digital advertising

Machine Learning; 7 Algorithms In Digital Advertising Platform [Part 2/2]

1024 632 Apurv Lungade

Click Here to read part 1 of this blog

Few points which can help Ad-Network do more business with same setup [macro-optimization]

Dear marketers and brands, please ignore whatever you read in this section 🙂 Helping brands reach & interact with their consumers is the most important thing; similarly, it is equally important for an ad-server to make money out of it & meet the ROI. There must be a sweet spot between these two goals – and when an ad-server system achieves this, it is a win-win situation for both – marketers/brands and the ad-server agency/network.

5. Dynamic Floor Price

80-20 rule says, 80 percent of revenue is generated from 20% of the clients (brands). In an ad-network ecosystem, the rule is a bit different – 90% of the revenue is generated from 10% of the SSPs. It is a game of demand and supply where ad-server system is the referee. When all demand players land the battlefield, they all want to hunt THAT user (most relevant & likely to interact with the brand) & ad unit. This is the opportunity for a referee to change the rules of the game and make it expensive to yield maximum.

Yeah, it is very cool thing to have an any RTB based ad-serving system; but there are two BIG challenges over here:

  1. It is not merely the site content that makes brands attract and bid higher, but it is majorly the quality and relevancy of the user that plays the vital role. So, defining a set of discrete rules to raise or lower down the floor price won’t help here. Again, Machine learning algorithms which continuously track demands at user and ad unit level would only help tackle the problem.
  2. Second challenge here is – if your ad-server keeps raising floor price then at one point in time, no/very few bidders will bid for the ad slot and most of your inventory will get unsold. And once your system gets a “Expensive” label, it becomes difficult to retain and gain more demand. So, the system should be intelligent enough to know if demand is consistent and the floor price is just below the optimal point beyond which if it is increased, they are not going to be satisfied with.

6. Know Your Inventory Treasure

What if your ad-server is not RTB based and still want to take benefit of variable pricing? Well, there are ways.

 

ML algorithms can keep a watch on inventory parameters like:

  • Site/Brand Popularity – based on trending/viral content being published
  • Monthly Traffic
  • Alexa Rank
  • Content Quality – based continuous sentiment analysis, reader engagements & sessions durations
  • Audience Quality – it is completely based on user’s response towards ads being served to it – it is measured in KPIs like ads visibility, user interaction with ads, clicks, leads and brand engagement
  • Inventory Type – it can be anything – a social media platform, SEM, websites, mobile app, push notifications etc. but the behaviour of each one of these is different – ML tracks the changes in the behaviour

So, the ultimate mantra here is to make marketers spend more on inventory in demand.

7. Platform Secrete Survey

It is ad-servers responsibility to make brands happy with quality performance of ads, best picked inventory and reach the unreachable audience. Similarly, the usability and experience of platform plays an important role to make brands happy and helps retaining them for product lifetime. So, the goal here is to understand user’s behaviour on the platform and mould the platform accordingly.

Make FAQs interactive as if a human is interacting with the user. This requires a very popular machine learning algorithm – Natural Language Processing. Again, a heavy piece of data required here to make system precise and accurate while answering the user’s questions.

Another example would be to track user’s interactions through click events, time taken to complete a process – say setting up a campaign. Using this data, system can make inferences as in – which features are most favourite, which are very rarely being used, which processes are time consuming and which quick ones. All the inferences made by the system if it consolidates and conveys to the project/product manager, they can work on the pain areas and work towards the betterment of user experience.

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Machine Learning; 7 Algorithms In Digital Advertising Platform [Part 1/2]

945 427 Apurv Lungade

Machine learning as the term implies, is the process of making machine learn on its own and make the decisions the way human brain takes. The learning process includes collection of information, reasoning for conclusions and self-correction. These algorithms are not limited to any specific industry or nature of business or kind of a product/platform.

Have you ever wondered, how Flipkart knows your choice and recommends a list of products which becomes very difficult for you not to buy them? How does Uber Eats know exactly when the delivery boy going to meet you and shows estimated delivery time?

All these are epic examples of machine learning algorithms. Be it Google, Uber or Flipkart, the systems are trained in such a way that they analyse all data points and come up with the most relevant results/suggestions. The process is continuous evolving and producing day by day better results.


