Modelplace.ai | Ready-To-Integrate Computer Vision Neural Networks

27 February 2024
Modelplace.ai | Ready-To-Integrate Computer Vision Neural Networks


Our initial task was to gauge the audience's interest in this product within the U.S. market. To achieve this, we opted for Google contextual advertising—a rapid and accessible tool for testing hypotheses.


To ensure the relevance of our hypothesis testing results, we implemented the following steps:

  1. Analytics System Setup: We established a robust analytics system to track user interactions, conversions, and other relevant metrics.

  2. User-Flow Optimization: We carefully designed the user journey (user-flow) to align with our testing objectives. This involved creating a seamless path from ad exposure to desired actions (such as clicks or conversions).

Goal: to check the demand for the product in the U.S.

What we are to do for the goal

Project planning

and main strategy formation

Implementation of analytics

Google, Facebook Pixel, Linkedin

The website’s integration set up

Notion (as CRM), MailChimp and Calendly

Google Ads campaigns

creating and managing

Results analysis

and Google ads budget optimization

Delivery package


We conducted an initial communication with the company's decision-makers, defined the project’s milestones, forecasted the impact and defined 
the implementation terms. Then we gathered the necessary materials 
for the launch of the advertising company, identified the target audience,
their "pains" and "benefits". Formulated an offer for the end client.

Implementation of analytics systems

As a result, we do audits of all the existing analytics systems, 
write a Terms of Reference to implement Google Analytics, Facebook Pixel 
and LinkedIn analytics systems and run tests to make regulations to the scripts and UTM-tags.

Website integrations

Task: we need to create a minimal viable system for receiving
and initial processing of an application for a startup.

Suggested scenario for the system
  1. A user fills out a form on the website.

  2. The information from the request is sent to Notion as a "New lead" with

    1. the request type,

    2. the user's email,

    3. UTM tags

  3. The information from Notion is sent to MailChimp
with the distribution of users into groups based on the type of request.

  4. A welcome-email based on the request type is sent through Mailchimp Customer Journey.

  5. After the user fills out the request on the website, a pop-up window
with the option to book a time to meet in Calendly is displayed.

  6. If the user books a time for an appointment, change in its status
to "First contact" should be made.

Workflow automation schema and description


Creating and managing advertising campaigns

We developed a promotional campaign in Google Ads, curated the semantic core, and removed irrelevant queries. Next, we launched targeted ad campaigns for the search engine and reallocated the budget. We then created a Terms of Reference (ToR) for designing banners. Finally, we tracked initial results and compiled a comprehensive report for transparent communication with the customer.

Results analysis and optimization

Ads campaign summary table



By leveraging Google contextual advertising and refining our user-flow, we obtained valuable insights into product interest, user behavior, and potential market viability.