Written by Jack Holloway, Sales Enablement Executive
Economic theory suggests that inflation is usually caused by two main drivers. Firstly, cost-push inflation, which is caused by prices within an economy going up, and those prices being passed onto consumers. The other is demand-pull inflation, where there is a sudden inflow of cash or disposable income into an economy causing demand to surge. In response, businesses push up prices and take advantage of the business climate at that time. It can be said that demand-pull inflation pushes up prices which then causes cost-push inflation, but that’s a discussion for another time and place.
Data Analytics is used to track information and datasets and pick out trends within that data. At the very core of data analytics the software is picking out patterns within the datasets and this technology can be used to see price trends. During times of inflation this becomes even more useful and can be used to track the changes of individual competitors and costs and give valuable information to users.
What are my competitors doing? Have they increased their pricing? Are my energy costs going up? Why am I making less profit on that service? These are all questions that Data Analytics can help answer during times of rapidly changing prices and inform decisions that businesses may need to make in the coming months and years. Even in times when inflation is low, this data can still be used to see trends in the marketplace and spot opportunities.
Here we’ll go through what Data Analytics can do and apply this to rising prices within the market.
Track trends in prices
I sell Product Z. Product Z is sold by lots of different companies in various locations, all with different prices and offerings, with discounts and sales. Data Analytics can be used to find trends in all of this data and see where prices are changing the most and where opportunities are being found.
Maybe London prices aren’t rising as fast as everywhere else so you can’t push prices to meet costs. There might be opportunities in locations to push prices higher than inflationary increases but less than competitors?
All of those decisions could be informed using Data Analytics tools with the Artificial Intelligence (AI) able to process data quickly and accurately.
Investigate individual datasets
Data Analytics software can analyse datasets and find outliers and trends as mentioned above.
I can buy a product from 500 places online. They all have different prices that recently have been pushed up and I need the most competitive price.
Using Data Analytics software and previous invoices I can create expectations based on current inflation rates on what I should now be paying. This can then be used to assess whether this new price is in line with expectations, or whether a supplier is taking advantage of the climate. Do I need to negotiate this price? Are there other suppliers that are now more valuable? Data Analytics can help me interrogate my own individual dataset and create expectations with that information that inform decisions for the business.
Combine multiple data sources
In a world of ‘big data’ and ever advancing spreadsheets there are vast and complex areas that data can be found. Unstructured data is difficult to use and often requires ‘cleaning’ before it is structured and able to be used.
Data Analytics software can improve the process of cleaning datasets before they can be used by finding gaps or removing unusual information automatically.
Are there lots of tweets with specific buzzwords? Data Analytics can find those words and sort the information to usable data.
Graphical packages
Data Analytics software often has pre-built bank of graphical packages and presentation tools to show the information that is gathered quickly. This can be used for board presentations when deciding that a customer is just too expensive to keep serving with these new costs.
Within different software, there is the knowledge that the output forms a key part of the process of Data Analytics as without this, the AI is sifting through data with no purpose.
Data Analytics packages are used by everyone in a small way every day, giving consumers trends in their spending habits on banking apps, or how many times that post and been liked. Financial Data Analytics software is used to create graphical representations in pension funds, investment decisions, audit presentations and statutory accounts amongst many more uses. Management information is key to making decisions – this emerging software type can help with these.
Spot anomalies in the data
As well as trends, anomalies can be spotted within datasets. If 50 competitors are selling a widget for £49.99, but one is selling for £29.99 – Data Analytics will spot and return the outlier in the dataset.
These tests can be run automatically without the need for human input meaning that, once loaded, resources can be used elsewhere knowing that the software and the artificial intelligence will pick up these changes.
Let the software do the work
Inflation increasing can mean changes in prices more rapidly and more often. There is the old story about hyper-inflation where bartenders stood on the bar and shouted the new price for a pint every 15 minutes. If prices change more, the work behind deciding the prices happens more. This is software and automation can help businesses keep up.
Decision making is often needed quickly, with answers to questions being needed before they have been asked. Software also has the ability to be able to compute information with less margin for error.
It’s become a cliché to say let the computer do the work, but the processing power that machines and programmes using artificial intelligence now have means that it’s not possible to process information in other ways at comparable speeds.
Data Analytics can be used to track price changes and spot those vital opportunities in uncertain times. Investing in software to assist with those decisions may soon become a necessity to stay ahead of the competition, and ensure you remain profitable.