Few pointers which can help brands reach the unreachable [micro-optimization]

1. User’s Browsing Journey

The most effective way to know a user’s choice is to think the way they think. Machine learning algorithms can trace the user’s everchanging choices and connects the dots to make a pattern. The continuous process makes user see the content and ad of his/her choice and thus increase in upselling a product or a brand. It may sound like traditional user categorization technique but the moral difference here is, categorization is a discrete method and it does not follow a pattern what ML algorithms do. Some platforms have taken a step forward and tried to build advance categorization by introducing scoring logic to each category user falls under. But again, bucketing a user in many categories makes the data skewed and this in turn leads in less accurate results than ML produces.

2. Audience Cloning

Continuing point 1 wherein machine learning algorithms continuously track user behaviour and makes a patter out of it; the process is applicable to all users in the network of an ecosystem. Once the system has a substantial amount of data, it can create samples of users having majorly same choices, interests & browsing patterns. These patterns change continuously with the change in audience counts and choices.

The best example of this algorithm is – Netflix

You have watched Sacred Games, Riverdale, 13 Reasons why, and suddenly you get a notification saying “Top Pick For You: Little Things”. Now, if you notice, all for web series are NOT of same a genre – so, it is definitely not picked by tracing your past taste. Netflix has got a huge user base and sampling those made it possible. Its ML algorithms continuously sample these users of same taste and try to suggest the unmatched shows across users in that sample – hoping that having same taste among these users, may also like the suggestion made by Netflix’s ML algorithm

3. System Suggestions

Have you ever seen a system talking to you? Yes probably – Google, Alexa, Siri etc. What if your advertising platform suggests you how to optimize your ads? Yes, it is possible with Machine Learning algorithms. System slice and dice the big data and correlates content’s meta data like – keywords, urls etc. That’s why, you type a single keyword and system suggests multiple around it.

If you notice here, there can be thousands of keywords relevant to Virat Kohli but, system filters out the recent ones – This only possible when system learns the publisher content on continuous basis and this is the beauty of ML algorithms over traditional keyword suggestion techniques.

Another example would be – system automatically crunches the inventory and user behaviour data for last T hours and suggests you change targeting accordingly.

4. Analytics with suggestions

Sometimes it is very difficult to define KPIs for you campaigns and taking decisions out of it. For video & rich media ads it can be views, engagements, sentiments and share of voice whereas for Native or Emailer ads, it can be CTR, eCPAs etc. What if you are using a comprehensive system which provides all possible stats of all possible ad formats and dimensions around these? It will be a mess! A straight away solution over this would be to have separate systems/analytics dashboards for separate ad formats – but, this will only help you analyse data separately and join the dots manually

It is highly possible that, you reach your consumer through more than one channel & for that matter, having different systems to analyse those will never tell you the common user specific insights. Machine learning probabilistic algorithms can predict and identify common users from different channels and their responses towards your brand. Moreover, by using these insights, you can re-target your consumers through different channels; as a flip side of it, at some point in time consumers may experience it intrusive if they feel it irrelevant or disturbing. So, it is very important to continuously slice the user specific data and fine tune your campaign settings accordingly. ML algorithms can make it happen in a single dashboard with suggestions in it.

So, in this episode we have seen how ML algorithms can help in betterment of user’s ad experience. We will see how ad-servers can make use of ML to get maximum of it to grow the business and make a responsive platform in the final part of this post. Click Here to access the final part

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Digital advertising

How to deliver brand promotion in Digital advertising using Gamification

538 310 Mayur Sonawane

In recent years the world has adapted to the change in the Advertising industry as its shifting towards the digital era.
Digital advertising plays a vital role in the expansion of a Brand or business. The whole deal is to market your brand correctly while utilising the swiftly developing digital platform.
Knowing the process is the trick of the test, out of the different options, one can either choose to make the ads very interactive or in the form of standard banners.
The mode of execution of the advertising strategy is an important factor for digital advertising.

Communication of a brand through a mobile is seemingly more effective as most generations now prefer to scroll over different topics of their interest, and it’s the handiest way to target a particular advertisement according to the users’ availability and convenience. In this way, the brand gets its awareness and the user is not forced to view an ad.
Most brands consider mobile as their main source of data/information gathering & sharing through various websites & apps.

