Pricing Tools – From Excel to SaaS

Have you heard the horror stories about companies that leave their pricing tools and algorithms to themselves, only to find that they’re constantly outperforming their competition, showcasing sky-high prices, and driving away customers in the process? Pricing algorithms can automate your pricing, but the downside is that they can be very risky for companies and result in poor outcomes and sales. It’s crucial to use tools that avoid such pricing mistakes and mishaps so that you’re correctly charging your customers and staying up to date with market demand.

Figure 1: Pricing tools over time

Pricing has always existed (Figure 1). From the ancient ruler method to the calculator of the 1960s to the standard 1980s Excel spreadsheets, people use the tools of the time to price a product. In fact, 85% of large enterprises are still using Excel as their tool of choice. But it’s time to upgrade beyond Excel for your pricing needs. There is a better way, and the innovative new tools that now exist can help to drive improved sales and customer return rates. That is why many companies are currently moving towards more advanced SaaS-based tools that run in the browser.

Before we get into the technology, let’s first evaluate the typical applications for pricing tools. 
Pricing consists of different tasks that are still primarily managed with Excel. Specifically, pricing managers must manage three key tasks as part of the optimization process (Figure 2):

Figure 2: Different tasks of pricing managers

1. The collection of data. This includes the responsibility to collect sales data, customer insights like willingness to pay, and competitor information. Then that data must be assessed. In order to do that, managers must measure price-demand relations and identify competitive patterns.
2. The next task is to optimize prices and products using four different methods that can range from popular price elasticity and competitor pricing to options like cost-plus or value pricing. Typically, if you do all of these, you’ll find different results and then must combine these methods to determine an aggregate (e.g., average) price. Hence, this aggregation of methods produces a price recommendation based on a combination.
3. The final task is to manage prices and offers with a logical system that is efficient and consistent. This includes numerous applications for many customers who each have specific needs and a different willingness to pay overtime, such as someone who wishes to purchase a heavy garment with down rather than a simple synthetic filing.

According to our Buynomics benchmarking survey of pricing managers across industries in 2022, we discovered that a whopping 85% of large enterprises use Excel as their pricing tool of choice, while only 10% use rules-based software, and a small 5% use the latest AI & machine learning technology. Innovation has stalled because although we live in a data-rich world, pricing operations are still stagnant in Excel spreadsheets. These documents are cumbersome and frustrating to navigate but contain everything from product, sales, competitor, and cost data to valuable customer insights.

Here is an example of an exemplary pricing model in Excel (Figure 3). It highlights that Excel models are often very simple, but they don’t produce optimal results.

Figure 3: Exemplary Pricing model in Excel

We can see that Excel has some advantages (Figure 4), like flexibility, speed, cost, and the ability to provide fast estimates, but those are clearly outweighed by its numerous disadvantages. These range from its lack of integrations and predictive modeling to its limitations of scale and automated learning. This makes Excel a poor choice for many enterprises looking to adopt the latest and modern SaaS pricing solutions for their teams that really takes accurate and realistic customer behavior into account.

Figure 4: Advantages of Excel in pricing

Although it has these advantages, Excel was not made to manage and make the best use of the vast amount of data now available. If you apply these different methods of pricing elasticity or value-based pricing, you can review and combine the results to obtain the optimal price that you should charge customers.

So, there is a better way. The next generation of pricing tools has arrived with many benefits and, in turn, makes the life of today’s pricing managers easier in the process. These new tools provide a way to interpret and integrate product, sales, competitor, and cost data sources with customer preferences. The benefits of using this updated technology include the meaningful integration of data sources, an easier way for teams to coordinate and work together, a faster and more secure approach, and an overall holistic pricing method and solution.

At Buynomics, our solution is based upon the Virtual Shopper technology that provides deeper insights into market behavior because it replicates real people with an accurate model of their shopping behavior. We accomplish this by using data sources to create a large number of shoppers who collectively behave like real shoppers in today’s market. After providing them with various offers, depending on their preference that is derived from data sources, you can predict which product they will select. This model of reality uses available data with a way to alter prices by replicating the behavior of people in a market to optimize your pricing choices.

 So, you must be wondering, how do these new tools work?

 Figure 5: Accuracy of Excel method using price elasticity

The comparison in Figure 5 demonstrates with actual customer data the challenges of pricing in Excel using price elasticities. Essentially, the Excel optimization is not as precise and accurate in its pricing model as the buynomics optimization forecast. This allows a company to make a more profitable price change than the traditional methods.

In summary, compared to an Excel solution, SaaS-based solutions are superior because they allow the user to take full advantage of the data with machine learning technologies. These advantages include:

• Comprehensive forecast of customer moves to other products
• Easily reflects product and portfolio changes
• Accurate market model given human behavior
• Very limited human error due to easy interface
• Unlimited computing power
• Sustainable profit increase across portfolio

Our Buynomics tool can integrate and assess all types of pricing data to precisely predict how demand will react to offer changes. This facilitates better and more profitable pricing decisions and advantages for pricing managers – who are often frustrated with the limitations of Excel.

Are you ready to upgrade to a new solution while upgrading your profits at the same time?

For more details and insights, check out our webinar here or set up a demo with us here to get deeper insights into how we can solve your specific challenges.