Quora has answers to EVERYTHING. Whether you need to find reliable assignment help in your preferred subject area or you want to know who discovered exams, Quora will never disappoint you.

Apart from providing you with a slew of answers, Quora is also an excellent platform for monetising your ads. This platform consists of over 200 million active daily users.

The revenue from ads on Quora is valued at approximately $1.8 billion. Quora was estimated to produce an ARPU of $0.50 in 2018. Thus, it has the potential to generate revenues of at least $112 million in the future.

Marketers can make the best use of Quora ads only if they are able to target the right audience.

You can’t expect to gain revenue if you display ads about food to people, who are looking for little black dresses, can you? Thus, Quora has brought forth three new targeting options for marketers to target the right audience with relevant ads.

Let’s see how to use these targeting options to make the most of this monetizing platform.

Keyword History Targeting

Quora already has a keyword targeting option and a question retargeting option for marketers to display the right ads. Keyword history targeting is a smart blend of these two options. This option lets you engage with the users who have already read the sets of questions relevant to your business objectives. You can connect with the people who have shared questions related to your nature of business on Quora.

This is what Quora has to say about keyword history targeting:

“For example, Keyword History Targeting works by taking a keyword like “Financial Planning” and finding matches with questions that contain the word or phrase, like “How do I start my financial planning?” Advertisers will be able to target ads towards users who visited groups of question pages in a given time period (ex. the last 20 days). Ads are shown on users’ feeds, topic feeds, and question pages.”

How can Keyword History Targeting help?

• You can retarget users who have posted or answered questions closely related to your business.
• It lets you bring quality prospect for users who expressed a strong intent for questions relevant to your field.
• This option increases the chances of profitable conversions since you can target the right set of audience.

Create a new ad set and import your existing keywords in it (if you want.) Add your new keywords as well that have proved to be effective on other advertising platforms. Now Quora will automatically include relevant and new questions to help your ad set target the right audience.  Manage the ad set and let Quora do the rest of the task.

Gender Targeting

Does your organisation have products that are designed specifically for men or women? In that case, you can use gender targeting to target the right gender through your ads on Quora.

You can choose to target your users based on their gender. You can even use the default setting ‘All genders’ to target your audience irrespective of their gender. Simply put, gender targeting helps advertisers to focus on their ads more specifically.

This is what Quora has to say about gender targeting:

“You can apply gender targeting to any ad set, regardless of the default targeting option, so it’s possible, for example, to create two topic – targeted ad sets, one with male-specific calls to action, and the other with female-specific calls to action. Similarly, if the audience for your product or service is only one gender, you can always exclude the other gender on all your ad sets.”

How can Gender Targeting help?

• It helps you align your products and the ad copy to reach your target audience better. You don’t have to bother your female users with the ads of men’s razors.
• You can apply this option along with a primary targeting option. Thus, it gets easier for you to create two-topic targeted ad sets. One can have a female-specific call to actions and the other one can have a male-specific call to actions.
• This option can come in handy even if you don’t have any gender-specific audience. 31.6% of men plan to spend more than $500, compared to 23% of women. Thus, you can create customised ad sets for men or/and women on Quora.  

Quora uses a set of attributes to determine the gender of their users by analysing their profiles and buying behaviours. In case the platform is unable to determine gender, it places them in the ‘Unknown’ category.

You can target this category along with a specific gender to boost your leads and conversions. Do not forget to include the ‘Unknown’ category in your ad sets to reach the maximum number of people.

Platform and Browser Targeting

75% of smartphone users access Quora on their phones. Imagine the number of the target audience that you may miss out on if your ads are not targeted at specific platforms and browsers.

Therefore, Quora has brought forth the third option- Platform and Browser Targeting. Now you can target your audience based on device and mobile platforms. Simply put, targeting the right set of people for your ads just got 10X easier.

According to Quora,

‘Advertisers can choose to target desktop and mobile devices, simultaneously or individually. With mobile targeting, you can choose to target iOS devices, Android devices, or all mobile devices. Campaigns optimising towards App Installs will automatically have their device settings changed to mobile only.’

How can Platform and Browser Targeting help?

• This option lets you optimise your budget for the desktop and mobile ads separately. Mobile auctions consist of lower costs as compared to other filters.  Thus, you can optimise your budget for the bids placed for both campaigns.
• You can further refine your options when you target users on the desktop. You can choose from a list of browsers such as Firefox, Chrome, Safari, etc.
• Having two separate campaigns based on desktop and mobile phones can help you evaluate the performance of your ads for optimisation purposes.

