We are building Sulie with a mission to unleash the power of predictive AI across marketing and product teams. Despite the numerous advancements in machine learning over the past years, such as the emergence of the transformers architecture, it is surprising that still a significant number of companies fail to embrace models used for predicting business-critical outcomes, whose foundations emerged decades ago.
On the other hand, existing platform providers restrict customers in terms of predictive scope and are too costly for small to medium-sized businesses. More importantly, these platforms place the responsibility of predictive modeling on data science or engineering teams, hindering non-technical teams like marketing from moving fast and autonomously.
Armed with knowledge, and in the context of marketing, predictive modeling enables you to make rapid campaign optimisation decisions without missing a heartbeat. Nip unsuccessful campaigns in the bud, or quickly double down on investment that can drive even better results.
We see Sulie as the driving force that empowers every team, regardless of their technical skills, with the predictive modelling superpowers, empowering data-driven business growth. How will we achieve this, and what are the key innovative aspects of our platform? To better grasp that, let's first delve into the most common issues why companies struggle to integrate predictive models within their processes and workflows.
Lack of engineering power
A recent study conducted by Wakefield Research, surveying 250 U.S. marketing executives at companies with a minimum annual revenue of $100M, revealed that 49% of marketers do not utilize predictive analytics for individual-level predictions. This is primarily due to the high cost of manual data science, lack of resources, and limited technical knowledge within the marketing and analytics team. IBM's Global AI Adoption Index also reported similar findings, highlighting that the main barriers to AI adoption are limited engineering resources and the high cost associated with model development. This lack of predictive power is particularly evident in small to medium-sized enterprises, which have more restricted budgets and resource constraints.
Predictive modelling is hard
Building and continually improving predictive models, such as those for lifetime value (LTV), return over ad spend (ROAS), or upsell probability, is challenging, time-consuming, and expensive. Once built, each predictive model needs to be carefully monitored for changes due to seasonality, shifts in user behaviour caused by feature or product releases, and variations brought about by changes in marketing campaigns.
Companies typically have to manage pipelines for data collection and processing, feature engineering, conducting training experiments to determine the optimal architecture or set of model parameters, and establishing infrastructure for model serving, among other tasks. Supporting these activities across all areas requires dedicated teams of skilled data scientists and machine learning engineers, resulting in a significant budget allocation and requiring months to complete.
AutoML to save the day?
The premise of AutoML is straightforward. Given an input dataset, such as user engagement or transaction data, the system automatically selects the most appropriate model architecture, tunes the parameters, and provides the end users with a model artifact that performs the best on a given training dataset.
However, most of today's predictive analytics platforms with AutoML capabilities were designed for technical users. For example, marketing teams who want to predict ROAS (Return on Advertising Spend) for a specific campaign still rely on engineers to prepare the training dataset, perform feature engineering, and train the model using these AutoML platforms. This underscores the importance of strong collaboration between non-technical and engineering teams to effectively fulfill predictive requests. However, achieving this synergy requires more than just processes; it must be driven by a data-driven culture, which many companies often struggle with.
Power to the people
We have set a goal to build a SaaS platform that will democratise predictive modeling across marketing and product teams. With that being said, we believe that Sulie will be the catalyst that infuses marketing and product teams with predictions of business metrics they care about, which in turn, will be used to:
Foresee the performance of campaigns to optimise them early on, allowing teams to cut unsuccessful campaigns in the bud, or quickly double down budgets on campaigns projected to perform on or above set benchmarks.
Automatically optimise campaigns by feeding ad networks with the predictive signals.
Personalise user journeys based on predicted metrics such as LTV, conversion, upsell or churn probability through variances in content or prices.
Each team, regardless of their level of technical expertise, will be able to independently train predictive models using plain natural language. Think of it as a Google Search like interface for asking questions about predictions or instructing Sulie to build a predictive model. Once you define the predictive problem, such as day 60 LTV for a specific cohort or campaign, our platform automatically reasons about the required data, joins and aggregates it from multiple data sources like Redshift and Singular, performs feature engineering, and trains the models.
In essence, our objective is to make traditional ML workflows and processes more affordable, efficient, and accessible to everyone, regardless of their levels of technical expertise, thereby saving companies valuable financial and time resources. With all of that being said, stay tuned for more product updates and don’t forget to sign up for our waitlist at sulie.co.
