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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. 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. 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. 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 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.

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.

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 Job 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.

Five outstanding examples of programmatic advertising campaigns Programmatic advertising has redefined the landscape of digital marketing, offering unprecedented opportunities for precision targeting. While programmatic encompasses a wide range of formats, banner ads remain one of the most widely used. The combination of programmatic buying with visually engaging banners can lead to powerful campaigns. Let's explore five memorable programmatic advertising example banner campaigns: Coca-Cola's "Share a Coke" Campaign: Coca-Cola's iconic "Share a Coke" campaign took personalization to a new level. Originally a traditional marketing campaign, Coca-Cola used programmatic buying to push personalized banner ads to users. Based on user data, these banners would display names or personalized messages encouraging users to find and purchase a Coke bottle with that name. The dynamic nature of these programmatic banners significantly increased user engagement, making it a digital counterpart to its real-world success. Cadbury's "Match & Win" Campaign: Cadbury leveraged programmatic banners to promote its "Match & Win" competition. Users would see banners with a unique code prompting them to buy a Cadbury product, enter the code online, and win prizes. What made the campaign special was its dynamic targeting. Using data, the banners were displayed to users more likely to participate, such as past competition entrants or those who had shown interest in similar promotions. Nike's Weather Sync Campaign: Nike decided to showcase its weather-appropriate gear using real-time weather data. Through programmatic banners, if a user was experiencing rainy conditions, they'd be shown Nike's latest waterproof running gear. Conversely, on hotter days, Nike would promote its breathable sportswear. The synchronization with real-time conditions made the ads timely, relevant, and highly effective. Lexus' Dynamic Retargeting Banners: Lexus utilized programmatic technology to retarget users who had shown interest in specific car models on their website. Depending on which model the user had shown interest in, they would see a customized banner ad showcasing that particular model, its features, and any ongoing promotions or discounts. This precise targeting made users feel that the ad was tailored for them, increasing the chances of them returning to the website and progressing further down the sales funnel. Burberry's Personalized Fragrance Ads: To promote its range of fragrances, Burberry used programmatic banners to target users based on their browsing behavior and previous purchases. If a user had previously shown interest in men's products, they'd see a banner promoting Burberry's latest men's fragrance. Similarly, those browsing female-centric products would see banners for women's fragrances. By ensuring that the ads were closely aligned with user interests, Burberry increased the resonance of its messaging. In conclusion, these programmatic advertising examples show how data, creativity, and technology can come together to deliver impactful messaging. As programmatic advertising continues to evolve, we can expect even more innovative and personalized campaigns that resonate deeply with audiences. Start running programmatic advertising in the Emerse DSP today by signing up for an account. No minimum deposit and you can launch your first campaigns quickly.

