Although a basic CBM system will offer some solid benefits and insights, to get the maximum value out of a CBM system, more advanced components are needed. Below we discuss a few of the most important components, such as having churn models with multiple time horizons, adding price optimization, using simulation-based forecasting and adding competitor pricing data.
Multiple Horizon Churn Models
Sometimes it makes sense to look at churn from different perspectives, and one of those angles is the time horizon — or outcome period — you allow the model to have. For some business scenarios, it makes sense to have a model with a short outcome period, while for others it can make sense to have a model with a 1-year outcome period.
To better explain this concept, assume you build a churn model with 10-week outcome period. This model can then be used to give a prediction whether a given customer will churn within a 10-week period. However, assume now that you have isolated a specific event that you know causes churn and that you have a short window of perhaps 3 weeks to implement any preventative measure. In this case it makes sense to train a churn model with a 3-week horizon, conditional on the specific event you know causes churn. This way you can focus any retention activities on the customers most at risk of churning.
This kind of differentiated approach allows for a more strategic allocation of resources, focusing on high-impact interventions where they’re needed most. By adapting the model’s time horizon to specific situations, companies can optimize their retention efforts, ultimately improving customer lifetime value and reducing unnecessary churn.
Pricing Optimization & Customer Price Elasticity
Price is in many cases the final part of strategy execution, and the winners are the ones who can effectively translate a strategy into an effective price regime. This is exactly what a CBM system with prize optimization allow companies to do. While the topic of price optimization easily warrants its own article, we try to briefly summarize the key ideas below.
The first thing needed to get started is to get data on historic prices. Preferably different levels of price across time and other explanatory variables. This allows you to develop an estimate for price elasticity. Once that is in place, you can develop expected values for churn at various price points and use that to forecast expected values for revenue. Aggregating up from a customer level gives the expected value and expected churn on a product basis and you can find optimal prices per product. In more complex cases you can also have multiple cohorts per product that each have their optimal price points.
For example, assume a company has two different products, product A and product B. For product A, the company wishes to grow its user base and are only willing to accept a set amount of churn, while also being competitive in the market. However, for product B they are willing to accept a certain amount of churn in return for having an optimal price with respect to expected revenues. A CBM system allows for the roll out of such a strategy and gives the leadership a forecast for the future expected revenues of the strategy.
Simulation-Based Forecasting
Simulation based forecasting provides a more robust way generating forecast estimates rather than just doing point estimation based on expected values. By using methods like Monte Carlo simulation, we are able generate probability densities for outcomes, and thus provide decision makers with ranges for our predictions. This is more powerful than just point estimates because we are able to quantify the uncertainty.
To understand how simulation based forecasting can be used, we can illustrate with an example. Suppose we have 10 customers with given churn probabilities, and that each of these customers have a yearly expected revenue. (In reality we typically have a multivariate churn function that predicts churn for each of the customers.) For simplicity, assume that if the customer churns we end up with 0 revenue and if they don’t churn we keep all the revenue. We can use python to make this example concrete:
import random
# Set the seed for reproducibility
random.seed(42)# Generate the lists again with the required changes
churn_rates = [round(random.uniform(0.4, 0.8), 2) for _ in range(10)]
yearly_revenue = [random.randint(1000, 4000) for _ in range(10)]
churn_rates, yearly_revenue
This gives us the following values for churn_rates and yearly_revenue:
churn_rates: [0.66, 0.41, 0.51, 0.49, 0.69, 0.67, 0.76, 0.43, 0.57, 0.41]
yearly_revenue: [1895, 1952, 3069, 3465, 1108, 3298, 1814, 3932, 3661, 3872]
Using the numbers above, and assuming the churn events are independent, we can easily calculate the average churn rate and also the total expected revenue.