We at Inuxu like to experiment with different strategies & techniques using our own resources and creating advertisements that create a major impact on a brand.
Keeping in mind the user has the thumb power to reject an unwanted ad, we spice up our creative advertisements to grip the user’s attention and create lasting engagement that satisfies the ‘brand awareness needs’.
In this blog, I am going to give you important insights on how to improve user interaction for a booming brand.

Nokia – http://demo.adgebra.in/adg_spark/nokia_snake_game.html

Gamification is the coolest way to attract audience towards a brand.
Building a game around a product/ brand is not as easy as you may think, you cannot lose your brand image to a mediocre game.
Every game is based on an Idea revolving around the USP (the unique selling point of the product/brand), it has to be entertaining as well as engaging. All you need to do is follow a simple rule “Keep it simple silly”. (KISS)
So let the fun begin!
Let’s create a mobile game!
Games are played for pure entertainment & enjoyment and thus there is no space for boredom.
– Make points on how you want to promote the brand/product, the targeted audience, what kind of offers/prize/price you want to highlight etc

Bingo – http://demo.adgebra.in/adg_spark/bingo_mad_angles_game.html

– Start working on a storyboard
The storyboard is the most important stage for crafting a design. Here you create a flow of direction for a brand/product, this clears the picture of a concept from the beginning to how it ends.
Make sure you frame the concept clearly, adding each of your key positions that you need in order to complete your game. Scribble on paper, Why paper? The more you visualise the more you will see what’s lacking and what’s not.
-Now that the storyboard is ready, start with designing.
Imagine an amazing painting hanging on a dull, dirty wall…. It will indeed ruin the essence of all the painting. In the same manner, a good bunch of characters can be ruined by a messy background. Your background has to be clear and appealing so that it can contrast the character or products. A good background is a key to emphasize your game or an ad. It will highlight your product or character with incredible entice.

Lays – http://demo.adgebra.in/adg_spark/lays_maxx.html

-Once the background is set, move to the character or products.
This is the core of the game. If you are using a character or a mascot, then the primary goal is to highlight the character to the audience.The character should be Mesmerizing as it will be the main source of interaction with the audience. For example, A talkative character will be fun & interactive with the audience thereby leading to the goal, that is advertising your brand or product. Make sure the character or product is sharp and not blunt, or the meaning can be lost.

-Moving to Typography.
‘A Pilot always drives his passengers to their desired destination with an accurate path’. In the same way, good typography can show a reader the path to read in the right direction.
Use a font that fits in your design and easily noticeable & readable as nobody likes to stress their eyes out figuring out the lettered mess. There is an ocean of the fonts available to enhance our design.

Munch – http://demo.adgebra.in/adg_spark/munch_nuts_puzzle.html

-At last the finishing of our game or design- ‘The reward’
Make sure the audience is getting awarded for their patience & subtly reward them with an offer/price/gift of the product intend to advertise.

Buggerking- http://demo.adgebra.in/adg_spark/burgerking_spin.html

Added throughout this blog are a few examples of the gamification mode, we created for different brand’s, Enjoy!
Don’t hesitate to broaden your creativity, as “Creativity takes courage”…

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Growing importance of native advertising in Digital strategy

538 310 Bianca Paes

Over the recent years, native advertising has become increasingly streamlined and standardized. And only seems to improve in finding ways to deliver and promote content. If you are an advertiser looking to expand your business or a publisher looking to monetize your website through the digital platform, here are a few reasons why you should do it.

Native advertising is a piece of content, article, video, infographic that is distributed to an audience through a publisher platform in a way that does not disrupt the native user experience of that platform. In simple words, non-intrusive ads; these ads blend well with the publisher’s website, therefore building trust and engagement with recurring/prospective customers than traditionally displayed ads.

There are plenty of advertising strategies to choose from. Native advertisement covers the area of sponsored content, videos, surveys, questionnaire forms, under one head. Some even provide widget-based native advertising which is great for content on a website.
Native ads help to create online and social presence across popular social networks and help monetize and optimize content engagement.

It promotes branding, popularity, and outreach of the websites and can be maximized with an effective native advertising strategy. When readers find that content with native ads are of high quality, a feeling of trust grows among them.

