Ad-Ops In A Nutshell And Why It Matters In Business

Ad Ops – also known as Digital Ad Operations – refers to systems and processes that support digital advertisements’ delivery and management. The concept describes any process that helps a marketing team manage, run, or optimize ad campaigns, making them an integrating part of the business operations.

Understanding Ad Ops

Ad Ops is a somewhat generic term describing any process that helps a marketing team manage, run, or optimize ad campaigns. Traditionally, this process was very simple. But many consumers now interact with brands in a multi-screen digital world with attention split between desktops, smartphones, and tablets.

Each of these devices provides a different user experience, displaying advertisements through banners, video, text, search, and mobile to name a few. Marketing teams must be able to interact with various platforms including ad networks, ad servers, ad exchanges, supply-side platforms (SSPs), and data management platforms (DMPs). Ultimately, each campaign must respect the device it is being displayed on while maximizing ad revenue for the company.

As demands increase, Ads Ops teams are utilizing new trends such as programmatic advertising, where automated software purchases digital ad space on their behalf. However, these teams must manage a range of unautomated tasks while understanding both sides of the advertising ecosystem. In other words, they must be sensitive to the needs of the sell-side (publishers) and buy-side (advertisers).

Some major responsibilities of Ad Ops teams

Individuals within an Ad Ops team enjoy a dynamic and varied role, with no two days being the same.

Nevertheless, there are some core responsibilities unique to most campaigns:

  1. Scheduling. In other words, when should the ad be scheduled for maximum ROI? Scheduling may revolve around a certain time of day, or it may focus on holidays, weekends, or special events.
  2. Trafficking. This requires a specialist role to oversee the monitoring and delivery of ads across various exchanges or servers. Tasks may include the tracking of third-party vendor ad tags or the implementation of ad campaigns using dedicated services such as Google Ad Manager.
  3. Optimization. Every ad must be optimized for the number of clicks it receives. Optimization can be achieved through correct ad placement, word choice, and proper SEO. In some instances, ads must reflect an awareness of changing trends or standards.
  4. Yield management. Each task that an Ad Ops team performs should have some relation to driving revenue. Yield managers search for opportunities to increase revenue generation through advertising. This may involve the restructuring of content based on user behavior analytics or the reallocation of inventory pricing to increase profits.

Ad Ops best practices

All Ad Ops teams should incorporate these best practices as the basis of a sound campaign:

  • Define objectives – what is the goal of the campaign? It is not enough to broadly state a goal of increasing profits or brand recognition. Use the SMART goals system to help define thoughtful, effective objectives.
  • Define the correct medium – how should the message of the campaign be delivered? Mediums such as email, social media, video, and content will be most suited to a specific target audience or device.
  • Craft the message – make sure that the ad campaign centers on one key idea or message. Importantly, it must be relatable to the target audience and company objectives.
  • Evaluate and adjust as necessary – continually evaluate campaigns for effectiveness and reconfigure if important targets or metrics are not being met. 

Ad ops vs. Programmatic

Ad ops comprise the advertising services that manage digital ad sales online. Advertisers who want to purchase ad space are connected with websites that have ad space to sell with tailored ad ops technology.

More generally, ad ops refers to any process or system that supports the delivery (or management) of advertisements via digital mediums. These mediums may include mobile, search, video, banner, rich media, and more.

Ad operations is managed by a team responsible for ad creation, management, and testing with a goal to generate revenue.

They interact with various ad networks, exchanges, or servers and also with supply and demand side platforms.

Major responsibilities of ad ops teams

  • Scheduling – this deals with decisions pertaining to when ads should run. Some are run at certain times of day, while others are optimized for holidays, weekends, or special events.
  • Trafficking – ad traffickers are those that manage an ad campaign with the assistance of an ad server such as Google Ad Manager. They manage and monitor delivery across ad exchanges and track third-party tags from multiple vendors.
  • Optimization – any initiative to optimize CTC (cost to click) factors such as ad location, SEO, trends, or standards.
  • Demand management – to cater to the enormous demand that sales teams of some publishers generate, ad ops teams are also responsible for many client-facing duties such as handling contractual matters, negotiating on behalf of the client, and following up on agreements to ensure compliance.
  • Yield management – where the team seeks to streamline operations to reduce costs and maximize profits. For example, an ad ops team may reconfigure the layout of a website based on user behavior.

What is programmatic advertising?

Programmatic advertising involves the use of algorithmic software to purchase digital advertising, drive impressions at scale, and deliver a better ROI for marketers. 

