Every business, big or small, needs an effective pricing strategy to maximize profits and ensure sustainability. While Excel is familiar and widely used for demand forecasting and pricing, it can not meet the demands of modern-day pricing. Setting up an optimal pricing strategy requires tracking real-time market fluctuations, multi-channel consistency, and predictive analytics outside of Excel’s capabilities.
Imagine investing hours to build an Excel-based pricing model, only to find it can’t handle the sophistication to predict outcomes accurately. In this blog, we’ll unpack the 8 key challenges you will likely encounter when building a pricing model in Excel.
The Main Drawbacks of Using Excel For Pricing Model
When it comes to pricing and demand forecasting, spreadsheets, Excel, and gut-feeling decisions are still the go-to methods. However, these methods come with challenges that could result in you leaving money on the table. Let’s understand the common challenges associated with using tools like Excel for pricing modeling.
1. Excel is vulnerable to human errors
Replacing a comma with a dot in spreadsheets may seem harmless, but it can have huge consequences on your data. This can affect your pricing model and lead to a massive financial loss. Even minor issues like missing negative signs or misaligned rows can result in serious losses, potentially costing millions. These kinds of errors could make or break the success of your business.
Recent studies have found that almost 9 out of 10 spreadsheets contain errors due to the lack of version control, manual data entry mistakes, copy-paste errors, and accidental overwrites. Human inputs are prone to mistakes, making it difficult for users to detect or correct errors since Excel doesn’t provide an efficient way to validate data. Automation is always better for managing large and complex datasets than manual processes.
2. Excel is not useful for advanced pricing models
While Excel is a useful tool for basic pricing strategies like cost-plus pricing, it falls short when dealing with more sophisticated approaches. Businesses relying solely on Excel may miss crucial factors influencing customers’ willingness to pay, such as value-based pricing, leaving money on the table.
Additionally, Excel proves less effective for advanced pricing strategies like demand-based and product specification-based pricing. Without a dedicated pricing system, it becomes challenging to understand, test, and optimize tactics like psychological pricing, quantity discounts, and attribute-based pricing.
Furthermore, businesses using spreadsheets often struggle to adjust prices based on markets, competitions or locations and may find it impossible to implement complex pricing strategies due to Excel’s limitations.
Must Read: How To Use Competition-based Pricing Strategy To Protect Your Margins?
3. Lack of collaboration in Excel
Large retail organizations typically have various subcategories within their product offerings, each with its unique pricing considerations and market dynamics. Having transparent access to data from all these categories can help category managers construct profitable pricing models. Therefore, for retail businesses, collaboration is not just a cultural mandate but a critical tool for productivity.
For instance, the Home category in retail has subcategories like fragrances, fresheners, and lighting. Fragrances and fresheners have expiration dates and shelf life constraints. Thus, when dealing with older inventory in such categories, category managers must implement substantial discounts to facilitate sales and meet targets without incurring losses. However, subcategories like lighting can feature exclusive merchandise, allowing category managers to price these unique SKUs at higher margins.
Overreliance on Excel for pricing can lead to bottlenecks, hindering swift pricing adjustments and adversely affecting a company’s profitability.
Excel’s limitations become even more evident when collaboration is required for pricing decisions. It necessitates a rigorous change management process involving central repositories, established procedures, and designated document owners to ensure accuracy and consistency. However, this entire process can be time-consuming and prone to errors, requiring extensive manual data consolidation and verification.
Furthermore, an integrated Excel solution, even with contributions from various subcategory teams, can only provide managers with periodic views, often limited to once or twice a day.
On the other hand, a robust pricing solution offers real-time data insights at the subcategory level, providing pricing managers with critical information to make informed decisions. This helps organizations remain agile and competitive in the long-run.
4. The pricing model in Excel cannot handle large databases
The pricing model in Excel cannot effectively handle large databases. To put this into perspective, Excel encounters performance issues or even crashes when dealing with data exceeding 20 megabytes (MB) in size.
Consider the data scale at which a retailer as big as Flipkart operates – processing an astounding 1 petabyte of data daily. Even if we exclude user-generated data, the magnitude of data remaining still amounts to hundreds of terabytes. Clearly, such a task is far beyond the capabilities of Excel files.
5. Managing spredsheets is time-consuming & lacks real-time data access
Excel can be a powerful tool for businesses of all sizes, but its lack of standardization can make it difficult for multiple people to work with the same document. It is because individual users often customize their Excel sheets to fit their preferences or solve specific troubles, making it hard for other team members to adjust when taking over the job. For pricing managers, it is essential to evaluate the effectiveness of decisions. Yet, conducting A/B testing and in-depth analyses can be arduous and time-intensive, as tracking all the alterations is challenging.
Furthermore, as the data supported on Excel are not updated in real-time, businesses often lose out on the opportunity to capture the change in market demand, leading to significant business loss.
6. Building a pricing model in Excel raises security concerns
Having all your pricing data stored in one Excel spreadsheet poses a major security risk. Even if you put some security measures in place, an experienced user could still bypass them. For example, if the spreadsheet accidentally gets sent to the wrong person or falls into the wrong hands due to theft, it could cause grave consequences for your business. Moreover, disgruntled employees may alter formulas without detection or send the contents to your competitors.
To prevent such consequences, leveraging the capabilities of modern pricing softwares with SSO-enabled logins and latest security protocol is a better choice.
7. No Access Control Features In Excels
One notable limitation of Excel for pricing models is its lack of access control. Within organizations, not all stakeholders should have identical access rights to pricing data.
