AI is transforming how businesses approach their digital marketing budgeting and forecasting processes.
Companies can develop robust forecasting and budgeting models that focus on data-driven decisions.
This approach enables customized strategies that align with specific business goals and can be adjusted based on organizational needs and channels.
AI is a key driver for transformation.
Up to 86% of organizations implementing generative AI report seeing revenue growth of 6% or more in their total annual company revenue, per a Google Cloud report.
This article covers how to leverage AI with the right data to come up with forecasting and budgeting prioritization, specifically for digital marketing efforts.
Below are the six steps to craft a model that aligns with your unique business needs.
Step 1: Define business goals, objectives and KPIs
This step is divided into two parts: setting goals and identifying key performance indicators (KPIs).
Clearly articulate business objectives
Specify the overall business objectives, such as increasing revenue, enhancing brand awareness, generating leads or boosting engagement rates.
Identify specific KPIs
Determine the relevant KPIs for each targeted channel, such as views, conversion rates or cost per acquisition (CPA).
Goals, KPIs, strategies alignment
After aligning on goals and KPIs, analyze historical trends to identify channels and strategies that can contribute toward achieving the goals.
Step 2: Trends, customer journey and channels
Channel distribution analysis
Gather historical data: Collect data on marketing spend, revenue and key performance indicators for each channel.
Identify performance levels: Analyze the data to determine which channels are high-performing and which are low-performing.
Calculate ROI: Know the return on investment (ROI) and other relevant metrics for each channel.
Market and trends analysis
Identify industry and market trends: Examine industry trends, including market demand and supply patterns for the upcoming year and the previous year.
Assess consumer behavior and emerging technologies: Identify shifts in consumer behavior and emerging technologies, such as AI, virtual agents and the shift to mobile platforms.
Analyze competitor activity: Evaluate competitor performance across different channels.
Search trends and customer journey
Analyze customer discovery channels: Determine how your customers are finding your business. While new marketing strategies may seem promising, ensure these channels align with your customer’s journey.
Use Google Search Console and Google Analytics: Leverage tools like search console and analytics to understand customer search trends and compare them with industry-wide search changes.
Evaluate content formats: Assess whether your business is gaining traction through videos, AI-generated overviews or images and compare these results with industry and competitor benchmarks.
Step 3: Data and infrastructure
Evaluate the existing technology stack
Assess the technology infrastructure for its ability to centralize data, maintain data quality and ensure data security.
Centralize data
Consolidate all data from various channels and touchpoints into a single location, such as a data lake. Test if data can be used to run analysis and reporting.
Data cleaning and pre-processing
With all the data collected, the next step is to prepare it for forecasting and budgeting models.
Begin by cleaning and organizing the data, focusing on the most relevant data points aligned with business goals and KPIs.
Ensure data accuracy and consistency by removing outliers and addressing any inconsistencies.
Conduct exploratory data analysis to identify patterns and correlations.
Step 4: Forecasting
Forecasting is key to budgeting because it helps manage risks, seize opportunities, optimize resources and make smart investment decisions.
The following machine learning and language-based models can be used to generate these forecasts:
ARIMA (Auto Regressive Integrated Moving Average)
Combines autoregression and moving average.
Flexible for various time series patterns.
SARIMA, or seasonal ARIMA, accounts for seasonal fluctuations.
Prophet
Developed by Facebook.
Decomposes time series data into trend, seasonality and holiday effects.
Works best with time series with strong seasonal effects and multiple seasons of historical data.
Chronos (language-based model)
Developed by Amazon.
A family of pretrained time series forecasting models based on language model architectures.
A time series is transformed into a sequence of tokens via scaling and quantization and a language model is trained on these tokens using the cross-entropy loss.
Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context.
Consider using Claude 3.5 Sonnet by Anthropic to easily generate Python code for implementing the forecasting models.
Step 5: Budgeting
Determining the optimum channel allocation
Determine the most suitable budget allocation method based on business objectives, such as percentage of revenue or a fixed amount per channel.
Consider factors like channel maturity, potential ROI and customer and market trends.
Use statistical techniques such as Linear Regression to generate a market mix model that optimizes the budget allocation across channels to meet your business goal.
Regular monitoring and optimization
Continuously track channel performance against budget and KPIs.
Identify underperforming channels and reallocate budget accordingly.
Optimize campaigns based on real-time data and insights.
Step 6: Use cases
Finally, create specific use cases for each step of your marketing plan. For example:
“As the chief marketing officer of an upscale hotel, I want to increase online revenue by 20% year over year. To help achieve this goal, recommend the best budget allocation across digital channels.”
Solution steps
Define business goals and KPIs
Goal – Increase revenue by 20% overall
KPIs – Revenue
Channel distribution, ROI, revenue and conversions