Selecting the right time series forecasting model can save your marketing budget and boost campaign performance. Here’s how to do it:
- Understand Your Data: Analyze trends, seasonality, and external factors. Clean your dataset and split it into training and testing sets.
- Align Models with Goals: Use ARIMA or SARIMA for simple trends, machine learning models like Prophet or LSTM for complex patterns, and hybrid models for a mix of both.
- Consider Resources: Match the model’s complexity to your data size and computational power.
- Test and Validate: Use metrics like MAE, RMSE, and MAPE. Validate with cross-validation and dynamic forecasting methods.
| Model Type | Best For | Complexity | Data Needs |
|---|---|---|---|
| ARIMA/SARIMA | Simple trends, seasonality | Low | Low |
| Prophet/LSTM | Complex patterns, missing data | High | High |
| Hybrid/Ensemble | Mixed patterns | Moderate | Moderate |
Key takeaway: Start simple, test thoroughly, and scale up complexity as needed. A well-chosen model ensures smarter budgeting and better campaign results.
Machine Learning for Time-Series Forecasting With Python – Francesca Lazzeri
Key Factors for Model Selection
Choosing the right forecasting model boils down to three main considerations: the characteristics of your campaign data, your business objectives, and the resources at your disposal. The right choice not only improves forecast accuracy but also helps refine budgeting and campaign strategies.
Analyze Your Campaign Data
Understanding the nuances of your data is the first step in selecting the right model. Time series analysis, for example, works with evenly spaced data points collected over time to identify patterns and predict future outcomes. To get reliable results, your dataset should cover at least two forecast periods.
A good practice is to split your data into training and testing sets. This lets you assess how well a model performs on unseen data before using it for real-world decisions. Additionally, be sure to address any outliers or missing values. Anomalies like unexpected spikes in click-through rates or drops in conversions due to system outages can skew forecasts if left unchecked.
Next, evaluate your data for stationarity (a consistent mean and variance) and seasonal patterns. Decomposing the time series can reveal trends and residuals, which different models handle in various ways. Don’t forget to consider external variables that could impact your campaign. For instance, if you’re in the automotive industry, tracking car sales data alongside your campaign metrics might improve forecast accuracy, as these variables often correlate.
Finally, feature engineering can enhance your model’s performance. Adding elements like day of the week, month, lag values, or rolling averages provides the context needed for more accurate predictions. Once you’ve thoroughly analyzed your data, you can align these insights with your campaign’s goals.
Match Models to Campaign Goals
Your choice of forecasting model should align with your strategic objectives. For example, time series models are ideal for identifying trends, causal models explore relationships between variables, and judgmental forecasting can be helpful for entering new markets.
Short-term decisions, like optimizing next week’s ad spend, require models that quickly capture immediate patterns. On the other hand, long-term planning, such as forecasting an annual budget or strategizing for market expansion, calls for models that can identify broader trends and handle extended time horizons.
Define clear, measurable goals for your forecasts. Whether you’re aiming to predict daily website traffic within a 5% error margin, estimate quarterly lead generation with 90% confidence, or calculate the ROI of boosting your social media budget, each objective demands specific model capabilities and performance benchmarks.
The timeline of your decision-making process also matters. Some campaigns may need daily updates based on fresh forecasts, while others operate on monthly or quarterly cycles. Choosing a forecasting horizon that matches your planning cycles ensures the predictions remain actionable.
Lastly, consider using scenario planning to prepare for best- and worst-case outcomes. Models that support multiple scenarios – such as changes in economic conditions, competitive shifts, or new regulations – offer more robust guidance for strategic decisions.
Consider Data Volume and Complexity
The size and complexity of your data play a significant role in determining the best forecasting model. You’ll need to balance accuracy with computational efficiency. Simpler models are easier to compute and interpret, while more advanced ones can capture complex, non-linear relationships but may risk overfitting.
