Market forecasting & Pricing Analytics

Market forecasting allows companies to make informed predictions regarding their performance in their target market. Market research and historic data can be used to make predictions about demand and trends, which will allow marketers to better predict their sales.

Forecasting helps you to understand your marketing strategy’s effectiveness and allows you to maximize your future efforts. Understanding the strengths and weakness of your marketing campaigns will help you better predict which techniques are likely to work, and those that should be avoided.

What is Marketing Forecasting?

Marketing forecasts help businesses analyze trends by providing information on future characteristics of the market, data from sales, and growth rates within their industry. By using forecasting, you can replace your guesswork and plan based on empirical data. Businesses can use both quantitative and qualitative methods to gather data with different forecasting techniques.

Business use behavior analytics and historical data to forecast things such as:

• Customer behavior predictions during the entire user journey
• The number of leads that are likely to be generated in a given period
• Sales funnel ______
• The effectiveness of marketing channels and campaigns in acquiring customers
• Potential market for the product : How much revenue you can expect to generate with your product in a particular market.
• Impact on revenue and future sales numbers
• Impact of critical product metrics in relation to acquisition, retention and monetization

Marketing forecasts combine all these projections into one report, giving your company a comprehensive view of the future. You can then plan more strategically, as you will have the information you need.

Benefits of Marketing Forecasting

It is the foundation of your product and marketing forecast. You can use it to plan for the future, and help your team make decisions.

This approach has many benefits:

Insight into future trends

The process of trend forecasting uses market and consumer information to determine how the behavior and buying habits of customers will change over time. Forecasting market trends helps you to stay ahead of your competition during periods of change.

You can use different types of trends forecasting patterns, including constant and linear data patterns. You can, for example, predict the likely time when certain product demand will rise or drop and plan accordingly. You can also use trend forecasting to determine changing customer expectations and behaviors. This knowledge can be used to modify your marketing strategy.

Marketing activity that is more targeted

You can also use customer predictive analytics in order to forecast the behavior of users and predict which actions will lead to higher conversion rates. This information will allow you to create more targeted messaging, improve your packaging and pricing, and boost your upsells and cross-sells.

Predictive analytics tools like Amplitude Audiences use algorithms to make links between certain behaviors and conversion. You might discover, for example that visitors who land on your page via social media are more inclined to sign up. This insight could help you invest more in social media marketing.

You can use forecasting to determine which marketing channels are most likely to be effective, based on market trends, data and behavior of users .

Customer retention:

A second benefit to using predictive analytics is that you can target customers who may be at risk for churning by performing cohort analysis. You can then experiment with marketing campaigns that will increase loyalty and retention. You could, for example, use inverse pricing – offering customers who are more likely to leave a greater discount or incentive.

Reactive planning vs. proactive planning

Planning and predicting multiple scenarios will help you to be proactive. By implementing contingency planning, you can build resilience against unexpected events. They could include external or internal factors such as changes in the economy, customer attitudes, technology advancements or loss of customers.

Budgeting with precision

Budget forecasting allows you to better allocate your funds between different parts of your company. Check your short-term and long-term sales forecasts against your expenses forecasts. You can then budget more effectively for costs such as:

• MarTech Tools
• Advertising that is paid
• Campaigns for marketing
• Launch of new products
• Costs of engineering and products

It can be risky to decide whether or not you want to spend money on things such as developing new products, adding more staff, and boosting your digital marketing. Understanding what the future financial state of your business will be can help remove some uncertainty.

Inventory management is a better way to manage your inventory

Inventory forecasting is essential for ecommerce business to ensure you are able to supply the products and services your customers need across all digital channels. Inventory management is the process of tracking inventory, including its location, price, mix, and amount. You won’t need to worry about ordering too much or not enough products if you base your order on an accurate forecast.

Employee allocation is more accurate

The HR Forecasting will ensure that you are able to have enough employees on hand to satisfy business needs and meet customer expectations, resulting in a more satisfying customer experience.

If you run an online business, for example, you may forecast that sales will increase during the holiday season and require additional customer service agents to answer inquiries. You might be planning a launch event to promote your new SaaS product, and you expect an influx of inbound requests for sales from customers and prospects.

Marketing forecasting methods

It may seem difficult to predict the future, but there are several ways you can get accurate predictions. You’ll get different metrics and insights from each, but you can combine them to create a comprehensive view of the future.

