[ad_1]
Using artificial intelligence, you can now predict user behavior using a low-code platform.
That’s the promise of Pecan AI, an AI-powered predictive marketing platform. Pecan uses state-of-the-art learning techniques to model marketing mix performance, lifetime value, churn and more.
We spoke with Zofar Bronfman, CEO of Pecan, to learn about this AI-powered marketing solution.
Describe Pecan in one sentence or statement.
Pecan helps marketing, revenue and data teams predict customer behaviors that impact revenue-generating KPIs.
How does Pecan use artificial intelligence in its products?
Cutting-edge machine learning technology is at the heart of Pecan AI. Machine learning underpins every aspect of our low-code predictive analytics platform. It provides automated data preparation, feature engineering, model creation and selection, and model deployment and monitoring. We have developed machine learning techniques that can solve critical business problems. We are constantly improving the platform based on the experience of thousands of models among users in various industries.
What are the primary marketing use cases for your AI-powered solutions?
Marketing mix modeling, predictive lifetime value, customer churn, demand forecasting
What makes your AI-powered solution smarter than traditional approaches and products?
Most marketing-oriented analytics tools only look back at what happened in the past. It is much more productive to use data to predict what will happen in the future. Some companies have in-house data science teams that can build predictive models, but most marketers don’t have access to effective advanced analytics and predictive modeling.
Pecan’s low-code platform brings machine learning directly to marketing teams. These teams can leverage their existing analytical skills and domain expertise and don’t have to rely on data scientists. Instead, they can use SQL skills, their marketing data analysts have already moved from analyzing the past to predicting the future with AI.
Pecan can help solve common marketing challenges with AI. For example, marketing teams can use our marketing mix modeling to understand and improve channel performance. Another example is predicting customer lifetime value for faster, better decisions about their campaigns, even within the first day or two of launch.
Are there any minimum requirements for marketers to get value from your AI-powered technology? (eg data, list size, etc.)
Data requirements depend on specific user models and goals. In general, you may need at least six months of behavioral, attribute, and/or demographic data to build reliable models. Other marketing data and product key data may also be useful. We help clients identify and evaluate relevant data sources that our platform can use to generate accurate, actionable forecasts.
Who are your ideal customers in terms of company size and industry?
1. Enterprise/Traditional B2C with direct customer relationship. e.g. Retail trade, TV company, consumer services; $100 million in annual revenue.
2. Digital natives, e.g. Mobile application/game. Pure play ecomm, D2C; $25m+ in annual revenue or $50m+ in VC funding.
How do you see the limitations of AI as it exists today?
Many businesses want to use AI to generate predictions, but their data is not ready to use AI models. Predictive models need clean data that is properly prepared to produce accurate results.
Additionally, many AI innovations focus on computer vision, NLP, and generative AI. While these are fantastic, they don’t address the core data type of most businesses: routine spreadsheet data generated by various business processes.
Traditionally, data engineers and data scientists spend a significant amount of time manually cleaning and preparing the data in this table. To eliminate manual effort and make AI more accessible, Pecan focused on automating data preparation and feature engineering. This capability, among others, helps marketing and other business teams gain AI capabilities easily and quickly. But there is still more to do to unlock the value of spreadsheet data for businesses. There is plenty of room for constant innovation.
What do you see as the future potential of AI in marketing?
AI has enormous potential as a partner for marketers in various fields. Generative AI can facilitate content creation. Natural language processing can interpret online interactions. Conversational AI can address customer queries and offer recommendations. And of course, predictive analytics using artificial intelligence can provide deeper insights into audience and consumer behavior; provide guidance for more personalized interactions with specific audiences and customer segments; and helps evaluate marketing messages, campaigns and channels.
While our tools to accomplish these tasks will only improve, it’s important to remember that marketers will always need to provide creative energy and expert guidance to AI. Ideally, this will reduce the tedious tasks that marketers enjoy less and allow them to focus on the strategy and creativity that drives their success.
Any other thoughts on AI in marketing?
For marketers starting with AI, it’s best to focus on a specific problem. “Using AI” sounds like a good goal, but the reality is that simply using AI won’t actually solve your team’s challenges or help you achieve your goals.
Instead, you should specify a way to use AI to solve a specific problem. For example, you may find it difficult for your team to understand how a variety of marketing channels contribute to your overall ROI. This challenge is what today’s marketing mix modeling, guided by machine learning, can help solve. Staying focused on solving problems and using action-oriented AI tools can help you quickly achieve success with AI and then continue to expand your use based on what you learn from that success.
[ad_2]
Source link