For smaller datasets, simple models like linear regression or moving averages might be sufficient. For instance, analyzing six months of weekly email metrics might not require anything more complex than these straightforward approaches, which are also easier to explain to stakeholders.
However, larger datasets with more variables often benefit from advanced machine learning models. These can uncover subtle patterns that simpler methods might miss. A good example is the UK’s CHAPS payment system, where ensemble models reduced forecasting errors by 13% and explained 97% of daily variability, outperforming traditional linear regression.
It’s often a good idea to start with a simple model and evaluate its performance before moving to more complex options. This helps establish a baseline, making it easier to determine whether the added complexity is worth it. Interestingly, in some cases, simpler neural models can deliver predictions nearly as accurate as more complex ones while requiring far less computational power.
Keep in mind that overfitting can be a problem with complex models, especially when training data contains noise or differs significantly from testing data. Studies show that using more than two years of training data often does little to improve forecast accuracy, proving that more data isn’t always better.
As Tom Davies of the Bank of England’s Payments Strategy Division wisely puts it:
"Forecasting must be both accurate and intelligible, lest its value be lost in opaque conclusions".
Ultimately, your model should strike a balance between predictive accuracy and practical usability, ensuring it meets the unique needs of your campaign.
Common Time Series Models
Once you’ve analyzed your data and set your goals, it’s time to explore the different forecasting models available for campaign time series predictions. Each model offers distinct advantages depending on your forecasting needs.
Statistical Models
Statistical models like ARIMA (AutoRegressive Integrated Moving Average) and SARIMA (Seasonal ARIMA) are the cornerstone of traditional time series forecasting. These models are excellent for identifying linear trends and seasonal patterns in campaign data, making them ideal for straightforward forecasting scenarios.
- ARIMA is particularly effective for data with consistent patterns. It’s easy to interpret and doesn’t require heavy computational power, which makes it a go-to option for short-term forecasting of stationary time series. For instance, if you’re monitoring weekly email open rates that follow predictable trends, ARIMA can quickly spot those patterns and deliver reliable short-term forecasts.
- SARIMA builds on ARIMA by incorporating seasonal variations. This makes it a great choice for campaigns with recurring seasonal fluctuations, such as holiday retail promotions or summer lulls in B2B campaigns.
That said, statistical models have their limitations. ARIMA, for example, struggles with missing data and requires stationary data to function effectively. Additionally, these models may fall short when dealing with complex, non-linear relationships in modern digital marketing campaigns. However, their simplicity and low computational demands make them accessible for teams with limited technical expertise, and their underlying principles are easier to explain to stakeholders.
For more intricate data patterns, machine learning models may offer better solutions.
Machine Learning Models
Machine learning models like Facebook Prophet and LSTM (Long Short-Term Memory) networks are designed to handle the complexities that traditional models often miss. These tools are particularly adept at capturing non-linear relationships and long-range dependencies.
- Facebook Prophet is popular for its ease of use and reliability in real-world applications. It handles missing data, trend shifts, and outliers with ease. Prophet is especially effective with seasonal data, automatically detecting multiple seasonal patterns in your campaign metrics. Whether you’re analyzing daily ad spend, weekly social media engagement, or monthly lead generation cycles, Prophet can identify and predict overlapping seasonal trends.
- LSTM networks are a more advanced option for complex forecasting challenges. They excel at identifying patterns in long, intricate data sequences, even when faced with irregular time lags. For example, a study on road traffic injury predictions in Northeast China from 2015 to 2020 found that LSTM delivered the highest accuracy compared to Prophet and SARIMA. However, LSTM models require large datasets and significant computational power. For campaigns spanning multiple channels or those with detailed attribution models, this investment can lead to more precise forecasts.
Hybrid and Ensemble Models
If single-model approaches aren’t cutting it, hybrid and ensemble methods can take your forecasting to the next level. These methods combine the strengths of statistical and machine learning models to address their individual weaknesses.
