Answers to your questions from the Kochava Foundry webinar

Grant Simmons, VP of Kochava Foundry, and Ethan Lewis, Chief Technology Officer at Kochava, recently hosted the webinar Navigating Google Privacy Sandbox—Part 1, where they unpacked the industry’s upcoming sea change with Google’s rollout of Privacy Sandbox for Android and spotlighted key trends in the shift in mobile toward user privacy. In this follow-up, they have compiled audience questions to address and elaborate upon in further detail.

Check out the full webinar on demand here.

#1 Has Google published the timeline for deprecating their Advertising ID (ADID) from Android?

Google has not yet published a definitive timeline for the deprecation of ADID, also sometimes called Google Advertising ID (GAID), from Android. The ADID/GAID is anticipated to follow a similar path as Apple’s IDFA insofar as its utility for tracking and measurement is expected to diminish. Given Google’s significant stake in the adtech ecosystem, their ADID phaseout may be more gradual compared to Apple’s rapid deprecation of IDFA. There are indications the deprecation process could begin with the phasing out of third-party cookies, expected to start this fall. Google’s active development of APIs for Privacy Sandbox signals a move toward testing with publishers later this year, with a broader rollout and ADID deprecation potentially starting next year. Marketers should prepare for a future where public unique identifiers such as ADID are no longer available and seek alternative privacy-centric measurement solutions.

#2 Is Google going to deprecate Google Play Install Referrer?

While Google has not made a formal announcement regarding this, there are indications they may deprecate the use of UTM parameters, which are critical for mobile tracking as they can be picked up via the Play Store and used to power Google Analytics. The potential deprecation of these links could begin next year, signifying a pivotal shift in mobile tracking and analytics.

#3 How does this compare to iOS App Store data restrictions?

Google’s Privacy Sandbox and Apple’s SKAdNetwork (SKAN) share the goal of enhancing user privacy while providing campaign performance metrics. Both are designed to be anonymous while offering event-level reporting. Their approaches differ, however, with Privacy Sandbox developed through broader community collaboration, while SKAN is an Apple-led initiative. Privacy Sandbox aims to provide tools for targeted advertising without individual user tracking, whereas SKAN offers a more limited framework for iOS app advertising attribution. Advertisers face challenges with both due to reduced granularity of data.

#4 How does this impact MMM, if at all?

Marketing mix modeling (MMM) is likely to thrive, as it relies on modeling of aggregated data as opposed to the granular data necessary for last-touch attribution. MMM platforms, such as AIM by Kochava, can ingest SKAN and Privacy Sandbox data to power their models and help marketers understand influence and incrementality across channel partners. Separately, mobile measurement partners (MMPs) will play a crucial role in understanding data connections, providing tailored measurement solutions, and syndicating measurement data as needed.

#5 How will Privacy Sandbox impact app remarketing, both gaming and non-gaming?

The exact mechanisms for user suppression or retargeting within Privacy Sandbox are not yet clear. However, it is expected that aggregate data will be managed via API, with flags indicating prior customers vs. new ones. Brands will need to differentiate between new and existing customers and communicate this information to the networks they engage with for remarketing. They should also continue to invest in owned media as a pillar of their remarketing strategy.

#6 How will Privacy Sandbox work for user acquisition? How is Kochava thinking about its role working with SDK-less partners, the delegation functionality in PAAPI, and PAS?

Google Privacy Sandbox is set to introduce new frameworks for user acquisition that prioritize user privacy. For instance, the Attribution Reporting API within Privacy Sandbox will enable advertisers to measure campaign performance without relying on traditional identifiers. As an MMP, Kochava is preparing to adapt to these changes by exploring SDK-less integrations and server-to-server clean room integrations. Kochava—an approved testing partner with Google—is actively involved in testing these new mechanisms. The second part of our Google Privacy Sandbox webinar series will delve deeper into how these integrations will function as well as the role of Kochava in this evolving landscape. It will also address to what extent Kochava will interact with the Protected Audiences API (PAAPI) and Protected App Signals (PAS).

#7 How will cookie deprecation impact DSPs and SSPs since they heavily rely on pixels? Do we know what Privacy Sandbox for app tracking will look like? What do we know of the differentiators as compared to SKAN?

Cookie deprecation will significantly impact DSPs and SSPs that have traditionally relied on pixels and third-party cookies for targeting and tracking. With Privacy Sandbox, Google aims to replace these methods with privacy-first alternatives, such as the Topics API for interest-based advertising and Attribution Reporting API for campaign measurement. These changes will challenge DSPs and SSPs to adapt their strategies, possibly leading to increased use of data clean rooms and data lakes. Google’s Privacy Sandbox for app tracking is expected to share similarities with Apple’s SKAdNetwork (SKAN), such as privacy-enhancing technology and anonymous reporting, albeit with its own unique approach to rollout, collaboration, and distribution effects.