In the digital age, advertisements have evolved beyond mere static images and texts on web pages. They’ve transformed into a dynamic, personalized experience, directly tailored to individual user preferences. The force behind this revolution? Programmatic buying platforms. But what are they, and why are they so crucial in the contemporary advertising ecosystem? What Are Programmatic Buying Platforms? Programmatic buying platforms, at their core, automate the purchase, placement, and optimization of digital advertising, as opposed to the traditional method of human negotiations. They leverage data insights and technology to acquire specific audience segments at optimal price points in real-time. In essence, it’s the "stock exchange" of the digital advertising world. These platforms fall under two main categories: Demand-Side Platforms (DSPs): DSPs such as the Emerse DSP allow advertisers and agencies to buy digital ad inventory across multiple sources, from websites to apps. They optimize based on specific targeting criteria such as demographics, geography, interests, and browsing behavior. Supply-Side Platforms (SSPs): SSPs are utilized by online publishers to sell their ad space to advertisers. They ensure the publishers get the highest possible prices for their ads through real-time bidding processes. How Do They Work? Imagine a user visiting a website. The moment this site loads, it sends a signal to an ad exchange that this particular user is available for an ad impression. The ad exchange assesses the ad impression's data (URL, demographic information, interests) and matches it with an advertiser's criteria. If it's a match, an auction between competing advertisers begins, and the highest bidder gets their ad displayed to the user. All of this happens in milliseconds, ensuring the user experience remains smooth and uninterrupted. Benefits of Programmatic Buying Platforms: Efficiency: Automation means quicker transactions and optimized pricing. Advertisers can reach their desired audience more effectively, and publishers can sell their inventory faster. Precision: By using data-driven insights, advertisers can target ads to specific user segments, ensuring the content is relevant and more likely to lead to user engagement. Real-time Analysis: Programmatic platforms offer real-time data analytics. Advertisers can see how their ads are performing and make immediate adjustments if necessary. Scale: Programmatic buying platforms connect advertisers to a vast network of publishers. This means more opportunities to display ads to the right audience, across various platforms and devices. Challenges and Concerns: While programmatic platforms have revolutionized digital advertising, they are not without challenges. The complexity of the ecosystem can lead to issues like: Transparency: With so many intermediaries involved, it can sometimes be unclear where an advertiser's money is going and how much of it is spent on actual ad placements versus platform fees. Ad Fraud: Automated systems can sometimes be exploited by malicious actors to generate false clicks or impressions, costing advertisers money with no real engagement. Privacy Concerns: Collecting user data for targeted advertising has raised privacy concerns. New regulations like the General Data Protection Regulation (GDPR) in Europe have been instituted to protect user data. Platforms such as the Emerse DSP are members of the IAB TCF Framework which works to ensure the privacy choices of consumers are handled correctly in digital marketing. Conclusion: Programmatic buying platforms have fundamentally altered the digital advertising landscape. They've made ad placements more efficient, targeted, and data-driven. As with all technological advances, they come with challenges. But with continuous innovation and appropriate regulations, they promise a future where advertisements are not just seen as interruptions, but as relevant, timely content that provides value to both the advertiser and the end user. Setup an account in the Emerse DSP today to get started with your programmatic buying or contact our team to schedule a demo and talk more.

Header bidding is an advanced programmatic advertising technique that allows publishers to offer their ad inventory to multiple demand partners (advertisers) simultaneously before making ad requests to their ad server, such as Google's DoubleClick for Publishers (DFP). This process increases competition for ad space, leading to higher revenue for publishers and more efficient ad placements for advertisers. Here's a step-by-step explanation of how header bidding works: Setup: The publisher installs a header bidding wrapper, which is a piece of JavaScript code, on their website or app. This wrapper is responsible for managing the header bidding process and communicates with different demand partners or ad exchanges. Ad inventory: The publisher defines the ad inventory (ad units and ad placements) they want to offer for header bidding. Bid request: When a user visits the publisher's website or app, the header bidding wrapper sends bid requests to multiple demand partners, along with information about the ad inventory and user (such as device type, location, and browsing behavior). Bid response: Demand partners review the bid requests and decide if they want to bid on the ad inventory. If they do, they return a bid response, which includes the bid price and the creative (the actual ad content). Auction: The header bidding wrapper collects all bid responses and conducts an auction to determine the highest bidder for each ad unit. Ad server request: After the auction, the header bidding wrapper sends the winning bids to the publisher's ad server. The ad server compares the winning bids from the header bidding process with other demand sources, like direct deals and ad networks, to determine the final winning bid for each ad unit. Ad rendering: The ad server returns the creative for the final winning bid to the publisher's website or app. The ad is then displayed to the user. By allowing multiple demand partners to compete for ad inventory, header bidding helps publishers maximize their ad revenue and ensures advertisers have access to premium ad placements, often resulting in more relevant and engaging ads for users.

