Machine Learning Based Predictive Analytics For Advertising Industry

Application Screenshots

Technology Stack

Problem Statement

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Measuring ROI (Return on Investment): It can be difficult to accurately measure the return on investment of an advertisement campaign, as there may be several factors that contribute to sales, and it can be challenging to isolate the impact of the advertisement.

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Assessing reach and engagement: Marketers need to assess the reach of the advertisement and gauge how engaged the target audience is with the advertisement.

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Dealing with limited data: Marketers often have limited data to work with, which can make it challenging to assess the success of an advertisement campaign.

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Identifying the right metrics: There are many metrics that can be used to measure the success of an advertisement campaign, and it can be challenging for marketers to identify the most relevant and meaningful metrics for their specific campaign.

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Managing budgets: Marketers need to manage budgets effectively, balancing the cost of the advertisement campaign with the expected return on investment.

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Solution Overview

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OptiSol partnered with a startup to develop a machine learning model to evaluate the performance of an advertisement campaign.

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We used various advertisement variables like cost per click, click rate, and cost per result to train our ML model.

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Marketers can access the results through a dashboard, where they can view performance reports for each advertisement, including the overall cost per click.

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The actionable insights from the solution helped the marketers determine the areas of improvement, and visualized trends, and patterns, to attain an informed decision.

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The solution also involved building a regression model to predict the cost per result of an advertisement campaign.

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Business Impact

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Improved decision-making: The insights generated by the machine learning model can help marketers make more informed decisions about their advertisement campaigns.

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Increased efficiency: The machine learning model can automate and streamline the data analysis process, saving marketers time and resources.

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Improved cost management: The machine learning model can help marketers optimize their advertisement campaigns by predicting the cost per result and identifying areas for improvement.

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Increased ROI: By making more informed decisions and optimizing their advertisement campaigns, marketers can increase their return on investment.

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Better targeting: The machine learning model can help marketers better understand their target audience and create more effective advertisements.

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Testimonials of Our Happy Clients

Rick Tigges

Rick Tigges

CFO, Bemodo

"They are very responsive and able to shift focus quickly as we have needed it for business reasons."
Ryan McClellan

Ryan McClellan

CEO, iLookin.com, LLC

"They [OptiSol Business Solutions] have been very friendly and they kept up with the work. They did the job as we had discussed and as everything was arranged."
Chris Gordon

Chris Gordon

CEO, Wait for me

“I can't speak enough about how pleased I am with their process; it was seamless and efficient.”
Oli Robinson

Oli Robinson

CEO, Rule:Fresh

"What impressed me the most was their willingness to go above and beyond what was asked."
Dan Talken

Dan Talken

Founder, My Equipment Library, LLC

"We're constantly impressed with their speed and quality of the development that they achieve on a consistent basis."
Ken Kisner

Ken Kisner

Molecule Corp

The best part about working with OptiSol Business Solutions is their commitment to being readily available to their partners. They’re even willing to work into late hours of the night depending on time differences. The solution they produced has already doubled efficiency levels.

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Key Features

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Ability to produce actionable insights in areas of improvements

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Visualized trends, patterns, to attain informed decision

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Ability to assess each advertisement’s performance

Trusted and Proven Engagement Model

  • A nondisclosure agreement (NDA) is signed to not disclose any sensitive information revealed over the course of doing business together.
  • Our NDA-driven process is established to keep clients’ data and IP safe and secure.
  • The solution discovery phase is all about knowing your target audience, writing down requirements, and creating a full scope for the project.
  • This helps clarify the goals, and limitations, and deliver quality products & services.
  • Our engagement model defines the project size, project development plan, duration, concept, POC etc.
  • Based on these scenarios, clients may agree to a particular engagement model (Fixed Bid, T&M, Dedicated Team).
  • The SOW document shall list details on project requirements, project management tools, tech stacks, deliverables, milestones, timelines, team size, hourly/monthly rate cards, billable hours and invoice details.
  • On signing the SOW, an official project kick-off meeting shall be initiated.
  • Our implementation approach, ecosystem, tools, solutions modelling, sprint plan, etc. shall be discussed during this meeting.

Our Award-Winning Team

A seasoned AI & ML team of young, dynamic and curious minds recognized with global awards for making significant impact on making human lives better

Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0

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50+

AI & ML
Engineers

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40+

AI & ML
Projects for
reputed Clients

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5 yrs

in AI & ML
Engineering

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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