How can you forecast digital performance metrics using historical data?

Ask yourself, what are you aiming to improve? It could be website traffic, conversion rates, or social media engagement. Pick metrics that directly reflect these goals. For website traffic, look at unique visitors and page views. For conversions, focus on conversion rates and the average value of each conversion. If it's engagement, track likes, shares, and comments. These metrics are your roadmap; they tell you where you've been and hint at where you're going. Be specific and realistic in setting these goals. They're the targets you'll use historical data to aim for... the clearer your targets, the more accurate your forecasts.

I'm not convinced there is too much to be learned from historical data in trying to forecast future "digital performance." Plus which "digital performance? One that is useful is tracking your past Share of Voice (share of digital shelf). If upi appear as one of the twenty results on page 1 of a product or category search, you have 5% SOV. For product sales, look at where you were in terms of rank and frequency of appearances on Google/Search and Marketplaces. Create an action plan to increase SOV and forecast based on expected improvement. Another X factor is to forecast your potential improvements based on unifying your data, tech stack, channel mix and marketing.

Vermutlich verkaufen Sie etwas. Als ultimatives Ziel sollte daher der Umsatz als KPI am Ende des Sales Funnels stehen. Der Weg dahin entlang der Customer Journey beispielsweise kann mit verschiedenen Metriken versehen sein, je nach Phase (Awareness, Consideration, Purchase, Loyalty oder ?hnliche Konstrukte). Jedoch h?ngt es stark von der Branche und Ihrer Zielgruppe (B2B/ B2C) ab, welche Metriken sich jeweils dafür eignen, genauer betrachtet zu werden. Alles hat in gewissem Ma?e Einfluss auf den Abverkauf. Wenn Sie ein starkes Kundenbindungsprogramm und viele wiederkehrende K?ufer haben, sollten Sie vermehrt darauf Acht geben und zB dort einen Fokus setzen.

Forecasting should start with ideation. Ask yourself these questions - What's the most important metric for you to track - What are some of the metrics that influence this metric - What's the seasonality for these metrics and how they are correlated - How do the industry trends affect any of these metrics - What values for these metrics could be considered outliers? - Is there a consistent trend that can be used as a baseline for forecasting? - Are there any changes on the business end to account for while forecasting? If there is a lot of seasonality involved, forecast manually instead of choosing automations in sheets etc.

Digital strategies should be adaptable to changing circumstances, emerging trends, and unexpected challenges. While setting SMART goals is important, being too rigid in adhering to predefined metrics and objectives might hinder the ability to pivot or innovate in response to new opportunities or threats.

Gather historical data related to your defined metrics over a significant period. Utilize analytics tools like Google Analytics for website performance or social media analytics for engagement metrics. Analyzing this data helps identify trends, patterns, and anomalies, providing a solid foundation for accurate forecasting. Pay attention to seasonal variations, market changes, and any anomalies to understand the factors influencing your metrics.

Look back at least a year to understand trends and patterns. Use tools like Google Analytics for website data, social media analytics for engagement trends, and CRM tools for customer behavior. Focus on the same metrics you've set as your goals. Notice the peaks and troughs – what caused them? Was there a marketing campaign, a seasonal trend, or an industry shift? Understand the context. This is you piecing together a story. The patterns you uncover are clues to what might happen in the future.

Historical data, especially behavioural data of your customers, is highly valuable to personalise the experience and tailor it based on their needs. A lot of companies however do not yet use the holy grail of personalisation and do not leverage their data enough. Make sure to eliminate data siloes and use technology to make your data actionable. A good CEP should be able to automatically personalise the user experience based on historical data, suggest the next best action for each user based on your conversion / campaign goals and even predict future actions of your customers, so you can take action early enough, e.g. to prevent churn.

But when can you determine that you have collected enough data to progress with your analysis? ARIMA requires 50 to 100 observation points, while Prophet needs at least two or three years of data. Personally, I would not feel confident with less than two years of data. Furthermore, I would lack confidence if I couldn't explain and interpret past occurrences. For example, changes in data collection policies in Google Analytics could render historical data invalid. If Covid-19 is present in my historical data, I would need to account for and weigh down its effect on those data points. While more data is beneficial, it's essential that the data are explainable.

On websites, identify periods of high traffic and target specific campaigns for those times. For example, you can target a Christmas campaign for the December period, when sales usually increase. Use this data to optimize the performance of your ads (ADS) by directing more budget to days and times of peak conversion. This can help you increase the ROI of your campaigns and reach more customers. Also analyze the types of devices used to access your website and the geographic location to create even more specific ads. By segmenting your campaigns, you can increase the conversion rate and engagement of your audience.

For straightforward trends, use linear regression; it shows how your metrics have increased or decreased over time. If your data has seasonal patterns, like holiday sales spikes, consider seasonal forecasting. This adjusts predictions based on these regular variations. More complex? Try time series analysis, which looks at data patterns over time. It’s more detailed, capturing subtle shifts and cycles. Start with simpler methods; as you get comfortable, explore advanced techniques. Tools like Excel or Google Sheets can handle basic forecasts, while more sophisticated tools are available for complex analysis. Remember, the method is just a tool. The real skill is interpreting the results and using them to inform your strategies.

In forecasting digital performance metrics, selecting the right method is key. I've found that regression is particularly effective for complex, reliable data analysis. However, it's key to match the method to your data's unique trends and characteristics for the most accurate predictions.

