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Forecasting and data science: 100 answers
Plain-language answers on forecasting and data science for small and mid-sized companies, from Zansa.
- What is demand forecasting and how does it work for a small business?
- What is the difference between a sales forecast and a demand forecast?
- How do I build a basic cash-flow forecast for my company?
- What is a reorder point and how do I calculate it?
- What is safety stock and how much of it should I hold?
- What is customer churn and how is it measured?
- How can a small company improve customer retention using data?
- What does pricing analysis involve for a small or mid-sized company?
- What is anomaly detection and when is it useful in business?
- What is customer segmentation and why does it matter?
- How can data help forecast staffing and rostering needs?
- What is machine learning explained in plain language for business owners?
- What is the difference between supervised and unsupervised learning?
- What does MAPE mean and how is it calculated?
- Why is MAPE misleading for products with low or intermittent sales?
- What is WAPE and when should I use it instead of MAPE?
- What is forecast bias and how do I detect it?
- What is the difference between MAE and RMSE for forecast error?
- What is backtesting a forecast and why should I do it?
- How is time-series cross-validation different from normal cross-validation?
- What is seasonality in forecasting and how do I handle it?
- What is trend in a time series and how is it different from seasonality?
- How should forecasts handle public holidays and moving events?
- How do promotions affect forecasts and how should I account for them?
- How much historical data do I need to build a useful forecast?
- When is machine learning not worth it for forecasting?
- What is a forecasting baseline and why do I need one?
- What is a naive forecast and a seasonal naive forecast?
- What is exponential smoothing and why is it popular for business forecasts?
- How do I explain a forecast to non-technical managers?
- What is a prediction interval and why show a range instead of one number?
- What does it take to deploy a forecasting model into daily operations?
- How do I monitor a forecasting model after it goes live?
- What is model drift and how often should I retrain a forecast?
- How can I safely use ChatGPT or other AI assistants with business data?
- Can AI assistants like ChatGPT be trusted to calculate numbers from my spreadsheets?
- What data privacy risks come with using AI tools on customer data?
- What are the most common forecasting mistakes small businesses make?
- What is a residual in forecasting and what does it tell me?
- How do I do error analysis to improve a forecast?
- What is data leakage and how can it ruin a forecast?
- What is overfitting and how can I tell if my forecast model has it?
- How should I split data into training and test sets for forecasting?
- How do I forecast intermittent or lumpy demand for slow-moving items?
- How can I forecast demand for a new product with no sales history?
- How should I treat outliers in sales history before forecasting?
- How do I deal with missing data in a time series?
- How do stock-outs distort demand forecasting and what can I do?
- What is forecast value added and how do I measure it?
- Why are my sales forecasts always too optimistic and how do I fix it?
- Should I use Prophet, ARIMA, or gradient boosting for forecasting?
- Is it better to forecast in Excel or use a machine-learning model?
- How do I forecast accounts receivable and customer payment timing?
- What data do I need to build a customer churn model?
- How do I know if a churn prediction model is any good?
- What is RFM analysis and how do I use it?
- What is price elasticity and how can I estimate it?
- How do I run a price test without damaging customer trust?
- What are the common methods for detecting anomalies in business data?
- How do I reduce false alarms from an anomaly detection system?
- How do I forecast foot traffic or customer arrivals for staffing?
- What is a feature in machine learning, in simple terms?
- Can I explain why a machine-learning forecast gave a particular number?
- How does poor data quality affect forecasts and what should I fix first?
- How can I tell whether my business is ready for data science?
- How often should I update my business forecast each period?
- What are the warning signs that a forecasting model is no longer reliable?
- Why does my forecast look accurate in testing but fail in real life?
- How do I measure the business value of a better forecast?
- How much does machine-learning forecasting cost for a small or mid-sized company?
- What is Zansa and what does it do?
- Why is the company called Zansa and what does the name mean?
- What is Zansa's process for working with a new client?
- What happens in a Zansa Data Health Check?
- What does a Zansa Foundation Build include and how long does it take?
- Who owns the models and dashboards that Zansa builds?
- Where do Zansa's machine-learning models run and who controls them?
- Does Zansa use AI tools on client data?
- Is Zansa's work designed to meet POPIA and GDPR?
- Can Zansa work with companies outside South Africa?
- Which industries does Zansa serve with forecasting and analytics work?
- Does Zansa offer ongoing support after the build is finished?
- When should a company hire a data scientist instead of using a consultancy?
- Can a small company really benefit from machine-learning forecasts, or is it only for big firms?
- How does Zansa use residuals to check a forecast is trustworthy?
- What should I ask a data science provider before hiring them to build forecasts?
- Is it safe to upload sales spreadsheets with customer names to an AI chatbot?
- What is the difference between anonymised and pseudonymised data?
- What are hallucinations in AI and how do they affect data analysis?
- Should my business have an AI usage policy for staff handling data?
- Do AI vendors train their models on the data I send them?
- How can LLMs help with forecasting and analytics without risking my data?
- How do I forecast when something big has changed, like a pandemic or new competitor?
- How do I forecast revenue for a service or professional-services firm?
- How can hospitality businesses forecast bookings and occupancy?
- What data is needed to forecast production and raw-material needs for a manufacturer?
- How can a logistics company use forecasting to plan capacity and routes?
- Can healthcare practices use forecasting while protecting patient privacy?
- How can e-commerce stores forecast demand across many products and channels?
- How do I get started with data science if my data is messy and scattered?