Better user engagement and optimum sales. Native advertising can help you to achieve
optimum sales for your business. It generates higher searches and sales for your website as it is directly proportional to the content marketing strategies applied. Native ads have a higher sale and purchase ratio compared to other traditional ads.

This results in spreading the name of the website which ultimately promotes branding.
Native advertising generates higher purchase intention as readers are highly accustomed to these kinds of ads compared to traditional banner ads.
So now you know, what native advertising is and how can it help you.
For more info on how Inuxu caters to native advertising needs via its ‘adgebra’ platform please visit www.native.adgebra.in

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20-50-30

Controlled approach the next big thing in campaign planning and optimization

649 288 Apurv Lungade

31st July, 05:30pm – “An amount of Rs. xxxxxx has been credited to your salary account…”  the most awaited SMS of the month! But what happens next? A lot of us face a similar challenge of maintaining a balance between our earnings and spends/expenses. The major hurdle is lack of planning. This scenario can be also observed in the case of digital advertising spends.

55/5 rule of problem-solving suggests that often it is preferable to spend more time on identifying and properly framing the problem before trying to solve it. The proportion 55/5 comes from a quote attributed to Albert Einstein who supposedly said that if he had only one hour to save the world he would spend 55 minutes identifying and formulating the problem and only 5 minutes solving it. If we try to relate this to digital marketing industry, a media planner should spend more time in planning the campaign objectives rather than taking it LIVE in a hurry.

At the time of campaign planning and optimization, media planner should try and make different categories of campaigns based on goals that he receives from the client.
For example: Daily delivery goals: 10k to 1L impression, 1L to 5L impressions, 500 to 2000 clicks, 2000 to 5000 clicks; CTR goals: 0 to 0.1, 0.1 to 0.5. 0.5 to 1.0, 1 to 3 etc.
Once you have these categories in place, try to fit the campaign in respective category.

In digital advertising industry, media planners should try to follow 20-50-30 rule for efficient delivery of campaigns.
What does it mean? It means the total spend should be divided into 3 phases – 20%, 50% and 30%.20-50-30First phase – Experiment
The first phase “Experiment” is the most important aspect. The first 20% of the budget should be spent by tweaking the campaign attributes in such a way that it would meet the campaign goals. For example, if campaign goals are – daily delivery of 5L impressions and CTR min 0.5% then it is clear that the campaign need to be focused on large delivery and less on its performance. In order to achieve the goals by keeping maximum possible gross margin, one of the experiments I would perform on this campaign would be – target it on inexpensive site at high frequency.
In addition to that, in this phase – try to target the campaigns on all verticals (sites and segments/audiences) which seem relevant to the campaign.

Second phase – Blast
Analyse results which you got in first phase and choose best combination of campaign attributes which are likely to give best results in terms of RIO and campaign goals. Take this combination and extrapolate it making a blast. 50% of the budget should be spent in this phase. Monitor the performance of the campaign and make sure it is giving expected ROI.
How to calculate ROI? – in general terms, ROI = Revenue/expense but in digital advertising industry campaign performance is equally important. So here the ROI concept is – what is output of campaign performance (CTR/CR) when input is campaign different attributes. Example: There are two sites – S1 which is premium site and S2 is a normal site; when a campaign is targeted on these sites separately, and you notice that there is a minimal difference in CTR on these sites then ROI of serving campaign on S2 is more than serving it on S1.

Third phase – Retarget
Before moving to this phase, planner should plan in such a way that all the remaining backend goals are met in this stage. In order to make this happen, historical data acts as a treasure here. Say for example, you did and auto company campaign few months ago and you again receive a similar campaign but from a different client. Still the historical data of the previous campaign can be utilized as a learning material. This will help you in choosing the best remark audiences and also the ones that never performed earlier.
Slice and dice the historical data and make inferences out of it which will be used for optimizing the current campaigns site wise – category wise – impressions, clicks, CTR
Segment wise – counts

Conclusion:
Don’t spend the entire budget at once instead play safe by breaking your budget in these 3 phases and treat all the phase as a new campaign setting. Try not to mix earlier settings. Utilize the learnings from historical data wisely and effectively.

