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Programmatic advertising platforms have revolutionized the way digital advertising is bought, sold, and managed. Key features of these platforms include: Automation: Programmatic platforms automate the process of buying and selling ad inventory in real time, allowing advertisers to quickly and efficiently target their desired audience. Real-Time Bidding (RTB): RTB is a core feature of programmatic advertising, where advertisers bid on individual ad impressions in real time. This process ensures that advertisers only pay for the ad space they want, while publishers can maximize their revenue. Data-Driven Targeting: Programmatic platforms utilize large amounts of data to identify and segment audiences, allowing advertisers to target users based on factors like demographics, interests, and browsing behavior. This results in more relevant and personalized ads for consumers. Cross-Channel and Cross-Device Integration: Programmatic platforms enable advertisers to run campaigns across multiple channels (e.g., display, video, social media, etc.) and devices (desktop, mobile, tablet, etc.), ensuring a seamless and consistent user experience. Transparency and Control: Advertisers and publishers have access to real-time reporting and analytics, allowing them to monitor campaign performance, make data-driven decisions, and optimize campaigns on-the-fly. Advanced Algorithms and Machine Learning: Programmatic platforms use advanced algorithms and machine learning to optimize ad targeting, bidding strategies, and ad delivery, resulting in improved campaign performance and increased ROI. Brand Safety and Fraud Prevention: Many programmatic platforms incorporate tools and features that help protect advertisers from displaying their ads alongside unsuitable content or falling victim to ad fraud. Dynamic Creative Optimization (DCO) : DCO allows for the automatic generation and customization of ad creatives based on user data, resulting in personalized ads that are more likely to resonate with the target audience. Cost Efficiency: Programmatic advertising often results in lower cost-per-impression (CPM) or cost-per-click (CPC) rates due to the increased targeting capabilities and automation, ultimately leading to a better return on ad spend (ROAS). Scalability: Programmatic platforms provide access to a large and diverse pool of ad inventory, enabling advertisers to reach a wide audience and achieve their marketing objectives more efficiently. Contact us to find out more about the Emerse programmatic advertising platform.

To calculate the number of variants in an A/B test, for example when running A/B-testing using Emerse Labs in programmatic campaigns, you first need to understand the components being tested and the variations of each component. An A/B test typically involves two or more versions of a specific element being compared to determine which one performs better. Here's a simple method to calculate the number of variants in an A/B test: Identify the components being tested: List all the elements you want to test in your experiment, such as headlines, images, call-to-action (CTA) buttons, or layouts. Determine the number of variations for each component: For each element you are testing, count the number of variations you want to include in the test. Make sure to count the original version as one of the variations. Multiply the number of variations of each component: To calculate the total number of variants, simply multiply the number of variations for each component. For example, if you're testing three different headlines and two different CTA button colors, the total number of variants would be: Total Variants = Variations of Headlines * Variations of CTA Button Colors Total Variants = 3 * 2 Total Variants = 6 In this case, you would have six different variants in your A/B test. Please note that the method above assumes a full-factorial design, where all possible combinations of variations are tested. In some cases, you may choose to test only specific combinations or use a fractional factorial design to reduce the number of variants, especially when testing multiple components simultaneously. To learn more about how Emerse can offer A/B-testing as a service, please contact us .

Programmatic Recruitment Advertising: A Modern Approach to Hiring In the age of digital transformation, nearly every industry is witnessing the integration of technology for optimization, efficiency, and innovation. The recruitment sector is no exception. Programmatic recruitment advertising has emerged as a game-changer, utilizing technology to automate the process of buying, placing, and optimizing job ads. Let's delve deeper into understanding this approach and how organizations can harness its potential. What is Programmatic Recruitment Advertising? At its core, programmatic recruitment advertising is about automating the distribution of job advertisements across various platforms. By using data analytics, algorithms, and real-time bidding, it ensures job ads reach the most suitable candidates at optimal times and places, maximizing visibility and engagement while minimizing costs. The Process J ob Ad Creation : Before launching a campaign, recruiters must craft compelling job descriptions. With the right content, tailored to the target audience, the subsequent steps in the programmatic approach become even more effective. Defining the Audience : With data analytics, recruiters can set detailed parameters on who should see the ad—be it based on skills, location, browsing habits, or any other metrics. This ensures the ad is shown only to those who are the best fit. Real-time Bidding (RTB) : Unlike traditional methods where job ads are bought for a set price on specific platforms, RTB allows for real-time auctions. The advertisement is dynamically bid on using a Demand Side Platform such as the Emerse DSP , ensuring it's placed where it will gain the most traction among potential candidates. Optimized Ad Distribution : The ad is then strategically displayed across various platforms—be it job boards, social media, or niche websites—depending on where the target audience is most active. Continuous Analysis and Adjustment : One of the biggest advantages of programmatic advertising is its dynamic nature. As the campaign runs, algorithms monitor its performance. Based on real-time data, adjustments are made—whether it's changing the platforms, tweaking the audience parameters, or adjusting the bid—to ensure maximum efficacy. Advantages of Programmatic Recruitment Advertising Cost-Effective : By targeting ads more accurately and relying on RTB, organizations can reduce wasteful spending. You're not paying for ads that reach the wrong audience. Wider Reach : With the ability to distribute across multiple platforms simultaneously, programmatic recruitment offers a wider reach. Moreover, it can tap into passive candidates—those not actively looking but might be interested if the right opportunity presents itself. Efficiency : Automated processes mean reduced manual intervention. The time taken from creating a job ad to it being viewed by potential candidates is drastically shortened. Data-Driven Decisions : Relying on data analytics means decisions aren't based on hunches. Recruiters get a clear picture of what's working and what's not, allowing for more informed strategies. Challenges and Considerations However, like any other method, programmatic recruitment isn't without challenges. There's a learning curve involved. Organizations need expertise, whether in-house or outsourced, to understand the intricacies. Moreover, while automation aids efficiency, the human touch in recruitment shouldn't be entirely discounted. Lastly, data privacy concerns, especially with increasing regulations, need to be meticulously managed. Conclusion Programmatic recruitment advertising is reshaping the hiring landscape. By blending automation with data analytics, it promises a smarter, more efficient, and cost-effective way to connect employers with potential employees. As the digital landscape evolves, it's an avenue organizations cannot afford to overlook if they wish to stay competitive in their hiring strategies. If you are interested in learning more about using programmatic advertising for recruitment, don't hesitate to contact us . If you want to create an account in the Emerse DSP and start running programmatic ads today, you can do so today using this link .

