Programmatic Trader - Job Description, Salary, & Qualifications | Digital Media Jobs

Programmatic Trader Job Description

The programmatic field is growing and changing every day. New wranglers are needed to keep on top of the work and help guide companies and agencies to success. In this guide, we'll help you learn more about the field and how to find your dream job!

What is Programmatic Trading?

Let’s begin with a quick history lesson! For a long time, advertisement purchasing was a simple process. Publishers sold their ad space directly to advertisers. When the Internet came around, this “Mad Men” type model was no longer possible. Online, ad networks acted as brokers. They helped organize the Wild West of the early internet by buying ad space from publishers and selling that space to advertisers. Eventually, ad exchanges that connected many ad networks opened.  As the Internet continued to grow, these tasks became difficult to perform manually. Programmatic advertising was born.

At its core, Programmatic is the process of buying and selling online ads through special software. These are usually not traditional search ads, like Pay-Per-Click (PPC) advertising. You may know these as Display, Video, or Mobile ads. Sometimes, even DOOH, or “Digital Out Of Home” ads, may be sold programmatically (you likely know these as video billboards). These ads are mainly bought and sold through an instant auction process, or real-time bidding (RTB), and are usually highly targeted, sometimes down to the individual level. The old days of buying ad space on a site or spewing ads across the web are over.

Programmatic ads trade across many systems, navigating complex interactions. Advertisers generally use Demand Side Platforms (DSP) to purchase ad space. Publishers and those selling ad space on their site use Supply Side Platforms (SSP) to make that space available.

Programmatic is on the rise for a number of reasons. It replaces much of the manual bureaucratic work involved in the “old world” of advertising with machines. It introduces AI, machine learning, and other automated improvements into the advertising workflow. Humans get sick, make mistakes, and fall asleep at work. Machines don’t.

What do Programmatic Traders do?

Programmatic Traders are in the driver’s seat of online ad buying. They can sit as part of an in-house team or, often, on a larger agency trade desk. A lot goes into a successful programmatic campaign. Programmatic Traders are the day-to-day managers keeping everything in line.

They help manage expectations from across the company – from what results are possible to what limitations a particular platform may have. They must be Excel masters, as they often need to pull together reports across multiple platforms. In some workplaces, Programmatic Traders may also act as client contacts, providing feedback and recommendations to clients on how to spend their advertising budget.

Each DSP has specific quirks and special instructions. It’s up to Programmatic Traders to both handle these differences and educate clients on what is in the realm of possibility.

What do Programmatic Traders need to be aware of?

As with anything on the Internet, Programmatic Traders need to be wary of various forms of fraud or unsavory content. Bot traffic is one form of fraud where automated scripts click on ads. This charges the advertiser for impressions never actually seen by a human and can cost big money. Traders also need to be aware of how their DSP vendor deals with brand safety. When you buy ad space programmatically, you’re buying an “audience” more than the traditional “space on a specific site”. It’s rare to be 100% sure of which specific site your ad will appear on. Ads that appear on websites or pages with unsavory content can damage a brand’s reputation.

Programmatic Traders should have an understanding of how the bid process itself actually works. The method most widely used is “waterfall” bidding, and happens at the time a user navigates to a webpage connected to an ad server. Some sites use “header” bidding, where the auction is held in advance of a user’s arrival. There are other important differences between these methods, which you can learn more about here.

It’s also important to note that at the vast majority of agencies, Programmatic Traders are not at all involved in the creative side of the business. Their job generally focuses exclusively on the management and purchase of digital ad space.

Programmatic Trader Qualifications

Programmatic trading is a rapidly growing field. More agencies and publishers join the web’s largest ad exchanges every day. While new Programmatic Traders are needed every day, it remains difficult to land a position due to the unique mix of job requirements.

Typical Education Requirements

  • A. / B.S. in Business Analytics, Math, Communications, Business Administration, Statistics or a related field

Programmatic Trader jobs typically require at least an undergraduate degree in a related field of communications, marketing, math or statistics. Employers hiring entry-level Programmatic Traders will want to ensure that their candidates are comfortable with both marketing and advertising concepts, as well as the advanced mathematics involved in interpreting complex output from multiple systems and analyzing data. As they grow in their field, Programmatic Traders should also be prepared to interface with clients, a task that often requires a specific decorum and detailed knowledge of their work.

