Data Maturity: Predictive Models

Where are we on our analytics journey? It’s a question we get asked all the time. In this series we will look at key questions to help you determine just this! We’ve graded common responses based on three levels of maturity: walk, run and fly.

Do you currently do any predictive modelling?

Walk: We are just setting up our data stack and then plan to hire a data scientist for this.

Run:?We have a data stack but are yet to leverage predictive analytics.

Fly: We have predictive models in place.

See which answer most closely matches your current situation and scroll down for some crucial advice.

Walk

Your answer: We are just setting up our data stack and then plan to hire a data scientist for this.

Our advice: It is not uncommon for organisations to be drawn to the more glamorous aspects of data science, leading them to hire a data scientist prematurely. However, without a robust data foundation in place, the effectiveness of a data scientist can be severely limited. The key lies in laying the groundwork for a solid data infrastructure before diving into advanced analytics.

Our recommendation is to prioritise foundational steps, such as thorough data analysis, before venturing into predictive analytics. By gaining a comprehensive understanding of the underlying patterns and dynamics within your data, you create a sturdy base upon which accurate and reliable predictive models can be constructed. This approach ensures the impact of your data scientist when they come on board.

Run

Your answer: We have a data stack but are yet to leverage predictive analytics.

Our advice: Before diving into the technical aspects of predictive analytics, it's essential to align your development efforts with your overall business strategy. Clearly define your business objectives and identify areas where predictive models can have the most significant impact. We have found that one effective approach is to use predictive models is for the automation of?'no brainer' scenarios. For example if you were to predict for example which of your customers would convert. You'd have a group at the top who were very likely where no action might be needed, a group at the bottom less likely to convert who might just get regular marketing communication, and in automating the actions (or inaction) to these groups, your team could then focus on how to entice the people likely to convert but not with a high percentage.

In terms of technical advice, It’s important to validate the effectiveness of any model early on. If you had a model for marketing effectiveness, you could do a simple AB test to validate what the model is showing you. There are many variables and data sets which go into a model and so testing you can get the desired results with your data set is key. it’s important that your data pipeline ingests new data, retrains your model, goes through some form testing and then is deployed.

?

Fly

Your answer: We have predictive models in place.

Our advice: Predictive analytics should always be designed around real people and real use cases. Consider how end-users who will be utilising the insights generated by the predictive models and tailor the solutions to meet their specific needs. User-centric design and user testing can help ensure that the predictive analytics tools and outputs are intuitive, actionable, and easy to interpret. By involving end-users from the early stages of development, you can create solutions that genuinely empower decision-makers.

The world as we know it is ever-changing and as new data becomes available, it’s important that this is reflected in our models, and they should be constantly evolving based on the feedback loop created by your data pipeline. Retrain your model on a regular basis to enjoy the best results.

Once you have developed your predictive models, it's time to activate them and turn the insights into action. Reverse ETL tools allow you to connect your predictive models to your operational systems, creating flags and notifications based on the predictions. For example, if a customer is predicted to churn, a notification can be generated for the customer success team to take proactive measures. Reverse ETL facilitates the seamless integration of predictive analytics into your existing workflows.

Great models deliver predictions where they are understandable and actionable and work alongside the tools your teams are already using.?

If you want to understand where you are on your analytics journey. Our Data Maturity review offers a holistic and independent assessment of the strengths and weaknesses of your current technology, processes and people.