Training AI models requires massive volumes of information. But not all information is the same. The data to train the model must be error-free, properly formatted and labeled, and reflective of the issue. This can be a difficult and time-consuming process. It might be challenging to debug AI models when they fail to function as planned. This is because the models are usually intricate, and various factors may contribute to a malfunction. Another potential source of mistakes is the training data used to create the models. There are always new advancements being made in the realm of artificial intelligence. Because of this, keeping up with new developments can be challenging. Furthermore, AI systems’ hardware needs are ever-increasing, making running AI models on older or less powerful machines challenging. Only some difficulties can arise when writing programs using AI components.
There are currently a variety of solutions/products on the market that can assist with the difficulties associated with coding AI structures. For example:
- No-code or low-code environments. Users of these systems can build AI models without touching a line of code. They commonly come with a graphical user interface streamlining model-making and training processes.
- Machine learning and AI hosting services. Cloud-based AI models and services are made available through these platforms. Companies without the manpower or funds to create and maintain their AI models may benefit.
- Experts in artificial intelligence. Numerous AI experts are available to assist companies in meeting the problems posed by AI. Whatever the AI needs, from learning the fundamentals to putting them into practice, it can help.
Pixis’ AI solutions enable AI-powered decision-making for cross-platform performance and growth marketing. Customers are leveraging the company’s codeless AI infrastructure, which uses purpose-built and self-evolving neural networks to meet and supersede their marketing objectives. The young company successfully closed a $100M Series C round of funding in 2022 for its robust codeless AI infrastructure , which aims to enable brands to scale all aspects of their marketing and efficiently augment their decision-making. Since its last round of funding, Pixis has introduced about 120+ new AI models to the infrastructure, putting them closer than ever to achieving their 200 proprietary AI models benchmark. These AI models provide marketers with robust plug-and-play AI products without having to write a single line of code. Also, Pixis’ distributed team of 300+ is focused on building incredibly transformative AI products to help customers get the most out of their marketing and demand generation efforts.
More than a hundred Pixis’s global customers are utilizing its AI offerings. Users of the Pixis AI infrastructure have reported monthly savings of at least 300 hours of manual labor in addition to a minimum of 10-15% reduction in customer acquisition costs. The brand promises customers of immediate AI activation without the need to write a single line of code.
Pixis’ Codeless AI Infrastructure for Performance Marketing: A Gist
Pixis’ Targeting AI, trained on billions of data points, uses cutting-edge neural networks to provide the most relevant cohorts for brands, and it gets better and better over time.
Brands can leverage user personas derived from conversion trends, behavior patterns, engagement levels, and other contextual insights to fine-tune targeting parameters and techniques. The infrastructure supports Customer Relationship Management (CRM) platforms, Attribution Platforms, design tools, and Web Analytics without hassle.
To improve targeting accuracy, Targeting AI uses unique clustering algorithms to construct highly relevant cross-platform audience cohorts and use the knowledge of the target audience to guide the marketing efforts in terms of both creativity and optimization.
Pixis’ Creative AI improves engagement and conversion rates across platforms by enabling customers to use their patented generative AI models to create engaging, relevant, and contextual visual and static assets.
Make it easy to get feedback on the effectiveness of the creative efforts so that you may fine-tune future campaigns for improved conversion rates. Increase engagement and sales by enabling persona-based creative advice across all channels. Through continuous feedback-based creative optimization, creative AI continually enhances the contextuality of communication.
Integrate contextual learning from past campaign data, seasonality-based patterns, attribution, analytics, and real-time performance data into an AI-powered marketing infrastructure that orchestrates smart decision-making across all channels.
Brands can automatically allocate and reallocate bids and resources with the infrastructure that also contains multi-objective converging AI models that detect microtrends across all channels. The goal is to maximize return on ad expenditure with real-time, performance-based budget reallocation.
Performing AI tracks and analyzing Spending and returns on ad spend (ROAS) during peak traffic times, predicting the best budget pacing techniques for future campaigns. Use hyper-contextual AI models to find the sweet spot between budgeting and optimizing for key performance indicators.
Pixis AI’s Standout Functions
● Cross-platform performance monitoring, optimization, and strategy
● Saves brands from the hassle of targeting mass audiences by enabling AI-powered segmentation and delivery.
● Quickly produces creative variants at scale
● Send timely, relevant, and contextual messages.
● With Pixis, customers could instantly scale their marketing innovation, efficiency, and optimization.
- DHL Express: 49% cost savings across campaigns with a 35% increase in CTR
- Joe & The Juice: Improved its Conversion Rate (CVR) by 14%
- CARSOME: Reduces Cost Per Lead By 40% With AI.
- Klar: Reduced its Cost Per First Transaction (CPFT) by 29%
- Madison World: Improved Performance for Multiple Clients. Madison was able to leverage Pixis’ AI infrastructure to scale campaign efforts for their multiple clients without breaking the bank.
- OMG Media: Improved Performance for Multiple Clients. Pixis’ AI infrastructure aided OMG media agency in helping its diverse clients achieve their unique goals simultaneously across platforms.
- Skoda: 35% improvement in CPL across Octavia and Superb models combined
Note: Thanks to the Pixis AI team for the thought leadership/ Educational article above.
Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.