digital marketing

    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.

    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

    20-50-30

    Controlled approach the next big thing in campaign planning and optimization

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    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.

    SECRET INGREDIENT FOR DIGITAL MARKETING STRATEGY, REALISM OR OPTIMISM

    Secret ingredient for digital marketing strategy, Realism or Optimism

    599 400 Shweta Gupta

    Is your digital marketing strategy on the right track?

    Have you reviewed your digital marketing strategy lately? Have a check whether it is based on realism or optimism.

    Realism is what makes humans act sensibly and cultivate practical and real life ideas of what can be achieved. In brief, represent things as they really are!

    Optimism is what makes humans look on the most favorable side of events. In brief, makes you feel awesome, happier, having fulfilled lives! Who doesn’t want this? Everyone wants to lead an awesome life.

    Unfortunately, with regards to the success of a digital marketing strategy, a lot of digital marketers tend to project goals on the basis of sheer optimism. Digital Marketers should embrace Realism instead of Optimism. Let’s understand why realism is the secret ingredient for a digital marketing strategy.

    There is no harm in being optimistic about performances and delivery, as this gives us the zeal to achieve goals, but on the contrary, being optimistic makes you emotionally blind, rather than allowing you to play well with your mind. Marketing strategies based on optimism might lead to under delivery and eventually unhappy clients as one might prematurely simplify digital marketing plans, efforts and outcomes and deviate from real facts and figures. In the process, a lot of important warnings might get neglected which can be dangerous in the long run. Forecasts and deliverables should invariably be based on realistic figures.

    Let’s take an example:

    You just bagged a branding campaign, with objectives to increase the brand awareness by 50% and traffic on their website by 70%. The timeframe to deliver the campaign is 3 months. Your visual designing team has created some very attractive display banners with compelling messages, which you think people will go nuts over. I mean, how could users not recall and click on your digital ads? (Yeah, here you go! Overly optimistic)

    Based on your optimism, you communicate the deliverables to the client. The campaign is set and the ball is rolling, but soon you discover that only half of the targeted numbers for the month show up in the reports. Your forecast and the reports are misleading. So, what went wrong? Let’s figure out.

    • Relevant audience actual count was not considered when numbers were derived.
    • Assured of attractive creative (display banner) deliverability.
    • Small advertising budget was not feasible enough to deliver the campaign.

    Checkpoints:

    • Be realistic and close to actual numbers when forecasting campaign goals.
    • Take insights from previous campaigns and customer experiences.
    • Always consider A/B testing before being over confident about one particular digital marketing strategy.

    How to be realistic:

    Over-commitment and an unrealistic figure can lead to client dissatisfaction. You need to be very careful when drafting your campaign goals. But, how do we attain the realistic goal? Here we go, (In reference to above example)

    a. List down the focused channels to run the campaign. Considering display marketing, let’s decide on the high impact innovative ad formats that should be appropriate for your branding campaign. Brand Recall as high as 50% by using high impact ad units. Say for example you can choose from these options;
    i. In-image
    ii. In-screen
    iii. GDN Ad Formats – Google Display Network

    b. Find out the precise count of relevant audiences that you have from your audience repository; this defines part of your reach. And rest can be pulled from GDN noting the advertising budget.

    c. State CTR based on past trends for each Ad Format. Now, you have the reach and CTRs in place. Hence, we know the traffic that we can deliver. That was easy! Isn’t it?

    d. Next step is measuring brand awareness, which is more of a qualitative parameter than quantitative. The best way to determine brand awareness is by asking the viewers/ clickers if they remember seeing the specific brand’s ad. If numbers in the first month don’t look that promising, the campaign settings can always be tweaked in terms of budget, ad channel and creative used, for the coming months. You shouldn’t be disheartened and pull the plug off soon.

    Grab your clients’ attention by promising delivery, backed by real numbers and not just your optimism. To execute a realistic and performance driven digital marketing campaign you need to find the right digital technology partner with the expertise to run digital campaigns via multiple delivery channels.

    Benefits:

    • Less chances of a catastrophe if you are close to reality.
    • High chances of achieving the final deliverables.
    • Happy clients, in return unshakeable client loyalty.

    Now you know how to keep your clients satisfied and loyal, by being realistic. We have won million hearts by using this secret recipe Secret ingredient for digital marketing strategy, Realism or Optimism

    To create some realistic digital marketing campaigns contact us.