For desktop targeting, you can target all kinds of browsers, as mentioned above. For mobile targeting, you can target both Android and iOS devices. It is always suggested to separate both the campaigns to increase or decrease the bids as and when required. Say you have designed a software that suits a specific browser only. Use the ‘Platform and Browser Targeting option’ to show the ad of your software to people who actually use that browser.

Wrapping Up,

Owing to its huge client base, Quora is most certainly one of the best advertising platforms for your business. The new targeting options will help you target the right set of audience easily.

You don’t have to waste money on bids that may not bring good revenue. Use these targeting options and display your ads to relevant Quora users.

— Henry Howkins is a content marketer at a reputed company in Australia. He also offers homework help to students at Assignmenthelp.com.sg. Henry loves to bake cookies in his free time.

The post 3 New Targeting Options for Quora Ads to Reach Your Target Audience appeared first on AiiotTalk - Artificial Intelligence | Robotics | Technology.

The concept and working model of machine learning have been in existence for quite a long time, but it has only been witnessing vogue since the year 2010 when mobile app development companies and mobile app owners have finally started understanding that machine learning is very helpful when it comes to serve the right content and boost the sales.

However, developing machine learning-enabled mobile app is not as easy as pie. Developers have to deal with big data challenges and they also need to find the perfect algorithm to process the big data. 

Sensing the urgency, in this blog, I will share 5 simple ways to develop machine learning-enabled mobile app. I will also give you the example of apps working on the machine learning technology and what are the business benefits they are deriving with machine learning.

But before this, let’s understand the difference between machine learning, AI & deep learning and some statistics about machine learning. 

Difference Between Machine Learning, AI and Deep Learning?  

To understand this in a more rational way, let’s keep [AI] and [Machine learning & Deep learning] away from each other.

• Artificial Intelligence, so-called AI, is both technology and technique to give the ability to machines to act like a human. In other words, if a machine responds to any problem like a human using some rules, that machine is called AI-enabled and its intelligent behaviour is called AI. The best example of AI-enabled machine or software is Siri and Alexa. They take user command as input, act on it using some rules (like a human) and give us the output. 

• Machine learning is the subset of AI and it is very useful to process big data, to know the hidden meanings between different attributes of data and using that found meanings, to do prediction. While deep learning is the broader term of machine learning. Here is the best example to understand the difference between machine learning and deep learning.  

You upload an image and ask the system to identify whether it is a dog or cat. In the case of machine learning, you have to define the major attributes like skin colour, nose shape, tail length which are supposed to be major defining factors.

Now, based on these factors only, the machine learning algorithm identifies whether the picture is of a dog or cat. While in deep learning, you don’t have to give attributes, the algorithm itself defines the attributes on which it should define identity. 

Most of today’s apps are working on all of these three technologies. But the most popular and useful technology (at least for mobile app development) is the machine learning technology. Thus, we will only talk about machine learning in this blog, but in a very comprehensive way.

Insight into the Machine Learning Technology Market  

• The market is concentrated with a few players occupying the market share. 
• The market will be growing at a CAGR of over 38%. 
• By 2022, the market size of machine learning as service will reach USD 4630 million. 
• Mobile apps and IoT will witness the most influence from machine learning. 
• Following of North America, the Middle-East and African countries will enjoy the most number of machine learning software sales by 2025. 

So, now when you know about the difference between machine learning, deep learning and AI and also the market and current trend of the machine learning, let’s move ahead and discuss several ways to apply machine learning technologies on the mobile apps. 

5 ways to Apply Machine Learning on Mobile Apps 

Before we move ahead, I would like to mention one basic learning that machine learning technology works with a machine learning algorithm and machine learning algorithm works with big data. Meaning, by collecting the data and by processing that data (using machine learning algorithms), you can actualize the machine learning technology. 

• Identifying user behaviour – using Genetic Algorithm 

Imagine you are running a grocery store and collecting all user and sales data digitally. Storing data digitally means you can apply a machine learning algorithm on it.

“This machine-learning algorithm, genetic algorithm, processes that data and finds the historic user behaviour, such as, X number of people who are living in Y neighbourhood and having age below Z years, bought milk and cookies together.”

Now think, how important this data would be for your business – you will put milk and cookies together, you will send promotional details to those users only, you will start thinking to start delivery service in that neighbourhood and many more.   