The Detailed Mechanics of Programmatic Advertising Programmatic advertising has emerged as a game-changer, reshaping how brands connect with audiences. But what exactly is it, and how does it function? For the curious reader, this article delves deep into the mechanics of programmatic advertising. Definition of Programmatic Advertising Programmatic advertising automates the decision-making process of buying and placing ads by targeting specific audiences and demographics. In essence, it's the algorithm-driven purchase and sale of advertising space in real-time. It eliminates the traditional manual methods, introducing precision, efficiency, and scale to the advertising process. The Intricate Process of Programmatic Advertising Programmatic advertising is propelled by technology platforms, the most integral being the Demand Side Platform (DSP) and the Supply Side Platform (SSP). Here's a breakdown of these platforms and their roles: Demand Side Platform (DSP) : A system that advertisers use to automate the purchasing of digital media across various inventories. Through the DSP , advertisers can set their targeting preferences, budget, bid for ad impressions, and monitor campaign performance. Supply Side Platform (SSP) : This platform allows digital media owners (publishers) to manage, price, and sell their ad space. An SSP assesses the value of incoming impressions, invites bids from potential buyers (via DSPs), and chooses the highest bid. The real magic unfolds when a user visits a web page. Here's a step-by-step process of what happens: User Visits a Website : The moment a user accesses a webpage with an ad space, the publisher sends a "bid request" to the SSP. This request contains information about the user, including their browsing history, location, and more. Auction Process : The SSP evaluates this data and sends the bid request to the ad exchange. The ad exchange then invites advertisers to bid for that ad impression. Advertisers Place Bids : Advertisers, through their DSPs, evaluate the user's data and decide if this user is valuable to them. If they're deemed valuable, the DSP places a bid on behalf of the advertiser. Selecting the Winner : The highest bidder wins the auction. The ad exchange then notifies the SSP, which in turn tells the publisher's platform to display the winning ad. Ad Delivery : The user's browser fetches the ad and displays it. This entire process, from the user visiting the site to the display of the ad, takes mere milliseconds. A Detailed Breakdown of an RTB Auction Many companies building technology for programmatic advertising follow the OpenRTB specification. Here's an overview of a typical RTB auction. 1. User Accesses a Web Page When a user navigates to a webpage that has spaces allocated for programmatic ads (like banner ads or video ads), this action triggers the start of an RTB auction. 2. Bid Request Initiated The publisher's ad server, often through a Supply-Side Platform (SSP), sends out a bid request to an ad exchange. This bid request contains a bundle of information about the user without revealing their personal identity. This can include: User Data: Browser type, device (mobile/desktop/tablet), operating system, IP address (often anonymized), and possibly historical data about the user's past browsing behaviors. Page Context: URL of the site, content category, page keywords, and other relevant metadata. Ad Details: Ad sizes/types available, ad formats, and placement positions. 3. Advertisers Evaluate the Bid Request Once the ad exchange receives the bid request, it broadcasts this request to multiple potential advertisers (or their representatives, which are Demand-Side Platforms or DSPs). These DSPs evaluate the bid request based on: Targeting Criteria : Advertisers have predefined criteria (like targeting users from a certain location or using a specific device) they look for. If the user's profile matches this criteria, they proceed with the bidding. Retargeting Lists : If the user had previously interacted with the advertiser's content (like visiting their website without making a purchase), they might be on a retargeting list, making them more valuable to the advertiser. Bid Algorithms : Advanced algorithms determine the bid amount based on the perceived value of the impression to the advertiser. 4. Bidding Interested advertisers submit their bids through the DSPs. This bid includes the amount they're willing to pay for the impression and the specific ad creative they want to display if they win the auction. 5. Selecting the Winning Bid The ad exchange reviews all the submitted bids and identifies the highest bidder. Some auctions may use a second-price auction model, where the winner pays $0.01 more than the second-highest bid, ensuring they pay the fair market value. Some auctions use a first-price auction where you pay what you bid if you win. 6. Ad Delivery Once the winning bid is determined, the ad exchange instructs the publisher's site (or the SSP) to display the winning advertiser's ad to the user. 7. User Sees the Ad The user's browser fetches the winning ad creative and displays it within the ad space on the webpage. The user can then interact with the ad, and the advertiser can record any relevant metrics, like clicks or conversions. 