#8 Is managing Google Privacy Sandbox on the roadmap for Kochava?

Kochava is an authorized testing partner with Google and actively engaged in managing the transition to Privacy Sandbox. The company is testing the new APIs and frameworks to assess their implications for mobile attribution and develop solutions that align with the privacy-first direction of the industry. As part of their commitment to adapting to these changes, Kochava will be integrating Privacy Sandbox features into services to help clients navigate the new landscape, with a strong initial focus on the Attribution Reporting API.

#9 Is Google Privacy Sandbox going to cost anything for the agencies that use it?

While there may not be direct costs associated with using Privacy Sandbox, the shift to privacy-first attribution methods will require agencies to adapt their strategies and potentially invest in new technologies or partnerships. The changes brought by Privacy Sandbox will be integrated into the adtech ecosystem, and agencies will need to evolve their practices accordingly. This evolution may involve indirect costs related to training, technology adoption, and changes in campaign management.

#10 What is the biggest challenge with Google Privacy Sandbox, and is there an upside of Google Privacy Sandbox from a marketing standpoint?

The biggest challenge with Privacy Sandbox is the shift away from deterministic attribution methods, requiring marketers to adopt more aggregated and model-based approaches to measurement. For the marketing industry, this will demand a new mindset and potentially new skill sets. On the other hand, the upside is an increased focus on consumer privacy, which may enhance trust and potentially improve the public perception of the advertising industry. Marketers will need to become more creative and strategic in how they target and measure campaigns, focusing on privacy-preserving methods that align with consumer expectations.

#11 Is there a POV on retention analytics and how this is going to be impacted/go away?

Retention analytics in the context of Privacy Sandbox remains an area of uncertainty. However, it is expected that technology solutions will be developed to assist with this aspect of analytics. Google has demonstrated a collaborative approach in the development of Privacy Sandbox, which suggests that feedback from stakeholders will influence shaping the future of retention analytics. It is important for marketers to stay informed and adapt to new tools and methodologies that emerge as Privacy Sandbox evolves.

#12 How does identity work in Privacy Sandbox for Android? Is it still based on advertising identifiers?

In Privacy Sandbox for mobile, identity will not rely on publicly available unique advertising identifiers. Instead, Google will utilize aggregated and anonymized data based on user information associated with Google accounts. This approach aims to preserve user privacy while still providing useful data for advertisers. The data will be structured to prevent the identification of individual users, aligning with the privacy-first initiatives of Privacy Sandbox.

#13 As a user, will I be able to opt out of certain interest topics within the Topics API?

While it is unclear whether users will have the ability to opt in or out of specific topics within Privacy Sandbox, it is expected that a new consent mechanism will be introduced on Android, similar to Apple’s App Tracking Transparency (ATT) framework on iOS. This mechanism will likely govern user consent for data collection and use in a privacy-conscious manner.

#14 What about gaming in the Topics API? Will it be broken down by subcategories?

The granularity of the Topics API, particularly for gaming, is not yet fully known. Initially, it is expected that categories may be broad and not provide the level of detail desired by performance marketers in the gaming sector. As Privacy Sandbox matures, however, it is possible that more specific subcategories would be introduced. In the meantime, marketers should focus on leveraging Event and Summary API data, which may offer more actionable insights in the early stages of Privacy Sandbox implementation.

#15 DSPs have spent a lot of time building out high-performance targeting products, but with Privacy Sandbox, they have to work within the browser or on device. How handicapped will their technical capabilities be if they can’t host massive amounts of campaign/targeting data in the browser memory? Or can they?

Demand-side platforms (DSPs) will face significant challenges as they adapt to the constraints of Privacy Sandbox, particularly with its limitations on using browser or on-device storage for campaign and targeting data. The extent to which DSPs can utilize such storage is uncertain, and it is likely that such capabilities will be restricted to ensure user privacy. DSPs may need to explore alternative strategies to comply with the new privacy regulations, relying less on extensive data storage within the browser.

#16 Will event-level reporting postbacks in Google Privacy Sandbox for Android have any kind of delay as with Apple’s SKAdNetwork?

Event-level reporting postbacks within Privacy Sandbox will indeed include delays similar to those in SKAdNetwork. These delays are part of the privacy-preserving features designed to prevent identification of individual users. The specific mechanisms and timing of these delays may differ from those in SKAN, and we expect to be able to clarify further details in the second part of our Google Privacy Sandbox webinar series. Marketers should anticipate adjustments to their reporting and analysis processes to accommodate these delays.