You can achieve the same thing in the mobile app. All genetic algorithm need is data which is really easy to collect and store as mobile apps are developed in that way. So, now when your app starts collecting data, give it the ability to process that data with a simple module which works on the Genetic Algorithm.

This module processes the data and lets the system know what a particular user wishes to see, based on his activities and activities of many more users like him (historic data). With the knowledge of the user’s interest, the app only shows him those contents which he is interested in and since he is rewarded with his interest, he would not uninstall an app.

This is how an app owner can increase the user engagement rate. One of the most popular entertainment apps Tiktok is working on the same principle.    

• Classification app feature – using the naive Bayes classifier algorithm  

Classification is the most fundamental module of the app. It basically means that the algorithm classifies the images, documents, or any other kind of screen content and places it in the relevant category. The best example of this feature is seen in news apps and apps like Pinterest, in which every content is categorized (automatically, using a Naive Bayes Classifier Algorithm). 

The reason why the Naive Bayes Classifier algorithm is a machine learning technique and not deep learning because it needs predefined attributes as it does not define it by itself. For example, if you want to put an Apple into the category of the fruit, you have to define attributes related to fruit like colour, diameter etc. 

• Clustering in e-commerce app – using K means clustering algorithm 

This is very useful in an e-commerce app. Here is why? 

In an e-commerce app, the main challenge is to show the result relevant to the user search. If a user is looking for iPhone and searches apple and search module of an e-commerce app considers it as fruit Apple, your business will soon meet the dead-end! 

To avoid such a scenario, you have to put products into relevant categories. But that does not mean that you have to do it manually and in fact, it is not possible for large e-commerce sites to categorize every product manually. Thus they rely on the machine learning algorithm, which learns by itself, categorizes the products and puts them into relevant clusters.  

K means clustering algorithm is the machine learning algorithm that is very popular because of its usefulness for cluster analysis. It makes the clusters and every cluster has only relevant data. Here is how it achieves so. 

• The algorithm works with data and in our case, it is product data which is stored in the online inventory. 
• The algorithm puts data of one single keyword (such as Apple) into cluster A and gives numbers to that data. 
• It then takes two mean values from that cluster and creates two different clusters (cluster A1 and cluster A2) with those mean values. 
• It now puts the nearest values of mean values from cluster A into cluster A1 and clusterA2. 
• It then calculates the average of cluster A1 and Cluster A2. The result of that calculation acts as the mean values of the next clusters. (cluster B1 and cluster B2)
• Again, it puts nearest values of mean values into cluster B1 and cluster B2. 
• The algorithm keeps doing this until it does not get the clusters with the same mean values. Clusters are called organized (having Apple iPhone details into Apple iPhone cluster and having apple fruit details into apple fruit clusters), when both clusters get same mean value.    

• Forecasting – using a linear regression algorithm 

What if before making a little change in price, you can know the future effect of price change in demand? You can revamp your pricing model more efficiently. Isn’t it? 

Though this kind of forecasting seems futuristic and hard, it is possible with a linear regression machine learning algorithm. This is the most significant algorithm we have talked so far. 

Linear regression algorithm basically studies the historic data you have and shows the dependency of each attribute on others. It also shows that what happens to one attributes if you change the values of other attributes.

Since this algorithm is very easy and very useful, it is very popular among companies. In fact, big companies like Target and Walmart are using it for making a better decision and be ready to lock horns with potential situations.

• Matching – using a matching algorithm   

Currently, the mobile app development trend is all about developing taxi apps and on-demand service apps. But without the matching algorithm, it is never possible to develop such an app. The matching algorithm is very useful to process the user request. It allocates the driver or service provider to users after considering the many factors. 

To understand everything about matching algorithm more rationally, let’s understand how Uber employs the matching algorithm to allocate the best driver who can reach rider in minimum time. 

As soon as a rider books the ride, the algorithm starts working. It scans all nearby driver and allocates the nearest driver to the rider. But there is a major problem in this algorithm. 

Many times, the nearest driver cannot pick-up rider faster enough than the driver who is a bit far away. Many factors are causing it, such as one-way street, traffic, weather, bridge, bike lanes, etc. 

So, to address this noteworthy issue, Uber rewrote its matching algorithm. Uber’s new algorithm analyses real-time data of surrounding which can be possible with Apache Spark like data analytic tool and allocates fastest driver to the rider and not the nearest. The algorithm also considers the past data to estimate the exact arrival time. 

