How do you monitor and respond to changes in customer demand, preferences, and willingness to pay?

Data-driven demand strategies for pricing in restaurants has been around for decades. Starting with RMS, Czar Metrics, Fishbowl and a litany of independent consultants. Previously it was based exclusively on POS data - which was time consuming and difficult to collect. With the enhancements in data availability and access that challenge has been solved. But now, there are even more robust data sets available to assist with these pricing engagements. Companies like @SignalFlare.AI (formerly Extropy 360) have fused together large external data sets that can more quickly and easily identify pricing opportunities based on REAL customer behavior -- beyond the restaurant's 4-walls... mobile data, credit card data, economic data & more!

Pricing information is an outcome of systematic data analysis. For example, we fostered collaborations with leading universities worldwide to ensure neutrality and eliminate biases in our pursuit of valuable insights. This enables us to enhance healthcare services in today's modern world by transitioning towards OPEX-based pricing models, prioritizing operational expenses over capital expenditures for greater adaptability and sustainability.

I cannot state this enough: pricing research or testing is not a “one and done” It has to be a continuous process with multiple sources. Here are some core ones: 1. Churn surveys - “why are you churning?” (Price as an answer option) 2. Pricing NPS - “would you recommend this product for the price you pay?” 3. Entry survey - “what other providers did you consider for this product?” Then you have standard market research tools (VW, GG, Conjoint, etc) that I would recommend 1-3 times a year plus various options for price testing/experiments based on your industry/market. Customer WTP is a moving target. If your pricing is static you are leaving money on the table.

In the fast paced modern commerce scenario, speed is the key; hence, automation is paramount. Automation needs to be applied for: (a) tracking competition pricing, products (assortment), promotions, (b) customer behaviour and demand - browsing patterns, search patterns, purchases, returns, etc. (c) inventory planning and tracking - procurement and maintaining velocity of saless A great tool for consolidating the data from multiple sources, and drawing actionalbe insights is brioanalytics.ai. It has helped increase the speed of action by 4x, volume of pricing actions by 2x, and quality of pricing actions by 5x.

In Pricing, data are crucial both to set a strategy and to assess its execution. Depending on the industry, the segment, and the business model (B2B, B2C, B2B2C, etc...) the means to collect those data will differ. Sometimes, you will be able to experiment more, and have a very straightforward return from the market, like in the retail, while in other situations, it might be more complex like in B2B. Data sources can come from various sources which don't necessarily need to be expensive market researches. For example, staying a day in the market listening, and watching at the reactions of your customers, friends, relatives, etc... Just don't forget that it is more important to understand well the different market segments.

Take time to study customer reaction. If you're in a hi-volume transaction industry, then prioritize reading "voice of customer" or "site feedback" on a daily basis. Usually this feedback is bi-modal (either 1s or 5s) but can be a lead indicator to larger problems. E.g. a new site flow that prevents easy access to product features - and thus reduces WTP, will show up quicker in site feedback than in sales volume. In a field sales based industry you're down to more pull-based loops which still can be as powerful : customer panels, mystery shopping or surveys.

To effectively monitor and respond to changes in customer demand, preferences, and willingness to pay, establish and maintain feedback loops with your customers. These loops allow you to gather direct and indirect feedback through questions, observations, complaints, and referral incentives. Use this feedback to assess customer satisfaction, loyalty, retention, and advocacy, while also identifying customer needs, problems, and expectations. Feedback loops also help you test assumptions, validate hypotheses, and refine your value proposition.

Make sure you give the right importance to customer pricing complains. Don’t ignore it, but also don’t give it too much importance. This is especially true in B2B where there are people on the other side who get paid to lower your selling prices. Focus on win rates instead. Remember: Customers must be happy with your product or service, not necessarily with your prices. You can think back for yourself: Think of purchase you made for which you were happy with the price paid. Chances are you would have been willing to pay to have it. There aren’t many people who are happy buying iPhones at above 1k. Yet, take a look at apples market cap…

One of the most important things that analysts will overlook when evaluating Price changes after they have been implemented is taking the time to understand the brand's normal customer purchase cycles & expected behavior ahead of time. The brick-&-mortar restaurant customer's purchase behavior post price increase is distinctive. When a customer sees a price on a menu that they think is over-priced, most customers don't walk out - they go through with the purchase, but they modify it - they might skip a beverage or not get dessert. It's the next 'purchase cycle' when restaurants will see customer traffic losses -- so depending on the frequency of the customer visits there may be a 2-8 week lag in traffic loss after a price increase.

