How AI Forecasting Really Works

How AI Forecasting Really Works – And Why You Can Trust It

A clear explanation of how TellMeNow uses historical data, statistical models, and machine learning to generate reliable, actionable predictions.

Introduction: AI Isn’t Smart – Just Instructed

More and more businesses are using AI to analyse customer service and spot trends. But when an AI suddenly declares, “Make this change and your FCR will increase by 12%”, it can be just as unnerving as it is exciting. How can it be so certain?

Here’s the truth: it isn’t. AI isn’t intelligent in the human sense. It’s not creative or self-aware. It’s like a hammer: powerful, but only if used correctly. It does exactly what it’s told, based on exactly the data it’s been given. That’s what makes it trustworthy—when used well.

In this article, we explain how TellMeNow works with different forecasting models. We show when traditional statistics is enough, when machine learning is necessary, and why instructions, data quality and traceability are absolutely key.

Why Forecasts Can Feel Like Magic

AI forecasts are not based on gut feeling. They are based on patterns in data. When a good model is matched with the right data, it can deliver surprisingly precise insights.

But remember: AI has no understanding. Only instructions and data. The better the input, the better the output.

Each insight must be traceable to a clear factor or data cluster – for example via PCA (Principal Component Analysis) or behavioural clusters impacting FCR, AHT or volume.

Model choice is shaped by your data – and your instructions

At TellMeNow, the selection of forecasting model depends on five key factors:

Type of data (time series, categorical, numerical, etc.)
Quality and volume of the data
Forecasting time horizon
Requirement for interpretability
Nature of the business decision being supported

But the model won’t figure this out on its own. That part is up to you. That’s why TellMeNow places strong emphasis on translating business challenges into clear technical instructions – and ensuring every forecast is linked to a data-driven insight that can be traced and validated.

Model Types Used in TellMeNow

Time Series Forecasting

When customer case volumes show regular patterns over time (e.g. monthly or weekly), we apply the following models:

SARIMA / SARIMAX – Ideal for handling seasonality and trend
Exponential Smoothing (ETS) – Simple yet reliable
Facebook Prophet – Flexible and suited for business data

These models serve as the default starting point in TellMeNow under the logic: fallback_to_simple_regression: true. They require at least 30 data points and only work reliably when the data follows a sufficiently regular structure.

Machine Learning

When relationships are complex or multiple influencing factors are present, we use:

XGBoost / LightGBM – Efficient with high-dimensional data
Neural Networks (LSTM) – Able to detect long-term dependencies

These models are only introduced after simpler approaches have been tested. They require thorough validation through residual analysis and significance testing.

Bayesian Models

To express uncertainty in unpredictable scenarios:

BSTS (Bayesian Structural Time Series) – Enables scenario-based forecasting with built-in uncertainty intervals

All Bayesian models in TellMeNow provide 95% confidence intervals and are used when the business question requires probability ranges rather than single-point estimates.

Hybrid Models

To combine strengths of different approaches:

SARIMA + XGBoost – Captures both regular trends and unexpected deviations
Ensemble Models – Combine multiple forecasts for greater predictive accuracy

Component weights are determined via historical backtesting – not summed up as total effects.

Rule-Based Logic Models

When domain-specific behaviours are critical:

Example: “If it’s the Monday after a public holiday → expect a 15% increase in demand”

These types of rules are only permitted when grounded in observed data and confirmed through before-and-after simulations.

The Forecasting Process in TellMeNow

Step 1: Classify the Question

Are we predicting a future value over time – or a future state? Do we need a percentage change or a probability distribution?

Step 2: Build a Baseline

We start with transparent models such as SARIMA or Prophet to detect fundamental patterns in the data.

Step 3: Test Advanced Models

If several influencing variables interact, we explore more advanced options like ensemble models or XGBoost – but only if the data volume is sufficient and the results can be validated using residual analysis (e.g. Durbin-Watson test), confidence intervals, and p-values below 0.05.

Step 4: Add Uncertainty Intervals

Bayesian methods allow us to forecast not only what might happen, but also how likely it is to happen.

Step 5: Backtesting and Validation

Every model is rigorously evaluated against historical data using:

95% confidence intervals
Residual analysis (to test model fit). Want to know what residual analysis is? Click me!
Statistical significance levels (p < 0.05)
Before-and-after comparisons for each recommendation

Example Case: Predicting Post-Campaign Case Volume

Data: 24 months of historical data, with four predictors (seasonality, campaign type, weekday, contact channel)
Model: Prophet combined with LightGBM
Simulated Result: 27% reduction in forecast error – specifically within the segment of repeat enquiries

Note: This is based on a simulated model component, not a total aggregate effect. TellMeNow never reports overall uplift figures – only disaggregated, traceable insights.

Why Transparency Builds Trust

We don’t position AI as a crystal ball. We position it as decision support.

We show which model was used, why it was selected, and how it performed. We link every insight to a clear data pattern – often through PCA or cluster modelling – and visualise everything in the dashboard.

Above all: AI doesn’t make decisions. You do.

Frequently Asked Questions (FAQ)

How much data is needed? It depends on the model. Some only need 30 observations. Others require years of data.
Can I understand why the AI recommends something? Yes. Every suggestion is tied to a clear driver or cluster and backed by traceable evidence.
Can I control the output? Yes. You choose which KPIs to optimise and receive insight-specific before-and-after estimates.

Key Takeaways

AI can’t think for itself. It has no context, no understanding, and no sense of responsibility. What it can do is exactly what you ask of it – quickly, consistently, and based on data.

So it’s not the AI that’s clever. It’s the person who uses it wisely.

With TellMeNow, you don’t just gain access to advanced models. You gain a structure, a process and a system where every forecast is:

Traceable to PCA or cluster-based insights
Validated through backtesting and residual analysis
Delivered with clearly defined uncertainty intervals
Presented without aggregated claims – only clear, actionable components

AI isn’t the clever one. You are – if you use it right.