Got more questions on Google Privacy Sandbox?

If you seek clarity on how Google Privacy Sandbox for Android will impact your mobile marketing strategies or have specific concerns about this landmark transition, Kochava Foundry is ready to assist. Our team of experts can provide guidance on navigating these changes and help you adapt your mobile app campaign strategies for success in a privacy-first landscape. Set up an expert consultation with us to explore how we can support your needs and keep you ahead in the evolving digital advertising ecosystem.

Answers to your questions from the Kochava Foundry webinar

Grant Simmons, VP of Kochava Foundry, recently hosted the webinar Measuring Incrementality & Lift, where he unpacked lift measurement best practices, common pitfalls with hold-out groups, emerging methodologies for incrementality testing, and Kochava solutions for incremental lift measurement. The Foundry team gathered some of the most engaging audience questions Grant answered during the webinar to elaborate on in further detail.

Check out the full webinar on demand here.

How exactly does Kochava Foundry measure incrementality & lift for content partners that run ads year round with no dark periods?

Foundry employs various measurement techniques to assess incrementality and lift for content partners with ongoing, year-round ad campaigns. While MediaLift is primarily designed for discrete campaign measurement, Foundry can utilize modeling techniques to understand the ongoing lift of always-on media.

By leveraging regression discontinuity, Foundry can analyze the impact of continuous advertising efforts and identify the incremental contribution of specific media partners. Additionally, AIM (Always-On Incremental Measurement) can be employed to measure the contribution of each channel continuously at the network, publisher, and campaign level. This enables content partners to gain insights into the effectiveness of their ongoing ad campaigns and make data-driven decisions to optimize their media mix.

Can Kochava MediaLift be used for an advertiser that is not using Kochava as their mobile measurement partner (MMP)?

Yes, Kochava MediaLift can be utilized by advertisers who don’t have Kochava as their MMP. MediaLift is designed to work with standardized ad signals and conversion signals, making it platform-agnostic. This means that even if an advertiser uses a different MMP, MediaLift can still analyze the ad and conversion data to measure incrementality and lift. By leveraging MediaLift, advertisers can gain valuable insights into the incremental impact of their campaigns, regardless of their chosen MMP.

What are some best practices for running incrementality tests while minimizing negative impact to my business?

• Define clear objectives: Clearly define what you want to measure and the specific metrics you are targeting. This guides the design of your test and ensures that you are capturing meaningful data.
• Consider opportunity and hard costs: Holdouts, or not marketing to a portion of your audience, can be costly and result in missed opportunities. Consider the potential impact on your business and weigh the costs against the benefits of the test.
• Determine timing and duration: Choose the appropriate timing and duration for your test to capture meaningful data without disrupting your ongoing campaigns. Consider factors such as seasonality, campaign duration, and audience behavior to ensure accurate results.
• Explore modeled options: Modeled approaches, such as synthetic control groups or machine learning models, can provide reliable results while mitigating the opportunity cost of holdouts. These approaches use historical data and statistical modeling to estimate the incremental impact of your campaigns.

Would you recommend measuring incrementality on your own or with a third-party company—or somewhere in-between?

I encourage brands to develop their own solutions. After all, it is YOUR money, and the measurement tooling you run must be up to your standards.

While it is possible to measure incrementality on your own, and Foundry has helped brands get there, it is often beneficial to work with a third-party company that specializes in incrementality measurement. Here’s why:

• Expertise and tools: Third-party companies have expertise in designing and executing incrementality tests. They can access advanced tools and methodologies that provide more accurate and reliable results. Their experience in analyzing large datasets and understanding statistical models ensures that measurements are conducted effectively.
• Unbiased and objective insights: Third-party companies provide unbiased and objective insights into the incremental impact of your campaigns. They are not influenced by internal biases or vested interests, allowing for a more impartial evaluation of your marketing efforts.
• Scalability and efficiency: Third-party companies have established processes and infrastructure in place to handle large-scale incrementality measurement. They can efficiently analyze and interpret the data, providing timely and actionable insights.

That said, it is important to find the right balance between in-house measurement capabilities and third-party expertise. Some advertisers may build internal measurement capabilities while leveraging third-party support for more complex analyses or to validate their findings.

Since leaders like to look at annualized numbers, but we don’t know decay, how can you scale the incremental numbers over a year? Any best practices?

