their 40 million monthly visitors to understand their patterns in behaviour and create a customer journey and propensity model to maximise revenue. Predictive pricing platforms will combine your own data with that of competitors alongside market demand to recommend the right thresholds for your products.

Healthcare

Predictive analytics could be a game-changer in healthcare. Trials in Hong Kong have shown that data gathered through patient records and images can diagnose some forms of cancel faster and just as accurately as doctors. There have also been trials around predicting the number of free beds required at a point in time to ensure availability and care. 

Manufacturing and Industry

The clear use case in manufacturing comes from what we call the smart factory. Using sensors to monitor all aspects of the supply chain, factories can improve productivity and efficiency while reducing costs. The global auto parts manufacturer Hirotec use predictive analytics to prevent system failures through real-time analysis of data to spot anomalies in behaviour. They achieved a 100% reduction in time manually inspected their machines. This more than accounted for the cost of equipment.

Education

In education predictive analytics can be used to create early alert systems of students who look like they could be falling behind the curve. Pastoral teams can reach out proactively rather than needing to wait for a student to fail. On a similar note, the masses of data collected can predict from a very early stage which students are likely to graduate. The continuous feedback opportunity of predictive analytics means students can get better support than they have ever done before. In August 2019, it was reported that 1,400 education institutions in the Georgia State of the USA are investing in predictive analytics. 

Agriculture

In agriculture, we are entering an era of digital farming and predictive analytics is a key part of the strategy. Data collected using drone technology can be used to do pest modelling, optimise soil conditions and nutrient movements all with the aim of improving yields. If data can be used to improve production without a farmer needing to walk 20 miles per day over land, there are clear cost and revenue benefits. To this end, agri-tech companies like The Climate Corporation and Gamaya already offer data-driven agricultural insights that take soil type, seed suitability and local weather patterns into account.

Almost every industry has a use for predictive analytics. The examples above only touch the surface of the benefits with law, construction, sports, entertainment and public sector companies all having cases for the technology. Within these industries, there are sub-sectors that have their own unique way of using predictive analytics. Two examples of this come from the worlds of plastic surgery and dentistry.

Predictive Analytics in Plastic Surgery

There are a few ways that predictive analytics could be the new best friend of plastic surgeons. First, using data on existing patients could help surgeons to predict what type of surgery they might want next. Patients, after a successful procedure, are likely to return for supplementary surgeries that compliment their first. For example, Botox and dermal fillers are two that go perfectly well together. Predictive analytics can aide cosmetic surgeons by upselling the right services to their patients.

The key to returning customers is success. A study by Galanis et al in 2013 showed that the reason most people fear cosmetic surgery is due to the risk of failure. Predictive analytics can help surgeons by forecasting the outcomes of their procedures using existing data. For example, it could simulate a facial aesthetic surgery before the patient undertakes it to negate their fears.

Predictive analytics can forecast the time it will take burn victims to fully heal, through analysing the depth and surface area covered. This could be quite ground-breaking in a space that is typically very difficult to determine final results. 

From an acquisition point of view, predictive analytics can be used to segment patients into the types of surgeries that they are likely to be interested in. For example, a research paper by Li et al in 2016, showed that patients aged between 19 and 34 accounted for 76% of surgical procedures and female patients are more likely to undergo Botox. In this case, clinicians can set clear target markets.

Predictive Analytics in Dentistry

In dental practices, AI and predictive analytics can be used to forecast the likely treatment paths of patients. For example, it could take their medical records and map them against existing data, detailing to patients what services they need. For example, if a patient has lots of fillings, it can tell them when they are going to need to have fillers. 

With AI having the ability to take unstructured data like images and turn it into business decisions, dentistry is ripe for disruption. This is known as computer vision. For example, if a dentist takes jaw x-rays, these can be converted to data and modelled against millions of other images in real-time. The options for treatment can then be presented accurately based on real examples. 

The larger the practice or network, the more data there is to work with, so the better the insights will be. For example, if in a large practice, you see a lot of patients with bone loss, you may consider buying an expensive dental laser. A solo practitioner who sees a similar trend might work with a specialist who already owns the equipment. Predictive analytics can help you find the best solution.

Summary

Big Data, machine learning and predictive analytics have gone from being a nice to have business function to a must have deployment to ensure businesses remain competitive. At a time where the technology has become affordable and scalable, small and mid size businesses can now integrate predictive analytics into their strategy in the same way that the tech giants do and do it on a shoestring budget.