Analysis of correlation

Correlational Analysis can help you to understand your customer’s relationship with your product. You may find out that the features implemented in your platform are having a positive or negative impact on customer satisfaction.

The information provided by this report allows product managers to understand what factors in their product lines contribute (or inhibit) retention of customers or engagement. This helps them improve their product for growth.

Also, you can analyze correlations relating to your marketing campaigns. If you find out that customers acquired via referral programs have higher Customer Lifetime Value (CLV), than through social media campaigns, optimize your marketing efforts accordingly.

Predictive Analytics

You can create cohorts using Audiences’ predictions to identify marketing and product tweaks that improve conversion. Can help with predictive analytics?

• Customize marketing messages
• The right price for your audience
• Increase CLV by cross-selling and upselling based on historical data
• Inverse pricing can be used to determine the best actions to take for various audiences, based on their likelihood to follow through with the action desired.

Experts and executives are sought to provide their opinions

You can get simple, knowledge-based opinion from industry experts and executives who are well informed. Although they might not be able to provide hard data to “prove their opinion”, their vast experience can help in forecasting.

To be accurate in this method, it is important to collect and analyze opinions using qualitative methods that have been tried and tested. Thematic analysis is one example, in which you can extract themes common to raw qualitative data such as transcripts of interviews.

Conducting customer surveys

Customer Surveys involves conducting research on potential customers to find out what they think about your new products, or asking your existing customers how they feel about them. To help you, you can gather information from current customers and future prospects.

• Understanding customer intention
• Gather demographic information about your customers
• Find out what their price range is

You can then analyze the data to determine what your customers are feeling. These sentiments should be used in your marketing predictions. Sales will be likely to increase if 90% of customers love your product.

Contact your sales team to gather information

Sales team members are the first to know about your marketing efforts. Your sales team has daily experience that can provide valuable insight into the performance of your products, your marketing efforts, and customer satisfaction. This information can be collected by interviews, surveys, or focus groups.

Your sales team is only able to provide you with information on your current products and marketing campaigns. You can still use their information and the insights you get from your funnel to better understand what marketing strategies will be successful. If customers are responding well to an ad that will be updated soon, then you can use the same ad in your new product launch. Your salespeople may not have the product or ad yet but they can offer useful insights.

Time series techniques

Time series techniques look at sales patterns over various periods. They can be used to discover patterns in the last month, quarter or year, which will help predict future sales. If, for example, sales have grown by 3% every year over the last three years, you can safely assume the same growth will occur next year.

Knowing what is going to happen over a certain period will allow you to make better strategic decisions about your product or marketing, which will increase the market share. You can, for example predict the number of items that you will sell via your e-commerce channels and how many people are likely to upgrade their digital products.

How to do a marketing projection

There are many different tools available to companies to use for their analyses, but there is one basic method to follow:

1. Plan out your revenue cycle. Customer journey analytics allows you to track a typical customer journey, from the first contact through to the final purchase. You will gain a solid understanding of your customers’ journey.
2. You can track leads by identifying them. Choose a small group of high-value customers whose journeys you would like to optimize. You have identified the most important market segments during your research.
3. Get information about how each customer lives their lifecycle. Use metrics such as conversion rate and abandonment rate if you are an ecommerce business to determine the percent of visitors that make purchases and those that place products in their shopping carts but do not complete their order.
4. Calculate the number of prospects who will pass through your funnel during a certain period. You can use the lead count to get a good idea about how many customers your B2B SaaS business will be able to attract. This is a useful starting point for forecasting. By looking at the trends in your current sales funnel and speaking to your team, you can estimate how many leads your company has.
5. Modeling the flow of leads, both new and existing ones through every stage in the customer journey is important. After you have gathered the data from previous steps, plot the typical customer journey. You can make more accurate predictions by using customer experience.
6. Predictive analytics based on customer behavior data. By analyzing past behavior of customers, Audiences is able to predict future behaviour with AI and machine-learning technology.
7. Finalize your forecast by analyzing your results. You’ll be better able to forecast future trends and consumer behaviour with this data.
8. Act on the insights you have gained. It is not helpful to forecast what the future will bring unless you act. You can use your predictions to test marketing campaigns, personalization of products, pricing strategies and other things.