• Calibrate attribution models: Data from incrementality tests is used to refine and calibrate attribution models. By understanding the incremental impact of different channels and tactics, you can adjust the attribution weights assigned to each touchpoint in the customer journey. This ensures that the attribution model accurately reflects the true contribution of each channel and tactic.
• Consider seasonality: Take into account any seasonal variations in your industry or market. Adjust the scaling of incremental numbers based on historical patterns during specific periods.
• Allocate budget: Use insights from incrementality tests to allocate budget toward channels and tactics that drive the most efficient incremental cost per acquisition (iCPA). By identifying channels and tactics that generate the highest lift and incremental conversions, you can prioritize budget allocation accordingly. This helps optimize marketing spend and maximize return on investment.
• Re-measure optimizations: Continuously re-measure the impact of optimizations based on the results of incrementality tests. By implementing changes to your campaigns, such as adjusting targeting parameters, creative elements, or bidding strategies, you can evaluate how these optimizations contribute to greater incremental contribution. This iterative process enables you to refine your strategies and make data-driven decisions to drive incremental growth.

Generally speaking, do you think online brands with smaller brand awareness can put more value or trust in incremental lift tests in some ways? Or do you find that even smaller brands can run into the same issues?

• Smaller brands may be in a better spot vs. big brands in that the smaller brands have less brand equity. So theoretically, ad spend should provide more of a pop because ad media is the only way some folks will come to know a new brand.
• Incremental lift tests can help smaller brands identify the specific channels, tactics, or campaigns that are driving incremental results and optimize their marketing strategies accordingly. By measuring the lift in conversions or actions compared to a control group, smaller brands can gain insights into the true impact of their advertising efforts and make data-driven decisions to allocate resources effectively.
• However, it is important to note that smaller brands may still encounter challenges similar to larger brands when conducting incremental lift tests, such as ensuring proper test design, data quality, and statistical significance (i.e., amount of data). It is crucial for all brands, regardless of size, to plan and execute their incrementality tests carefully to obtain reliable and actionable insights.

Do holdout biases exist with geo-based holdouts, or is this exclusive to audience-based splits?

This is likely so, but geo holdouts can be a useful tool. Assuming that two markets move in concert, if one is treated with media and the other goes dark, the marketing effect lift may be understood as the performance of the two markets in direct comparison. Note that this usually takes an inordinately large amount of spend in the target market and having to go dark in the control market, which isn’t how you would actually run the campaign, so the results may not actually reflect reality.

While geo-based holdouts can be a useful tool for comparing the performance of different markets, it is important to consider factors such as market dynamics and the significant amount of spend needed in the target market.

Conducting holdout tests in a way that accurately reflects real-world campaign execution can be challenging. Going dark in the control market may not truly replicate how the campaign would actually be run, which can introduce biases and affect the validity of the results obtained from the holdout test.

What are your thoughts on a cross-screen campaign? Using an always-on approach and multi-touch attribution, we would want to tell the power of our multiple products.

• A cross-screen campaign with an always-on approach and multi-touch attribution can be a highly effective strategy to showcase the power of multiple products. By maintaining a consistent presence across various screens, you can engage with your target audience at different stages of their customer journey. This allows for a more holistic and integrated marketing strategy—maximizing the exposure and impact of your multiple products.
• Multi-touch attribution enables you to understand the contribution of each touchpoint in the customer journey and measure the incremental value generated by each product. It provides insights into how different screens and touchpoints work together to drive conversions or actions, allowing you to optimize your campaign and allocate resources effectively. To execute a successful cross-screen campaign, it is important to have a robust measurement framework in place, including proper tracking, attribution mechanisms, and advanced analytics tools. Continuous monitoring and analysis of campaign performance will help ensure its effectiveness.

Got more questions on measuring incrementality and lift?

Are you looking for expert consultation on your incrementality testing strategy? Need help measuring the lift of your campaigns? Get in touch with the Kochava Foundry team for an expert consultation.

Answers to your questions from the Kochava Foundry webinar

Grant Simmons, VP of Kochava Foundry, recently hosted the webinar “All Things iOS,” where he unpacked topics including Apple’s SKAdNetwork (SKAN), the AppTrackingTransparency (ATT) framework, Apple Search Ads, and more. The Foundry team gathered some of the most engaging questions that came up during the session to answer in more detail.

Check out the full webinar on demand here.

What are Apple’s plans with privacy manifests? What do you make of the list of SDKs they published? Will ad monetization SDKs be impacted?

Apple’s plans with privacy manifests provide more transparency to users about the data collected by apps and how it is used. Privacy manifest also empowers app developers by providing them with new control over whether third-party software development kits (SDKs) can transmit signal off device when user consent isn’t granted through Apple’s ATT framework. This level of control means that developers can safely integrate third-party SDKs, potentially avoiding the dreaded “contact your SDK provider” to change the default behavior of the SDK.