8. Post-Auction Analysis Advertisers often analyze the results of their bids to refine their strategies. They might look at metrics like click-through rates, conversions, or viewability to determine the success of their bids. The Fuel: Data in Programmatic Advertising Programmatic advertising's prowess lies in data. Various sources, from websites, apps, social networks, to even offline sources, feed data into programmatic platforms. This rich data allows for: Audience Segmentation : Advertisers can identify micro-segments within broader categories. Instead of targeting "males aged 25-30", they can target "males aged 25-30 who are vegan, enjoy hiking, and recently searched for eco-friendly products." Retargeting : Users who have interacted with a brand but didn't convert can be retargeted. This increases the chances of conversion as the user is already familiar with the brand. Types of Programmatic Purchases Real-Time Bidding (RTB) : This involves buying and selling ads in real-time auctions, much like stock trading. Advertisers bid for impressions based on the value of the user, and the highest bid wins. Programmatic Direct : This is a more traditional approach where advertisers directly purchase guaranteed ad impressions from publishers. The price and volume are pre-determined. Private Marketplaces (PMPs) : These are exclusive RTB auctions where premium publishers invite select advertisers to bid on their inventory. It offers more control and transparency to both parties. PMPs can also be bought through using a DSP. Advantages of Programmatic Advertising Efficiency : Automation streamlines the ad buying process, eliminating the need for manual negotiations. Precision : Advanced algorithms and rich data allow for hyper-targeted ad placements. Flexibility : Advertisers can adjust campaigns in real-time based on performance data. Scale : Access to a vast array of publishers means advertisers can expand their reach easily. Challenges in Programmatic Advertising Transparency : "Black box" operations of some platforms mean advertisers don't always know where their ads appear. Ad Fraud : Automated systems can sometimes display ads to bots, leading to wasted ad spend. Privacy Concerns : With data as the driving force, there's an ongoing debate about user privacy and data misuse. Conclusion Programmatic advertising has transformed the digital advertising sphere with its efficiency, scalability, and precision. By leveraging technology and data, it has allowed brands to engage with their target audience like never before. However, as with any technological advancement, it's essential to navigate the ecosystem with knowledge and awareness, ensuring that user trust and privacy are upheld, even as advertisers work to craft compelling, personalized ad experiences. If you want to get started with programmatic advertising, you can either setup an account now in the Emerse DSP using this link. Or contact us to learn more and schedule a meeting.

Vill du komma i kontakt med oss, veta mer om Emerse Labs eller programmatisk marknadsföring? Kontakta oss genom formuläret här eller Johan Bertilsson på mejl johan.bertilsson@emerse.com samt telefon +46 76 144 30 54. Bakgrund UniverCity är en bostadsutvecklare med fokus på hyresbostäder i Sveriges starkaste tillväxtregion Stockholm-Uppsala. UniverCitys syfte med marknadsföringen var att få in intresseanmälningar till deras nybyggda hyresrätter i Upplands Väsby. De ville därför, genom enkla medel och kreativitet, hitta rätt hyresgäster och skapa uppmärksamhet kring sitt projekt i Upplands Väsby. Utmaning UniverCity ville ha in kvalitativa intresseanmälningar från personer med möjlighet att flytta in omgående. Lägenheterna stod ju trots allt redo för inflyttning. Vilka är dessa personer, vart hittar vi dem och hur fångar vi deras uppmärksamhet? Personer som bor i närheten och går i separations- och skilsmässotankar är sannolikt intresserade av snabb inflyttning. Personer boende på annan ort i Sverige som fått jobb i Stockholm var också en potentiell målgrupp. Här gällde det att hitta rätt budskap anpassat till rätt målgrupp som sedan det anlitade uthyrningsteamet på Fastiella kunde ta hand om. Att synas i sociala medier är en självklarhet men här ville vi tillsammans även göra en extra insats för att kliva utanför ”ankdammen” och både skapa och stärka intresset hos målgruppen på ett annat sätt. UniverCity var öppna för att testa nya kreativa idéer för att se vad som ger bäst effekt. Med tanke på vår tidigare väldigt positiva erfarenhet av automatiska materialtester, s.k. A-B/N-tester, genom programmatisk marknadsföring så bestämde vi oss för att addera detta till strategin. Social Media + Programmatisk annonsering + Chat GPT = SANT Social Media - På UniverCitys sociala medier låg