Pricing

The impact of pricing activities on the performance of a business is significant. Quantifying pricing is essential to ensuring that decisions are made for the maximum profit. Pricing analytics can help companies understand their markets better by analyzing pricing trends and patterns.

What is pricing analytics?

The collection, aggregate and analysis of data on pricing from different sources is called Pricing Analytics.

This tool allows companies to understand their customers’ reactions to various price strategies and identify potential revenue opportunities.

Companies can identify the products that are most profitable and then adjust prices based on this information.

Pricing analytics are used in different ways by businesses depending on their industry.

Pricing analytics is a great tool for businesses that are subscription-based to understand their lifetime customer value. It can also help set the prices for each tier of customers.

Retail businesses can use pricing analytics to determine seasonality in their sales, and then make adjustments to the dynamic pricing.

Synonyms

• Analysis of Price: A process that involves examining the costs of products/services and determining the best pricing structure.
• Pricing Optimization A strategy to find the most profitable prices for products and services.
• Pricing Intelligence : Ability to gather, analyze and interpret information about the market in order to improve pricing decisions.

Price Analytics: It’s Important

Pricing analytics can be beneficial to companies in almost any industry. Especially large companies, which tend to have complicated pricing structures as well as large catalogs, benefit from being able to analyze price data in order to find revenue opportunities.

Pricing analytics has many benefits.

Price Opportunities

Pricing analytics can help companies identify new revenue opportunities.

It is particularly true for companies that are looking to move from a transactional revenue model to one of recurring revenues. Understanding the demand of customers for various products and services allows the business to create packages with different price points in order to maximize sales.

Price analytics can be used to find discounting opportunities, without risking margin loss. The data can be analyzed by companies to identify which discounts generate the highest sales, while maintaining healthy margins.

Optimize Pricing Strategies

Companies often lose revenue due to complexity.

Business can be complex in many ways.

• Customers with different average transaction sizes, e.g. enterprise, mid-market and SMB buyers
• Multi-product lines, with different pricing models (e.g. one-time service, subscription model, tiered/package offering)
• Market conditions that fluctuate
• Transaction volume
• Multi-channel and multi-market strategies
• Pricing based on quotes

It is unlikely that an organization will find the ideal pricing structure for their products or services.

Businesses can fill in the gaps of their pricing strategies by analyzing historic data and making more informed, accurate pricing decisions.

Increase Profitability

Pricing analytics is used to determine optimal pricing. This is the price at which the most customers are likely to buy the product from the business, while maintaining a profit margin.

Price analytics can improve profitability for businesses in many ways.

• Higher average revenue per user. Businesses that can accurately predict trends and closely match pricing models to customer demand can generate more revenue per customer.
• Margin improvements. By analyzing pricing data, companies can identify discounts and create new revenue streams.
• Reduction in churn. If organizations can identify where they are falling short on pricing, then they will be able to take the necessary steps to improve retention.

Customers Insights

The data that pricing analytics provides companies allows them to adjust prices according to demand.

Companies can better understand the customer by analyzing their behavior, demographics and seasonality. They will also be able to provide them with a more suitable product through pricing.

Pricing analytics can be used to customize offers for specific segments of buyers, so that they receive the most value from their budget.

Sales and marketing can better identify their Ideal Customer Profile and communicate with them, thus lowering total cost of acquisition (CAC).

Concentrate on profitable channels

Pricing analytics can be used to determine profitable channels.

Over 40% of salespeople cite prospecting as the hardest part of selling. Pricing analytics can help organizations identify which customers are the biggest revenue generators for their business.

Businesses can allocate more budget and resources to the key areas that generate most of their sales and steer away from less profitable channels by using data.

Enhance Operational Efficiency

Pricing analytics helps businesses to save money by reducing the time spent on marketing campaigns and sales team prospects and allowing them to focus their efforts.

Data can be used by companies to automate some aspects of their businesses, for example creating bundles or promoting discounts at certain times.

These activities can be time-consuming, error-prone and involve a lot of guesswork. By providing businesses with reliable and trustworthy data, pricing analytics reduce human error.

Pricing Analytics Metrics

In order to understand the behavior of customers and establish the best pricing plan, companies must follow the correct data.