We are building Sulie with a mission to unleash the power of predictive AI across marketing and product teams. Despite the numerous advancements in machine learning over the past years, such as the emergence of the transformers architecture, it is surprising that still a significant number of companies fail to embrace models used for predicting business-critical outcomes, whose foundations emerged decades ago.
On the other hand, existing platform providers restrict customers in terms of predictive scope and are too costly for small to medium-sized businesses. More importantly, these platforms place the responsibility of predictive modeling on data science or engineering teams, hindering non-technical teams like marketing from moving fast and autonomously.
Armed with knowledge, and in the context of marketing, predictive modeling enables you to make rapid campaign optimisation decisions without missing a heartbeat. Nip unsuccessful campaigns in the bud, or quickly double down on investment that can drive even better results.
We see Sulie as the driving force that empowers every team, regardless of their technical skills, with the predictive modelling superpowers, empowering data-driven business growth. How will we achieve this, and what are the key innovative aspects of our platform? To better grasp that, let's first delve into the most common issues why companies struggle to integrate predictive models within their processes and workflows.
Lack of engineering power
A recent study conducted by Wakefield Research, surveying 250 U.S. marketing executives at companies with a minimum annual revenue of $100M, revealed that 49% of marketers do not utilize predictive analytics for individual-level predictions. This is primarily due to the high cost of manual data science, lack of resources, and limited technical knowledge within the marketing and analytics team. IBM's Global AI Adoption Index also reported similar findings, highlighting that the main barriers to AI adoption are limited engineering resources and the high cost associated with model development. This lack of predictive power is particularly evident in small to medium-sized enterprises, which have more restricted budgets and resource constraints.
Predictive modelling is hard
Building and continually improving predictive models, such as those for lifetime value (LTV), return over ad spend (ROAS), or upsell probability, is challenging, time-consuming, and expensive. Once built, each predictive model needs to be carefully monitored for changes due to seasonality, shifts in user behaviour caused by feature or product releases, and variations brought about by changes in marketing campaigns.
Companies typically have to manage pipelines for data collection and processing, feature engineering, conducting training experiments to determine the optimal architecture or set of model parameters, and establishing infrastructure for model serving, among other tasks. Supporting these activities across all areas requires dedicated teams of skilled data scientists and machine learning engineers, resulting in a significant budget allocation and requiring months to complete.
AutoML to save the day?
The premise of AutoML is straightforward. Given an input dataset, such as user engagement or transaction data, the system automatically selects the most appropriate model architecture, tunes the parameters, and provides the end users with a model artifact that performs the best on a given training dataset.
However, most of today's predictive analytics platforms with AutoML capabilities were designed for technical users. For example, marketing teams who want to predict ROAS (Return on Advertising Spend) for a specific campaign still rely on engineers to prepare the training dataset, perform feature engineering, and train the model using these AutoML platforms. This underscores the importance of strong collaboration between non-technical and engineering teams to effectively fulfill predictive requests. However, achieving this synergy requires more than just processes; it must be driven by a data-driven culture, which many companies often struggle with.
Power to the people
We have set a goal to build a SaaS platform that will democratise predictive modeling across marketing and product teams. With that being said, we believe that Sulie will be the catalyst that infuses marketing and product teams with predictions of business metrics they care about, which in turn, will be used to:
Foresee the performance of campaigns to optimise them early on, allowing teams to cut unsuccessful campaigns in the bud, or quickly double down budgets on campaigns projected to perform on or above set benchmarks.
Automatically optimise campaigns by feeding ad networks with the predictive signals.
Personalise user journeys based on predicted metrics such as LTV, conversion, upsell or churn probability through variances in content or prices.
Each team, regardless of their level of technical expertise, will be able to independently train predictive models using plain natural language. Think of it as a Google Search like interface for asking questions about predictions or instructing Sulie to build a predictive model. Once you define the predictive problem, such as day 60 LTV for a specific cohort or campaign, our platform automatically reasons about the required data, joins and aggregates it from multiple data sources like Redshift and Singular, performs feature engineering, and trains the models.
In essence, our objective is to make traditional ML workflows and processes more affordable, efficient, and accessible to everyone, regardless of their levels of technical expertise, thereby saving companies valuable financial and time resources. With all of that being said, stay tuned for more product updates and don’t forget to sign up for our waitlist at sulie.co.
