Alternative Data in Financial Modelling
- Alexander Kiel
- Jan 28, 2024
- 4 min read
Over 80% of the world's population uses social media platforms for an average of 151 minutes daily.
In a world where data is the new currency, have you ever wondered how much untapped potential lies within the endless streams of social media activity? What if you could harness this data to not just react to the market but to predict its very movements?
Traditionally, financial models forecast future events using historical data, industry benchmarks, management plans and economic forecasts. However, a recent study showed social media data can significantly improve forecast accuracy.
The study found that using social media data with standard forecasting methods reduced forecast error by 3-5%. Similarly, a consumer goods company improved item forecasts by 15-25% and overall forecast accuracy by 7% using social media comments and location data.
This trend aligns with the growing importance of social listening in marketing. 63% of social media marketers believe listening will be even more crucial in the coming year and 71% already share consumer insights gleaned from social media with other departments unlocking deeper customer insights, boosting forecast accuracy.
So, let's have a look at the metrics to consider and how to integrate alternative data in financial modelling:
1. Identify Relevant Social Media Metrics
Engagement Metrics: Likes, shares, comments, retweets, etc.
Reach Metrics: Follower count, impressions, reach, etc.
Conversion Metrics: Click-through rates (CTR), conversion rates, etc.
Brand Sentiment: Positive/negative mentions, sentiment analysis, brand mentions.
Audience Demographics: Age, gender, location of followers.
Competitor Analysis: Monitor competitors' social media metrics.
2. Integrate Social Media Metrics into Financial Modeling
Define Key Performance Indicators (KPIs): Choose metrics relevant to your business goals.
Data Collection: Use social media analytics tools or APIs to gather relevant data.
Normalise Data: Convert social media metrics into comparable units (e.g., daily averages, monthly totals).
Correlation Analysis: Analyse the correlation between social media metrics and financial performance (e.g., revenue, sales).
Feature Engineering: Create new features based on social media data (e.g., average engagement per follower, sentiment-weighted engagement).
Time Series Analysis: Track changes over time to identify trends and patterns.
3. Incorporate into Financial Models
Revenue Forecasting: Use social media data to predict future revenue based on historical correlations.
Customer Acquisition Cost (CAC): Analyse how social media metrics affect CAC and adjust marketing strategies accordingly.
Customer Lifetime Value (CLV): Determine how social media engagement impacts CLV.
Market Share Analysis: Monitor how social media presence correlates with market share.
Risk Management: Identify risks and opportunities based on social media sentiment and engagement.
4. Machine Learning and Predictive Modeling
Regression Analysis: Build regression models to predict financial metrics based on social media KPIs.
Time Series Forecasting: Use algorithms like ARIMA to forecast financial performance using social media data.
Machine Learning Models: Train models to predict future financial outcomes based on social media metrics, considering seasonality, trends and external factors.
5. Continuous Monitoring and Optimisation
Real-time Monitoring: Continuously monitor social media metrics and financial performance.
Feedback Loop: Use insights to optimise marketing strategies in real-time.
A/B Testing: Test different social media strategies and measure their impact on financial metrics.
“Information is the oil of the 21st century, and analytics is the combustion engine.” - Peter Sondergaard
Example Use Case
Visualise a scenario where every social media interaction is a predictor of your company's success. What if you could anticipate sales spikes or dips simply by analysing engagement trends?
Scenario: A retail company wants to predict sales based on social media activity.
Approach: Gather data on daily social media engagement (likes, shares) and historical sales data.
Modeling: Use regression analysis to build a model where sales are the dependent variable and social media metrics are independent variables.
Prediction: Forecast future sales based on projected social media engagement.
Optimisation: Adjust marketing efforts based on the model's insights to maximise sales.
Integrating social media metrics into your financial modeling offers several benefits. You can enhance predictability by understanding how social media impacts financial performance, leading to more accurate predictions. You'll improve marketing ROI by optimising spend based on social media effectiveness.
Additionally, it serves as an early warning system, allowing you to detect trends or issues through social media sentiment analysis before they affect your financials. This comprehensive view of performance drivers enables you to make more informed decisions for sustainable growth.
“Where there is data smoke, there is business fire.” - Thomas Redman
Achieving this integration requires a proactive approach to data analysis and a willingness to adopt innovative methodologies. To start, identify the social media metrics that are closely aligned with your business objectives and integrate them with your financial KPIs. Establishing a feedback loop between social media performance and financial results enables agility in navigating a dynamic market landscape and positions your organisation for sustainable success.
The idea of integrating social media metrics into financial modeling and forecasting is not only fascinating but also holds substantial promise for predicting sales based on current social media trends.
As you reflect on the possibilities, how will you leverage the power of alternative data to not only predict financial outcomes but to gain a competitive edge? What steps will you take today to integrate these insights and position your organisation for the future?