The list of software development kits (SDKs) published by Apple has a mix of well-known SDKs and obscure outliers. It appeared to be an addendum to a previous developer-focused communication. While the spirit seems to be attenuated to some SDKs that collect personally identifiable information (PII), it is important to note that the list hasn’t evolved much since it was first released. However, it is possible that more updates and additions will be made in the future. As for the impact on ad monetization SDKs, it is crucial for brands to review the list and ensure compliance with Apple’s guidelines to avoid any potential impact on ad monetization strategies.

I would love to hear about brands that are smaller advertisers and may not meet the thresholds of SKAN—what are alt models to attribution?

For smaller advertisers who may not meet the privacy or crowd anonymity thresholds of SKAN, alternative models to attribution can be utilized to understand campaign performance.

The first recommendation is to maximize owned media opportunities by hydrating special, usually hashed, first-party identifiers to the extent possible. This allows for better tracking and attribution within advertisers’ own media channels.

Another suggestion is to use network and publisher cost as an input and conversions as an output, implementing an MMM (Marketing Mix Modeling) or contribution model. This approach can provide valuable insights into the effectiveness of different marketing channels without reliance on deterministic, last-touch attribution. Exploring solutions like our AIM (Always-On Incremental Measurement) product can offer further guidance and support for smaller advertisers seeking to future-proof their attribution efforts.

How are you thinking about over-attribution related to SKOverlay elevating impressions to a Fidelity Type 1 in the SKAN waterfall (30-day attribution window)? Tangentially, how should brands navigate the loss of user intent signal with networks individually defining/justifying passing impressions as clicks on the mobile measurement partner (MMP) side with SKOverlay signaling and Engaged View type “click” signals? Ultimately, this becomes a measurement problem, but how can brands partner with their MMPs to address?

Over-attribution related to SKOverlay, which elevates engaged view impressions to a Fidelity Type 1 in the SKAN attribution waterfall, can be a challenge for brands. The passing of impressions as clicks on the MMP side, especially with SKOverlay signaling and Engaged View type “click” signals, can lead to inaccurate measurement and attribution. It is important for brands to recognize the misalignment that can occur, particularly with in-app ad units that generate multiple “tap-like” activities without significant user interaction.

To navigate this issue, brands should work closely with their MMP to establish clear guidelines and processes for differentiating between impressions and true user engagement. Standardizing reporting across offers and DSPs can also help address this measurement problem, allowing for better normalization and accuracy in attribution.

With Apple building their own DSP and possibly an SSP as well, what is the future for third-party ad platform access to iOS?

The future for third-party ad platform access to iOS may become more restricted with Apple building their own DSP and possibly an SSP. Apple’s focus on privacy and control over user data may lead to tighter restrictions on data sharing and targeting capabilities.

It is conceivable that Apple may push for a more direct integration with publishers, effectively making them integrate directly with Apple and requiring all internet traffic to go through the iOS operating system. It is important for brands to adapt to these changes and explore alternative advertising strategies, such as leveraging Apple Search Ads and other first-party data sources, to ensure continued access to iOS users.

What do you think Apple will release at WWDC 2024? And what do you think about broader private relay implementation?

While the specific releases at Apple’s Worldwide Developer Conference (WWDC) 2024 are uncertain, it is expected that Apple will continue to focus on privacy and user control. They may introduce updates to their privacy features, additional components to privacy manifest, data granularity improvements to the SKAN framework, and new tools for developers and advertisers.

Apple is aware that there are still many unsolved challenges in the advertising space, and they have already implemented visibility around Apple Search Ads that Google cannot match. As for the broader private relay implementation, it is an interesting development aimed at enhancing user privacy by masking IP addresses. However, its broader impact and adoption are yet to be seen, and it will be important to monitor how this aligns with industry practices and regulations.

Got more questions on iOS?

Need help with your ATT prompting strategy, SKAN or Apple Search Ads, or any other iOS marketing topic? Get in touch with the Kochava Foundry team for an expert consultation.

Whether you are an advertiser or a publisher, Kochava has your lift needs covered

The multifaceted realm of digital advertising technology is a tale of two entities: advertisers and publishers. While both are engaged and intertwined within the same ecosystem, each has distinct perspectives and challenges as well as goals and strategies. Let’s take a look at some of these.

Advertisers vs. Publishers

Advertiser Viewpoint

• Values volume and overlap of impressions and influences
• Needs to gauge overall contribution and value of publishers

Advertiser Objectives

• Identify which platforms or mediums yield the highest conversions
• Navigate the maze of ad spend efficiency
• Assess the true incremental impact of campaigns

Publisher Viewpoint

• Wants to account for every impression they serve
• Needs effective measure for ads while respecting privacy constraints
• Must optimize targeting strategies and ad placements continuously and dynamically

Publisher Objectives

• Demonstrate value and contribution to advertisers
• Maximize the value of each ad impression
• Stay relevant and competitive in an increasingly saturated market

Both entities, while operating in the same space, have diverging viewpoints and requirements. With powerful tools for measurement and optimization tailored to each group’s distinct needs, Kochava is unique among mobile and omni-channel measurement providers in profoundly understanding this intricate dynamic.