Preferred Skills

This represents a quick summary of what recent job listings in Programmatic Trading have requested of their applicants:

  • Become a product expert in programmatic buying
  • Advanced knowledge of Microsoft Excel and PowerPoint
  • Work with customers to develop achievable campaign goals
  • Experience with web languages including HTML, JavaScript, and SQL
  • Desire and ability to learn new technical skill sets such as using an API to automate campaigns
  • An eagerness for career progression in AdTech
  • Highly analytical mindset and skills
  • Passion for your work and your clients’ satisfaction
  • Outstanding written and verbal communication
  • Creative Thinking
  • Research and problem solving skills

Many of these skills are essential to success as a Programmatic Trader as they will be needed regularly.

The ability to learn and adapt quickly is critical to success. While many employers provide on-the-job training, the field of programmatic advertising is evolving extremely quickly. Employees need to stay up-to-date on the latest information and be aware of any issues so they may be proactively managed.

While programmatic trading is highly focused on results and data, it remains important to have good communication and presentation skills. Client relationships are paramount in this field, and everyone at an agency needs to focus on their client’s success.

Programmatic Trader Salary Expectations

The programmatic industry is still very young, so trader salary expectations may vary significantly. Programmatic trader pay is extremely variable based on experience and job functions. Set your expectations based on the job description and assess your experience in the field.

Operations and Trafficking

Some Programmatic Traders focus specifically on functions traditionally handled by Ad Ops or Ad Trafficking positions. These team members typically focus on setting campaigns live in the assorted DSP systems and maintaining live campaigns, or flights. Traders in these sorts of positions may perform some analytical work, but usually focus on ensuring the best client return. They may also act as a line of defense in terms of brand safety and staying on plan. A salary of $30,000 - $50,000 is typical for these sorts of roles. This number may vary based on the overall market, your agency’s size, and many other factors.

Data Scientist and Analyst

Another specialization within the overall programmatic marketing space focuses on the analysis side. These team members generally project outcomes, forecast revenue, and other tasks to help account leadership teams make better decisions. Some agencies may refer to these positions as “Insights Specialist” or, a bit more creatively, “Data Wrangler”. While they collect and format the data, these traders generally do not perform the optimizations themselves. An average salary for this sort of position would fall around a midrange salary

Optimization and Arbitrage

These team members perform the actual optimization work. This is where the moneymaking happens for the client, as these are often the best opportunities to reach a new audience. Traders working at this level often require advanced skillsets and a very good sense of the outcomes of a given decision. Client results and brand safety are paramount here, and a poor decision could spell trouble. Given the high demands placed on this part of the team, salary expectations are normally far higher.

Find the Perfect Programmatic Trader Job!

Now that you know what it’s like to be an Programmatic Trader, find your dream role in Programmatic at Digital Media Jobs!

Visit our Job Listings page to find positions available near you!