Customer feedback isn't just for product improvement – it's a goldmine for crafting a winning pricing strategy! Here's how, with a real example: 1. Perceived Value: Gather feedback on how customers perceive your product or service's value. Are they willing to pay more for certain features or benefits? 2. Price Sensitivity: Surveys or focus groups can reveal customer sensitivity to price changes. Can you offer tiered pricing or discounts without sacrificing perceived value? 3. Willingness to Pay: Directly ask customers their maximum willingness to pay for specific features or the entire product/service. This helps gauge the ideal price range.

In the current fast paced modern commerce scenario, companies need to stop treating pricing experimentation as an adhoc activity. Instead, companies need to build in price experimentations as part of their pricing or business strategy. To achieve this, companies need to develop speed in their experimentation, which will not only allow them to increase the number of experiments, but also quickly revert back from experiments that are not headed in the right outcomes. Online travel agencies (OTAs) such as expedia.com, and online food ordering apps (like Zomato) are setting new benchmarks here by experimenting with a varied set of discounts with different customer cohorts and figuring out what's best.

A/B testing is a powerful tool for monitoring and responding to shifts in customer demand, preferences, and willingness to pay. Companies can empirically test how different pricing strategies affect customer behavior by comparing two versions (A and B) of a variable, such as price, structure, or format. For instance, they may vary the price level or frequency in two different market segments and analyze which yields better customer engagement or sales. This experimentation helps identify the most effective pricing strategy, ensuring that the company adapts to changing consumer preferences and maximizes revenue. It's a methodical approach to understanding customer behavior and optimizing pricing to meet market demands. #pricing #marketing

One of the mistakes businesses make is 'testing' pricing as if it's a one-time event that can be measured like a laboratory test with a clean A & B group. The trouble is that there is no such thing as a 'clean' control group in the real world. Businesses that take data-driven-decision making seriously should be tracking initiatives such as price changes, promotional activity, marketing spend, new product rollout and major event on a constant basis -- tagged in an analytical table & optimized for multivariate analyses. There are many multivariate techniques, and isolating what is driving changes in demand does not need to be elusive anymore. When data is properly specified Price elasticity & attribution analysis is straight forward

Experimentation in pricing within the SaaS startup space is like A/B testing in coding—crucial for optimization. We deploy controlled experiments on pricing tiers, observe how changes affect customer acquisition and retention, and gather concrete data on price elasticity. This method allows us to fine-tune our pricing in real-time, ensuring it aligns with customer value perception and willingness to pay. The key is to move with data, not hunches, allowing the market's response to guide our strategy. It's a dynamic, evidence-based approach that keeps our pricing competitive and our value proposition clear.

The third step is to experiment with different pricing options and strategies. Test various pricing variables like price levels, structures, and formats to see how they affect customer behavior and outcomes. Compare scenarios such as discounts, bundles, and subscriptions to optimize your pricing strategy based on your goals and customer preferences.

Make sure pricing strategy adjustment are not based on 1 or 2 episodes. Always look at the bigger picture. Tip: When pricing your products or service, think upfront of an “adjustment period” and agree with your stakeholders on what needs to happen for a pricing strategy adjustment to be made - and stick to that model. Always best to plan versus react

The fourth step is to adjust your pricing strategy based on the insights from data, feedback, and experiments. Use these insights to refine your pricing by adjusting price levels, structures, formats, and customer segmentation. Additionally, consider offering different pricing options or incentives. Clearly communicate any price changes to your customers and stakeholders, and monitor their reactions and responses to ensure alignment with your business goals and customer expectations.