# Calculate the total expected revenue using (1 - churn_rate) * yearly_revenue for each customer
adjusted_revenue = [(1 - churn_rate) * revenue for churn_rate, revenue in zip(churn_rates, yearly_revenue)]
total_adjusted_revenue = sum(adjusted_revenue)# Recalculate the expected average churn rate based on the original data
average_churn_rate = sum(churn_rates) / len(churn_rates)
average_churn_rate, total_adjusted_revenue
With the following numbers for average_churn_rate and total_adjusted_revenue:
average_churn_rate:0.56,
total_adjusted_revenue: 13034.07
So, we can expect to have about 56% churn and a total revenue of 13034, but this doesn’t tell us anything about the variation we can expect to see. To get a deeper understanding of the range of possible outcomes we can expect, we turn to Monte Carlo simulation. Instead of taking the expected value of the churn rate and total revenue, we instead let the situation play out 10000 times (10000 is here chosen arbitrarily; the number should be chosen so as to achieve the desired granularity of the resulting distribution), and for each instance of the simulation customers either churn with probability churn_rate or they stay with probability 1- churn_rate.
import pandas as pdsimulations = pd.DataFrame({
'churn_rate': churn_rates * 10000,
'yearly_revenue': yearly_revenue * 10000
})
# Add a column with random numbers between 0 and 1
simulations['random_number'] = (
[random.uniform(0, 1) for _ in range(len(simulations))])
# Add a column 'not_churned' and set it to 1, then update it to 0 based on the random number
simulations['not_churned'] = (
simulations['random_number'] >= simulations['churn_rate']).astype(int)
# Add an 'iteration' column starting from 1 to 10000
simulations['iteration'] = (simulations.index // 10) + 1
This gives a table like the one below:
We can summarize our results using the following code:
# Group by 'iteration' and calculate the required values
summary = simulations.groupby('iteration').agg(
total_revenue=('yearly_revenue',
lambda x: sum(x * simulations.loc[x.index, 'not_churned'])),
total_churners=('not_churned', lambda x: 10 - sum(x))
).reset_index()
And finally, plotting this with plotly yields:
The graphs above tell a much richer story than the two point estimates of 0.56 and 13034 we started with. We now understand much more about the possible outcomes we can expect to see, and we can have an informed discussion about what levels of churn and revenue we we find acceptable.
Continuing with the example above we could for example say that we would only be prepared to accept a 0.1 % chance of 8 or more churn events. Using individual customer price elasticities and simulation based forecasting, we could tweak the expected churn_rates for customers so that we could exactly achieve this outcome. This kind of customer base control is only achievable with an advanced CBM system.
The Importance of Competitor Pricing
One of the most important factors in pricing is the competitor price. How aggressive competitors are will to a large degree determine how flexible a company can be in its own pricing. This is especially true for commoditized businesses such as utilities or telcos where it’s hard for providers to differentiate. However, despite the importance of competitor pricing, many business choose not to integrate this data into their own price optimization algorithms.
The reasons for not including competitor pricing in price algorithms are varied. Some companies claim that it’s too difficult and time consuming to collect the data, and even if they started now, they still wouldn’t have all the history they need to train all the price elasticity models. Others say the prices of competitor products are not directly comparable to their own and that collecting them would be difficult. Finally, most companies also claim that they have price managers who manually monitor the market and when competitors make moves, they can adjust their own prices in response, so they don’t need to have this data in their algorithms.
The first argument can increasingly be mitigated by good web scraping and other intelligence gathering methods. If that is not enough, there are also sometimes agencies that can provide historic market data on prices for various industries and sectors. Regarding the second argument about not having comparable products, one can also use machine learning techniques to tease out the actual cost of individual product components. Another method is also to use different user personas that can be used to estimate the total monthly costs of a specific set of products or product.
Ultimately, not including competitor prices leaves the pricing algorithms and optimization engines at a disadvantage. In industries where price calculators and comparison websites make it increasingly easy for customers to get a grasp of the market, companies run a risk of being out-competed on price by more advanced competitors.