- Hybrid models blend the capabilities of statistical and machine learning approaches. For example, a hybrid ARIMA-LSTM model outperformed standalone ARIMA and LSTM models, achieving an RMSE of 13.252 and a MAPE of 0.072 – significantly better than ARIMA’s RMSE of 16.745 and MAPE of 0.121, or LSTM’s RMSE of 21.757 and MAPE of 0.150.
- Ensemble methods take a different route by combining multiple forecasts using techniques like averaging or weighted combinations. This approach not only enhances overall accuracy but also reduces the risk of relying on a single model.
Hybrid models are particularly useful when you need to capture both linear seasonal trends and more complex, non-linear behaviors in your campaign data. This dual capability makes them a powerful choice for campaigns that require a balance between simplicity and precision.
| Model Type | Best For | Complexity | Data Requirements |
|---|---|---|---|
| Statistical (ARIMA/SARIMA) | Simple trends, seasonal patterns | Low | Low |
| Machine Learning (Prophet/LSTM) | Complex patterns, missing data | High | High |
| Hybrid/Ensemble | Comprehensive forecasting | Moderate | Moderate |
Choosing the right model depends on your campaign’s specific needs, available resources, and desired accuracy. A practical approach is to start with simpler models and move to more advanced options as your forecasting requirements evolve.
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Model Testing and Validation
After selecting a model, it’s essential to test and validate its forecasts to ensure they hold up in real-world applications. This process helps separate models that perform well in theory from those that deliver reliable results when applied.
Forecasting Performance Metrics
Forecast accuracy isn’t captured by a single metric. Instead, use a variety of measures to get a comprehensive view of performance:
- Mean Absolute Error (MAE): This metric calculates the average error in the same unit as your data. For example, an MAE of $50 in daily ad spend means the forecast is off by $50 on average. It’s simple and easy to interpret [30,31,32].
- Root Mean Squared Error (RMSE): RMSE measures the standard deviation of forecast errors, emphasizing larger mistakes. Like MAE, it uses the same units as your data, making it straightforward to understand. For instance, if you’re predicting monthly lead generation and RMSE is 25 leads, it reflects the typical variation in your prediction errors. However, RMSE is especially sensitive to outliers [30,32,33].
- Mean Absolute Percentage Error (MAPE): This expresses error as a percentage of actual values. A MAPE of 15% means forecasts are typically off by 15% of the real value. However, this metric can be unreliable when actual values are close to zero, as it penalizes underestimations heavily [30,32].
- Weighted Absolute Percentage Error (WAPE): WAPE is particularly helpful for low-volume campaigns. It weights errors relative to the total true values, making it useful for smaller or niche campaigns like new product launches where traditional metrics may not offer an accurate picture.
To complement these statistical measures, consider business-specific metrics. For instance, tracking the accuracy of Customer Acquisition Cost (CAC) forecasts or monitoring Return on Ad Spend (ROAS) predictions can provide practical insights into how well your forecasts align with revenue goals.
Once you’ve quantified errors with these metrics, validate them using rigorous testing methods.
Campaign Forecasting Validation Methods
Time series cross-validation is a reliable way to test forecasts by evaluating them in chronological order. For example, if you have two years of data, train your model on the first 18 months and test it on the remaining six months. The test set should cover at least the maximum forecast horizon and typically accounts for about 20% of your total data [38,40].
Dynamic multi-step forecasting is another effective approach. It tests how well your model predicts multiple future periods, which is crucial for campaign planning. A model that performs well for one-week forecasts might falter with monthly predictions, so this method provides a more realistic evaluation.
Tailor your validation to different campaign scenarios:
| Scenario | Description |
|---|---|
| Best-case scenario | Focus on high-performing periods to see how the model handles optimal conditions. |
| Worst-case scenario | Include low-performing periods to test the model’s resilience. |
| Seasonal scenario | Assess the model’s ability to handle recurring trends and holiday impacts. |
These techniques help ensure your model aligns with your campaign’s strategic goals.