If you want to raise the price, try to avoid a blank price increase (more for the same), as it alienates customers. Instead, increase prices with new versions or products that allow for market segmentation and maximize revenue by catering to different customer needs and willingness to pay. Cheaper Offer (Less for Less) ?? Offer the basic version at a lower price Attracts price-sensitive customers Serves as an entry-level option Main Offer (More for a Bit More) ?? Standard product with enhanced features Slightly higher price for added value Appeals to the majority of customers Decoy Offer (Much More for More) ?? Premium version with exclusive features Significantly higher price Creates perception of value in the main offer

Observe relative metrics that are available to you that put absolute performance (units, gross sales, revenue) adjacent to intent (website visits, unique searches, etc). Conversion rate is a start, but much better is thinking about pattern shifts in "of people who looked specifically for x, what % bought". Online travel does this via a "look to book ratio", eg: of the people who searched in x destination for y date, what % booked. Comparing the metric across dates allows them to see real-time relative pricing efficiency issues. A retailer could contemplate something similar - eg. of people who searched for product x during y time period, what % booked. This unlocks signal and helps prioritize pricing teams' time.

One suggestion: Keep it simple. I suggest using win rates - and agree upfront after how many observations the number becomes relevant for your market. Also define upfront what is your target % win rate and measure versus those. At minimum segment by current customers and new customers. Segmentation is key - as you only care about the win rate for your target segment (ideal customer).

The fifth step in evaluating pricing strategy is to monitor and respond to changes in customer demand, preferences, and willingness to pay. Evaluation metrics help measure the effectiveness of your pricing strategy by tracking performance against objectives, benchmarks, and competitors. These metrics assess the impact on key performance indicators like sales volume, revenue, profit, market share, and ROI. By identifying strengths and weaknesses, you can make necessary adjustments to improve your pricing strategy.

To continuously enhance your pricing strategy, it's crucial to cultivate "pricing excellence" as a key organizational skill. This involves several dimensions: Strategic Pricing: Aligning pricing with business strategy to achieve long-term goals. Price Setting: Price based on market research, cost, and value perception. Price Variation Management: Flexibly adjusting prices in response to market dynamics. Price Execution: Ensuring effective implementation of pricing strategies and discounts across all channels. Monitoring and Improvement: Regularly analyzing pricing performance and making adjustments for improvement. By focusing on these aspects, your organization can develop a robust approach to pricing.

An excellent Pricing analytics program is not just about pricing, it's about having the data and capabilities to understand the underlying demand fundamentals of the business. Over the long run the benefit of maintaining and improving the data and the analytics program that supports it far is far better than a project-based approach where knowledge and efficiency is lost each time.

The sixth and final step is to continuously improve your pricing strategy. This involves ensuring that your pricing remains aligned with your value proposition, competitive advantage, and market position. Continuous improvement helps your strategy stay responsive to evolving customer needs and expectations. It also fosters innovation and differentiation, keeping your pricing ahead of competitors.

It's an exciting time to be helping restaurants measure consumer demand and willingness to pay. The industry is so new to the digital age relative to others and there is so much to be done. Mining historical data and combining that with new, relevant, external data sources is opening up a whole new world of insights, ideas and ways to make more money for an industry that works extremely hard. Now that digital ordering plays such a significant role in restaurants, they can finally participate in price optimization tactics employed by similar industries for decades. The first, and arguably the most important element, is effectively measuring consumer demand and willingness to pay.

A German B2B market study with 324 participants revealed that managers often have a skewed perception of their pricing. By definition, half of the companies should have prices above the median and half below. Contrary to this statistical expectation, the study found that only 7% of managers believed their prices were lower than competitors, while a significant 78% perceived their prices to be higher (Homburg et al. 2004). This indicates a widespread misjudgment in price positioning among managers, suggesting a potential disconnect between perception and market reality.

Monitoring and responding to changes in customer demand, preferences, and willingness to pay is an ongoing, dynamic process. Leveraging multiple data sources, including sales data, customer feedback, and market research, allows us to gain a comprehensive understanding of our customers' behavior. These insights are crucial for staying attuned to shifting trends and emerging preferences. By fostering a culture of constant learning and adaptation, we can refine our methods and strategies over time, ensuring sustained growth and customer satisfaction. This involves not only encouraging team members to stay updated with industry trends and best practices but also promoting an environment where feedback is actively sought and valued.

To effectively monitor and refine your pricing strategy, evaluate its impact using metrics such as sales volume, revenue, and ROI, and adjust based on performance against objectives and competitors. Additionally, continuously improve your pricing to stay aligned with your value proposition and market position, ensuring responsiveness to customer changes and maintaining innovation to differentiate from competitors.