Kochava’s Suite of Incremental Lift Products

MediaLift

Utilizes consented device-level datasets to formulate a control group, modeling an audience likely to view a particular ad.

• Ideal for: advertisers deciphering lift from varied media sources; publishers aiming for superior evaluation toolkit
• Superpower: Delivers comprehensive insights 7 to 10 days post campaign, can assess wide spectrum of media, and offers integrated, cross-channel view
• Granularity/Insights: Lift due to media on installs, post-app-install conversions and footfall visitation; insights by creative or tactic, reach, and frequency
• Required Inputs: Exposure and conversion with timestamps, 30 days of pre-period conversions
• Ideal Media: OOH/DOOH, CTV/OTT, linear
• Delivery: Comprehensive study and underlying workbook
• Feasibility: Requires higher volume of data
• Level of Effort: More intensive at outset

RDiT (Regression Discontinuity in Time)

Uses time series regression to highlight conversion impact of initial ad impression.

• Ideal for: advertisers and publishers interacting with platforms like DSP or SAN and needing prompt, insightful feedback
• Superpower: Offers insights within rapid 2-day turnaround, enabling profound understanding of early impressions, a critical element for shaping effective campaigns
• Granularity/Insights: Assesses lift from first ad impression to conversions, split by various categories such as creative, campaign, ad set, and more
• Required Inputs: Exposure and conversion with timestamps
• Ideal Media: AdTech DSP, SANs, CTV/OTT platforms
• Delivery: Periodic, recurring reports
• Feasibility: Suitable for lower data volume
• Level of Effort: Relatively low

AIM (Always-On Incremental Measurement)

Leverages machine learning to build real-time dynamic models.

• Ideal for: advertisers trying to improve their attribution signal in the wake of evolving privacy policies, as this next-gen marketing mix modeling tool uses only privacy-first data
• Superpower: Analyzes cost curves to identify areas where marketing efficiencies can be maximized and inefficiencies minimized; measures the impact of offline media on digital sales
• Granularity/Insights: Spend recommendations focusing on contribution of each publisher at network, publisher, and campaign level
• Required Inputs: Daily cost and conversions by app/network/geo
• Ideal Media: DSPs, SANs, and any platform with aligned real-time cost and conversions
• Delivery: Interactive dashboard within UI; full reporting API
• Feasibility: Higher initial data requirement, minimal thereafter
• Level of Effort: Initial setup requires more effort, largely self-running thereafter

Always-On for Publisher’s Suite

Offers continuous (daily to intradaily) insights and adjustment modeling to neutralize prior impression biases.

• Ideal for: publishers aspiring to pioneer in evolving CTV marketplace
• Superpower: Crafted especially for emerging connected television (CTV) domain, guaranteeing that publishers remain at forefront of innovation
• Granularity/Insights: Similar to RDiT but constantly updated, ensuring removal of prior-impression bias
• Required Inputs: Exposure and conversion with timestamps
• Ideal Media: Specifically crafted for CTV
• Delivery: UI, integrated platform, and postback systems
• Feasibility: Works well with lower data volume
• Level of Effort: Generally low

Find the Best Tools for Your Needs

The modern advertising landscape is intricate, with advertisers and publishers alike requiring tools honed to their distinct challenges. Kochava, with its unique understanding of the ever-evolving AdTech landscape, offers precision tools tailored for every nuance. Whether you’re an advertiser seeking ROI clarity or a publisher aiming to showcase your value, we equip you with industry-best tools for harnessing actionable insights for optimal decisions. Welcome to the future of ad measurement and optimization!

Interested in learning more? Contact us for a free consultation and product demo.

What Kochava Foundry Can Do for Your Brand

In today’s digital age, data is the unsung hero behind successful marketing campaigns. It’s like the wizard behind the curtain, making the magic happen. Brands need accurate, timely, and trustworthy data to make informed decisions, optimize their advertising efforts, and connect with their target audience effectively. Enter Kochava Foundry, our trusty sidekick, here to help you harness the power of data with a bit of a wink and a nod. In this blog, we’ll explore what Foundry can do for your brand, all while sneaking in a dash of wry humor.