Reserve a portion of recent data as a holdout set for final testing. This step evaluates how well the model performs on entirely fresh data before going live. Additionally, backtesting and visual inspections (like spotting negative conversion rates or unrealistic click-through rates) can flag potential issues.
When selecting a model, one common practice is to choose the one with the smallest RMSE based on time series cross-validation.
Ongoing Updates and Data Validation
Models need regular updates to stay accurate. Refresh them monthly with the latest campaign data and market trends. Compare forecasted indicators – such as monthly ROI, campaign costs, and revenue – to actual results to refine your approach over time.
Statistical tests can also help in choosing between models. For example:
- Friedman test: Compares the medians of error distributions across multiple models.
- Wilcoxon signed-ranks test: Useful for comparing two specific approaches.
Robust data validation is another critical step. Set rules to flag unusual inputs. For instance, if a campaign budget suddenly jumps from $5,000 to $50,000, investigate the anomaly before proceeding. Systematic checks of inputs, calculations, and outputs ensure data reliability throughout your forecasting process.
Putting Forecasting Models to Work
Once your forecasting model is validated, the next challenge is integrating it into your marketing workflow. This shift from testing to active use demands careful data preparation and smart deployment decisions to ensure your forecasts deliver actionable insights.
Prepare Data for Forecasting
Start by bringing all your campaign data together in one place. Marketing teams often juggle data from platforms like Google Ads, Facebook Ads Manager, email tools, and analytics dashboards. Centralizing this information gives you a complete view, which is essential for accurate predictions.
Define your forecasting goals clearly. Are you tracking monthly ad spend, weekly conversion rates, or quarterly customer acquisition costs? Knowing what you want to forecast helps you structure and clean your data effectively.
Pay close attention to data quality. Clean your data by addressing anomalies, filling in missing values, and ensuring consistency over time. These steps are crucial for improving the accuracy of your forecasts.
Attribution windows are a key consideration when preparing your data. For example, if your sales cycle takes 30 days but you’re using a 7-day attribution window, you might miss conversions that happen later in the customer journey. Align attribution windows with how your customers actually behave to avoid skewed forecasts.
If you’re using Multi-Touch Attribution (MTA), the process becomes even more detailed. MTA assigns credit to every touchpoint in the buyer’s journey based on its role in driving conversions. When incorporating MTA data, ensure you’re applying the same attribution model consistently across all historical data.
Aim for at least 2-3 years of monthly data to build reliable forecasts. This timeframe helps capture trends like seasonality, market cycles, and shifts in campaign performance. If you’re working with weekly metrics, you’ll need even more data points to achieve similar reliability.
Once your data is clean and complete, you’re ready to put your forecasting model to work. Each step in this process is critical to ensuring your forecasts guide meaningful campaign decisions.
Run Your Forecasting Model
Choose the right type of forecasting for your needs. Real-time forecasting is ideal for performance campaigns that require immediate budget adjustments, while batch forecasting is better suited for strategic planning, such as monthly budget allocations.
Keep your models updated on a regular schedule. Monthly updates work well for most campaigns, but some businesses may benefit from weekly refreshes. Each update should incorporate the latest data and reflect current market trends to maintain accuracy.
Standardize your forecasting processes across campaigns. Instead of building separate models for every campaign, create templates that can handle a variety of campaign types. This approach not only ensures consistency but also speeds up deployment for new campaigns.
Monitor your model’s performance by comparing forecasted metrics – like ROI, campaign costs, or revenue – with actual results. If discrepancies arise, investigate whether they’re due to model limitations, data issues, or unexpected market changes that require adjustments.
Always reserve a holdout dataset to validate your model’s performance in real-world conditions.
Set up feedback loops to analyze campaign outcomes and use any discrepancies to refine your forecasts. Post-mortem reviews after each forecasting cycle can uncover patterns in errors and help improve your model over time.
Take advantage of real-time data streams to adapt forecasts quickly to market changes. This is especially useful for digital marketing campaigns, where performance data flows in continuously from advertising platforms. By staying flexible, you can respond promptly to shifts in the market and keep your campaigns on track.
