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Real talk: Ana Sušac on developing her career as a woman in tech

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Real talk: Ana Sušac on developing her career as a woman in tech

We’re celebrating this Women’s History Month by featuring one of Remerge’s Software Engineers, Ana Sušac. Ana has been with Remerge since 2017, having started as the first female member of the tech team and has been part of the growth from eight to twenty nine members. In this interview, Ana talks about her story in tech, one that is of courage, discipline, and passion, but also of joy.

Your journey into tech has been adventurous so far. Tell us more about that experience.

Unlike many of my colleagues, I didn’t start programming at the age of 12. I grew up in Bosnia and Herzegovina in the 90’s and only got internet access at home when I was 17. I wasn’t as tech-savvy then, but I managed to apply online and land a scholarship to Duke University. My plan was to study math. I loved its rational, deterministic nature, and was always good at it, but I had no clear idea what I wanted to do in life.

The math department was the first time I found myself the only woman in the room. Getting my young colleagues to take me seriously was a struggle. I remember proposing a solution in a study group, only to watch them argue for hours before arriving at my suggestion. They were louder, I was 18 and too shy for my own good. This was demoralizing, so I looked for alternatives. Through twists and turns I ended up in the Psychology department studying, instead of rationality, what is it that makes humans irrational.

From day one I was also trying to register for the introductory CompSci course, curious to see what that was all about. The course was highly sought-after, so I only managed to enroll in my final year. Taking it made me at once ecstatic and disappointed. Ecstatic I finally found something that captivated my attention, and disappointed my scholarship was running out, and there was no time to pursue it further.

From then on, my direction slowly but surely drifted towards where I am today. I went on to do a Masters in Neuroscience at FU Berlin. I picked that program over a similar one because it had a programming class. It was basic scientific programming, writing matlab scripts to show slides and collect participant responses - nothing too exciting. Meanwhile, the Computational Neuroscience department at TU Berlin was doing more fun things like using computational modelling to analyze functional brain neuroimaging data, so I joined a lab there for a short internship. Later I learned this is what the tech industry refers to as Data Science.

« I wanted to solve problems faster, have a quicker feedback loop, and keep learning new things, so tech was the obvious choice. »

Living in Berlin introduced me to the industry, and in turn made me reconsider my commitment to academia. I wanted to contribute to the scientific community, but it became clear to me that an adequately rigorous scientific process was unfortunately slow. I wanted to solve problems faster, have a quicker feedback loop, and keep learning new things, so tech was the obvious choice. I took all the online courses I could find, and immersed myself into Berlin’s Meetup community. I spent evenings and weekends at OpenTechSchool, learning the basics of HTML, CSS, JavaScript, Ruby, you name it.

After my focus working on my first website was broken one night by the rising sun, I knew I'd found my passion. The next step was quitting my student job, and looking for someone willing to take a chance on a wanna-be developer with 9 months of community training. Through a stroke of luck, I found Remerge.

You seized the opportunity. What was it like working at Remerge when the team was still small?

I started as a part-time student. I was terrified those first weeks! I thought everyone would realize I knew very little, so I would pretend to understand something, go home and stay up all night learning it, just so I could do it the next day. It took me months to get out of this mindset and understand that it was not just acceptable, but expected of me to not know things.

No one was testing me - all I had to do was ask, and the team was eager to teach me.

We were way smaller back in 2017, altogether around 40 people, most sitting in the same room. The atmosphere was quite relaxed, and the group tight-knit. In a way it was different than today, easier to know every face and less processes to follow, but in a way it was the same. It always felt as it still does, that everyone is important, and that people come first.

How has the journey been so far and what was it like growing into your role today?

When I joined we were eight on the tech team. Our system was already powerful and complex, but it was still easy to keep track of all the changes. This made learning quite straightforward. It also allowed me to get exposed to our whole stack, and find my place on the team.