1. Data Source Validation: Separating the Gems from the Cubic Zirconia

Let’s face it: not all data sources are created equal. Some are as reliable as your GPS, while others might lead you down a rabbit hole. Foundry starts by doing what we like to call “data source validation.” We’re like the data bouncers checking IDs at the door. We ensure that your data sources are the real deal—accurate, complete, timely, and as secure as a secret agent’s briefcase.

With Foundry on your side, you won’t have to worry about data that’s faker than a spray-on tan. We’ve got your back, and we won’t let your brand fall victim to unreliable data.

2. Data Quality Assurance: Polishing Your Data Crown Jewels

Data quality is the crown jewel of marketing success. It’s like having the Hope Diamond in your marketing toolkit. Foundry takes data quality seriously, making sure your data shines brighter than a supernova. We perform meticulous data quality assurance checks to spot any data blemishes or imperfections. Think of us as the data beauty therapists, making sure your data looks flawless.

3. Timely Data Delivery: We’re Not a Pizza Delivery Service (But Close)

In the world of digital marketing, timing is everything. Foundry ensures that your data arrives on time, every time. We understand that delayed data is like cold pizza—nobody wants it. So, rest assured that your data will be as punctual as a Swiss watch.

4. Data Security: Better than Fort Knox for Your Data

Security is our middle name (well, not really, but you get the point). We treat your data like it’s a national treasure. Foundry takes stringent measures to protect your data during its journey, making sure it’s secure every step of the way..

5. Data Source Reviews: Tea Time with Data Providers

Foundry goes the extra mile by establishing a tête-à-tête with data source providers. It’s like having tea time with your data buddies. We keep the lines of communication open to address any data-related issues promptly. We’re like the friendly neighborhood data watchdogs.

6. Actionable Insights: The Sherlock Holmes of Data Analysis

With Foundry, you gain access to actionable insights that Sherlock Holmes himself would envy. We help you decipher data, spot trends, and make data-driven decisions. Think of us as your trusty Watson, guiding you through the mysteries of your data.

7. Compliance and Industry Standards: Staying on the Right Side of the Law

We make sure your data sources play by the rules, just like a stern school principal. Foundry helps ensure you understand compliance with industry standards and regulations, keeping your brand out of hot water.

Foundry is Your Data Superhero

In summary, Foundry is your data superhero, here to help you make data-driven decisions with a hint of wry humor. Don’t let your brand’s success be left to chance—partner with Foundry, and let’s embark on a data-driven adventure together.

Reach out to us today to learn more about how Foundry can bring a touch of levity while we help supercharge your data management efforts. After all, who said data had to be boring?

How Insight Packs from Kochava Foundry Light the Way

In the intricate landscape of digital marketing, brands confront a plethora of challenges. These obstacles, ranging from misattributed user acquisitions to the ever-changing ad realm, often leave advertisers in a perplexing situation. How can they confidently allocate their precious ad spend, knowing they’ll be scrutinized over the final outcome (success or failure)?

Kochava Foundry, with its revolutionary Insight Packs, emerges as a beacon, guiding brands to make informed and impactful decisions based on expert analysis.

Deep Dive into Insight Packs

Foundry, always at the forefront of innovation, offers two trailblazing Insight Packs tailored for today’s marketing conundrums:

Incremental Intent: In a world where every network claims superior customer acquisitions, Incremental Intent emerges as the truth-seeker. By meticulously calculating the variance between organic and driven installs, this tool offers a crystal-clear perspective. Brands can, therefore, redirect their budget towards avenues that genuinely amplify their advertising impact.

Loyalty and Engagement: The modern consumer is discerning and volatile. Retaining their loyalty is a Herculean task. This Insight Pack offers a magnifying glass into your media strategy’s real impact. By highlighting how different channels and campaigns influence customer loyalty and engagement, brands get a roadmap. Following this, they can judiciously adjust their spend, maximizing ROAS and fine-tuning acquisition strategies.

Dissecting and Addressing 10 Key Pain Points

Let’s delve deeper into key marketing challenges and explore how Kochava Foundry’s tools and expertise pave the path to solutions:

1. Attribution Confusion:
Our sophisticated attribution platform delves beyond surface-level data. By leveraging both deterministic attribution and probabilistic modeling, we ensure an unambiguous view of user acquisition sources. Brands can then confidently reward the deserving networks for the conversions they actually drove.

2. Suboptimal Ad Spend:
The Incremental Intent Insight Pack stands out as the sentinel guarding against wasteful ad spend. By distinguishing between organic and campaign-driven acquisitions, it provides a nuanced understanding, helping brands streamline their budgets for optimal impact.

3. Low Customer Engagement:
Our advanced engagement analytics dive deep into user behavior post-installation. When merged with insights from the Loyalty and Engagement Insight Pack, brands receive a comprehensive view of any discrepancies. This enables a recalibration of ad messaging and the user experience to better align.

