Any business owner who wants their enterprise to be profitable needs to make sure their CRM team works the best way possible. The only way to make sure your CRM team can thrive and hugely increase profitability for your business is to make sure they are using data.
Essentially, CRM is about reaching out to the customer with various communication and sales campaigns, with the goal of increasing their lifetime value. So where does data fit into it?
Data driven campaign optimisation
The first step to optimise your CRM with data is data driven campaign optimisation.
Data driven campaign optimisation refers to always measuring campaign performance and testing as many improvement hypotheses as possible. Before doing anything else, make sure you have the points below checked, because without them your strategy is to try and pray.
- You track your campaign performance against campaign goals (eg, purchase)
- You check necessary sample sizes before emailing, and perform as many relevant experiments as the data volume allows
- You verify the statistical validity of your test results. (check your p values)
- You perform as many A/B tests as possible with your volume of data
Data driven segmentation
Once you have the basics of data driven campaign optimisation nailed down, it’s time to look at data driven segmentation.
Segmentation refers to grouping users together by specific attributes (gender, age, marketing channel, purchased items etc). Such, it is vital to keeping the number of campaigns manageable, but still relevant for the user.
- You know the customer acquisition details (acquisition channel, time, season)
- You know the customer behaviour (currently active/inactive, purchases, interactions etc)
- You leverage unsupervised learning (data science) to classify behavioural profiles in the data and group users in manageable similar clusters
Data driven personalisation
Data driven personalisation is where data science becomes necessary. Creating relevant, specialised, personalised communications for your users is the best way to optimise goals such as purchases, reactivation, referral etc. All of the above require some form of statistics or data science.
- You leverage predictions of the conversion reasons
- You use the most likely to convert recommendation for the user.
- You tackle churn by identifying churn risk and reasons of each individual user
- You leverage the performance data of your campaigns correlated with the profiled data to identify which campaigns work best for which users
How to get there
To check off the entire checklist you will not only need a data driven CRM team, but also a data team.
At minimum, you will want to build some automation pipelines to provide a customer view into your CRM system.
But in order to leverage the majority of the advantages in the long run, you will need a data warehouse and a business savvy data scientist to run the numbers on the experiments and provide valuable support in CLV optimisation.
However, improving CRM campaigns can start well before having everything. In fact, the sooner you start, the sooner you can start testing improvement hypotheses. And let me know if you need a hand.