I’ve always felt supported and encouraged. My colleagues would find time to sit down with me and guide me. They knew how to provide me with the right level of detail I needed to make progress on the task at hand and in general. For this I’m especially grateful to my mentor from day one, and later also my manager, Richard. He always had the exact command I was missing, as well as an encouraging word, and definitely helped shape (and still does) the engineer I grew into. I was also lucky enough to join while Mike was still on the tech team, and had a chance to learn from his example how to work diligently, effectively, and efficiently, while not taking myself too seriously.

Looking back, I think I joined Remerge in the perfect moment to grow with it. Whenever I got comfortable in my role, there would be a new challenge: a new technology we decided to use, a new team structure, a new process, or most recently a new style of work, with remote-first. In tech, we went from eight people using a single kanban board, to now 29 in rotating mini-teams working on shaped-up projects. I’ve had the opportunity to help our product team shape some of the projects, getting to better understand the business side of things. I also served as the tech lead on several mini-teams, expanding my skills into project management.

What do you hope to achieve in the next six months?

This is a very exciting time for us all, given the changes the industry is facing. In R&D we have been busy getting us ready for the post-IDFA world, and I think we’ve done a great job until now. Obviously some things are still unclear, and in the next months we’ll have to keep innovating to stay on top of them.

One thing I can say for sure is that we won’t run out of interesting problems to solve. What we hope to achieve is for Remerge to continue as a thought-leader in the industry, with our scientifically grounded and privacy friendly solution for the identity-less future.

For me personally, the upcoming challenge will be taking on my first direct reports. I am very excited to grow in this direction. I see it as a chance to apply what I have learned throughout my whole journey to help our new members thrive as well.

Any last words or learnings that you’d like to share?

I often read stories about difficulties people from underrepresented groups experience in tech. I truly empathise, and I do my part to make sure that topics of diversity and inclusion are on the table within our team and company.

« I often read stories about difficulties people from underrepresented groups experience in tech. I truly empathise, and I do my part »

I also feel privileged never to have experienced any similar difficulties at Remerge. Despite being the only woman on the tech team for quite a while (but happy to say no longer!), I have never again felt the way I did back at the university where others would easily talk over me. Part of that is due to the fact that since then I have found my voice and learned not to be afraid to speak up (and be wrong sometimes!). Another part is definitely due to working with exceptional, supportive, amazing people I am happy to call my team.

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Retargeting lexicon
Programmatic Advertising

The automated process of buying and selling advertising space through digital platforms.

View-Through Attribution
view-through-attribution

Refer to: Attribution Methodology

Uplift Test
uplift-test

A randomized control trial test conducted by Remerge to measure the incremental impact of one or more campaigns.

See also: Randomized Controlled Trial

Uplift Report
uplift-report

A report by Remerge showing the results of an uplift test. It presents the incremental revenue generated, on top of organic and other marketing-driven conversions. Also contains observed values such as ad spend, group sizes, amount of conversions, converters, and revenues per group, plus other metrics.

SKAdNetwork
skadnetwork

Stands for Store Kit Advertising Network. Apple’s measurement framework for tracking mobile attribution. Introduced in 2018 and widely implemented in 2020 with the iOS 14.5 update.

Segment
segment

A group of users with common attributes such as location, demographics, activity level, value or amount of purchases, and how recently they last opened a specific app.

Retention Rate
retention-rate

The share of users active in the app within certain time frames after install, reengagement, or other events.

Retargeting
retargeting

A type of marketing channel used by an app owner to engage with their existing users through other channels within the same device. Usually, the aim is to encourage users to complete a particular task e.g. completing a purchase, buying in-game currency, placing a first order. The conventional way of retargeting relies on user IDs, such as AAID and IDFA.

Reshuffle
reshuffle

Reshuffle indicates the randomization and marking of users when they were once part of a test or control group.

In incrementality measurement, reshuffling the group assignment for a specific application fights aggregated bias over time where one group doesn't see any ads while the other group is constantly exposed to them.

