When you start to automate your CRM, you can scale your activity beyond what is possible with purely manual execution. If you want to grow, the only alternative to automating player engagement is to continually expand your team, which isn’t very cost effective or sustainable.
While scaling your CRM with automation is exciting, it’s important to also keep track of how well all these new engagements are performing.
Moving from running campaigns based on “gut feel” to a more science-led approach requires putting experimentation and analysis in focus.
To do this, you need the right tools and processes in place. In this blog we will explore how to find bright spots within your data so you can keep incrementally improving your CRM.
We will look at:
Creating a culture of experimentation
When a scientist is in the lab trying to find a solution, they know that it might take 10 or 100 attempts to find the perfect formula. With that in mind, why would we expect to get our CRM campaigns right the first time?
Moving towards a more scientific approach requires a mindset shift. We can’t just set up lifecycles and leave them running in the background. Nor can we send out repeated “one size fits all” campaigns to our player without questioning if we could do something different for better results.
We need to instil a culture where failure is an accepted part of the process and we question the status quo. This involves things like:
Providing the tools for digging into data
Once we start to experiment, how do we measure what is working and what isn’t? How do we identify those bright spots?
In order for CRM teams to continuously improve player engagement, they need access to centralised, real-time reporting. Easily accessible data is the most important tool you can provide CRM teams with to get an idea of how campaigns and other engagements are performing.
For example, we provide the Fast Track Data Studio to our partners. The Data Studio gives CRM teams the capabilities to build reports about whichever KPIs (or other information) they are interested in and load lightning-fast dashboards full of real-time data whenever they need to.
The most important things to consider when thinking about reporting on your CRM activity are:
Taking action on your findings
Through your experiments, you may have found out that players under the age of 30 are more responsive to a clean email design and that players in Italy are more responsive to SMS messages in the mornings. These are two bright spots! What can you do with these discoveries?
The most basic thing you can do is to use this information to update your automations. You can, for example, create a new version of your onboarding flow that takes into account the tonality preferences of different age groups.
If you want to take things a step further you can use this to inform machine learning models.
For example, we provide operators with the Singularity Model. This is a flexible machine learning model that they can shape based on their player base. So, in the case of our players from Italy, we can feed this information into the model so that it knows that when we contact these players we shouldn’t use SMS messages in the afternoons. It can select an alternative channel to communicate our message. Perhaps we would even like to use machine learning to see if the same applies to other markets we are operating in? We could train the model on this data and see if it can find patterns in the rest of our player base.
Want to know more?
We have spoken to hundreds of operators over the past few years and supported brands with a flexible, custom CRM platform packed with awesome features.
You can reach out to us with any questions you have about the future of CRM, what data and reporting you need to reach your goals, or our platform.