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7 terms in digital marketing you need to know beyond SEO, SEM & SMM

300 200 Ankita Panjwani

Since its evolution, a lot has been spoken and written about digital marketing and its terminologies, When I initially started reading about digital media it was all Greek to me until my mentor came and explained few terminologies which all beginners should be aware of if they wish to start their career in digital media.

I being a tyro in digital media would like to elucidate a few terms for those who are trying to latch onto the same field from my experience of internship of 2 months in a Inuxu Digital Media Technologies.

  1. REMARKETING –

Remarketing is targeting consumers who have already visited your website, shown interest in your product or service, but did not make a desired action on the website.

For example-When people leave your website without buying anything/ or filling up a contact form, remarketing helps you reconnect with them by showing relevant ads as they browse the web, as they use mobile apps, or as they search it on a search engine.

The biggest advantage of remarketing is that you’re only showing your ad to people who are genuinely interested in your product.

    2. AFFILIATE MARKETING –

 Affiliate marketing is performance-based marketing in which a business rewards affiliates for each sale generated by the affiliate. In affiliate marketing, advertisers pay to the ad networks and they in turn pay to the publishers for every lead generated. Payment is done on the basis of online advertising ad models, two of which are –

Cost per action (CPA)is a pricing system where advertisers pay for a specific action like form filled, purchase/sale, lead generated any similar conversion happening.

Cost per sale– is pricing system where the publisher or website owner is paid on the basis of the number of sales that are directly generated by an advertisement.

   3. PUSH NOTIFICATION –

An App notification is a message that is received by the user and is sent by the app publisher. This notification is a message that pops up in the status bar in a smartphone. This message maybe regarding any latest updation, latest event/activity that might be important to the user.Getting the content primarily outside the app, paradoxically yields increased engagement inside the app.

Desktop Notification or Browser push notification is a short message that appears on the desktop of any user who has opted in to receive notifications from any site. A desktop notification is accompanied by a ‘notification sound’ to alert the user, and inform them of whatever message, update, or content a site wishes to provide.

Push notification is an eminent channel to communicate with users.They deliver the right information at the right time to the users by emerging on the lock screens of the user.He/She doesn’t have to be in the app or using their device to be receiving them.

Push notifications are promotional drivers designed to drive “open rates.” Instead, we think of notifications as a way to push a value to the user.This way businesses get hold of their customers’ interest in their products and keep the profit graph high.

  4. PROGRAMMATIC ADVERTISING –

Programmatic Advertising refers to an automated process of buying media online.

This basically means -use of software for buying ad slots on websites, without any human involvement and manual insertion order. Programmatic Advertising also allows “Real Time buying”. RTB refers to the purchase of ads through real-time auctions and these auctions typically happen within 100 milliseconds.

  5. CROSS-DEVICE TARGETING –

Cross-device targeting is the ability to serve targeted advertising to consumers across multiple digital devices.

If any correlation isanalyzedd in the behaviour of the browser, across multiple devices through cookies and mobile ids, “match rate” arises, which means that the separate devices are of the same individual profile. Advertisers are now able to serve targeted advertising to these uniquely identifiable users.

Whereas When we rely on cookies or mobile IDs in isolation, each device or mobile app is viewed as an individual user. Cross-device targeting helps advertisers deliver a specific offer to an individual at a given time, regardless of the device they’re using. This way it promises the reduction of wasted impressions and more effective audience engagement and attribution.

  6. RICH MEDIA –

Rich media ads are pretty and their good looks attract consumer. Rich media is a digital advertising term for – an ad that includes advanced features like video, audio, or other creative elements that encourage viewers to interact and engage with the content and brand.

While text ads sell with words, and display ads sell with pictures, rich media ads offer more ways to involve an audience with an ad. The rich media ad can expand, float, etc. This is why they generate high click through rates (CTRs) and engagement rates. You can measure the success of your campaign by measuring the user engagement rate by gauging number of expansions, multiple exits, video completions etc. Rich media lets agencies create complex ads that can elicit strong user response.

  7. NATIVE ADVERTISING –

Native Advertising is a type of disguised advertising, that matches the form and function of the platform upon which it appears. It matches the visual design of the content of the web page. It looks and functions just like the natural content.