fokus på att driva trafik till Homeq.se som är den externa sidan där man hittar mer information om lägenheterna och fyller i sina kontaktuppgifter. Chat-GPT - Vi visste inte i förväg vilket budskap som skulle ge oss bäst resultat. Därför ville vi testa olika budskap för att kunna avgöra vad som funkar bäst. Dvs ge oss trafik och konverteringar. Här använde vi oss av Chat-GPT för att få fram variationer på texter. Vi valde 10 förslag att testa i EMERSE LABS som är en plattform för att just testa olika variationer av annonser s.k. A/B/N-tester. Här kunde vi snabbt se exakt vilken variation som gav vilket resultat. Efter det var det enkelt att optimera mot de bästa budskapen. Programmatisk annonsering - Till den programmatiska annonseringen använde vi bland annat oss av en metod som kallas för kontextuell targeting för att nå målgruppen. Detta är en strategi som innebär att annonserna hamnar i en redaktionell miljö som innehåller ”sökord” som vi har angett och som är relevanta för annonsen. I detta fall angav vi ord såsom skiljas, skilsmässa, flytta isär, flytta ihop, sambolagen med flera. Utöver den kontextuella annonseringen använde vi även oss av relevanta sajtlistor som Boli, Hemnet och Blocket Bostad. Resultat Under perioden då annonseringen pågick (april – juni) fick UniverCity en jämn ström av sökande och med mycket hög träffbild på de kriterier som satts upp för att godkännas som hyresgäst. Det skrevs 106 nya hyreskontrakt och 100% av projektet är nu uthyrt. ” Genom annonseringen och de smarta lösningarna nådde vi vårt mål. Det har varit ett kreativt och lärorikt samarbete. Dessutom att vi har fått inspiration till hur vi kan jobba med kommande projekt för att få dem uthyrda snabbare. Att vi haft roligt och skrattat mycket längs resans gång är ett plus som gett oss ökad arbetsglädje! ” säger Christina Sundman som är vd på UniverCity. " Det har varit väldigt tacksamt att ha ett flöde med bra intressenter som vi kunna jobba löpande med tillägger Eva Andersson Ericson, vd på Fastiella. Detta har underlättat vårt arbete och bidragit till att vi kunnat fylla fastigheten på rekordtid med glada och förväntansfulla hyresgäster. " Eva Andersson Ericson, VD på Fastiella Sammanfattning Att få till en bra räckvidd och skapa kännedom associeras ofta med dyra köp via TV eller Radio. Programmatisk annonsering tillhör också ett kraftfullt räckviddsmedia men till ett betydligt lägre pris. Dock är programmatisk annonsering ett alternativ som är komplext och okänd mark för många och att därtill koppla på en A/B/N-testplattform för att optimera annonsmaterialet kan kännas överväldigande. Därför är det viktigt att kunna arbeta med en partner som inte bara lägger upp annonser och låter annonserna bara göra sitt. Här krävs kunskap, optimering och nitty gritty arbete på djup nivå för att få valuta för investeringen. Sociala medier är däremot ett självklart val för många, en kanal som är stark längre ner i säljtratten både trafikdrivande och konverterande. Att kombinera programmatisk annonsering för att skapa räckvidd med sociala medier för trafik och konverteringar är en strategi som UniverCity använde och gav bra effekt. Ett vinnande koncept helt enkelt. Här får tilläggas att den öppenhet som UniverCity haft, att faktiskt våga och vilja testa nya metoder är givetvis en stor nyckelfaktor till att vi överhuvudtaget kunde leverera det resultat som vi gjorde. Om UniverCity UniverCity är en bostadsutvecklare med fokus på hyresbostäder i Sveriges starkaste tillväxtregion Stockholm-Uppsala. Deras filosofi är att sätta människors behov i centrum och erbjuda bostäder som svarar upp mot medvetna människors förväntningar om ett boendet som är en viktig del i deras livsstil. Med tonvikt på ekologisk och social hållbarhet, med gott om gröna ytor och möjligheter att umgås, skapar vi en boendemiljö där människor trivs och mår bra. Länk till projektet i Upplands Väsby Om Fastiella Fastiella är ett privatägt konsultföretag som arbetar med fastighetsförvaltning och är specifikt inriktade mot intäktssidan. Fastiella är fast beslutna om att basen i en välfungerande förvaltning består av de tre parametrarna: Stabil plattform, kunniga individer och välfungerande processer. Mer information om Fastiella här: https://fastiella.se/ Om Emerse Emerse har varit med i branschen sedan 2007 och var en av de absolut första att börja skriva egen kod för att skapa ett programmatiskt verktyg, en DSP. Det ger Emerse en oerhört djup kunskap om algoritmer, machine learning, AI och hur man nitiskt hanterar programmatisk annonsering för bästa resultat. Med denna kunskap hanterar vi samtliga digitala verktyg på ett seniort och unikt sätt. Emerse sitter med i den internationella kommittén W3C som är en global organisation för att sätta standarder och guidelines för webben. Vi sitter också med IAB (världsorganisation för onlinemarknadsföring). Vill du komma i kontakt med oss, veta mer om Emerse Labs eller programmatisk marknadsföring? Kontakta oss genom formuläret här eller Stina Larsson på telefon +46 709 769 901.

