Marketing Forecasting 101: Using Analytics for Future Insights

Marketing forecasting is how companies make educated predictions about their future performance within their specific target markets. By using market research and historical data, marketers can make forecasts about demands and trends that will help them better predict sales.

The forecasting process helps you understand the effectiveness of your marketing strategies and puts you in a better position to optimize your efforts going forward. By understanding the strengths and weaknesses of your campaigns, you can better predict what will work and what techniques to omit altogether.

What is marketing forecasting?

A marketing forecast helps businesses conduct trend analysis by predicting future market characteristics, sales data, and the growth rate within their sector. Forecasting means you replace guesswork with an empirical, data-focused approach to planning. There are several different types of forecasting techniques that allow businesses to obtain data using both qualitative and quantitative methods.

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

• Predicted customer behaviors throughout the user journey
• Number of leads likely generated within a period
• Rate of leads moving through the sales funnel
• Effectiveness of different marketing campaigns and channels in acquiring new customers
• Market potential of the product: how much potential revenue your product or service will likely generate within a specific market.
• Future sales numbers and revenue impact
• Impact on critical product metrics around acquisition, retention, and monetization

A marketing forecast takes all of these predictions and consolidates them into one analysis, allowing your business to get a complete picture of the future. These insights enable you to carry out more strategic planning, knowing you have all the necessary information.

Main benefits of marketing forecasting

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

Several benefits come from taking this approach:

Insight into future trends

Trend forecasting involves using market and consumer data to predict how customer behaviors and purchasing habits will likely shift over time. Predicting future trends in the market helps you outpace your competitors during times of change.

There are several different types of trend forecasting patterns that you can analyze, such as constant and linear patterns in data. For example, you can predict when demand for certain products will likely rise or fall and prepare accordingly. Trend forecasting also provides you insights to predict shifting customer behaviors and expectations. You can use this knowledge to adjust your marketing or product strategy.

More targeted marketing activity

You can use predictive customer analytics to understand user behavior and forecast which behaviors will likely have higher conversion rates. These insights will help you craft more effective messaging, refine your pricing and packaging, and increase your cross-sell and upsells.

A predictive analytics tool like Amplitude Audiences leverages algorithms that make connections between specific behaviors and conversion. For example, you might find that people who arrive on your landing page from social media ads are more likely to sign up for a free trial. You might invest more heavily in your social media marketing efforts with this insight.

Forecasting helps you understand which marketing channels will be most effective based on trends, market data, and user behavior.

Increase customer retention

Another benefit of utilizing predictive analytics is the ability to target customers who are at risk of churning through churn rate cohort analysis. Once you’ve identified these at-risk customers, you can experiment with the most effective marketing campaigns to increase retention and boost loyalty. For example, you might employ inverse pricing—offering customers with a high likelihood of churning a larger discount or incentive.

In this inverse pricing example, a streaming company might offer customers with a low likelihood of upgrading a larger incentive than those with a high likelihood of upgrading.

Proactive vs. reactive planning

Predicting and planning for several possible scenarios helps you be more proactive in your approach. Implementing contingency plans allows you to build more resilience to otherwise unexpected events. These could be external or internal events such as shifts in economic trends, changes in customer sentiments, technological advancements, or losing customers to competitors.

Precise budgeting

You can better allocate funds to different areas of your business through budget forecasting. Look at your sales forecasts and check them against your expense forecasts for both the short and long term. This way, you can budget smarter for different costs like:

• MarTech tools
• Paid advertising
• Marketing campaigns
• Product launch events
• Engineering and product costs

Deciding to invest in things like developing new products, hiring more employees, or boosting digital marketing efforts can be risky. But understanding what your company’s financial situation will look like further down the line helps remove a lot of the uncertainty.

Better inventory management

For ecommerce businesses, inventory forecasting ensures you have the right supply to meet customer demand across your digital channels. Ecommerce inventory management involves tracking the location, amount, pricing, and mix of available inventory. By basing your orders on an accurate forecast, you don’t have to worry about over or under-ordering products for your online store.

