If you are looking to upgrade your pricing by leveraging the tools and benefits of a pricing software solution, then you will have heard that you need to get your data ready for implementation. But why is that exactly and how does your historical data drive your pricing strategy? For your software to work at its optimum level, the scope of the historical data that supports it and the ways it helps you plan your pricing strategy needs to be well understood. Otherwise, it will be like trying to build a house without a foundation. That is exactly what we will address in this article and give you 5 ways to make the best use of your historical data.
At Pricefx, we spent more than a decade enjoying getting our hands dirty with data and helping our customers achieve pricing excellence with theirs. But over the years, we have seen many companies stumble at the first hurdle of getting their data ducks in a row, and not entirely come to grips with how their historical data affects the quality of their pricing insight outcomes.
The goal of this article is to take that pain away and show you 5 ways to use your historical data to achieve maximum pricing efficiency. But first, let’s examine precisely what historical data is.
What is Historical Data?
Your Pricing Manager uses historical data as an insight into how to predict the right price for your products at the right time.
The history of what has gone on in the past with your customer, your products and your past sales and transactions is a critical driver for quality pricing.
The Big 3 Historical Data Sets
You are the core of any pricing project from price setting to price analytics, price quoting, rebate management etc., you name it.
All pricing projects will usually require ‘the big 3’ data types, however, it may depend on what you want are your organization’s unique set of business objectives.
For example, if for some (unusual) reason your organization is not fussed with seeing the margin, the win-loss ratios or being able to understand the effects of tweaking the price up or down that applies to the sale of your products, you might not need transactional data.
HOT PRICING TIP – Remember, your pricing strategy is where your product strategy meets your customer strategy.
If your organization does not have a defined pricing strategy or set of goals or business objectives, no matter how wonderfully complete and clean your company’s historical data sets may be, it will count for little.
If your company does not have defined business objectives for how you will utilize your historical data, there will be little point in collecting it.
If you find you are doing pricing analysis simply for the sake of analysis without any clear objective or defined reason for why you are doing it, it may well be a waste of your organization’s time, money, and other resources.
Take into consideration that your business needs and objectives may change over time, and as such, the best possible scenario is to have all 3 data types prepared to implement your pricing software project. Your company can always use these data sets to grow additional functionality and data ‘subsets’ (such as accruals, business rules, formulas etc.) further down the path of the project.
The Top 5 Uses for your Historical Data in Your Pricing Strategy Planning
Now we understand fully historical data is, let’s examine the top 5 ways you can use to plan your organization’s pricing strategy.
1. Segmenting Your Data for Betting Pricing Insights
As a Pricing Manager, the first use you should be thinking about for your historical data is segmenting that data.
Segmenting your historical data delivers you with a way to go from a massive, generalized picture of everything in the business, millions of transactions, thousands of customers, thousands of products into more manageable smaller units.
Distilling your data down into smaller segments in the shape of micro markets, micro transactional sets, or micro products sets will enable you to perform the pricing analysis within or across those market or product segments or across those segments.
With that segmentation, you’ll be able to analyze small but powerful and significant pricing details to understand which data attributes really drive pricing behavior.
For example, for your organization; is it the customer size, or is it the salesperson driving pricing behavior? Or is it the day of the week that they purchased on? Does your business have huge price variances across small, medium, and large customers?
Going through and looking at all these different variants of data points as granularly as possible will help your business understand what drives price variance and by how much.
To get there, you must break up your data into smaller sections to make the insights of your pricing analysis executable.
2. Identify Outliers in Your Historical Data
When your price segmentation is a robust and accurate reflection of the differences in willingness-to-pay across the marketplace, identifying a pricing outlier (an exception in other words) in your historical data may be an indication that a salesperson has offered an unwarranted and unnecessary price break, or it may be something else entirely. The first step is to identify the price outliers.
The outlier could be a low margin, it could be a low price, it could be related to the number of products in a transaction. Whatever they are (and all businesses have them – it is perfectly normal) defining your organization’s outliers is every little bit as important as defining what is normal for your business.
Outliers are often a particularly good starting point to go ahead and make some price changes and increase your prices. In other more extreme cases, identifying a price outlier can lead to a decrease in your prices.
When beginning their journey with pricing software (even before the implementation of the entire pricing software is complete), many users find that using the pricing analytics function can deliver them quick pricing wins.
In our experience, many of our customers have begun doing analysis of the data and have located these outliers very quickly.
It can be valuable for users to deal with the outliers quickly as start the value journey even before their pricing software is fully up and running.
