Conversion Forecasting: Proven Methods to Predict and Optimize Your Conversion
How much ever the business wishes, no crystal ball can allow you to predict the future of your conversions! However, Quilytics, a leading data analytics firm based in the USA, offers a solution that comes remarkably close.
By harnessing advanced data analytics techniques, Quilytics provides businesses with powerful tools to plan and forecast conversions with unprecedented accuracy. Keep reading to discover how their innovative approach can bring you closer to the predictive insights you need for strategic success.
To begin with, let’s explore the fundamentals of conversion forecasting:
What is a Conversion?
If you’re reading this blog, you’re likely familiar with the concept of a conversion. However, for a quick refresher, a conversion takes place when a user takes a desired action.
This can include:
- Sales: A completed purchase on an e-commerce site
- Leads: Submitting a form or signing up for a newsletter
- Engagements: Actions such as downloading an app or clicking on an ad
What is Conversion Forecasting?
As the name implies, conversion forecasting predicts future conversion rates or the number of conversions. Accurate forecasting can provide businesses with a significant edge.
Data analytics-driven conversion forecasting involves using data analysis techniques, such as predictive analysis, to examine historical data and current trends to predict future conversions. This approach allows for more accurate forecasting, better inventory allocation, and optimized marketing strategies.
Read More – The Ultimate Guide to Digital Marketing Analytics: Maximizing ROI and Conversion Rates
Importance of Conversion Forecasting
Conversion forecasting is a valuable tool for businesses. Businesses can gain insights about their audience and make data-driven decisions to enhance their conversion.
From a marketing POV, you can allocate your resources to accurate campaigns based on high-quality data rather than mere instincts. From a sales perspective, it can help identify the cause of a drop-off or reduction in conversions. From an operations perspective, it can help order and maintain inventory of specific products.
In terms of overall business, conversion forecasting is beneficial in the following ways:
Plan Budgets :
Budget planning requires using data-driven insights to predict future conversions and strategically allocate your budget to optimize marketing and sales efforts. Conversion forecasting allows you to identify effective channels and allocate resources efficiently based on expected conversions. This means maximum ROI and better financial planning.
Identify bottlenecks:
A conversion funnel bottleneck refers to a stage in the conversion funnel where the flow of potential customers slows down significantly or stops, resulting in a higher drop-off rate. Identifying this means better-targeted strategies and optimized conversion.
Optimize Strategies:
Conversion forecasting allows businesses to identify effective campaigns and customer segments. Thus, you can focus on activities that generate better conversion, target the right customer segment, maximize ROI, and waste fewer resources on ineffective strategies.
Set Goals:
With conversion tracking, you can compare actual conversion rates with forecasted rates and set realistic targets. This means a better measure of progress and better performance over time.
Read our case study to see how conversion forecasting can make a tangible difference in your business: Quilytics’ Predictive Analytics Elevates Sales Strategy for a Leading E-commerce Platform
Now that you’ve understood the benefits of conversion forecasting, let’s di scuss how to implement it in your business effectively
How Do You Predict and Plan Your Conversion With Analytics-Driven Conversion Forecasting?
Data analytics-based conversational forecasting involves much more than analyzing historical data. It leverages the power of data analytics and predictive modeling to predict and plan conversion accurately.
Data scientists typically follow structured steps to build and implement predictive models in model-based conversion forecasting. Here’s an overview of the key steps involved:
Data Collection and Storage:
Ideally, any data modeling process should begin by collecting relevant data. In terms of conversion models, this involves
- Collecting data from analytics and ad platforms, sales data, and other pertinent metrics are also considered.
- Consolidate your data into a comprehensive dataset
- Storing data in a local or cloud server like GCP or Azure
Data Pre-processing:
Once the data is collected and stored, the data engineer processes the data. There are varied steps involved in pre-processing the data, such as:
- Address missing values using statistical methods or through more complex imputation techniques
- Identify and remove duplicate records to ensure each entry is unique and accurate
- Correct any inconsistencies in the dataset
- Standardise the data
- Develop new variables that provide additional insights from the data
- Identify the feature most relevant to your data & problem
- Remove redundant data features
- Apply advanced analysis techniques to reduce the number of features while preserving essential information
- Summarize the data
- Convert non-numeric data into a numeric format suitable for modeling
Model Building:
The next step is to model your data to identify and establish the relationships among different data points. It involves several key steps such as,
- Conduct an initial analysis to understand data distributions, relationships, and patterns
- Use charts, graphs, and plots to visualize data and uncover insights that can inform model-building
- Select apt algorithms to choose the models that is based on your problem
- Balance complexity & interpretability of data
- Distribute your data into subsets
- Select a unique subset of data for analysis or model training to manage large datasets.
- Use the training data to fit the model, adjusting parameters to learn from the data and capture underlying patterns
- Apply relevant performance metrics to gauge the accuracy and reliability of your model to solve the defined problem.
Several effective models exist for conversion forecasting. Based on our extensive research, we recommend the following effective models as business intelligence service experts, such as:
- Prophet Model: Handle time series data with strong seasonality (daily, weekly, yearly) and special events
- SARIMA: For time series with seasonality and trends.
- LSTM: Capture complex, nonlinear relationships in time series data.
- DeepAR (Amazon Forecast): Predict e-commerce conversions across various products during seasonal sales.
- Catboost: Forecast conversions based on historical data from multiple marketing channels.
- Random Forest Regressor: Forecast conversion by analyzing historical data related to customer behavior, marketing spend, website traffic.
Model Deployment:
Once the trained model is tested and ready to be used in the real world, it is deployed to make predictions. The process includes:
- Finalize the model that has been tested
- Deploy the model in real-time on local server or cloud platforms such as GCP, AWS or Microsoft Azure
- Prepare the necessary hardware and software environment for the model’s deployment, including servers, databases, and APIs
- Double-check your model dependencies and system requirements
- Create application programming interfaces (APIs) that allow external applications or systems to interact with the model and request predictions
- Develop user-friendly interfaces for end-users
- Embed the model within existing applications or workflows to ensure seamless operation
- Conduct testing in the production environment to ensure the model performs as expected with real-world data
- Constantly monitor key performance indicators (KPIs) and metrics to maintain the model’s accuracy and reliability
- Set up alerts for anomalies or performance degradation to make prompt corrections
- Ensure the model and data handling processes comply with security standards and regulations to protect sensitive information
- Provide comprehensive documentation for the model’s functionality, API usage, and deployment procedures
- Offer ongoing support to address issues, provide updates, and assist users with deployment-related queries
Best Practices for Conversion Forecasting:
- Establish goals
- Define clear objectives that align with your business objectives
- Ensure data accuracy
- Choose the right models
- Evaluate model performance
- Identify opportunities for improvement
- Monitor and Adjust
- Use advanced tools
Read More: Essential Tools Every Data Engineer Should Master
- Ensure data Security and compliance
Conclusion:
Effective conversion forecasting involves understanding your current metrics, analyzing historical data, predicting future trends, setting clear goals, developing strategic plans, and continuously monitoring performance. By leveraging data and predictive analytics tools, you can make informed decisions and optimize your conversion strategies for better results.