Most marketers don’t manage with prediction, even the data-driven ones.
That is changing with automated data science that leverages no-code AI technology. The technology puts the power of cutting-edge machine learning algorithms in the hands of marketers with just a few simple clicks.
As a marketing leader, you likely get your data delivered to you in reports or dashboards. Your team worked with technologists who use business-intelligence skills and technologies to access data in your Martech stack to build those reports. Maybe your team has even built data models to provide data that answers marketing performance questions via various methods – from ad hoc reporting to dashboards and data visualizations and other data artifacts. As the team executes the marketing plan, you consult this data to determine what to do next.
For many years, this standard, basic, and now dated approach to marketing analytics was considered “state of the art.” Get a BI tool, connect to it, build reports and visualizations, and have your analysts point at things and suggest what to do. You heard data stories and made data-informed decisions, but it was still a bit like a fortune teller looking at the tea leaves trying to figure out what to do next because this data only tells you what has happened in the past. It doesn’t show you what’s ahead.
The next evolution was Data Science, or “Predictive Analytics,” as it was commonly referred to, and it represented a sea change for thinking about how data was used. Data Science is a method that uses processes, algorithms, and systems to extract knowledge and insights from data. The challenge was its complexity. Data scientists need specialized knowledge of statistics, programming, data engineering, algorithms, business, management, leadership, analytics, and data in addition to the hardware and software systems.
According to IBM, less than 13% of all data science projects reached production. In some cases, the research indicated that only 4% of data science projects had yielded expectations. The difficult thing was often finding the data scientists with the right experience and giving them the resources and space to focus on their projects, as one project could take several months to complete. By and large, companies that have less than $1B in revenue are unable to allocate the capital expenditure to hire and equip data scientists. Even in those cases, the additional challenge is meeting and keeping with the needs of the business. As Jonathan Francis, the Global Head of Analytics at PayPal and former Chief Digital Officer and Chief Analytics Officer at Starbucks said in a recent VentureBeat panel with Squark,
“I’ll never find enough data scientists or have enough budget to hire enough people to quell the demand that we have and so thinking more and more about opportunities to create, through technology augmentation, that would allow other folks in the organization to do more inferential and data science work on their own.”
No-code data science is a new approach to data science that leverages AI for business users to predict future outcomes. Tools like Squark allow marketers and analysts to load data quickly from your systems (via our intelligent connectors or API) and run their own projects to get the best possible data science predictions, in just clicks. By automating nearly, the entire data science process from data ingestion to the delivery of predictive results, Squark enables your marketing team to create predictive action plans across the entire customer lifecycle that let you know what will happen so you can make better decisions.
You can take the reports that your team spent time and money building, the ones that tell you what your customers did in the past and extract their historical value. Machine learning AI algorithms get better and better over time as they acquire more information. Squark allows you to unlock their full potential by learning from and applying those past learnings to future customer actions. The end-to-end automation is so nimble and flexible, you can quickly iterate and run new projects.
Free up your data scientists to work on the more complicated projects and empower your marketers to answer questions like:
Squark is the marketer’s next-generation predictive capability. The use-cases for Squark’s business-focused automated machine learning SaaS exist across all your marketing activities and your customer lifecycle. As easy as a spreadsheet, Squark allows the modern marketing team to augment their intelligence and deliver their own predictions without technical complexity, with high accuracy, and at a much lower cost than ever before. Squark enables you to apply prediction as you make decisions and execute.
Start actioning onto success with Squark, speak with a rep.
Squark is a no-code AI as a Service platform that helps data-literate business users make better decisions with their data. Squark is used across a variety of industries & use cases to uncover AI-driven insights from tabular and textual data, prioritize decisions, and take informed action. The Squark platform is designed to be easy to use, accurate, scalable, and secure.