Conclusion: Choose the Right Model
Selecting the right forecasting model starts with a deep understanding of your data, clearly defined goals, and decisions grounded in evidence. The key to success lies in analyzing past performance, customer behavior, and the complexity of campaigns within your marketing framework.
Once you’ve assessed your data, align your model choice with your campaign’s specific objectives. For instance, if you’re focused on demand forecasting, budget distribution, or spotting performance anomalies, different models will suit each task. ARIMA is a solid choice for trending data with little seasonality, while SARIMA is better equipped to handle strong seasonal variations. On the other hand, machine learning models excel at uncovering intricate, non-linear patterns that traditional statistical methods might overlook.
The quality and volume of your data should also influence your decision. Start with simpler models and only increase complexity when necessary. Businesses that tailor their forecasting models to meet industry-specific patterns see 20-35% higher forecast accuracy compared to those relying on generic solutions.
"What companies need is an analytical, forward-looking approach that allocates marketing dollars to customer segments as well as products or geographies that have the highest growth potential rather than to those that have traditionally performed well." – Jonathan Gordon, McKinsey
This highlights the importance of basing your forecasts on thorough analysis and aligning them strategically with your goals – key ideas we’ve explored here.
Advanced analytics can lead to tangible benefits. For example, businesses often see 15-25% improvements in scheduling accuracy, while those with fully integrated marketing and workforce planning systems report up to 30% lower labor cost variance.
Treat forecasting as an evolving capability rather than a one-off task. Use cross-validation techniques to test your model’s real-world performance and embrace scenario planning to stay agile in the face of market changes. Prioritize data preprocessing to address missing values and outliers, foster collaboration between marketing and operations teams, and regularly recalibrate your models with updated historical data for the most accurate predictions.
FAQs
How can I tell if my campaign data is ready for advanced forecasting models like LSTM?
To figure out if your campaign data is ready for advanced forecasting models like LSTM, there are a few things to check:
- Sequential Data: LSTMs work best with time-ordered data. If your data follows a clear timeline, you’re on the right track.
- Enough Historical Data: You’ll need a solid amount of past data to spot trends and patterns. A good rule of thumb is having at least 30 times the number of model parameters.
- Stationarity: Your data should have consistent statistical properties over time. If not, preprocessing might be necessary to make it stationary.
- Seasonality or Cyclical Patterns: LSTMs excel at identifying recurring trends, like seasonal spikes or cycles, so having these patterns in your data can be a big plus.
Finally, ensure your data is formatted properly for LSTM models, typically as [samples, time steps, features]. When these conditions are met, your data is better positioned to produce accurate and insightful forecasts with LSTM.
What’s the best way to split data into training and testing sets for accurate time series forecasting?
To make accurate time series forecasts, it’s important to split your data while keeping its chronological order intact. This step avoids data leakage and ensures your model is tested on future data it hasn’t seen before.
One popular method is the time-based split, where earlier data is designated for training, and later data is reserved for testing. Another approach is the rolling window technique. This method involves training the model on a fixed window of past data and then testing it on the following data. It closely simulates real-world conditions and enhances the reliability of your model.
Using these techniques ensures your forecasts align with marketing campaign objectives and generate actionable insights for better decision-making.
How can I include external factors in my time series forecasting to improve accuracy?
To make your time series forecasts more reliable, try including external factors – often referred to as exogenous variables – that can impact your target data. Think about seasonal patterns, economic trends, or marketing efforts. For instance, integrating holiday schedules for retail forecasts or weather data for energy usage can add helpful context to your predictions.
Leverage models like ARIMAX or SARIMAX, which are built to incorporate these external variables effectively. Make sure to update your model regularly to account for shifting conditions, and assess its accuracy using metrics like Mean Absolute Percentage Error (MAPE). This approach helps keep your forecasts precise and actionable as circumstances evolve.
