Scaling Your Data: The Fly Stage
In today’s rapidly evolving business landscape, organisations are increasingly turning to predictive analytics to gain a competitive edge, but embedding data across an organisation is not an easy job. Analytics is a journey and we’ve separated out that journey into three stages: Walk, Run and Fly. At the fly stage, because you’ve built solid data foundations and have demonstrated the value that data can have, you can now start to work on more complex modelling and in particular think about propensity models/predictive models.
Propensity modelling looks at the past behaviour and characteristics of your customers to make predictions about their future actions.They act as a useful indicator, a guiding light rather than a laser beam and will most often take the form of scores. For example, your marketing department might want to understand the likelihood that different groups will open an email. A propensity score would show them how different groups would respond to emails at different times, and with enough data could even show you on an individual basis. Here are our top tips to embed predictive analytics into your data pipeline.
Let Strategy Guide Your Development Efforts
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. Whether it’s improving customer retention, optimising supply chain management, or enhancing marketing campaigns, the strategic alignment will guide your development efforts and ensure that you focus on the right priorities.The second point of consideration is good quality data. A successful model is underpinned by your first-party customer data, and where this is not in a clean and organised state you may need to look further afield to third party or category data for a more general approach.
Identifying High-Impact Opportunities
To maximise the value of predictive analytics, it’s crucial to identify high-impact opportunities within your organisation. Consider areas where a data science (DS) model can drive substantial improvements and generate actionable insights. These could include predicting customer churn, forecasting demand, optimising pricing strategies, or identifying fraud patterns. By focusing on high-impact opportunities, you can ensure that your predictive analytics initiatives deliver tangible business value. If you are unfamiliar with the concept of modelling, it is essentially writing code that takes into consideration different metrics from different data sources and combines them. It’s one of the more time consuming aspects of data, and this is especially true of advanced predictive modelling and so spending your time on use-cases with a clear impact is key.
Building Automated Processes for 'No Brainer' Scenarios
Typically when you apply predictive analytics, you will score your customers based upon an action, and in most cases this creates three distinct cohorts. Let’s say for example you were looking at the propensity for customers to churn. You will have one cohort with a very low score, not likely to churn and so you don’t need to take any action. Then you’ll have the opposite scenario, a cohort with a very high likelihood of churning and finally those customers who fall in the middle of those two ends of the spectrum. In this scenario, a ’no brainer’ might be to have an automated flag to the account team on all the customers at risk of churning so they can intervene. Let’s say though that you had millions of customers and thousands are flagged at risk of churning, you may simply not have the people-power to follow up with each one and so you could automatically offer a discount to those people.
The ultimate goal is that a human only needs to make a decision where the action isn’t clear cut.Typically you will need to layer a few different predictive models to understand what someone is likely to do, and what decision you need to make. So it might be one model to see who will churn, and another to see which communication channel to engage them on.
Implement Predictive Analytics on the Back of In-Depth Analysis
Before implementing predictive analytics, it’s essential to conduct in-depth analysis to understand the underlying patterns and dynamics of your data. This analysis provides the foundation for building accurate and reliable predictive models. Data scientists and senior analysts play a critical role in this stage, utilise their expertise to explore and model the data effectively. By building on top of clean, well-modelled data, you can enhance the accuracy and reliability of your predictive analytics solutions.
Building Around Real People and Use Cases
There is nothing less predictable than human behaviour! Sometimes the fault of data scientists is they get too caught up in the numbers and don’t take a step back and consider the way that people actually act. This is where your data team should work closely with subject matter experts to understand the motivations and drivers of customers in any given scenario. If you were creating a model on the propensity to convert for example, talk to your sales team, what activity do they usually see that means a prospect is warm?
Train and Review Models
Predictive models are not set in stone; they require ongoing training and reviews to remain accurate and effective. As new data becomes available, it’s crucial to continuously update and retrain your models. This ensures that they adapt to changing patterns and maintain their predictive power. Regularly review the performance of your models, analyse any discrepancies, and fine-tune them accordingly. The iterative process of training and reviewing models is essential for maximising the value of predictive analytics.
Strive to make your model interpretable, especially if it will be used in critical decision-making processes. This will help users understand why the model makes certain predictions and build trust in the system.
Activate Using Reverse ETL
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 inside their CRM. Reverse ETL facilitates the seamless integration of predictive analytics into your existing workflows.
In Conclusion
In conclusion, embedding predictive analytics into your organisation requires careful consideration of your business strategy, high-impact opportunities, and user-centric design. By leveraging clean, well-modelled data and utilising the expertise of data scientists and senior analysts, you can build more accurate predictive models. Implementing automated processes and utilising reverse ETL tools enable you to activate the predictions and turn them into actionable insights. Remember, ongoing training and review of models are vital to maintain accuracy and effectiveness.
By embracing predictive analytics, you can unlock the power of data-driven insights and gain a competitive advantage in today’s dynamic business landscape BUT don’t forget that there’s a lot of value in the walk and run stages of data maturity!
If you’re looking to unlock the hidden in your data, why not get in touch with the 173tech team today?