Here are some key metrics to consider when analyzing pricing:

• Demand Elasticity is the degree of change in product or service demand that can be attributed to changes in prices. This change in demand is measured as a percentage relative to an increase in price. A high elasticity means that changes in prices have a dramatic impact on demand.
• Pricing Sensitivity : A measure of the degree to which customers’ behavior is affected by price changes, as measured by how much sales and purchases change after a new price. A high level of sensitivity indicates that the customer is either actively seeking better prices or looking to avoid certain ones.
• Revenue Per Customer Measures the contribution of each client to revenue. The revenue per customer can be relative to companies or business models. Measuring against the industry standard is an effective way to determine the success of pricing strategies.
• Quotation-to-Cash Rate of Conversion: Ratio between quotes accepted and sales. High conversion rates indicate the efficiency of pricing models.
• The Average Order Value is the average order value of a client. The AOV of a company is relative. Companies with high-priced goods may have an AOV that’s higher than average, but they could have lower revenues per customer, or even fewer customers. This would mean a poorer pricing strategy.
• CLV (Customer Lifetime Value): Total amount of money that a client is expected to spend over the course of their life with a company (closely correlated to customer loyalty). A business that is losing customers before CLV can compound should examine its pricing to see if it matches customer expectations.
• Gross margin: The profitability of a company on a transaction basis. This includes all the costs involved in generating sales revenue. High sales can offset low margins and vice-versa.
• Product Profitability: A business’s income from one product or service compared with the resources it used to produce that income.

Different types of pricing analytics

Pricing analytics can be classified into three types: descriptive, prescriptive, and predictive.

Description

The descriptive pricing analysis provides an overview of past data such as sales history and customer behaviour.

Examples include:

• Average order value
• Revenue per customer
• Conversion rate from quote to cash

Descriptive analytics is used by businesses to identify trends, understand buyer behavior and perform feature value analyses. They can use this information for their product and marketing strategy, pricing and pricing strategies, as well as their overall pricing.

Predictive

Data mining and machine-learning are used to find patterns and predict future outcomes using predictive pricing analytics.

Examples include:

• Demand and price elasticity
• Prices are sensitive to price
• Customer lifetime value

Businesses can use predictive analytics to anticipate customer behavior and determine the most effective pricing strategy for their product or service.

They are best suited for businesses with predictable revenue.

Prescriptive

The algorithms used in predictive pricing analytics are advanced and identify the optimal pricing plan. They also provide immediate feedback about how changes in price will affect future performance.

This is similar to predictive analysis, but it provides specific recommendations to businesses rather than relying on them.

Software that has price optimization capabilities can produce this type of analysis. Businesses can adjust prices to maintain competition by answering the question “what should be done?”

The use of predictive pricing analytics is also useful for customer segmentation, and identifying profiles that have the greatest potential lifetime value.

Price Analysis Software Features

Pricing analytics software should have the features to allow businesses set retail prices at the correct level or offer the most suitable software subscriptions without having their bottom line affected.

Some of the features are:

Real-time Monitoring

Businesses can quickly adapt their pricing strategy by monitoring customer feedback, competitors’ prices and the market in real-time.

This can be done by using a pricing analysis tool that tracks customer purchasing habits as well as pricing changes made by competitors.

Alerts

Businesses receive alerts when customer behaviour changes, or when competitor prices fall. Alerts can be used to update customer or market data. For example, when customer behavior changes and seasonality impacts the market.

Competitive Intelligence through automatic alerts helps companies to stay abreast of list prices, discounts, and promotions.

Integrating the Internet

Software that has data analytics and price optimization built in is available. It should be integrated with the rest of your company’s tech stack.

ERP

The ERP software keeps track of all customer transactions, including purchases, prices, and history.

Integration with CRMs gives companies access to information about their customers, such as purchasing habits, preferences and contact information. It allows them to personalize their offers and reach the correct audience.

BI Tools

Analytics tools and business intelligence software should be integrated to avoid silos of data. Businesses can then use the same data source for marketing, pricing and product decisions.

CPQ

Configure-price-quote (CPQ) software helps companies quickly generate accurate quotes and configure complex products. This integration allows businesses to accurately reflect changes in pricing into their CPQ systems, ensuring that customers always get the best price possible for any product or service.

Billing

The bill process plays a major role in a company’s pricing strategies. Pricing analytics should be able to track revenue and profit for every customer using automated billing.