For example, if your company has a customer that’s low margin and low volume, clearly your pricing strategy is not being carried through to transactions with that customer and there is no point to maintaining that approach.
On the other hand, you may be selling to another customer at a low margin but extremely high volume. There is a good reason for keeping that customer because that volume could be giving you good purchase power with your vendors, or it may be making your production processes more efficient.
Along the outlier identification process you may discover that your organization might be better off without some customers, or you may discover prices that need to be fixed and/or contracts updated at new prices.
3. Identifying Static Pricing
Use your historical data to identify prices for a long time. That might be price lists that are not being updated often enough (particularly at the current rate of increase in many raw materials used in your production), or it be even more innocent than that.
Static pricing can often take the shape of contracts that do not get updated or maintained and are quite simply forgotten about. It could be that something about the contract’s nature or the negotiation process may be too difficult or complex for account managers, or somebody else in sales operations to find the data or even be aware that there is a contract in place.
Static prices are often caused by companies working in organizational silos such as only one person is aware of the history as the contract in sitting on their PC only and not shared with the rest of the team.
Or worse still, the reasoning behind the static pricing is locked away in the head of a long-term employee and is not recorded at all. It happens.
If your salespeople are selling again at an outdated price, just because they simply don’t know any different or don’t know what else to do, consider integrating your historical data with a quality pricing software solution to provide the level of pricing guidance they need.
4. Locating Product & Customer Misalignments
Do you have a product that is priced differently 4 ways across 4 different countries? Sure, there may be an exceptionally good reason for the variation in price (such as varying production or distribution costs), but do you know for sure?
If those 4 product prices have simply evolved randomly as different across 4 distinct locations, then that is misaligned pricing, and it could be costing your company money.
Similarly, you may have different customers being charged different prices for the same product even when they are sold at similar volumes. Do you know why that is occurring?
There may be a particularly good reason for different prices in different jurisdictions, or selling at different prices to different customers, but there may not.
Wouldn’t you like to know?
Your historical data can supply the answers that you are looking for.
Use your historical data to track when, where, how and why your prices are what they are.
It is possible that these types of misaligned prices may first become identified when you are identifying your price outliers (in step 2 above).
One of the beauties of switching from Excel to a pricing software tool is that it can do all that heavy lifting and number crunching in addition to storing massive amounts of data. You can also build charts that will automatically highlight your price outliers and find price misalignments easily. So rather than hunting and pecking through reams of information to find that 1 in 10000 SKUs that is price misaligned, you can build these exception charts in as well and even automate misalignment alerts.
5. Use a Pricing Software Tool to Facilitate Transparency in Your Historical Data
Whatever your price misalignments, outliers, and exceptions are, there is a high chance you will find the reasons why in your historical customer, product, and transactional data.
However, trying to find an outlying pin in your theoretical data haystack is going to be a tricky challenge to overcome, regardless of what type of technology you are going to use to track them down.
You could employ an army of workers manually sifting through hundreds upon hundreds of thousands of lines of data across tens of thousands of different spreadsheets (good luck remembering which version is current mind you), and eventually you may find the price misalignments you are looking for. But it will be painful, and because the information is as if it is ‘hidden in a black box,’ it will be slow.
On the other hand, you could use an AI-informed (Artificial Intelligence-informed) pricing software solution to track those exceptions in your historical data set. As AI is a pattern seeking tool, it will simply be looking for patterns in your data and not everything will fit into those patterns. Long story short, AI simply transparently reflects the biases of the past, tracks down what doesn’t fit quickly and allows you to flexibly change your prices, transform decisions and reboot your pricing strategy as soon as possible.
That’s Great – But How to Choose Pricing Software to Make the Most from My Historical Data?
Now you know 5 ways to use your historical data to plan your pricing strategy.
Many of you may be looking to continue their learning journey on data and how data work determines how effective your pricing strategies can be. By reading this article you may have already decided that the innovative technology of pricing software like the award-winning Pricefx or similar is something that can help your organization achieve its business objectives, but you would like to make sure.
To dive a little deeper into price optimization software, check out the handy article below for further benefits of the technology and to drill down to who it is right for and who may not need it at all.
Or if you have already made up your mind that Pricefx is exactly what you are looking for, talk to one of our pricing experts now.
Iain Lewis has worked in pricing as a practitioner for 27 years working at Automotive, industrial goods, business services and Distribution companies. Iain brings his unique perspective to each engagement to guide companies through complex buying decisions and has helped companies throughout Europe and South-East Asia continue to improve their pricing approach.