Consider a scenario where a team member has the authority to view and edit pricing rules for a specific product category. However, they might require access to data from other categories for comprehensive decision-making. In such cases, it becomes crucial to prevent them from modifying pricing rules for categories beyond their scope. On the other hand, executives and central leadership teams often require broader access, granting them oversight of pricing rules across all categories.
This level of access control helps prevent unintended alterations and ensures that stakeholders can access specific information they need without compromising the integrity of the pricing model. Unlike more advanced solutions, Excel lacks the granularity of access control required to manage these varying needs.
Must Read: Price Optimization Models: How Retailers Can Use It For Business Growth
8. Excel does not provide "smart insights" and "deep analytics"
While Excel can perform basic price analyses, it falls short compared to modern, sophisticated ML/AI-based pricing programs. It does not consider external factors and cannot provide smart insights.
On the other hand, modern pricing software is designed to handle more complex calculations and easily incorporates external data into pricing strategies. Such pricing softwares also offers insights like alerts on inflection points, brand sentiment analysis (providing feedback on customer sentiments about specific brands or SKUs), and tracks competitors’ price refresh rates and list of updated pricing on a regular basis. This provides a much more comprehensive approach to pricing strategy than Excel can offer.
Why you need dynamic pricing software
While Excel served as a valuable pricing software in the past, it now falls short of meeting the demands of today’s dynamic pricing landscape. Its limitations in flexibility, efficiency, and scalability hinder businesses from optimizing their bottom line with real-time pricing strategies.
Although Excel offers various chart types, it lacks built-in drill-down capabilities. Extracting precise pricing insights becomes a laborious task, involving cross-referencing millions of data points, monitoring KPIs and metrics, and frequently necessitating extensive ad-hoc analyses to reveal potential business opportunities. To maintain a competitive edge in the market, you need more than what Excel can offer.
You need a specialized pricing software like Pricing Manager by Flipkart Commerce Cloud (FCC). Leveraging advanced AI and ML algorithms backed with game theory principles, FCC offers intelligent pricing recommendations that maximize profitability while accounting for market dynamics.
Its user-friendly dashboards present complex data in an easily digestible format, facilitating rapid decision-making. FCC’s Pricing Manager promotes inter-departmental collaboration for synchronized pricing strategies and implements rigorous security protocols and SSO-enabled logins to safeguard sensitive pricing data.
FCC’s innovative pricing platform is tailored to help businesses of all sizes thrive in the digital age. It seamlessly integrates with other systems, streamlining workflows and reducing operational friction. Its scalability accommodates businesses of any size, and AI-powered continuous learning ensures pricing strategies remain up-to-date. Additionally, Flipkart Commerce Cloud offers extensive customization to meet each business’s unique needs and objectives.
Ready to see how FCC’s pricing solution can help you elevate your business to new heights? Schedule a demo with our pricing expert today!
FAQ
“Pricing Model in Excel” means a structured spreadsheet built using Microsoft Excel or similar spreadsheet software that uses formulas, data tables, and defined inputs to calculate and determine product or service prices. This model typically incorporates various factors like costs, profit margins, competitor prices, and demand estimates, and allows a business to simulate different pricing scenarios based on manual data entry and defined logic.
Many businesses build pricing models in Excel primarily because of its accessibility, low cost, and flexibility. Excel is widely available, requires virtually no specialized training beyond basic spreadsheet skills, and offers a high degree of immediate control, allowing users to quickly create, modify, and customize complex formulas without needing a dedicated software development team.
Excel can only handle advanced pricing strategies like value-based pricing or attribute-based pricing in a very basic or manual way. While you can create formulas to calculate the price based on a limited set of attributes or perceived value components, Excel lacks the ability to integrate real-time market research, customer willingness to pay data, or machine learning models necessary to accurately and dynamically execute these complex, data intensive strategies at scale across a large product catalog.
No, Excel is not suitable for pricing models when databases or SKU lists are very large. Excel encounters performance issues, becomes extremely slow, or may even crash when dealing with data files exceeding a certain size, typically around 20 megabytes. Managing hundreds of thousands or millions of SKUs, along with their associated cost, competitors, and sales data, is far beyond Excel's functional capacity and introduces major risks of data corruption.
No, Excel-based pricing models cannot adapt easily to changing business conditions such as seasonal demand, market fluctuations, or competitor moves because they are fundamentally static tools. Adapting requires a user to manually update input data, recalculate formulas, and save new versions, which introduces significant delays and makes real-time responsiveness or agile pricing completely unfeasible.
No, using Excel for pricing models does not support real-time price updates or dynamic pricing because Excel is an offline, manual tool that is fundamentally not integrated with e-commerce systems or price execution software. Calculating a new price in Excel requires manual data entry and saving the file, after which a separate manual process is needed to upload that price to the e-commerce platform, making real-time, instantaneous adjustments impossible.
Excel-based pricing might lead to missed revenue or profit opportunities because it is too slow and too rigid to react to market changes. Since Excel cannot track competitor price shifts, stock levels, or real-time demand signals and update prices instantly, businesses often maintain static, conservative pricing. This results in missing opportunities to charge a premium when demand spikes or to aggressively undercut competitors during a lull, a gap that can be filled by dedicated, integrated software solutions like those from FCC.
A business should consider switching from Excel to a specialized pricing software when the volume of its SKUs becomes unmanageable (e.g., thousands of items), when errors in pricing start to materially affect profitability, or when it needs to implement advanced strategies like dynamic, competitive, or value-based pricing. The switch is necessary when manual processes can no longer keep up with the data volume, complexity, and speed required by modern commerce.
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