Too much marketing is based on guesses not backed by data. Paid tactics, like pay-per-click (PPC) and social media advertising, can burn through your budget when guesses are wrong. How can you use data to make marketing more predictable to forecast performance and adjust to shifts in trends to increase your ROI?

Today’s guest is John Readman from BOSCO, a digital analytics and predictive modeling platform for retailers and eCommerce companies. He discusses what it takes for predictable marketing to be successful. It involves understanding historical data, performance, and trends across a client's channels.

 

Some of the highlights of the show include:

• What is predictable marketing? Getting all data in one place on an ongoing basis
• Why should marketers make decisions driven by data, not gut instinct/intuition?
• Data and Decisions: Depend on volume, understanding data to base decisions
• Challenges: Digital marketing data is used to scale ROI in one particular channel
• COVID Comparisons: Causal effect of supply and demand during the pandemic
• BOSCO: Helps marketers predict future w/ machine learning, Bayesian statistics
• Out-of-Date Numbers: Forecasting runs scenarios, planning, and model analysis
• Different Data Sources: Connect platforms to quickly predict what could be done
• Wasted Budget? Run and identify data models that are hugely, scarily accurate
• Two Key Metrics: Cost per acquisition and understanding that by channel
• Clients/Conversations: People make decisions emotionally, justify them with data
• Control: People get nervous about not doing things the traditional way
• Predictive Analytics Platform/Practice: Get buy-in by leading conversation with potential results, starting small, and using data to quantify progress and success

 

Links:

• John Readman on LinkedIn
• BOSCO
• Ben Sailer on LinkedIn
• CoSchedule

 

Quotes from John Readman:

“If we've got the right data in the right format, and we understand what is going on around certain targets, what makes it predictable is understanding the metrics and the outputs we are trying to achieve.”

“Fundamentally, why do people need to make data-driven decisions to really explain where they're spending their money, where are they getting their ROI, and then how can they scale it?”

“It all starts with getting all your data organized in one place, then looking at what I am willing to pay to acquire a customer, and then maybe looking at customer lifetime value.”

“The thing to stand out will be a better proposition, a better product, and a better promotion, which is sort of the traditional marketing going around in a full circle.”