Reshuffling is relevant in cases where a test has been running for a long time and/or in campaigns the experience more extensive changes to the campaign setups, segmentation, or creative strategy.

Real-Time Bidding (RTB)
real-time-bidding-rtb

The process by which individual ad placements are bought and sold via programmatic auctions that happen instantaneously. With real-time bidding, ad buyers bid on an ad space, which, if the auction is won, instantly displays the buyer's ad. This lets demand-side players such as advertisers or DSPs optimize the purchase of ad placements from multiple sources.

Randomised Controlled Trial (RCT)
randomised-controlled-trial-rct

A method that randomly separates a specific population into two groups that are as similar to each other as possible, namely the test group and control group.

further reading
Queries Per Second (QPS)
queries-per-second-qps

The number of ad placements a DSP is able to process in order to determine on how to bid on them.

Publisher
publisher

Within the sphere of app marketing, a publisher is an App Developer that gets paid for placing ads within their app. For example, an advertiser wants to reach their users via App Y, so they pay App Y to display their ads.

further reading
Public Service Announcement Ad (PSA Ads)
public-service-announcement-ad-psa-ads

An incrementality testing methodology where devices in the control group are shown PSA ads, like donation drives or road safety reminders. By serving real ads, information on the devices within the control group that would have been exposed can be obtained. Unexposed devices are excluded from the measurement to reduce noise.

Probabilistic Attribution
probabilistic-attribution

Refer to: Attribution Methodology

Organic Behavior
organic-behavior

A user’s behavior not directly attributable to specific marketing efforts.

Multi-Touch Attribution
multi-touch-attribution

Refer to: Attribution Methodology

Mobile Measurement Partner (MMP)
mobile-measurement-partner-mmp

Within the sphere of app marketing, MMPs are a service provider that specializes in measuring activities that are happening within and leading to the app. An app publisher may incorporate an MMP into their app to track activity and events e.g. time spent on a certain screen, sources of incoming traffic, app opening frequencies etc.

Lifetime Value (LTV)
lifetime-value-ltv

The amount of revenue generated by the user for the App Developer during the entire duration of the relationship with the user, beginning with the app install.

Last-Click Attribution
last-click-attribution

Refer to: Attribution Methodology

Key Performance Indicator (KPI)
key-performance-indicator-kpi

The key metrics used to assess the effectiveness of an effort in achieving its objective. In programmatic advertising, the common types of performance indicators depend on the goals and nature of each campaign. These can include ROAS, cost per action, and retention rate.

Intent-to-Treat (ITT)
intent-to-treat-itt

An incrementality testing methodology where no ads from the campaign are shown to devices within the control group. Also known as a ‘holdout test’. Cost-free and easy to implement, but with a relatively high level of noise.

This method compares the behavior of all users in both groups. In the test group, this includes both exposed and unexposed users

Incrementality
incrementality

A method of measuring the impact of a specific activity, on top of organic and other activity.

Incremental Revenue (iRevenue)
incremental-revenue-irevenue

The estimated revenue caused directly by the campaign.

Formula:Revenue from test group – revenue from control group = iRevenue

Incremental Return On Ad Spend (iROAS)
incremental-return-on-ad-spend-iroas

A KPI used in calculating how cost-efficient a campaign is. This is used to evaluate the relationship between incremental revenue and the amount of money spent on the campaign. The figure is typically represented in percentage.

Formula:
Percentage: [IRevenue ÷ ad spend] × 100 = IROAS%
Ratio: IRevenue ÷ ad spend = IROAS

Incremental Cost Per Action (iCPA)
incremental-cost-per-action-icpa

A KPI used to evaluate the cost of incremental conversions.

Formula:Ad spend ÷ [test group actions – control group actions] = iCPA

Incremental Conversions
incremental-conversions

The estimated amount of conversions caused directly by the campaign.

Formula:
Test group conversions – control group conversions (scaled) = Incremental conversions

In-app Event
in-app-event

Actions made by a user within the app, such as log-in, registration, completion of onboarding, or purchases. These events can be tracked with the help of an MMP.