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Google Display Network (GDN) GDN is Google’s ad network of AdSense publishers, accessible via Google Ads (formerly AdWords). GDN is one of the largest, easiest, but simplest ad exchanges, making it a good choice for those who are new to the industry, or who only want to run basic programmatic marketing campaigns. Experienced Programmatic Traders will typically avoid GDN since its options are limited, however, compared to more modern DSPs. Demand Side Platforms (DSPs)   DSPs offer advertisers access to a wide collection of the available display, video, native and mobile inventory in real-time. In addition, many DSPs offer more advanced programmatic advertising technology, such as geofencing and IP address targeting capabilities, as well as access to a large bank of third-party data providers. Leading DSPs examples include The Trade Desk and DV360. Typically, DSPs are a better option for more advanced campaigns with restrictive or specific targets. GDN vs DSPs GDN has basic audience and content targeting capabilities, but the DSPs have additional targeting capabilities and sophisticated optimization tools. With more targeting options, advertisers using DSPs are typically better able to reach the exact group of people who will respond to their marketing efforts, making DSPs more effective than GDN. GDN isn’t bad though, it’s just better for small advertisers with limited budgets, while DSPs are best for large advertisers who require advanced targeting capabilities and greater reach. Using GDN & a DSP Simultaneously One thing virtually all programmatic trading experts agree on is that it’s best to NOT use both a GDN and a DSP simultaneously, as there are problems to running both systems at the same time, including: Inability to Control Frequency Capping – When running both GDN and a DSP, it becomes impossible to effectively control frequency capping, which can be a big problem as this is an effective way to reduce waste, avoid audience burnout and negative brand associations related to over-exposure. With a single DSP, it’s possible to cap frequencies, but with more than one DSP, or a DSP & GDN, frequency cannot be easily controlled. Multiple Bids on the Same Impression – DSPs participate in auctions for placements, and the highest bid wins each impression. If two DSPs are used to bid on the same audience (or cookie pool) then there will be competing bids from the same advertiser on the same impression, meaning advertisers using two DSPs, or DSP and GDN, will be bidding against themselves, needlessly driving up the price of each impression. Attribution Dilemma – Using more than one DSP, or a DSP and GDN, makes attributing and ROAS determinations much more difficult, especially when targeting a limited audience (like with a retargeting campaign). It’s likely that both DSPs will have ad exposures to many of the same converters, but only one will receive credit for the conversion. This makes it difficult to determine which platform is performing better, due to the randomness with which attribution gets assigned. Reach – Running two DSPs, or a DSP and GDN may lead to a slight increase in reach, but most top DSPs have access to nearly all of the same inventory, so the incremental increase in reach is probably not worth the other negative issues outlined above. Note: GDN has less inventory than the top DSPs (discussed below) and niche DSPs like Amazon are an exception, with exclusive inventory, but in general, all of the top DSPs have enough reach that advertisers don’t need to worry about running two platforms at once. For all of the reasons above, we recommend picking a single DSP and running with it. If you’re not happy with the results of the campaign, and want to try another DSP, it’s best to pause the existing campaign before starting up again on a new platform. Reach DSPs are superior to GDN when it comes to reach, since they can access hundreds of exchanges/suppliers such as PulsePoint, OpenX, AppNexus, Sonobi, Rubicon, PubMatic, PMPs and others, while GDN is limited to primarily to just the Google AdSense network. One example where DSPs are almost guaranteed to be superior to GDN would be running a re-targeting campaign, where you serve ads to customers who have already visited your site, but who have not yet converted. In this case, you’re working with a relatively small targeted population of valuable potential converters, so you’ll want to reach as many of those individuals as possible, and typically, a DSP will give you much more penetration into that audience than GDN. Any time programmatic traders are running retargeting campaigns, campaigns targeting a small geographic area, or campaigns focused on reaching a niche audience, using a DSP will typically produce better results than GDN. Inventory Availability DSPs distinguish themselves by having more premium inventory, whereas GDN inventory is primarily sourced from AdSense for publishers, which could include a lot of lower quality sites. Many large, established publishers such as CBS and The New York Times are on DSPs, but these types of premier brands are often not found on GDN. GDN is typically considered a good basic or beginner place for advertisers to start testing campaigns, just like Google AdSense for Publishers is considered a good place for publishers to start testing ad placements on their sites. Large and established publishers almost always eventually navigate to making their inventory available on the open exchange or through Private Marketplaces instead though, away from Google AdSense and GDN. Inventory Quality DSPs can buy the inventory across all value levels, whereas GDN is open auction only and includes many smaller publishers who cannot qualify to sell their inventory elsewhere, including Google’s premium exchange. This means that advertisers are probably getting a lot of “bottom of the barrel” inventory from GDN. In comparison, with DSPs, advertisers are able to set up deals that provide access to higher-quality inventory higher up the food chain, which typically leads to better converting traffic. CPC Vs CPM Pricing GDN gives advertisers a choice as to how to pay, but even though advertisers will be invoiced on a CPC (cost-per-click) basis, the yield management algorithms inside GDN are calculating an effective predictive CPM when deciding where, and how often, to serve each ad. In contrast to that system used by GDN, nearly all inventory purchased through DSPs is made available for real-time bidding on a CPM exchange, including Google and others. This tends to make pricing more attractive on DSPs than GDN. Targeting & Optimization Capabilities GDN has basic audience and content targeting and optimization capabilities, but DSPs have additional targeting and optimization capabilities. With more targeting and optimization options, there is a better chance for advertisers to reach the exact group of people who will respond to their ads, increasing the ROAS of the campaign. GDN is limited to location and language targeting, keyword targeting, device targeting and retargeting, whereas DSPs offer all sorts of additional options. DSPs have advanced capabilities, such as the ability to locate and target current and desired customers based on specific demographics, interests and their purchase intent, audience frequency caps for excluding users based on the number of impressions they have been served (across media, channels, and identity spaces) and advanced algorithms to adjust and optimize bids and budgets. 