This is our third article in the series Emerse on Quality where we discuss topics in quality control of programmatic advertising campaigns. In our first article we discussed too fast ad reload times and in our second article we discussed ad stacking , both important quality problems to manage in programmatic advertising. Before we move on to the interesting topics of this article we would like to mention that Emerse provides fully managed quality controlled services for delivery, analytics and optimization of programmatic advertising for brands, advertisers and agencies. Our tools and processes for quality control goes beyond the ordinary. On a daily basis, we help brands deliver ad campaigns with more impact, more quality and at lower cost. Contact our sales team today to get started working with us . Intro: What is click discrepancy? Click discrepancies between Google Analytics sessions and DSP (Demand-Side Platform) clicks occur when the number of clicks reported by a DSP doesn't match the number of sessions recorded in Google Analytics. Several factors contribute to these discrepancies: Bot Traffic: DSP clicks might include non-human (bot) traffic, which inflates click numbers. Google Analytics applies filters to exclude some bot traffic, reducing session counts. This creates a gap between what the DSP reports as clicks and what GA reports as sessions. We see this is a very common issue in programmatic campaigns and will discuss this more below. Tracking Differences (Sessions vs. Clicks): Google Analytics Sessions: A session is a group of interactions on a website within a specific time frame. A session is initiated when a user lands on a site and typically ends after 30 minutes of inactivity. If a user clicks an ad multiple times or revisits within that time, only one session may be counted. DSP Clicks: Every click on an ad is recorded by the DSP, even if the user doesn't complete the landing process, encounters errors, or navigates away quickly. Discrepancy Example: One user may click an ad multiple times but trigger only a single session in GA. Tracking Issues: Some users may block tracking scripts or have disabled JavaScript, preventing Google Analytics from recording their session. DSPs, however, record the click because it happens on the ad server side, not on the user's browser. Here are for example some stats about browsers that use some kind of blocking app that will disable Google Analytics from running (of course many of these apps also block ads so don't assume they see or click your ads either but some ads might pass the filter): In total 700 million or more browsers have some of these apps blocking GA from tracking their visits to your website. Redirects and Page Load Failures: DSP clicks are counted when the user clicks the ad. However, if the landing page fails to load properly (slow connections, server errors, user closes the page before it loads), Google Analytics may not track the session. This results in clicks being reported without corresponding GA sessions. UTM Tagging and URL Mismatches: If the landing page URL in the ad is incorrect or lacks proper UTM parameters for Google Analytics tracking, the session may not be attributed correctly. DSP clicks will still be counted, but GA will fail to register the session, leading to a discrepancy. Session Timeouts: Google Analytics considers a session inactive if there's no activity for 30 minutes. If a user clicks an ad but waits too long before interacting with the site, Google Analytics may not register it as a new session, even if the DSP reports multiple clicks. Tracking discrepancies using a chart For any programmatic campaign it makes sense to track discrepancies. Both to be aware of current levels but also to try to reduce them over time, keep track of what DSP and GA configurations impact different changes in discrepancy and so forth. At Emerse we make it part of our job in managing programmatic strategies for customers to create charts like this and keep them populated with data. Before starting to tackle discrepancies what we like to do at Emerse is to create a chart to track data. It shows the measurement points across a timeline with values for averages/mean and a few upper and lower standard deviation levels (typically 2 and 3 standard deviations). If you'd like our help setting up and running a chart like this just contact us and we'll help you Here's an example chart based on mock-up data (not a real campaign) that shows how we track and visualize discrepancies at Emerse: The control chart in this example includes weekly measurement data of discrepancies between Google Analytics sessions and DSP clicks. Here's a deeper look at the chart itself: We can see in the sample data here that there is a clear issue with discrepancies. During some weeks discrepancies are very high. So this would indicate there is a problem and we need to do something about it. We have built a tool specifically for creating control charts that connects to Google Analytics and your DSP to produce automatically updated charts. It's called AdQMS and you can find it by clicking the logo below. Feel free to sign up to start tracking your own charts today: Analysing the cause of discrepancies As listed above there can be many reasons behind discrepancies. It's important to rule out any configuration settings are causing issues such as looking for the clicks under a certain UTM tag but that tag then not being used properly in the DSP. Or the GA tag not firing on the page traffic is directed at. Once we can establish that the configurations look ok it is time to look into the traffic itself. Traffic analysis To help the client improve their campaign, we first take a look at impression level data to see what level of bot traffic is visible when the ad tag is firing in the DSP. This is only sampled data and not the entire data set of the campaign: From the impression level ad-tag data we can see that there is an amount of bot generated impressions in the campaign (about 5%). This is of course interesting in itself but it does not show the whole reason for the discrepancy (which is higher). There are as we showed above many natural reasons even a quality controlled campaign will have some levels of bot impressions (such as scraping bots frequently visiting major news sites to scrape their content). The biggest news sites for example usually have good content, because of this they are very popular for others to scrape. So other sites, services and tech firms send their bots to the large news sites and just read and download their content, save it and do something with it. Some might be news aggregators, some might be AI services reading news to learn, some might use AI to rewrite the articles into their own. Etc. An important point here: Even if you buy ads directly from the largest publishers, you will still get this bot traffic on your campaigns. So if I go out and buy an ad campaign directly from the largest news publishers in my region, the bots will still go there and see my ads. Because bots also download ads, not just articles. So it doesn't