Impression
impression

The deployment of the ad to the ad placement. An impression might not necessarily mean that the ad has been viewed.

Identifier for advertisers (IDFA)
identifier-for-advertisers-idfa

A unique random device identifier Apple generates and assigns to every iOS device. Advertisers can use this to track user activity across apps, show them personalized ads, and attribute ad interactions.

Ghost Ads
ghost-ads

A testing methodology that shows devices in the control group an ad ran by another advertiser on the platform, therefore removing any additional cost for clicks and impressions. The control group behavior is then marked with a ‘ghost impression’, which gives the information on which control group users would have been exposed.

further reading
General Data Protection Regulation (GDPR)
general-data-protection-regulation-gdpr

A regulation under the EU (European Union) law on data protection and privacy within the EU and the EEA (European Economic Area), that grants users control over how their data is stored and used by organizations. To comply with GDPR, programmatic sellers must clearly communicate to users how their data will be stored and used. When a user gives consent to an organization to process their data, it enables targeted advertising.

Exposure Rate
exposure-rate

The percentage of devices within a test group that received at least one ad impression, versus the total number of devices within the test group targeted within a campaign during an uplift test. For example, if 900 out of 1,000 users are shown an ad, the exposure rate is 90%.

See also: Uplift Test

Deterministic Attribution
deterministic-attribution

Refer to: Attribution Methodology

Deep link
deep-link

A link that sends users directly to a specific in-app location, instead of the app marketplace. Deep links bypass the steps needed to go through to reach a conversion point, bringing the user directly to where they can perform the intended action e.g. completing a purchase, buying coins, placing an order.

Test Group
test-group

Within the sphere of app marketing, this refers to the group of devices that may be shown ads from a specific campaign in the test. The actions on these devices are then compared to the actions on the devices in the control group.

Compare with: Control Group

further reading
Control Group
control-group

Within the sphere of app marketing, this refers to the group of devices within the target audience that are not shown ads from a specific campaign in the test. The actions on these devices are then compared to the actions on the devices in the test group.

Compare with: Test Group

further reading
Contextual targeting
contextual-targeting

A type of targeting that works with contextual signals only, such as location data (country, city, postal code), language setting, mobile operating system, device model, as well as publisher information.

California Consumer Privacy Act (CCPA)
california-consumer-privacy-act-ccpa

A bill that enhances privacy rights and consumer protection for residents of California, United States. The CCPA took effect on January 1, 2020.

The CCPA provides these rights to consumers:

- Know what personal data is being collected about them.
- Know whether their personal data is sold or disclosed, and to whom.
- Say no to the sale of personal data.
- Access their personal data.
- Request a business to delete any personal information that was collected from that consumer.
- Equal service and price, even if they exercise their privacy rights.

Attribution Window
attribution-window

A specific time frame that is taken into consideration when determining the source of a user’s action.

Attribution Provider (AP)
attribution-provider-ap

A role played by an MMP to credit the in-app activity of users to the correct media sources.

Attribution Methodology
attribution-methodology

Refers to the process of identifying which conversions belong to which preceding click or impression. Common attribution methodologies include:

  • Click-Through Attribution - Determines the source of a conversion based on the user’s click activity.

  • View-Through Attribution - Determines the source of a conversion based on the ad impression delivered to the user.

  • Deterministic Attribution - A model that establishes the origin of a user’s conversion from a specific click or impression, based on unique device IDs.

  • Probabilistic Attribution - A model that establishes the likelihood of a user’s conversion originating from a specific click or impression, based on the data logged on both occasions, such as device language, timezone, IP address, and OS version.

  • Last-Touch Attribution - A model that establishes a match between the action taken by a user (e.g. app open, purchase) and its corresponding ad click or impression. When a user converts from an ad, the DSP that delivered the respective ad is given full credit for that conversion event.