3 rd Party Data GDN does not have third-party targeting capabilities, meaning that you are reaching relevant users on  GDN only. With DSPs, you can target based on demographics, interests, topics, and even seek out users currently interested in a specific product or service through display and search.   Brand Safety/Ad Fraud/Audience Verification DSPs have fraud protection and brand safety controls that are integrated with leading third-party verification providers.  In contrast, GDN only offers Google Ads-only brand safety controls, without third-party integrations. GDN also doesn’t offer any transparency, so it’s virtually impossible to conduct impression-level analysis to determine what is performing well, and if that performance is real, or potentially fraudulent. GDN does self-grade the inventory that they are selling, but this isn’t transparent either, and GDN’s reporting is also only available through Google Ads, which further limits transparency for performance. Basically, DSPs offer much better options for brand safety, fraud prevention, and audience verification. Creative Options Google Ads: Image, Text, HTML5, Dynamic Creatives and Video Ads. Display & Video 360: Image, Rich Media, HTML5, Native, Video Ads and Dynamic Creatives. Pick the Right Platform for Your Campaign As this post established, simpler campaigns may be better suited for GDN, whereas more sophisticated campaigns are going to perform better on DSPs. When determining which platform will work best for you, you’ll have to review the differences outlined above, weighing the pros and cons, and deciding which system is in your best interest. But when it comes to deciding between running on the GDN vs. DSPs, typically, DSPs will win out thanks to their more-modern, more-sophisticated targeting, optimization and reporting capabilities.
What is Lead Scoring and How Does it Work? When it comes to acquiring new customers, most businesses focus all their time, money, and effort on one of two processes: acquiring leads or nurturing leads. Tons of resources are poured into lead generation activities (e.g., marketing, advertising, blogging, etc.) or lead nurturing activities (e.g., sales calls, email follow up sequences, etc.). In other words, most businesses either focus on what happens before someone becomes a lead or what happens after they become a lead, but what about the process in the middle? Many marketers and even businesses don’t even know that lead scoring exists, which means that the process of selecting which leads to pursue, and which to ignore, often goes overlooked entirely. For businesses that get very few leads, this is fine. However, businesses getting hundreds or even thousands of leads per day need to have a process in place that allows the sales team to prioritize which leads to pursue. It’s important to realize that not all leads are created equal. Some leads are cold and at the top of the funnel, while other leads are at the bottom of the funnel and are literally ready to buy right when they’re submitted. Still other leads won’t even be in the funnel at all, like those sent in by researchers or bloggers just poking around, and would be a complete waste of time to chase down. This is why it’s so important to have a lead scoring system to handle the sales process. Having a system that assigns numerical values to leads in such a way that will allow a sales representative to go after high-quality leads and disregard low-quality leads will result in better time efficiency and more revenue in the long run. That’s where lead scoring comes in, and that’s what we’ll explain to do in this post. What is Lead Scoring? Lead scoring is the process of assigning values to your leads so that you can label those that are most likely to convert and those that are not. This way, you’re able to prioritize which leads your sales team will contact next. Without a lead scoring system, leads would be chosen one by one which is fundamentally flawed because many of those leads will end up being a dead end and a huge waste of time. Having a lead scoring system is an extremely valuable tool for a business since it helps improve the efficiency of the company’s sales team, generates more revenue, and helps prevent the sales staff from experiencing burnout.   Better time efficiency means no time is wasted on bad leads, and no time wasted on bad leads means higher conversion rates and more revenue for the company. Additionally, since the sales team can see what characteristics and attributes make a high-quality lead, they can relay this information to the marketing team, which should help improve the overall marketing strategy too. With improved marketing, the leads coming into the sales funnel will be even more qualified than they were before, making the marketing and sales process easier, faster and more profitable for the company. How Does Lead Scoring Work? To give a prospect a lead score, you must first come up with a point system with rules. This point system is at the heart of lead scoring and what ultimately allows you to know which prospects are hot and which are cold. The first step in creating a point system is looking at past and present customers. Look for commonalities between customers—the attributes that they all share or the common actions that these customers took just before becoming a customer (e.g., a download, a click, a form submit, etc.).   