Marketing Forecasting 101: Steps, Benefits, and Data to Use

Originally published on September 28, 2022

Marketing forecasting is the process of making educated predictions about a company's future performance within specific target markets. Using market research and historical data, marketers can forecast demands to better align marketing efforts with sales trends.

The forecasting process helps you understand the effectiveness of your marketing strategies and enables you to improve your future efforts. By understanding your campaigns’ strengths and weaknesses, you can adjust marketing actions to get the most out of your budget.

What is marketing forecasting?

A marketing forecast helps you conduct trend analysis by predicting future market characteristics, sales data, and the growth rate within your sector. Forecasting enables you to replace guesswork with an empirical, data-focused approach to planning. You can use various qualitative and quantitative forecasting techniques to predict trends.

You can use behavioral analytics, market research, historical data, and forecasting methods to make predictions on things like:

• Customer behaviors throughout the user journey.
• The number of new leads generated within a period.
• The rate at which leads move through the sales funnel.
• The effectiveness of different marketing strategies in acquiring new customers.
• The impact of marketing on critical product metrics around acquisition, retention, and monetization.
• Future sales numbers and a product’s market potential.
  

A marketing forecast consolidates all of these predictions into one analysis, empowering your teams with a complete picture of the future. With these insights, you can plan more strategically, knowing you have all the necessary information to make your marketing as targeted and efficient as possible.

Benefits of marketing forecasting

Your marketing forecast is foundational to your marketing plan and product forecast. It helps you understand how your marketing campaigns and products will perform so you can guide your team’s decision-making.

Forecasting brings several benefits:

• Insight into future trends: Understanding potential customer behavior and demand for certain products enables you to plan proactively and prepare for different outcomes.
• More efficient marketing: Predictive customer analytics enables you to target your marketing efforts to the customers with the highest likelihood of converting or having a higher lifetime value. Let’s say you notice that people who arrive on your landing page from social media tend to retain for longer and upgrade their subscriptions more often. Based on this insight, you might invest more heavily in your social media marketing efforts.
• Higher retention: Predicting customer behavior also enables you to mitigate churn. You can identify customers you believe are at risk of churning, then run campaigns to re-engage them, for instance, with a discount or extra in-app product guidance.
• Precise budgeting and resource allocation: Marketing forecasting reduces risks associated with investing in new products, hiring, or marketing efforts because it provides clarity on future financial situations. For example, anticipating a holiday sales spike enables you to hire additional customer service reps.

Better inventory management: For ecommerce businesses, inventory forecasting ensures you have the proper supply to meet customer demand across your digital channels. You don’t have to worry about over- or under-ordering products for your online store when you base your inventory purchases on an accurate forecast.

The data you need for marketing forecasting

Data collection is essential to creating a marketing forecast. Gathering data from various sources provides a comprehensive view of current performance, customer behavior, and market trends. It’s essential to ensure your data is both relevant and accurate. Here are some types of data to consider:

Goals and objectives

Gather data on your business and marketing goals for the forecast period. Defining these goals provides critical context and direction for your forecast.

For example, if your goals are centered around monetization and activating more customers, your forecast will focus on expected customer behavior within the app and conversion rates. Conversely, if your objective is to increase your free customer base, your focus will shift toward acquisition channels and how you expect them to perform.

Historical data

Historical data enables you to recognize patterns so you can predict future performance. By analyzing past sales figures, such as monthly revenue across product lines, you can identify trends and seasonal variations to anticipate future sales. Marketing performance data, like the ROI of previous email campaigns, along with customer data (like repeat purchase rates and lifetime value), helps you understand the results you can expect from different strategies and channels.

Current metrics

Current marketing metrics give you a snapshot of your marketing performance, a baseline for your forecast, and a benchmark against which to measure future performance. For marketing forecasts, you’ll typically want to look at:

• Website analytics: Use metrics like page views, number of engaged sessions, and bounce rates to understand visitor behavior and engagement on your site.
• Funnel metrics: Monitor the number of leads you generate over a specific period and how they convert at different points during the sales process (e.g., visitor to free plan conversion and free to paid conversion.)
• Channel performance: Assess the effectiveness of various marketing channels based on their engagement and the number of leads they generate.

Analysis of external factors

Your business doesn’t exist in a vacuum. External factors can significantly influence market demand and business performance, and understanding these helps create more accurate forecasts. Data expert and former analyst at Gartner Doug Laney explains that companies that consider external data, such as weather patterns, consumer spending power, and employment rates, achieve “significant business results.”

Collect external data around:

• Industry trends: Identify shifts in industry standards and emerging technologies and their impact on consumer preferences. Forrester and McKinsey often produce industry-specific reports.
• Competitors: Review competitor websites, press releases, and marketing materials to monitor their product launches and marketing campaigns.
• Economic health: Government publications, like the US Bureau of Economic Analysis and the Office for National Statistics in the UK, and financial news outlets provide information on economic indicators like gross domestic product (GDP) growth and inflation rates. This information helps you predict consumer spending power.
• Consumer behavior: Analyze market research reports and consumer surveys from firms like Nielsen or Ipsos to understand consumer behavior and preferences changes.

Regulatory changes: Monitor new regulations and policies that might impact your business or marketing. Regulatory bodies, industry associations, and legal advisories often publish updates on new laws and regulations on their websites, along with guidance on staying compliant. For example, in the US, the National Institute of Standards and Technology from the US Department of Commerce shares guidance in line with the government’s Executive Order on AI.