More accurate employee allocation

HR forecasting ensures you have the right number of employees to meet business and customer needs, leading to a better customer experience.

For example, if you have an ecommerce business, you might forecast a spike in sales during the holidays and need extra customer service representatives to respond to inquiries. Or perhaps you plan on hosting a marketing event for your B2B SaaS tool’s new product launch and forecast an increase in inbound sales requests from prospects and customers.

Common marketing forecasting techniques

Predicting what will happen in the future might sound tricky, but you can use 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.

Analyzing correlations

Correlational analysis helps you understand the relationships between your customers and your product. Through your analysis, you might find that certain features you implement in your platform have positive or negative effects on your customer experience.

This information provides product managers with the knowledge of what aspects of their product line 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.

Predictive analytics

With Audiences’ Predictions, you can build cohorts based on specific attributes or behaviors that will help you identify product and marketing tweaks to improve conversion. Predictive analytics can help you:

• Personalize your marketing messaging
• Choose the right pricing for your target audience
• Cross-sell and upsell based on historical data to increase CLV
• Use inverse pricing techniques to develop the most effective actions for different audiences based on how likely they are to perform the desired actions.

Seeking executive and expert opinions

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

For this approach to be accurate, opinions must be collected and analyzed using tried and tested qualitative methods. One example could be thematic analysis, where you extract common themes from raw qualitative data, such as interview transcripts.

Conducting customer surveys

Customer surveys involve carrying out research with potential customers about new products or finding out how your current customers feel about your existing products. You can collect information directly from your current and potential customers 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 get a feel for your customers’ sentiments. You should then use those sentiments in your marketing forecast. If 90% of your customers say they love your new product, sales will likely be high.

Gathering information from your sales team

Your sales team is at the front of your marketing activities. They have insight from their daily experiences into how your products perform, the effectiveness of your marketing activities, and your customer sentiment. You can collect this information by conducting interviews and surveys or hosting focus groups.

One limitation is that your sales team can only provide information about your existing products and current marketing efforts. However, you can use the information they give you and insights from your sales funnel to understand how other marketing efforts will work. For example, if customers respond well to a specific ad for a soon-to-be-updated product, you know you should use a similar ad when you roll out the new version. Yes, the new product and ad don’t exist yet, but your salespeople can still offer valuable insights.

Implementing time series techniques

Time series techniques look at sales patterns over various periods. You can use them to uncover patterns over the past month, quarter, or year that will 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.

It’s helpful to know what will happen in a specific period to make more strategic product and marketing decisions that will help you 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 the premium version of your digital product.

How to conduct a marketing forecast

Even though there are several different forecasting tools that companies can use to carry out their analysis, there is a basic methodology to be followed:

1. Plot out the stages of your revenue cycle. Track a customer’s typical journey from start to purchase using customer journey analytics. This gives you foundational knowledge about your customer journey.
2. Identify the leads that you would like to track. Pick a few high-value customer cohorts whose journey you want to optimize. These are market segments you identified as most valuable to you during your market research.
3. Obtain information on how every customer experiences their 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 who will move 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, which offers 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. Using insights from past customer behavior, a tool like Audiences can predict future behaviors using AI and machine learning technology.
7. Analyze your results and finalize your marketing forecast. With this information, you’re in a stronger position to predict future sales, trends, and general consumer behavior.
8. Take action on your insights. Forecasting what will happen in the future is only helpful if you take action. Use your predictions to test new marketing campaigns, product personalizations, pricing strategies, and more.

Learn how you can leverage the power of predictive analytics with a personalized Audiences consultation. Or see how customers are behaving in your digital product today with a free Amplitude account.

References

• What is Ecommerce Inventory Management?, Big Commerce
• What Is Trend Forecasting?, Chron
• Customer Acquisition vs. Retention, Invesp