Short Answer:

Approximately 4 weeks.

Full Answer:

Typically, from end-to-end, this process spans about 4 weeks. This duration allows us to understand your specific requirements, train and build your models, and ensure that everything is functioning perfectly.

Short Answer:

Yes, AIM only uses ‘privacy-first’ market level data.

Full Answer:

AIM doesn’t require any user-level data to function effectively. Our reliance on aggregated market-level data not only ensures robust performance but also naturally aligns us with stringent privacy policies, reinforcing our commitment to user protection.

Short Answer:

AIM uses market-aggregated data, focusing on client KPIs and ad spend. 

Full Answer:

AIM’s foundation is anchored in aggregated market-level data, which represents the total amount of events per market. This encompasses client-specific Key Performance Indicators (KPIs) that they employ for optimizing their marketing campaigns, as well as data on ad spend broken down by different networks or media channels. AIM doesn’t utilize any attribution data.

Short Answer:

AIM integrates with your marketing stack using an API.

Full Answer:

AIM has been purposefully designed to smoothly integrate with your existing marketing toolkit. Not only is it fully integrated with all mobile measurement partners, but it also seamlessly connects with over 30 ad networks. No matter the nature of your data, whether it’s sales figures, KPIs, or ad spend data, AIM ensures hassle-free integration with all systems via a simple API connection. This makes data transfer and analysis efficient and straightforward.

Short Answer:

Through the ‘Kochava Cost’ product.

Full Answer:

Supplying AIM with your ad spend data is streamlined thanks to our integration with the ‘Kochava Cost’ product. All you’re required to do is enter your network credentials into the system. Once done, the ‘Kochava cost’ product takes over, automatically fetching all the pertinent ad spend information, ensuring AIM has the data it needs to deliver comprehensive analyses.

Short Answer:

AIM provides advice at the region, app, media source, and campaign levels.

Full Answer:

AIM offers insights on multiple levels, including region, app, media source, and campaign. However, it’s important to note that the granularity extends to the campaign level only if the campaign-level data is made available.

Short Answer:

The threshold is likely quite low.

Full Answer:

It’s essential to consider the volume of ad spend being analyzed by each AIM-built model. Presently, we cater to clients with ad spends as minimal as $25,000 monthly for a single model. At this expenditure, the model’s accuracy remains high. However, we haven’t catered to clients with spends below this, so the exact lower limit remains uncertain.

Short Answer:

Yes, AIM is versatile across industries.

Full Answer:

Absolutely. AIM’s modeling framework is very versatile, catering to the distinct nuances and KPIs characteristic of various industry sectors. Whether you’re in retail, gaming, dating, or any other domain, AIM adjusts and tailors its approach, ensuring results are both relevant and actionable for your specific industry landscape.

Short Answer:

Yes, AIM can analyze offline media sources.

Full Answer:

AIM is capable of analyzing offline media sources. However, the effectiveness of this analysis hinges on the availability of the relevant offline data to be incorporated into the AIM system.

Short Answer:

Yes, AIM can integrate with multiple sources.

Full Answer:

Certainly, AIM is capable of integrating with a wide range of data sources. We have several clients for whom we’ve connected with various data providers. If a client grants us access to a specific source, we ensure we retrieve the data without any complications.

Short Answer:

Reliability varies based on the spend levels. 

Full Answer:

Reliability depends on the amount of data provided. For major platforms like Google, Facebook, and Apple, suggestions are notably reliable. However, when it comes to platforms where the daily spend is, for instance, only $500 across six campaigns, the campaign-level reliability might waver. Yet, this distinction is transparent within the AIM platform. It offers a confidence band around campaign-level suggestions. At network level, with a $500 daily spend, the confidence remains high.

Short Answer:

AIM uses Bayesian and non-linear regression modeling.

Full Answer:

AIM’s approach is deeply anchored in a Bayesian methodology coupled with non-linear regression. This unique blend facilitates the incorporation of prior knowledge and the consistent refreshing of models with incoming data. Consequently, AIM can produce predictions that are not only accurate but also adaptive and informed. Unlike some traditional models that remain static, AIM’s Bayesian foundation enables dynamic adjustments and refinements based on fresh data inputs.

Short Answer:

AIM customizes models based on client-specific KPIs.

Full Answer:

AIM meticulously crafts its models to align with the distinct KPIs pertinent to each client. This means that regardless of whether you operate within the retail domain, the gaming sector, or any other industry, AIM’s models are calibrated to precisely reflect and cater to your exclusive performance benchmarks.

An AIM model represents a unique blend of an app’s title, platform, and operational region.

Full Answer:

An ‘AIM model’ is defined by three primary attributes: the app’s specific title, the platform it runs on (like iOS, Android, or Web), and the region where it operates. Think of it as a distinct identity for a brand. To put it in perspective, Disney+ on iOS in the UK constitutes one specific AIM model. This combination allows for precise and targeted analysis in the AIM system.

Short Answer:

AIM is a real-time learning system that auto-updates, while traditional MMM is static and can become outdated.

Full Answer:

While both AIM and traditional marketing mix modelling (MMM) employ probabilistic models to analyze and attribute marketing benefits, they function quite differently. AIM is designed as a dynamic learning system. It features an automated data pipeline that consistently checks, enriches, and transforms incoming data. This continuous flow and optimization ensure the models always reflect the most recent and accurate information. Users can access this real-time data with ease through AIM’s user interface.

On the other hand, traditional MMM lacks this adaptability. It doesn’t self-update with new data, which means that as the market or data evolves, MMM can quickly become outdated. This static nature means it demands significant resources for both creation and upkeep. Often, by the time users access its data, it no longer reflects the current landscape.

Therefore, AIM offers a more agile, cost-effective, and timely approach to marketing measurement, granting businesses a distinct competitive advantage.

Short Answer:

Improved marketing efficiency.

Full Answer:

AIM augments your marketing strategies by providing fresh and actionable insights. It’s not just about analyzing data; AIM’s approach actively elevates the efficiency of your marketing mix. By leveraging AIM, you gain clarity on how to direct your resources and make decisions that amplify your return on investment. In essence, AIM transforms raw data into strategic guidance, ensuring your marketing efforts yield maximum benefits.

Short Answer:

Boost in target KPI without an increase in budget.

Full Answer:

With AIM, many clients have witnessed a notable uptick in their desired KPIs without having to inflate their marketing expenditures. This means that AIM doesn’t just offer a return on your investment but also optimizes your existing budget to deliver better results. In essence, you’ll see your sales or other target metrics improve while maintaining your current marketing spend.

Short Answer:

AIM makes incremental changes, ensuring stability for long-term planning.

Full Answer:

While AIM does operate on a continuous learning basis, it’s important to understand that it primarily introduces minor, incremental adjustments rather than drastic shifts. This means that even as the model refines itself, the core insights remain relatively stable over time. As a result, businesses can confidently integrate AIM’s insights into their financial reporting and long-term goal setting without fear of erratic shifts or disruptions.

Short Answer:

Sales funnel data and ad spend are the primary inputs.

Full Answer:

While sales funnel data and ad spend serve as the core inputs to the AIM model, the system is also capable of integrating additional data elements. This includes metrics like CPMs, updates related to products, and even competitor data for a more comprehensive analysis.

Short Answer:

AIM tests accuracy against actual results, targeting 95%+ accuracy on 2-week forecasts.

Full Answer:

To instill confidence in the model’s reliability, AIM undergoes rigorous testing against real-world results, not attributed results. Specifically, we aim for a 95% or higher accuracy rate for our 2-week forecasts.