Marketing forecasting methods

Predicting what will happen in the future might sound tricky, but you can use and combine several techniques to obtain accurate forecasts. Each one will give you different insights and metrics, but a mixture gives you a more comprehensive picture of what you’re trying to predict.

Correlation analysis

Correlation analysis helps you understand the relationships between your customers and your product. Your analysis might reveal that certain features in your platform positively or negatively affect your customer experience.

This information gives product managers insight into the aspects of their product line that contribute to (or hinder) customer retention or engagement—which helps them optimize their products for growth.

You can also analyze correlations related to your marketing efforts. You might find that customer cohorts acquired through referral programs tend to have a higher customer lifetime value (CLV) than those from social media campaigns and optimize accordingly.

Expert opinions

These are simple knowledge-based opinions you can obtain from well-informed executives in your company and external industry experts. Though they may not have hard numbers to prove their opinions, their extensive experience lends weight to their views and can be helpful in forecasting.

Tried-and-tested qualitative methods can also help you collect and analyze options. One example is thematic analysis, which extracts common themes from raw qualitative data, such as interview transcripts. The Delphi Method is another option that involves reaching a consensus forecast by running multiple rounds of questioning with experts.

Customer surveys

Customer surveys involve getting feedback from current or potential customers about new or existing products. You can collect this information directly to help you:

• Understand customer intent
• Collect demographic data about your target customers
• Get an idea of their preferred price range

Once you have the raw data, you can analyze it to gauge your customers’ sentiments and use them to guide your marketing forecast. If 90% of your customers say they love your new product, sales will likely be high. Equally, if you know which customers are power users of your product, you can tailor beta releases of new features to target them.

Sales team insights

Your sales team is at the forefront of your marketing activities. Their daily experiences give them insight into how your products perform, the effectiveness of your marketing activities, and customer sentiment. You can collect this information via interviews, surveys, or focus groups.

One limitation is that most sales teams can only provide information about your existing products and marketing efforts. However, you can use their insights to predict how other marketing efforts might work. For example, if customers respond well to a specific ad for your product, you know to use a similar ad when you roll out that product’s newest version.

Time series

Time series techniques look at sales patterns over various periods. You can use them to uncover past month, quarter, or year patterns that predict future sales. For example, if there was a 3% growth in sales every year for the past three years, it’s safe to assume that the next year will see similar growth.

Knowing what will happen in a specific period can help you make more strategic product and marketing decisions to acquire a larger market share. For example, you can predict how many items you’ll sell through your ecommerce channels or how many customers will upgrade to your digital product’s premium version.

AI-powered predictive analytics

Predictive analytics solutions use AI to anticipate future behavior based on users’ past actions. Predictions are essentially advanced forecasts that use deep learning models to identify how likely users are to perform a specific action, such as converting or churning.

You can identify which users are very likely to convert versus which require engagement with an email or ad campaign to drive their conversion. From there, you can take action by creating a targeted marketing campaign. Predictions also help you to set the right pricing for your target audience and cross-sell and upsell to increase CLV.

How to conduct a marketing forecast

Though there are several different forecasting tools you can use to carry out your analysis, there is a basic methodology you can follow:

1. Plot out the stages of your revenue cycle: Using customer journey analytics, track a customer’s typical journey from start to purchase. This will give you foundational knowledge about your customer journey.
2. Identify the leads you want to track: Pick a few high-value customer cohorts whose journeys you wish to optimize.
3. Obtain information on your customer lifecycle: If you’re an ecommerce company, use metrics like conversion rate and cart abandonment rate to understand the percentage of online store visitors who make a purchase and those who place items in their cart but never complete their purchase.
4. Determine the number of leads moving through your sales funnel in a given period: If you’re a B2B SaaS company, knowing the number of leads will give you a rough idea of how many new customers you can expect, giving you a great start to your forecast. You can determine the number of leads by looking at your recent sales funnel trends and talking to your sales team.
5. Model the flow of new and current leads through each customer journey stage: Once you’ve gathered all the information from the previous steps, you can plot out the typical journey of a customer lifecycle. This helps you make better predictions based on tried and tested customer experiences.
6. Make predictions based on behavioral customer data: Make your predictions for the future using insights from past customer behavior. A platform like Amplitude helps you predict future behaviors using AI and machine learning technology.
7. Analyze your results and finalize your marketing forecast. With this information, you’ll be in a stronger position to predict future sales, trends, and general consumer behavior.
8. Take action on your insights: Forecasting is only helpful if you take action. Use your predictions to test new marketing campaigns, product personalizations, pricing strategies, and more.

Real-world example: using forecasts to assess campaign performance

ACKO, an InsurTech company, uses Amplitude to forecast business metrics. When they run marketing campaigns, they compare the outcomes of these campaigns against the forecasted baselines to check the impact. This helps them understand how effectively their campaigns improve metrics like website visits and sales compared to the baseline.

Improve your marketing forecasting with Amplitude

Marketing forecasts are powerful and vital to your marketing and product strategies. Using the right data, forecasting methods, and processes, you can empower your teams to make better decisions for the business.

References

• Qualitative Data Analysis, Thematic
• AI weather forecasting is now possible, Cepsa
• Predictions 2024: Generative AI Transitions From Hype To Intent, Forrester