matter if it is programmatic or direct buying from big sites, the bot traffic is there regardless. Next we dig into the actual click traffic from the ad-tag in the DSP to see what amount of clicks (not impressions) in the ad-tag originate from bots: We see some interesting data here. About 8% of the clicks on the ad-tag are from bots. Again there is very little the publishers can do to prevent bot traffic but in some cases the amount can be larger on certain publishers and that data can be interesting to look into further. Bot clicks can of course cause reporting errors and discrepancy in the GA/DSP data ratios. Next steps Once you have (with our help if you like) identified the cause of discrepancies, the next step is to work to reduce it. Here it is clear that we need to identify which traffic sources are driving the bot clicks through and find ways to block them out from the campaigns. If clicks like this are used in CTR/CPC or (worse) even CPA optimization then they will mess up the optimization algorithms causing them to drive more and more traffic from the wrong places. We are able to identify exactly which publishers, sites and apps are driving the bot clicks. This will help you remove them from your campaigns. It's important to note that some level of bot traffic will occur on any site or app. For example, reputable high quality news sites are often scraped for content by bots that feed that content into AI and convert it into content on other sites (typically made-for-advertising sites). This is something the publisher being scraped has nothing to do with. So that level of bot traffic will be hard to avoid. But then there are publishers that buy traffic from bot farms or ad networks that send tons of bot traffic and generate both impressions and clicks. The bots can manipulate DSP algorithms by clicking ads and thereby fooling the algorithms to allocate more budget to them as they seem to have a high CTR. Emerse delivers quality controlled programmatic advertising Our services to deliver quality controlled campaigns and programmatic advertising for customers means we take care of quality assurance techniques such as the ones in this article for you. If you are interested letting Emerse manage your programmatic advertising with our quality and cost control processes as well as performance optimization, please contact our sales team today to discuss more .

This is our second post in the series Emerse on Quality (our first article in the series was about too fast ad reload times ). Join us as we delve into key aspects of digital advertising quality control. We've opted to simplify the complex issue of quality by dividing it into manageable, bite-sized sections. We will explore what constitutes 'defect' ad impressions and offer strategies for advertisers to steer clear of them. Our goal is to enhance the quality and performance of ad campaigns. Before we begin, we’d like to highlight that Emerse is dedicated to providing advertisers with quality, cost-controlled programmatic advertising through a managed service. We apply quality management processes typically seen in industries like manufacturing to advertising. If you’re interested in learning more about our services, please don’t hesitate to reach out to our sales team . What is ad stacking? Ad stacking refers to the practice of stacking multiple ads on top of each other in a single ad slot, but only the top ad is visible to users. The other ads underneath remain unseen, though they are technically served and registered as impressions. This can happen in both display and video advertising and is often associated with fraudulent intent to generate revenue by delivering unseen ads. The implications of ad stacking are primarily negative and multifaceted, affecting advertisers in several ways: Wasted Spend: Advertisers pay for impressions that are never actually viewed by users. This drains advertising budgets, as a significant portion of the expenditure does not contribute to actual ad engagement or brand exposure. Skewed Analytics: Since all ads in a stack report impressions, ad stacking leads to inflated impression counts, misleading advertisers about the true reach and effectiveness of their campaigns. This can skew performance analytics, leading to poor decision-making based on inaccurate data. Damaged Reputation: Brands unknowingly involved in ad stacking may suffer reputational damage if their ads are associated with fraudulent activities, even indirectly. This can erode trust with both consumers and advertising partners. Reduced Campaign Effectiveness: Real engagement metrics such as click-through rates and conversion rates are adversely affected. The disparity between high impressions and low engagement can lead to incorrect assessments of campaign performance. How do we detect it? At Emerse we use a number of tools to analyse ad impressions and ad inventory for quality assurance. We've spent many years refining our technology and methods to find ways to keep track of ad impression quality and avoid low quality ad inventory. Some examples of ways to detect ad stacking: Geometric Monitoring: This involves checking the z-index (a CSS property that specifies the stack order of elements) and other CSS properties of ad elements to determine if multiple ads are layered over each other in the same ad space. Page Layout Analysis: This method analyzes the entire layout of a webpage to ensure that ad placements are visible and not obscured by other content or ads. Browser Visibility Tests: These tests determine whether an ad is within the visible area of the browser window and not hidden behind other content. How we can help you run campaigns with less quality issues We help customers on a daily basis to deliver ad campaigns using high quality configurations that we have fine tuned over many years of working. We analyse large flows of impression data every day and take action to improve settings and configurations for our customers step by step. Each improvement is for the benefit of all our customers. So the combined flow of ad impressions and the quality control knowledge they generate is of benefit for all advertisers we work for. Conclusion: Excluding inventory with ad stacking Measuring and keeping track of potential ad stacking for each publisher and inventory source you buy ads on is important. Once a publisher has been identified as engaging in ad stacking, they can be removed from your targeting site lists (or if you run open targeting, added to a black list). Removing ad impressions with ad stacking will improve the performance of your campaigns as more people will actually see the ad impressions you are buying. At Emerse we provide customers feedback and input on which traffic we find use methods such as ad stacking. Don't miss our third article in the Emerse on Quality series, about discrepancies between Google Analytics sessions and programmatic DSP clicks . If you'd like to explore letting Emerse managed your programmatic advertising using our quality and cost control processes, then make sure to reach out to us today .