4. Short-term User Retention Woes:
Our retention analytics meticulously chart out user behavior trajectories post-install. Brands gain unparalleled clarity on user drop-off points, enabling them to refine onboarding and engagement touchpoints.

5. ROI Uncertainty:
Our detailed ROAS reports break down the performance of networks and campaigns, segment by segment. This granular view empowers brands to discern the genuine high-performers, ensuring investments that promise tangible returns.

6. Over-reliance on a Few Networks:
Our exhaustive performance metrics catalog offers a panoramic view of multiple networks. Brands, thus, are nudged to venture beyond their comfort zones, discovering uncharted territories in the advertising world.

7. Lack of Actionable Insights:
Kochava Foundry transcends traditional data offerings. With a blend of strategic consultations and expert-backed recommendations, brands receive a clear, actionable blueprint for the future.

8. The Ever-Changing Ad Landscape:
At Kochava, we pride ourselves on our agility. As digital advertising undergoes metamorphoses, from privacy regulations to emerging platforms, we ensure brands aren’t left in the lurch. With timely guidance, integration advice, and adaptive strategies, brands remain ahead of the curve.

9. Siloed Data Interpretation:
Our holistic dashboard amalgamates diverse metrics, offering brands a cohesive narrative. This unified perspective, enriched with data visualization tools, ensures brands grasp the intricate dance of different metrics and their cumulative effects.

10. Long-term Strategy Struggles:
We believe in a 360-degree approach. By synergizing historical data insights with forward-looking predictive modeling, we ensure a brand’s short-term tactics seamlessly merge with its long-term visions.

Insights for the Dynamic World of Digital Marketing

In the dynamic world of digital marketing, a brand’s survival hinges on its adaptability and informed decision-making. With Foundry’s Insight Packs, brands are equipped with a compass and a roadmap. As they navigate the tumultuous terrains of the digital realm, Kochava ensures their journey is not just safe but also supremely successful.

Visit Kochava.com/Foundry-Insight-Packs/ to learn more about Insight Packs and request a free consultation.

• The Non-Performer
  • User installs the app and then disappears without completing a KPI

• The One-and-Done
• • User installs the app and performs one post-install action but never returns
• The Performer
• • User installs the app and performs consistently through regular engagement with KPIs

You can determine which KPIs to evaluate, the specific time frame of installs, and when the post-install events occurred. Then, use the Query tool to write SQL scripts to analyze, cohort, group, slice, dice, and pivot your data in unique, customized ways. Or, the Kochava team can help you customize the query for your specific brand’s needs.

In addition, measuring ad partners by how many users are classified as “one-and-done” is beneficial in comparing user quality by media partner. Partners that heavily over-index in one-and-done users can be trimmed or eliminated, whereas partners with the lowest percentages can be prioritized. 

The potency of this analysis can be further increased by making sure the app is measuring a healthy mix of KPIs throughout the user funnel. See Post Install Event Examples for a list of recommended events to track by app vertical.

Below is an example of how you can visualize the data from the query to help distinguish the one-and-done vs. performer segments by media partner, providing insight into which partners drove quality user engagement. 

Sample use case 

A financial service provider specializing in money transfers has a goal to increase this conversion event and are exploring ways to grow their user base. In seeking assistance from the Client Analytics team at Kochava, they learned that a percentage of their converted users were “one-and-done.” Additionally, they learned which media partners repeatedly delivered this user type. They can now apply their learnings toward their next campaign by removing certain media partners and creating a lookalike campaign of their newly segmented performing users.

How can you apply the One-and-Done Analysis results?

There are a couple of ways in which you can apply your learnings from the One-and-Done analysis to grow your audience:

Audience Targeting: Use your performers as a seed for a lookalike model. Activate a campaign with the lookalike audience to acquire more users with attributes that commonly define performers, getting stronger ROI on your acquisition efforts.

Push Engagement: If your goal is to retain the one-and done user, load this audience segment into your push provider platform for a push, in-app message or SMS campaign to reengage them with a contextually relevant offer.

Marketers can also export a list of device IDs by segment and activate them with a preferred media partner.

Grow your audience with the One-and-Done analysis

While you may already be segmenting your audience, the One-and-Done analysis brings highly customizable and flexible turnkey segmenting. If you haven’t identified and segmented one-and-done users, you are probably mistakenly considering them active performers.

If you are looking for greater insight into the quality of installs, the One-and-Done analysis may be a solution to explore. The output is easy to understand and can be automated on an ongoing basis to provide actionable audience segments.

Interested in refined and customizable user segmentation? Contact your client success manager or support@kochava.com.

Not a Kochava customer? Contact Us Today.