Prebid is an open-source set of solutions designed to streamline and optimize the process of header bidding in digital advertising. Header bidding is an advanced programmatic advertising technique that allows publishers to offer their ad inventory to multiple ad exchanges simultaneously before making a call to their ad server. This competition among ad exchanges typically results in higher CPMs (cost per thousand impressions) and increased ad revenue for publishers. Prebid offers a suite of tools, including a JavaScript library for implementing client-side header bidding, server-side header bidding solutions, and additional tools for analytics and reporting. Prebid.js is the most widely used component of Prebid, as it allows publishers to easily integrate multiple demand partners (e.g., ad exchanges and networks) into their websites or apps. The main benefits of using Prebid include: Increased ad revenue: By fostering competition among multiple demand sources, Prebid helps publishers maximize their ad revenue. Improved transparency: Prebid provides publishers with greater insight into how their ad inventory is being valued and sold across different demand partners. Enhanced control: Publishers can customize and optimize their header bidding setup according to their specific needs. Faster page load times: Prebid is designed to minimize the impact of header bidding on page load times, ensuring a better user experience. Strong community support: As an open-source project, Prebid benefits from a large community of developers, users, and contributors who work together to improve and maintain the platform.

There are two primary types of auctions in Real Time Bidding (RTB) and programmatic advertising. First-price auctions and Second-price auctions. First-price auctions and second-price auctions are two distinct types of auctions that differ in how the final price paid by the highest bidder is determined. Here's a brief overview of each: First-price auction: In a first-price auction, bidders submit sealed bids, and the highest bidder wins the auction. The winner then pays the exact amount of their bid. Since bidders are unaware of what others are bidding, they must strategize and balance their desire to win the auction against the risk of overpaying for the item. This might lead to bidders submitting lower bids than their true valuation to avoid overpaying, a strategy known as bid shading. Second-price auction: In a second-price auction, also known as a Vickrey auction, bidders also submit sealed bids, and the highest bidder wins the auction. However, the winner pays the amount of the second-highest bid rather than their own bid. This auction format encourages bidders to submit bids that reflect their true valuation of the item, as they only pay the price of the next highest bid if they win. This mechanism is designed to reduce the winner's curse, which occurs when the winner overpays for an item in a first-price auction. In summary, the main difference between first-price and second-price auctions is the way the final price is determined. In a first-price auction, the winner pays their own bid, while in a second-price auction, the winner pays the second-highest bid. This difference in pricing mechanisms affects bidders' strategies and the overall efficiency of the auctions.

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 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.

Here's a step-by-step guide to help advertisers effectively leverage ChatGPT for their campaigns: Define objectives: Clearly outline the goals of the A/B-test, such as increasing click-through rates, conversions, or engagement. Generate variations: Use ChatGPT to create different versions of ad headlines, copy, and calls-to-action. Provide the AI with context, target audience, and desired outcome to ensure the generated content is relevant and effective. Create ad visuals: While ChatGPT does not generate visuals, you can pair it with a visual design AI tool to create ad images or use your in-house design team to develop visual elements for the different variations. Set up the A/B-test: Set up the A/B-test using an ad platform like Emerse Labs, ensuring that you evenly distribute traffic between the different ad variations or let the Emerse Labs machine learning algorithm automatically distribute more impressions to the better performing version as quickly as possible. Monitor performance: Keep track of key performance indicators (KPIs) for each ad variation during the test period. These KPIs may include click-through rates (CTR), conversion rates, cost per click (CPC), and return on ad spend (ROAS). Analyze the results: After the test has run for a sufficient duration, analyze the data to determine which ad variation performed better based on your predefined objectives. Refine and iterate: Utilize the insights gained from the A/B-test to make data-driven decisions about your ad content. You can use ChatGPT to refine the winning variation further, generate new variations, or optimize other aspects of your advertising campaign. Continuous optimization: Regularly perform A/B-tests to stay ahead of market trends, audience preferences, and the competition. Use ChatGPT as an ongoing tool to help you quickly and efficiently generate new ad content ideas for testing. We help our customers setup A/B-tests, adapt (if necessary) the creative content assets needed to upload into Emerse Labs then run the A/B-tests using our programmatic advertising platform (DSP). Our A/B-testing solutions are available both as self-service and as a managed service. Contact our sales team today to get started.

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.