In our Optimization Edge series we focus on performance optimization of programmatic advertising campaigns. Performance generally means driving more sales, leads or results in some form. In this article we explore how advertisers can use insights from their Google Ads search campaigns as signals to inform the settings of their programmatic campaigns. Since Google Ads campaigns can be late-funnel and catch buyer intent signals, these can be useful to better target programmatic display, video and out-of-home campaigns with similar parameters. About Emerse and managed programmatic advertising services Emerse has for more than 10 years provided managed services in programmatic advertising to customers worldwide. Focusing on quality, cost and performance we help customers improve their advertising beyond 'normal' methods. To find out more about our services and explore becoming a customer, please contact our sales team today here . How smart bidding signals can inform targeting in programmatic campaigns Google Ads and campaigns using tools such as Performance Max often focus on late funnel customers with buyer intent and can have a high degree of conversions. As such, the campaign insights from these campaigns can provide interesting signals that can be useful to target campaigns also in programmatic channels. With information such as which geographic areas or times of day are better performing, similar settings can be deployed across programmatic campaigns to improve performance. The programmatic campaigns do not have to run in Google platforms. You can simply use insights from the smart bidding signals to make settings in any platform you use for your programmatic campaigns. This way, search campaigns can fuel data into programmatic campaigns. If you are running campaigns using Google Ads, you have access to this data and can apply it to your programmatic campaigns in other platforms. The dynamics of search campaigns in Google Ads are quite different from the dynamics of for example a programmatic banner campaign. Therefore the signals can provide different value in the optimization process of your programmatic campaigns. Simply put, they provide a different "view" on the optimization from an (often) keyword driven, late funnel perspective. Where to find smart bidding signals Bid strategy reports can be found using the steps described on this link: https://support.google.com/google-ads/answer/7074568?hl=en&ref_topic=6294205&sjid=3975267608713313156-EU For example, at campaign level you can find the reports this way (quoted from the link above): In your Google Ads account, click the Campaigns icon . Click the Campaigns drop down in the section menu. Click Campaigns, then navigate to Campaigns table. Add the "Bid strategy type" column, if it doesn’t exist already. Find the relevant campaign, hover over highlighted bid strategy type (for example, Maximize conversion value (tROAS) or Maximize conversion value). Click to view bid strategy report for that campaign. What smart bidding signals look like Once you find your bid strategy report, you will see something similar to this under signals: Here under Top signals you will find signals used by smart bidding in Google Ads to optimize your bidding: So here we can see that certain geographic locations (in this example, Stockholm) and certain days of the week (in this case weekends) seem to perform better. At the same time certain locations (Gothenburg in this case) seem to perform less well. Top smart bidding signals are also easily available in the Google Ads mobile app: How to deploy these insights to your programmatic campaigns To test the performance of these signals on your programmatic campaigns, simply create a new line item or bidding agent that targets the top positive signals. For example the same geographic location, days of week and times of day. Then keep track of how the top signals change in Google Ads, so you can update and test new optimization strategies in your programmatic campaigns. Interested in explore working with us? We provide programmatic advertising through banner, video, CTV and Digital Out-of-Home as a managed service for customers in multiple countries. Contact us today to find out more and start up a partnership .

This is the first article in our series 'Emerse on Quality' where we discuss important topics in digital advertising quality control. We have decided to break down the big topic of quality into several small and more easily accessible chunks. We identify what we call 'defect' ad impressions and what advertisers can do to avoid buying them. With the purpose of increasing quality of ad campaigns and thereby also ad campaign performance. Before we start we want to mention that Emerse works for advertisers to deliver quality and cost controlled programmatic advertising as a managed service. We do this by employing a process of quality management which is traditionally more often found in areas such as manufacturing. To learn more about the work we do please feel free to contact our sales team to set up a meeting . What is ad reload time? "Ad reload time" refers to the interval between when one advertisement is displayed and when it is replaced or refreshed with another advertisement on a web page or in an app. This concept is particularly relevant in the context of digital advertising where ads can be dynamically loaded and swapped without needing to refresh the entire page. The reload time can be set to different durations depending on the strategy of the advertiser or the publisher. A shorter reload time can increase the number of ads shown to a user, potentially increasing revenue. However, it can also impact user experience negatively if ads refresh too frequently, which might be distracting or annoying for users. Why is ad reload time important for advertisers to be aware of? If you buy for example display banner advertising and run using programmatic channels on thousands of websites and apps, each of these publishers can set their own ad reload times. Some might reload the banner once for every page visit, others might reload the banner position once every 30 seconds. But some might reload it every 3 seconds. If you consider this and the impact it has on the value of the ad impression bought it is easy to see that an ad position that reloads after 3 seconds offers a significantly lower chance of being seen or read by a visitor while an ad that is available on screen for the whole duration of the page visit is more likely to be seen by the visitor, and when seen the visitor will also have time to read and see the content of the banner. Too fast ad reload times reduce the chance that the visitor will even see the ad on the page before it is reloaded. Chances are even if they see it they won't have time to click it before it is reloaded with another ad from another advertiser. So this can be a big problem for advertisers. Example Heres an example of what fast ad reload can look like. Here you have 3 banners all reloading with 2 second intervals: What can advertisers do to handle this? First of all being aware of the differences in quality various publishers offer is important. Not all publishers offer the same number of seconds in ad visibility for an impression. The ad reload time is often regulated by policies by the ad networks, ad exchanges and supply side platforms used by the publishers. Demand Side Platforms can also set their own policies on what ad reload times they permit. Being aware of this enables the advertiser to buy from sources with good quality ad reload times permitted. When curating sitelists for campaigns or when reviewing traffic from delivery reports, having ad reload time as a parameter in quality control should be considered. If the reload time is too short, chances are ad impressions will not have the effect they are intended to. The quality control process at Emerse includes ad reload time and we work to ensure our customers are not buying impressions from inventory with too low such times. If you would like help buying display, video, banner and programmatic advertising campaigns with quality and cost controls in place, please contact us to schedule a meeting with our sales team . Don't miss our next article in this series, about ad stacking .