  • Multi-Touch Attribution - Also known as multi-channel attribution. A model determines the value of every touchpoint on the way to a conversion. Rather than giving full credit to one ad, multi-touch attribution divides the credit among all advertising channels that the user has interacted with, leading to the conversion.
Attribution
attribution

A method of identifying the touchpoints a user has encountered within a specified period before making a conversion.

App Tracking Transparency (ATT)
app-tracking-transparency-att

The privacy framework from Apple that, among other things, manages the process of obtaining user consent before accessing their Identifier for Advertiser (IDFA).

App Monetization
app-monetization

The strategy a publisher employs to earn money from their app. This can be done through in-app advertisements, paid membership, and charging for premium features or an ad-free experience, among others. For example, some gaming apps are free to download and play, but users may need to pay in order to progress to the next level quickly.

Android Advertising identifier (AAID)
android-advertising-identifier-aaid

Also known as Google Advertising Identifier. A unique device identifier that Android generates and assigns to every device. Advertisers can use this to track user activity across apps, show them personalized ads, and attribute ad interactions.

Advertisers
advertisers

The advertiser is a person or legal entity focusing on generating sales and leads through serving ads that convey the right message to the right audience at the right time.

In mobile advertising, the advertiser is on the client-side and is the one interested in promoting an app.

Causal Impact Analysis
causal-impact-analysis

A measurement framework developed by Google that works without device IDs. It measures the incremental uplift of one or more conversion events, removing the influence of other campaigns and organic conversions. Used to assess the effect of ID-less campaigns.

Similar to measuring the effect TV ads have, the principle is based on running campaigns on identifiable sub-markets (test group), while leaving other sub-markets unexposed (control group).

Ghost Bids
ghost-bids

An incrementality testing methodology based on Ghost Ads, adapted for retargeting campaigns. The difference is that it removes all devices that are not seen on ad exchanges, or that would not be bid on, from both test and control groups, to reduce noise. A bid is placed as usual for the test group, while the control group is tracked with ‘ghost bids’ (bids that could have been placed, but weren’t in the end).

Return on Advertising Spend (ROAS)
return-on-advertising-spend-roas

A KPI that measures the relationship between the revenue generated by specific advertising efforts and the money spent on them.

Formula

Percentage: [Revenue ÷ ad spend] × 100 = ROAS%

Ratio: Revenue ÷ ad spend = ROAS

See also: Incremental Return On Ad Spend

Supply-Side Platform (SSP)
supply-side-platform-ssp

A company that works with publishers to sell ad inventory across ad networks.

Demand-Side Platform (DSP)
demand-side-platform-dsp

A company that works with advertisers to purchase ad inventory across ad networks. Their platforms are built to identify a desired ad space and place bids on it.

Compare with: Supply-Side Platform

Open RTB
open-rtb

A digital marketplace where ad inventory from multiple publishers are available for advertisers to bid on in real time.

See also: Real-Time Bidding

Self-Attributing Network
self-attributing-network

An ad network like Meta, Snap, and Twitter, that attributes its traffic internally, without the involvement of third-party MMPs.

Variable Bidding
variable-bidding

The dynamic adjustment of bid prices based on a user's in-app behavioral patterns, contextual information, time of day, and ad placement performance.

Dynamic Product Ad (DPA)
dynamic-product-ad-dpa

Also known as a dynamic ad. It is dynamically assembled based on the user’s behavior and information sourced from a feed. This type of ad delivers a tailored experience for individual users.

Real-Time Audience Segmentation
real-time-audience-segmentation

The division of an audience into distinct segments based on real-time events, thus enabling targeted advertising and alignment with a user's behavioral patterns and preferences.

User Acquisition (UA)
user-acquisition-ua

A mobile marketing effort used to attract new users to an app. Paid UA may refer to ads shown in mobile ad networks or social media channels, while non-paid UA involves app store optimization and promotion on the advertiser’s own channels.

Programmatic Advertising
programmatic-advertising

The automated process of buying and selling advertising space through digital platforms.

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