Vous pouvez tester différentes méthodes et comparer leurs performances. Privilégiez des modèles simples et interprétables pour une meilleure compréhension des résultats. Adaptez votre méthode au fil du temps en fonction de l'évolution de vos données et de vos besoins.

Select appropriate forecasting methods based on the characteristics of the data and the nature of the metrics being forecasted. Common techniques for forecasting digital performance metrics include: Time-Series Analysis: Use techniques such as moving averages, exponential smoothing, or ARIMA (AutoRegressive Integrated Moving Average) models to forecast based on historical patterns. Regression Analysis: Build regression models to identify relationships between digital performance metrics and other variables (such as marketing spend, advertising campaigns, and seasonality) and use these relationships to make forecasts.

In selecting forecasting methods for corporate communications, it’s essential to balance analytical sophistication with the practicalities of large, slow-moving organizations. Predictive analysis, using AI and machine learning, offers forward-looking insights that aid strategic planning. Yet, the approach must remain adaptable, allowing for adjustments in ongoing change management processes, thus ensuring communication strategies are both strategic and flexible.

Pensez à ajuster votre méthode et vos prévisions en fonction des résultats réels. La prévision n'est pas une science exacte, mais une compétence à affiner avec la pratique. Suivez ces étapes et méthodes pour améliorer vos prévisions et votre stratégie numérique. Utilisez vos prévisions pour éclairer vos décisions et optimiser vos actions. Communiquez vos prévisions aux parties prenantes pour une meilleure compréhension et collaboration.

After forecasting your digital metrics, put your predictions into action. Adjust your strategies based on what the data suggests. For instance, if your forecast shows a dip in traffic, beef up your SEO or PPC campaigns. If it predicts higher engagement, plan more interactive content. But, don’t just set it and forget it. Regularly check the accuracy of your forecast against real-time results. Are the predictions holding up? If not, tweak your forecast model and strategies. This isn’t a one-time exercise; it's an ongoing process of prediction, action, and adjustment. Think of your forecast as a compass, not a map. It points you in the right direction, but you need to navigate the journey, adapting to the changing terrain.

Choose an appropriate forecasting method based on the nature of your data and the objectives of your analysis. Common forecasting methods include time series analysis, regression analysis, and machine learning algorithms. Consider factors such as seasonality, trend, and autocorrelation when selecting the method. Apply your chosen forecasting method to generate future values of your digital performance metrics based on your historical data. Use the forecasting capabilities of your selected tool to extrapolate trends, make predictions, and visualize the forecasted values over time.

To apply and validate your forecast effectively: Choose a forecasting method based on historical data. Implement the forecast using tools like Excel, R, Python, or Tableau. Visualize the forecasted values to identify trends and patterns. Validate the forecast against actual data using measures like MAE, MSE, or R-squared. Refine your approach based on validation results for improved accuracy over time.

The final step is to apply and validate your forecast. You need to use your chosen forecasting method to generate future values of your metrics based on your historical data. You can use various tools and software to perform and visualize your forecast, such as Excel, R, Python, or Tableau. You also need to validate your forecast by comparing it with actual data as it becomes available. You can use various measures and tests to evaluate the accuracy and error of your forecast, such as mean absolute error (MAE), mean squared error (MSE), or R-squared. Forecasting digital performance metrics using historical data is not an exact science, but a skill that requires practice and refinement.

All these pointers are definitely needed. I would, however, be mindful of the journey point in the transformation lifecycle. Idea is not to get locked into metrics and indicators. Also, in my experience, best is, if broad parameters across Dx, Ux and Cx can be created for every interaction point which is then followed by historical data analysis as it ensures that moving variables are better understood and applied and not the other way round.

While forecasting can provide valuable insights, it’s important to remember that external factors such as market changes, consumer behavior shifts, and unforeseen global events can impact digital performance metrics. Incorporate a degree of flexibility and regular review cycles into your forecasting model to adapt to changes. Additionally, fostering a culture of data-driven decision-making within your organization can enhance the strategic value of your forecasts, ensuring that insights lead to actionable strategies.

Consider external factors like market trends, competitor actions, or economic shifts. They can significantly impact your outcomes. Stay adaptable; if real-world results start veering off your forecast, be ready to adjust. Use your forecast as a guide, not gospel. Yes, it's about setting expectations, but also about responding to change. Keep updating your forecast with fresh data – this isn’t a one-time task. And most importantly, learn from discrepancies. If predictions don’t match reality, dig into why. It’s a continuous cycle of prediction, observation, and adaptation. Stay curious, stay flexible, and use every forecast as a learning opportunity to refine your digital strategy.

Data Quality: Ensure historical data is accurate and consistent. External Factors: Consider seasonality, events, and trends influencing metrics. Leading Indicators: Incorporate variables signaling metric changes. Granularity: Adjust data resolution for relevant insights. Feedback Loops: Recognize self-reinforcing dynamics for accuracy. Uncertainty Management: Use probabilistic techniques for risk mitigation. Interpretability: Ensure transparency for stakeholder trust. Continuous Improvement: Iterate models based on feedback and insights. By addressing these factors alongside standard forecasting steps, you'll enhance the accuracy and reliability of digital performance metric forecasts using historical data.

Remain adaptable, refining models with new data and market changes. Blend qualitative insights with quantitative analysis. Monitor external factors like industry trends and regulations. Cultivate a data-driven decision-making culture within the organization