In contrast to traditional advertising methods which require proposals, quotes, tenders, or negotiation with the seller, programmatic advertising’s use of software makes it an automated buying and selling solution.

This frees up time that can be better spent on ad optimization to improve the company’s likelihood of success.

Companies are increasingly turning to programmatic advertising to streamline their approach as the world emerges from COVID-19 and AI tech becomes more advanced.

How does programmatic advertising work?

Programmatic advertising utilizes data insights to serve ads to targeted users at the right time and the right place.

In short, this ecosystem is supported by three main components:

  1. Demand-side platform (DSP) – a place where advertisers can purchase ad inventory.
  2. Supply-side platform (SSP) – comprised of software that enables publishers to sell ad impressions to buyers in real-time. These impressions may be for display, mobile, or video ads.
  3. Ad exchanger – where the SSP feeds inventory into the ad exchange itself and connects to the DSP. At this intersection, advertisers, networks, publishers, and agencies can buy and sell ad space with prices normally set in a real-time auction. Remember, this process is automatic. 

What is the difference between ad ops and programmatic advertising?

In very general terms, programmatic advertising is a type of ad ops that is best suited to less technical users who desire a more hands-off solution.

The obvious difference in automation level, some argue, also means that traditional ad ops requires individuals to have a more robust and specialized skill set than those employed in programmatic. 

In many companies, programmatic advertising is simply one of many tools in their ad ops arsenal. Ad ops also tends to be more functional and versatile because it has been around longer and has had more time to develop.

Key differences between ad ops and programmatic

  • Ad ops comprise the advertising services that manage digital ad sales online and refers to any process or system in support of the delivery or management of advertisements via digital mediums.
  • Programmatic advertising involves the use of algorithmic software to purchase digital advertising, drive impressions at scale, and deliver a better ROI for marketers.
  • In general, programmatic advertising is a more automated type of ad ops that requires less specialized knowledge and client interaction.

Key takeaways

  • Ad Ops refers to a suite of systems and processes that support digital advertisement delivery across multiple devices.
  • Ad Ops is a broad and dynamic industry requiring responsive and multi-skilled teams. These teams must be well versed in the scheduling, optimization, and trafficking of ads.
  • Some aspects of Ad Ops have been automated to reflect the increasing complexity of online advertising. Nevertheless, Ad Ops requires that practitioners be competent using a range of ad networks, servers, exchanges, and platforms.

Connected Agile Frameworks


AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.

Agile Methodology

Agile started as a lightweight development method compared to heavyweight software development, which is the core paradigm of the previous decades of software development. By 2001 the Manifesto for Agile Software Development was born as a set of principles that defined the new paradigm for software development as a continuous iteration. This would also influence the way of doing business.

Agile Project Management

Agile project management (APM) is a strategy that breaks large projects into smaller, more manageable tasks. In the APM methodology, each project is completed in small sections – often referred to as iterations. Each iteration is completed according to its project life cycle, beginning with the initial design and progressing to testing and then quality assurance.

Agile Modeling

Agile Modeling (AM) is a methodology for modeling and documenting software-based systems. Agile Modeling is critical to the rapid and continuous delivery of software. It is a collection of values, principles, and practices that guide effective, lightweight software modeling.

Agile Business Analysis

Agile Business Analysis (AgileBA) is certification in the form of guidance and training for business analysts seeking to work in agile environments. To support this shift, AgileBA also helps the business analyst relate Agile projects to a wider organizational mission or strategy. To ensure that analysts have the necessary skills and expertise, AgileBA certification was developed.

Business Model Innovation

Business model innovation is about increasing the success of an organization with existing products and technologies by crafting a compelling value proposition able to propel a new business model to scale up customers and create a lasting competitive advantage. And it all starts by mastering the key customers.

Continuous Innovation

That is a process that requires a continuous feedback loop to develop a valuable product and build a viable business model. Continuous innovation is a mindset where products and services are designed and delivered to tune them around the customers’ problem and not the technical solution of its founders.

Design Sprint

A design sprint is a proven five-day process where critical business questions are answered through speedy design and prototyping, focusing on the end-user. A design sprint starts with a weekly challenge that should finish with a prototype, test at the end, and therefore a lesson learned to be iterated.

Design Thinking

Tim Brown, Executive Chair of IDEO, defined design thinking as “a human-centered approach to innovation that draws from the designer’s toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success.” Therefore, desirability, feasibility, and viability are balanced to solve critical problems.