In the Nutshell: 

To dominate the already-intensified market, machine learning-enabled algorithms and features working on them is the most fundamental need. Using a genetic algorithm, you can add a module in the app which understands the user behaviour and shows him only relevant content.

Using native Bayes classification algorithm, you can classify the images and other contents of the app and present a well-organized app to users. Using K means clustering algorithm, you can organize the poorly-distributed items into relevant clusters and serve the right result to users.

Using a linear regression algorithm, you can know the effect of price changes on demand prior to changing the price. And in taxi and on-demand apps, using machine learning-enabled matching algorithm, you can save the time of both users and service providers. 

— Vishal Virani is a Founder and CEO of Coruscate Solutions, a leading taxi app development company.

Also Read: Reasons to Learn Machine Learning

The post 5 Easy Ways of Applying Machine Learning on Mobile Apps appeared first on AiiotTalk - Artificial Intelligence | Robotics | Technology.

What is a social media marketing plan?

It is basically a summary of everything that you are planning to do on social media. This plan or strategy guides you to take the necessary actions and also helps you determine if you are succeeding or failing. If you want the execution to be effective and smooth, you must make your social media marketing plan as precise as possible. 

“ 71% of consumers who have a good experience with a brand’s social media are more likely to suggest the brand to others. ”

If you want your brand to make a mark on social media platforms, read this blog immediately. This blog will teach you how to make a successful social media marketing plan from scratch.

Follow the below-mentioned steps to prepare your effective social media marketing plan:

• Set specific goals 

You will be able to create an effective plan only when you learn what your objective or goal that you want to achieve from your social media marketing plan. Make sure that all your goals have the following characteristics:

• Specific
• Achievable
• Measurable
• Time-specific
• Related 

You need to ensure that your social media goals are in line with your entire marketing strategy.  

• Review your brand’s presence on social media

Before you make your strategy, you need to know where you are standing. You need to check different details of your social media presence in order to make an effective plan. Here are a few areas which you need to take into consideration while reviewing the social media presence of your business:

• Which social media platforms your business mostly use? 
• Which platforms are actually providing you value? 
• Is your business’s profile better/worse/equal to your competitors’ profiles?

Learn about your target audience

You must already have a clear idea about who your target audience is. Now, every social media platform serves a different purpose and has a different type of users. You need to figure out which platform will be ideal for reaching out to your target audience. For example, if you provide programming help services, you can consider Facebook for promotions, and use Twitter for offering customer service. Prepare a buyer persona by considering the followings 

• Age 
• Income
• Location
• Frequently used social media platform
• Job title
• Pain points

“ 90.4% of Millennials, 48.2% of Baby Boomers, and 77.5% of Generation  X use social media actively. ”

• Optimize your profiles 

Your profile is the first thing that users will check. Therefore, it must be appealing and should portray your professionalism. Make sure that all the fields are completely filled, and you have a complete and updated profile.  You must maintain consistency throughout your profiles. Apart from this, also make sure that your bio section provides the users with helpful information they might be looking for. 

Brands need to ensure that a clear and precise social media profile that helps in describing the brand is necessary. According to experts at Igloo Agency, the more information you are able to give your customers, the higher are the chances that the audiences will engage with you and get in touch with the brand. You can also use some creative and innovative jargon in the bio section to make it fun and appealing. Some great brands use the bio section for trivia and content-related posts.

• Prepare your strategy 

One thing about social media is that you need to be very consistent there. Hence, posting content regularly is a must if you want your social media marketing plan to be successful. While creating the plan, you will also have to decide how often you are going to post and what should be your strategy. Usually, pictures and videos work the best when it comes to social media posting. You can also plan a particular time for posting every day. 

“According to 95.8% of social media marketers, Facebook is a source that produces the best ROI.”

• Track and analyze 

You can always take advantage of the analytics that most of the social media platforms provide. You can see at what time your ideal audience stays the most active, which types of posts they like the most, etc. Based on this information, you can further change and improve your plan.

Without tracking your performance, you won’t be able to understand whether your social media plan or strategy is successful or not. You can also run surveys or ask your website visitors and followers about your social media strategies. This way, you can tailor your posts based on their reviews.

Conclusion

While creating your social media marketing plan, keep this mind that things can change fast in the social media world. So, you have to continuously measure your progress, see where you can improve, and make the necessary changes. Only by doing so, you can make your social media marketing plan a success. 

The post How to Create a Social Media Marketing Plan From Scratch appeared first on AiiotTalk - Artificial Intelligence | Robotics | Technology.