It is difficult for the user to distinguish between the content and the part of it which is a native ad, only until he doesn’t click on the ad and reaches the landing page. These are known as the “sponsored content”. Native ads are catching with other formats of advertising lately because of their disguised nature and a probable solution to ad blocking.

Conclusion: A lot of individuals think that digital marketing is only about SEO, SEM & SMM, but there is more to this buzzing word. My internship is about to get over and I have learned a lot of new trends that will rule digital advertising in the coming times. These trends are changing the face of the digital marketing industry. I am thankful to my mentor and all the peers at Inuxu for the guidance and support.

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trends in digital advertising 2016

5 digital advertising trends that will rule in 2016

599 401 Prerna Mehta

2015 is about to end and we will enter another existing era of digital advertising in 2016

Here are the top 5 trends that will rule digital advertising space in 2016. Digital advertising has always been a complex and volatile industry. With new trends paving way every passing year, advertisers and publishers need to stay tuned to all the latest updates. Let’s talk about some hot topics or trends that will keep advertisers and publishers on their toe’s in 2016.

  1. Mobile video advertisement
  2. Big Data usage for predictive analytics
  3. IoT and advertising on wearable device
  4. Native advertisement
  5. High impact ad units

Mobile Video advertisement: As per IAMAI study, 60% of internet users in India access the internet via their mobile phones now. The number is expected to reach 315 million by 2017, estimates IAMAI-KPMG report. A recent report from comScore reveals 100% growth in Online Video consumption in India in last 2 years. All this leads to advertisers spending their dollars on video ads on hand held devices in 2016. There are specialized companies which deliver mobile video ads programmatically. Advertisers will think mobile first when making their digital advertising strategy. With more telecom operators in India ready to launch 4G services in 2016, video consumption on mobile will rise even more thus pushing ad spends on mobile video ads.

digital advertising

Big data usage for predictive analytics: Big data is the backbone of predictive analytics. We will see a rise in new generation marketers using big data technologies for delivering better-targeted campaigns. Marketers can also find and target their best potential customers both existing and new, using predictive analytics. Decision making using this modelling method will give a new dimension to digital advertising strategy. 2016 will see a surge in startups offering, big data, predictive analytics & audience targeting solutions to all the stakeholders in the digital adverting landscape.

IoT and advertising on wearable device: Wearable devices & IoT will make more room for themselves in 2016. The Wearable device market in India is predicted to boom in the coming year. Some advertisers believe, that wearable devices (smart devices) are an extension of user’s phone and users will be more connected to these devices. When consumers moved from desktop to mobile, advertisers and technology companies studied and gained insights into behaviors and buying pattern of their consumers.  The same can be predicted for users moving from their mobile phones to wearables devices. Advertisers in India might not spend a lot on wearable technology as of now, but big data and analytics companies might end up collecting data for future. This transition will help us gather information and understanding of consumer habits across multiple devices. We might see some experiments happening in this space to see how consumers react to seeing ads on their wearable devices. We foresee healthcare companies utilizing health wearable as another device to target their audiences.

Native advertisement: Native advertising seems to be a lucrative option for advertisers and publishers in 2016.
Data from BI Intelligence finds that spending on native ads will reach $7.9 billion this year and grow to $21 billion in 2018, rising from just $4.7 billion in 2013. Natives ads are gaining popularity because of its non-intrusive nature and its immunity to ad blockers. Even, social native advertising is growing at a fast pace. Content marketing and native ads will go hand in hand. Advertisers will have to create appealing native content to generate more clicks. Companies like Taboola and Outbrain will be able to harness this growth in 2016.

High Impact ad units: 2016 will see advertisers and publishers working together to focus on user experience across devices. Non-standard ad format is the new kid (spreading across display, mobile & digital video) in town and is expected to attract advertisers and publishers to deliver desired revenue. We might see new standards and best practices being set for these ad units in 2016. International bodies like IAB, has already included a lot of innovative ad units like Billboard, Sidekick, Portrait, Filmstrip etc. in their rising stars catalogue. 2016 might see other non-standard ad units like in-screen, in-article and mobile innovations topping the charts. These innovative ad units deliver exceptional user engagements without disturbing the user experience or publishers’ content. We will see a rise in a lot of companies and ad exchanges delivering innovative ad units programmatically.

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