DevOps refers to a series of practices performed to perform automated software development processes. It is a conjugation of the term “development” and “operations” to emphasize how functions integrate across IT teams. DevOps strategies promote seamless building, testing, and deployment of products. It aims to bridge a gap between development and operations teams to streamline the development altogether.

Dual Track Agile

Product discovery is a critical part of agile methodologies, as its aim is to ensure that products customers love are built. Product discovery involves learning through a raft of methods, including design thinking, lean start-up, and A/B testing to name a few. Dual Track Agile is an agile methodology containing two separate tracks: the “discovery” track and the “delivery” track.

Feature-Driven Development

Feature-Driven Development is a pragmatic software process that is client and architecture-centric. Feature-Driven Development (FDD) is an agile software development model that organizes workflow according to which features need to be developed next.

eXtreme Programming

eXtreme Programming was developed in the late 1990s by Ken Beck, Ron Jeffries, and Ward Cunningham. During this time, the trio was working on the Chrysler Comprehensive Compensation System (C3) to help manage the company payroll system. eXtreme Programming (XP) is a software development methodology. It is designed to improve software quality and the ability of software to adapt to changing customer needs.

Lean vs. Agile

The Agile methodology has been primarily thought of for software development (and other business disciplines have also adopted it). Lean thinking is a process improvement technique where teams prioritize the value streams to improve it continuously. Both methodologies look at the customer as the key driver to improvement and waste reduction. Both methodologies look at improvement as something continuous.

Lean Startup

A startup company is a high-tech business that tries to build a scalable business model in tech-driven industries. A startup company usually follows a lean methodology, where continuous innovation, driven by built-in viral loops is the rule. Thus, driving growth and building network effects as a consequence of this strategy.


Kanban is a lean manufacturing framework first developed by Toyota in the late 1940s. The Kanban framework is a means of visualizing work as it moves through identifying potential bottlenecks. It does that through a process called just-in-time (JIT) manufacturing to optimize engineering processes, speed up manufacturing products, and improve the go-to-market strategy.

Rapid Application Development

RAD was first introduced by author and consultant James Martin in 1991. Martin recognized and then took advantage of the endless malleability of software in designing development models. Rapid Application Development (RAD) is a methodology focusing on delivering rapidly through continuous feedback and frequent iterations.

Scaled Agile

Scaled Agile Lean Development (ScALeD) helps businesses discover a balanced approach to agile transition and scaling questions. The ScALed approach helps businesses successfully respond to change. Inspired by a combination of lean and agile values, ScALed is practitioner-based and can be completed through various agile frameworks and practices.

Spotify Model

The Spotify Model is an autonomous approach to scaling agile, focusing on culture communication, accountability, and quality. The Spotify model was first recognized in 2012 after Henrik Kniberg, and Anders Ivarsson released a white paper detailing how streaming company Spotify approached agility. Therefore, the Spotify model represents an evolution of agile.

Test-Driven Development

As the name suggests, TDD is a test-driven technique for delivering high-quality software rapidly and sustainably. It is an iterative approach based on the idea that a failing test should be written before any code for a feature or function is written. Test-Driven Development (TDD) is an approach to software development that relies on very short development cycles.


Timeboxing is a simple yet powerful time-management technique for improving productivity. Timeboxing describes the process of proactively scheduling a block of time to spend on a task in the future. It was first described by author James Martin in a book about agile software development.


Scrum is a methodology co-created by Ken Schwaber and Jeff Sutherland for effective team collaboration on complex products. Scrum was primarily thought for software development projects to deliver new software capability every 2-4 weeks. It is a sub-group of agile also used in project management to improve startups’ productivity.

Scrum Anti-Patterns

Scrum anti-patterns describe any attractive, easy-to-implement solution that ultimately makes a problem worse. Therefore, these are the practice not to follow to prevent issues from emerging. Some classic examples of scrum anti-patterns comprise absent product owners, pre-assigned tickets (making individuals work in isolation), and discounting retrospectives (where review meetings are not useful to really make improvements).

Scrum At Scale

Scrum at Scale (Scrum@Scale) is a framework that Scrum teams use to address complex problems and deliver high-value products. Scrum at Scale was created through a joint venture between the Scrum Alliance and Scrum Inc. The joint venture was overseen by Jeff Sutherland, a co-creator of Scrum and one of the principal authors of the Agile Manifesto.

Read Also: Business Models Guide, Sumo Logic Business Model, Snowflake

InnovationAgile MethodologyLean StartupBusiness Model InnovationProject Management.

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Read Also: Fastly Business Model, Snowflake Business Model, Sumo Logic Business Model

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