While we might all appreciate the importance of the KPI that is Customer Lifetime Value – for any business – but there are multiple challenges faced while attempting to implement it.
Based on our analysis and first-hand industry experience, here are five key challenges we see faced by most businesses when implementing CLTV. We’ll also share how we’ve applied Acrotrend’s CLTV Capability and Maturity Model to overcome them.
As businesses expand with multiple teams coming online, each team may have its own department-specific goals and in different scenarios apply different calculations for some of the same metrics, leading to confusion for executives in reporting.
CLTV is one such metric and you should dig deeper to see how it is calculated in sales vs. marketing vs other departments.
The standard formula we use and one that is applied across the industry is this:
CLTV = Average Order Value X Number of Transactions X Retention Time Period X Profit Margin
But, calculations in the ingredients of that formula can vary too – so you need to dig even deeper. For instance, these are all extracted from just on article (and there’ll be plenty more on the internet!)
- Average Revenue Per User (3 months) = Total Revenue (3 months) / Total Customers (3 months)
- Customer Lifetime Value = (Average Number of Transactions Per Month x Average Order Value x Average Gross Margin x Average Customer Lifespan in Months) / Number of Customers for the Period
- Customer Lifetime Value = Gross Margin Per Lifespan x ( Retention Rate Per Month / ( 1 + Discount Rate Per Month – Retention Rate Per Month) )
To resolve this, it’s not just about working together and deconstructing silos, but about centralizing of company resources: implementing a company-wide knowledge base for commonly used calculations across the board means companies standardize formulas.
It’s true that your basic data sets will include internal transactional data, demographics, revenues, and costs for the calculation of key metrics and generating insights. But while these are useful in their own right, what makes them a richer, more comprehensive set of data is to integrate these internal metrics with external data sources. By combining the two, you’re able to enrich and supplement the data for a more holistic picture of customer and/or lead behaviours.
Discover external data by utilising other applications that go beyond what you yourself are measuring – and currently have the capabilities to measure. This data can be engagement data from Google Analytics or Adobe Analytics, or data from third-party data sources like Acxiom to enhance the customer profiling information. Be sure to triangulate correctly and remove erroneous third-party data as you would your own.
“Bad data costs U.S. businesses more than $611 billion each year.”Dirty Data, ZoomInfo
Is Big Data overwhelming your business? So many times, we hear companies telling us that they have so much data and that they don’t know what to do with it – or even how it can be used effectively for tracking and decision making. It’s overwhelming! In the vast majority of cases, only a few data sets are available to form reports, which are used by analysts to generate insights.
One issue is that businesses have manual or semi-automated processes of data processing, that can inadvertently lead to poor quality data sets, and so incomplete and inaccurate analysis by analysts – skewing results.
The end goal of data maturity within a business should be utilizing defined and structured datamarts with real-time pre-aggregated data and very low latency. Choose your data scientists (or data specialists), and data organisation techniques wisely.
While a great analyst may have the power to delve into the data, having clean, complete, organized, and reliable data to start with is the difference between obtaining true insights or having a biased and potentially useless result. This requires investment in the right people and tools to make sure you have good data.
Most businesses take a ‘rear-view mirror’ approach and use accumulated historical to calculate historical CLTV, rather than instead of looking at the road ahead and considering robust projections for CLTV. Historical methods look at past data and calculate the value of each customer based on transactions they have made – without investigating and truly predicting what value can be generated by these customers in the future.
With historical data, the focus is only on customers that have been acquired and are already in the system. Today’s market requires understanding the customers at the start of the funnel itself, i.e., calculating CLTV for prospects and accordingly spending money to acquire them.
Yes, it has never been easier to do data science badly, but with help of data science models (like Acrotrend’s with a foundation in AI and ML), it is now possible to acquire customers with minimal acquisition cost and retain them for a longer period.
Where is your data coming from? Despite a plethora of apps and internal signals, if your data isn’t all automatically fed into the same place, if can be tricky to remember which bits to combine and when. Data is always coming from multiple sources, and with more manual interventions, it’s more likely that data points get missed. This leads to incomplete and inaccurate analysis.
“Deliver the data to automated systems for use: repositories, applications, mobile apps, analytics, BPM, web services, RPA, etc.”Why Data Capture And Automation Are Key To Digital Transformation, Frost & Sullivan
It should be a priority for any organization to automate data collection and sync, bringing disparate data in to a centralized repository so that it can be easily accessed by analysts. Its also best practice to have Data/MLOps with automated monitoring implemented to ensure continuous data science model enhancement.
The ultimate fix – The Acrotrend Capability and Maturity Model
Acrotrend Capability and Maturity Model framework helps businesses resolve these five challenges, by benchmarking and implementing capabilities across five different stages of maturity; from Aware, to Reactive, Proactive, Managed, and finally, Effective.
The five areas cover:
- Decision Making: Level of CLTV utilization in driving actions
- Data Availability: Data required from CLTV is accessible and consumable
- Data Processing: Required data is structured and modelled along with integrated data applications
- Technology and Automation: Automated machine learning mechanism with increased pace of model development and deployment
- CLTV Calculation and Data Science: Sophistication of algorithms and modelling to achieve higher accuracy
Businesses generate significant value when they move from one stage of maturity to the next, even right from the very beginning of the maturity model. If you’re reading along now, you’re at the very least at the Aware stage. And while you may work at a Managed level in Technology and Automation, you may only work at a Proactive level in CLTV Calculation and Data Science – each axis may have a different maturity level.
At the Effective stage across all aspects, businesses tend to learn and update new subscriber behavior with minimal oversight using advanced technologies.
Acrotrend helps businesses benchmark and analyze maturity of key areas based on industry research and detailed study of your business. We help you move up the stages of maturity to achieve optimal ongoing performance and informed review.