Master’s thesis carried out at Emerse Sverige AB for the Department of Computer Science, Lund University. Author: Erik Smith Supervisors: Pierre Nugues, Department of Computer Science, Faculty of Engineering, Lund University Elin Anna Topp, Department of Computer Science, Faculty of Engineering, Lund University Carl-Johan Grund, Emerse Sverige AB Rasmus Larsson, Emerse Sverige AB Link: https://lup.lub.lu.se/student-papers/search/publication/8994653 Link to full-text PDF: Reinforcement Learning for Real Time Bidding Today, the most common software-based approach to trading advertising slots is real time bidding: as soon as the user begins to load the web page, an auction for the slot is held in real time, and the highest bidder gets to display their advertisement of choice. But each bidder has a limited budget, and strives to spend it in a manner that maximizes the value of the advertisement slots bought. In this thesis, we formalize this problem by modelling the bidding process as a Markov decision process. To find the optimal auction bid, two different solution methods are proposed: value iteration and actor–critic policy gradients. The effectiveness of the value iteration Markov decision process approach (versus other common baselines methods) is demonstrated on real-world auction data.

Master’s thesis carried out at Emerse Sverige AB for the Department of Automatic Control, Lund University. Author: David Rådberg Supervisors: Karl-Erik Årzén, Department of Automatic Control, Lund University. Martina Maggio, Department of Automatic Control, Lund University. Anders Rantzer, Department of Automatic Control, Lund University. Carl-Johan Grund, Emerse Sverige AB Rasmus Larsson, Emerse Sverige AB Link: https://lup.lub.lu.se/student-papers/search/publication/8953440 Full-text PDF: Optimal Real Time Bidding in Online Advertising This thesis explores some of the possibilities of demand side optimization in online advertising, specifically how to evaluate and bid optimally in real time bidding. Theory for many types of optimizations is discussed. The thesis evaluates auctions from a game theory and control theory perspective. It also discusses how big data sets can be used in real time, and how agents can explore unknown stochastic environments. All items are valued through an estimated action probability, and a control system is designed to minimize the cost for these actions. The control system aims to find the lowest possible price per item while spending the entire budget. Periodic market changes and censored data makes this task hard and imposes low pass characteristics on the closed system. Using data to evaluate items is a high dimensional problem with very small probabilities. When data is limited the algorithm is forced to choose between low variance and precision. The choice between exploring and exploiting the unknown environment is crucial for long and short term results. An optimization algorithm was implemented and run in a live environment. The algorithm was able to control the spend optimally, but distributed it suboptimally.

Master’s thesis carried out at Emerse Sverige AB for the Department of Computer Science, Lund University. Authors: Carl Dahl, Pontus Ericsson. Supervisors: Pierre Nugues, Department of Computer Science, Faculty of Engineering, Lund University Jacek Malec, Department of Computer Science, Faculty of Engineering, Lund University Carl-Johan Grund, Emerse Sverige AB Rasmus Larsson, Emerse Sverige AB Link: https://www.lunduniversity.lu.se/lup/publication/8995181 Link to PDF: Managing Programmatic Advertising Using Machine Learning Articles in this series are theoretical and involves a substantial part of mathematics and computer science. This thesis is an exploratory study into the possibility of using machine learning to manage advertisement campaigns and agents involved in real-time bidding. The norm for the industry of real time bidding is currently having human operators managing campaigns by changing settings to maximize the number of clicks. The goal was to investigate the possibility of automating this process, to at the very least assist the human operators with making better decisions. The first part of the project was to build a model for predicting the clickthrough rate (CTR) of the ad campaigns. The second part was to use the model to suggests optimal settings for bidding agents. The outcome was a model with an accuracy of 92% in predicting whether an ad was to generate any clicks or not, and with an accuracy of 58% to predict the outcome of an agent in the different categories “few clicks”, “some clicks” and “many clicks”.