Pricing - creating GS master

Background

Create a GS and label it [COMPANY NAME] Pricing MASTER
Have a list of all of the customers in a sheet and add columns that relate to the data/analysis highlighted below.
The idea is to have an all encompassing database with key metrics/information for each customer so we can identify how best to implement price increases for each.
There is no blanket price increase that we can apply to every customer, it must be tailored to each depending on their metrics.

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For each customer look at the price increases for at least the least 2-3 contract renewal cycles, by analysing the historical data we can see whether there has been much movement in the contract costs.
Compare the % price changes in the renewal cycles with inflation, we expect prices to increase at RPI + X%. (include a tab in the Pricing Master GS).
Compare the inflation rate as well by the contract if it fixed e.g. 3 year fixed contract for £1,000 a year, the renewal price should increase by a minimum of RPI + X% compounded by 3 years.

Compare the annual contract value with the total revenue of the customer e.g. if a customer generates £10M and their fixed costs are £2M but your contract with the customer is £50K, then they would be less resistant to a price increase.

For each customer gather the data to show the usage of the products e.g. how active are the customers, number of users logging in, proportion of people using the systems vs number of people registered.
Make sure you interpret the data properly, you may have a lot of people using the system but we need to understand how important the system is to the customer. E.g. you can look at usage alone for example customer X could have 600 users but there are 3,000 people at the company, compared to customer Y who has 100 users out of 150 people. This is a good indication on how reliant the customers are of the system.

If a customer were to switch to a competitor how long would it take? E.g. if our apps are fully integrated into their systems and to switch to a competitor it would take 3+ months, that makes the product more sticky.
How many API integrations and coming in and out for the product? E.g. a software gathers the pricing data from 400 different websites and outputting to the end user. It makes the product more sticky as it may be hard to find a new provider that does the same job.

How many competitors does the company have and how good are their products compared to ours?

For each customer gather the data on the number of support tickets raised. If the customer raises a lot of support tickets it could mean multiple things. E.g. it could just mean that they are having issues, it could also mean that the customer wants free customisation of the application so its important to look at the nature of the tickets.
If a customer doesn’t raise a lot of tickets it could indicate that they are happy with the system OR it could mean that they are likely to churn as they’ve given up on the system.
Calculate the no. support tickets / total cost of the contract. The higher number of support tickets mean a higher cost to us, we can go back to the customer and say that their tickets are costing us too much which could help justify a price increase. Higher support tickets = higher proposed price increase.
How many users on the customer’s side who are raising support tickets have been trained? We could implement a policy that we do not take support tickets from users who haven’t been trained.
This allows us an opportunity to charge for training but also reduce our costs for addressing support tickets.

Identify how many different products a customer has purchased from us. The more products integrated into their systems = more stickiness.
Compare the rate card vs the customer’s actual figures e.g. if the number of users on the contract exceed their rate card bracket then we can justify a price increase