The next step would be to assign a numeric value to each of the attributes you have chosen to be good indicators of potential customers. There are different ways to assign points to attributes, but to keep it simple, one way would be to add a higher amount of points to crucial attributes that all customers share and fewer points to those attributes that occur less frequently in customers. Three Types of Scoring There are three main ways most systems and organizations score leads. The first one is demographic scoring which is when you score a lead based on the data you have collected on the lead submitter. For example, people who live in a certain location or those that are a certain age would receive a higher score. This way, you can ensure that those who fit your target demographic will get prioritized. The next type is behavioral scoring. With behavioral scoring, you score a lead based off of how they interacted with your website or business. Using behavioral scoring, you might award more points to people who have visited multiple pages of the site, people who have visited the pricing page, people who have downloaded a brochure, etc. Lastly, there’s negative scoring which is where you deduct points based on attributes that would automatically disqualify someone from being a good lead. For example, you might want to deduct points from leads who have visited the employment page, or who did not include either a phone number or email address on the lead form they submit. Having a system that utilizes these 3 different types of scoring will allow you to qualify and rank your leads more accurately.   How to Find the Important Attributes A lead score model needs a set of rules that tell you when to add points to people with certain demographic and behavioral attributes. But how do you know which attributes to award points to? First, try asking your sales team. Since your sales team is constantly in the trenches, actually interacting with your leads before they become customers, they usually have valuable insight as to what type of attributes indicate that someone will become a customer. Next, you could ask your advertising department. It’s no secret that advertisers must deliver the right message to the right people, otherwise, their advertising efforts will have been for nothing. To do this, advertisers have a predetermined set of attributes that they use when they’re configuring their targeting settings just before launching a digital ad campaign. Viewing your advertising department’s targeting settings just might give you the insights you need to discover those important attributes that you can assign points to. You also might be able to find important attributes from your marketing department. Like the advertising department, the marketing department also has a target market—a set of attributes—which they hone in on when they deliver their creatives, content, and collateral. Looking at the marketing department’s targeted demographics can reveal hidden attributes that might not have been obvious at first. Additionally, you could ask the customers themselves since their inner thought processes are something that no marketer could replicate. Although surveying your customers is more time consuming, it might be worth asking them why they think they became customers or when in the sales cycle they knew they would become customers. You could do this by interviewing your customers, sending them a questionnaire, or by sending them a poll. Lastly, you could also check your analytics, which is ripe with valuable insights on your customer’s demographics, interests, and behavior (all three types of lead scoring data!). Running an attribution report can uncover which marketing activities convert leads into customers. You could also check which pages of the site leads have viewed before turning into customers or which pages visitors viewed before turning into leads and use this info to assign a higher score to leads that have viewed those pages. How is it Done? There are a few different ways of lead scoring, however, some methods take longer and are less effective than others. The first way of doing this is manually, although almost no one ever does it this way anymore as it becomes too time-consuming. Another method is using a data mining technique called logistic regression. For this method, you must build a formula (typically in excel) that takes into account all of the customer’s attributes and analyzes how they interact with one another, which in the end, will determine the probability that a lead will convert into a customer.    And finally, there are some automated approaches that do all of the heavy lifting for you. For example, some email autoresponders have a lead scoring system already built into them, with a few examples being Active Campaign, Get Response, and Drip. But even that isn’t as effective as another automated lead scoring method called Predictive Lead Scoring. Predictive Lead Scoring In an ideal world, your lead scoring system wouldn’t be static, but instead should be a living, breathing thing that changes as the market and your customers change. The criteria and attributes that make a good, high-quality lead might not even be the same from month to month—this is especially true for businesses that are heavily influenced by seasonality. This is why it’s recommended that you constantly need to be making tweaks to your scoring system so that it stays as accurate as possible. But this can quickly become too time-consuming, stealing the time you need to dedicate towards other elements of running your business. How can you automate the lead scoring system tweaking process and essentially “set it and forget it”? The answer is predictive lead scoring. Predictive scoring is an AI-based scoring system that uses machine learning to look at thousands of data points to see which attributes indicate hot leads. The scoring algorithm is constantly evolving, automatically making tweaks, updating itself, and sorting your leads to ensure that those most likely to convert remain at the top of your sales funnel. Admittedly, predictive lead scoring isn’t the right tool for every business. Because the sorting algorithm needs a large number of data points for it to work, predictive lead scoring is best suited for Businesses that have thousands of customers. If you’d like to try out predictive lead scoring, some popular lead scoring tools are MadKudu, Infer, Mintigo, and 6Sense. What Should You Do? No matter what lead score software or method you do end up choosing to use, the important thing is that you try something other than treating every single lead as being equal, as that’s an incredibly inefficient way to handle sales. Lead scoring is incredibly powerful and the truth of the matter is that most lead-based businesses could benefit from implementing a lead scoring system of some sort, even if it’s incredibly simplistic and done entirely manually. If you’re looking for ways to increase lead conversion rates, improve sales rep productivity, save time and money, and improve profits, then you need to look into lead scoring! About The Author Darden Faulkner is a freelance writer and product reviewer living and working in Irvine, CA. He enjoys long walks on the beach, learning everything he can about Google products, and has just discovered Twitter!  Follow him over on Twitter  for the latest life updates
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