We are building Sulie with a mission to unleash the power of predictive AI across marketing and product teams. Despite the numerous advancements in machine learning over the past years, such as the emergence of the transformers architecture, it is surprising that still a significant number of companies fail to embrace models used for predicting business-critical outcomes, whose foundations emerged decades ago.
On the other hand, existing platform providers restrict customers in terms of predictive scope and are too costly for small to medium-sized businesses. More importantly, these platforms place the responsibility of predictive modeling on data science or engineering teams, hindering non-technical teams like marketing from moving fast and autonomously.
Armed with knowledge, and in the context of marketing, predictive modeling enables you to make rapid campaign optimisation decisions without missing a heartbeat. Nip unsuccessful campaigns in the bud, or quickly double down on investment that can drive even better results.
We see Sulie as the driving force that empowers every team, regardless of their technical skills, with the predictive modelling superpowers, empowering data-driven business growth. How will we achieve this, and what are the key innovative aspects of our platform? To better grasp that, let's first delve into the most common issues why companies struggle to integrate predictive models within their processes and workflows.
Lack of engineering power
A recent study conducted by Wakefield Research, surveying 250 U.S. marketing executives at companies with a minimum annual revenue of $100M, revealed that 49% of marketers do not utilize predictive analytics for individual-level predictions. This is primarily due to the high cost of manual data science, lack of resources, and limited technical knowledge within the marketing and analytics team. IBM's Global AI Adoption Index also reported similar findings, highlighting that the main barriers to AI adoption are limited engineering resources and the high cost associated with model development. This lack of predictive power is particularly evident in small to medium-sized enterprises, which have more restricted budgets and resource constraints.
Predictive modelling is hard
Building and continually improving predictive models, such as those for lifetime value (LTV), return over ad spend (ROAS), or upsell probability, is challenging, time-consuming, and expensive. Once built, each predictive model needs to be carefully monitored for changes due to seasonality, shifts in user behaviour caused by feature or product releases, and variations brought about by changes in marketing campaigns.
Companies typically have to manage pipelines for data collection and processing, feature engineering, conducting training experiments to determine the optimal architecture or set of model parameters, and establishing infrastructure for model serving, among other tasks. Supporting these activities across all areas requires dedicated teams of skilled data scientists and machine learning engineers, resulting in a significant budget allocation and requiring months to complete.
AutoML to save the day?
The premise of AutoML is straightforward. Given an input dataset, such as user engagement or transaction data, the system automatically selects the most appropriate model architecture, tunes the parameters, and provides the end users with a model artifact that performs the best on a given training dataset.
However, most of today's predictive analytics platforms with AutoML capabilities were designed for technical users. For example, marketing teams who want to predict ROAS (Return on Advertising Spend) for a specific campaign still rely on engineers to prepare the training dataset, perform feature engineering, and train the model using these AutoML platforms. This underscores the importance of strong collaboration between non-technical and engineering teams to effectively fulfill predictive requests. However, achieving this synergy requires more than just processes; it must be driven by a data-driven culture, which many companies often struggle with.
Power to the people
We have set a goal to build a SaaS platform that will democratise predictive modeling across marketing and product teams. With that being said, we believe that Sulie will be the catalyst that infuses marketing and product teams with predictions of business metrics they care about, which in turn, will be used to:
Foresee the performance of campaigns to optimise them early on, allowing teams to cut unsuccessful campaigns in the bud, or quickly double down budgets on campaigns projected to perform on or above set benchmarks.
Automatically optimise campaigns by feeding ad networks with the predictive signals.
Personalise user journeys based on predicted metrics such as LTV, conversion, upsell or churn probability through variances in content or prices.
Each team, regardless of their level of technical expertise, will be able to independently train predictive models using plain natural language. Think of it as a Google Search like interface for asking questions about predictions or instructing Sulie to build a predictive model. Once you define the predictive problem, such as day 60 LTV for a specific cohort or campaign, our platform automatically reasons about the required data, joins and aggregates it from multiple data sources like Redshift and Singular, performs feature engineering, and trains the models.
In essence, our objective is to make traditional ML workflows and processes more affordable, efficient, and accessible to everyone, regardless of their levels of technical expertise, thereby saving companies valuable financial and time resources. With all of that being said, stay tuned for more product updates and don’t forget to sign up for our waitlist at sulie.co.