Predictive and recommendation models

What can predictive models do for a business?

Predictive models can help businesses turn past data into future decisions.  While many businesses know how to view historical data to analyse performance, it’s more difficult to use that information to work out what could happen next.

Predictive modelling does just that, using your business information and an AI-powered algorithm to make informed projections about the future of your organisation.  These become actionable recommendations, which can help you optimise inventory levels, identify customers who are about to churn, forecast sales of a specific product or range, or work out which customers to target in your marketing.  Predictive modelling can also be used for fraud detection, risk assessment and even in healthcare settings – for example, it could be used to predict patient outcomes or identify treatment pathways.

How does predictive modelling work?

Predictive modelling works by looking for patterns in your historical business data and then extrapolating from those patterns to make informed predictions about future events.  Because it works on vast amounts of information – or ‘big data’ – it often involves AI or machine learning to make calculations that humans can’t manage manually.  Predictive modelling isn’t a one-off process – models are regularly reviewed and updated as new data emerges or as business strategies or market conditions change.

This technique can be used to predict almost anything – from future sales numbers and product trends to the number of customers likely to leave or convert during a specific period.

How does a recommendation model work?

A recommendation model works by using past data to recommend products to your customers in real-time.  This type of modelling uses AI-powered calculations to find trends or patterns in data from within your organisation and other sources – for example, it could use transaction and search data as well as demographic and location information.  These trends are used to predict products or services that a particular customer is likely to purchase.

Unlike predictive modelling, which feeds insights back to your marketing or inventory teams, recommendation models are customer-facing.  Your recommendation model can be integrated with your e-commerce site to suggest products as a customer shops, or used by your salespeople to make informed recommendations during a sale.

Datamine’s predictive modelling solutions merge concepts and techniques from our broader range of analytics and data services.  Drawing on decades of experience, our team works closely with yours to guide the whole process, from development to implementation.  

Key steps include:

1. Initial discussion: What question are you trying to answer? What is the model trying to predict, for what length of time?  

2. Workshop: This determines how you will implement and use insights from the model and ensures you have the resources in place to do so. This could mean building a technical environment to host and run the model, adapting existing workflows, or integrating with your e-commerce site.

3. Exploratory analysis: This pre-build stage helps teams understand what’s happening with current data. This involves scoping out the quality and source of your business data, looking at outside sources, and finding out whether another service like data cleaning will be needed.

4.Model build: This step involves choosing the variables to consider and then building your analysis model. Our team’s years of expertise are invaluable here, helping you choose variables and a model type that will be most useful to your business.

5. Validation: We validate your model by using it to assess past performance data and compare the predictions with actual outcomes. This helps our team ensure that we’re using an effective technique. It can also be important for internal sign-off, helping prove the model’s value to leadership or accounting teams.

6. Implementation: The final stage involves implementing the model, measuring against control groups, and calculating the benefit to your business. Of course, as the market or internal conditions change, your predictive model may need to be adjusted.

There are several types of predictive models and techniques, ranging from relatively simple to very complex.  Although you don’t necessarily need to understand each model and type, it’s good to have a broad understanding if you plan to use predictive modelling in your organisation.

Common models include:

Classification modelling

This technique can be used to answer a direct yes/no question: for example, ‘Is this customer likely to churn?’ or ‘Is this transaction fraudulent?’ Classification models analyse past data, put each transaction or interaction into a category and use that information to classify each new transaction as it comes in.  For example, if your historical data shows that a certain behaviour pattern indicates fraud, future transactions that match this pattern can be flagged as likely fraud.

Cluster modelling

Cluster modelling groups customers or transactions based on shared characteristics – whether that means demographic data, behaviour or a combination of both.  These groupings can be used to drive your marketing strategy or inform the way you manage different customers.  For example, a bank or lender might use cluster modelling to group customers by their level of credit risk, so they can approve or deny loans more effectively.

Forecast modelling

Forecasting models can be used to analyse any number-based dataset – for example, sales figures for a specific product or average customer numbers for a range of store locations.  The model maps out historical data over time, then continues the ‘graph’ to show you what’s likely in future.  Put simply, if sales of a specific product are trending upward at a set rate over time, forecasting can predict sales numbers over the next months or years.  Forecast modelling is invaluable for inventory or staffing decisions – if you know how many customers are likely to visit a store during a period, your operations team can ensure you have enough staff on-site at those times.

Outlier modelling

Outlier models look at atypical data points instead of commonalities or broad trends.  This type of model can be used to spot unusual activity that could signal an issue – for example, an overseas transaction or unusually large purchase could be a sign that credit card details have been stolen.  

Time series modelling

Time series models look at change over time and use that information to predict future events.  Instead of looking at commonalities between groups, it measures a single metric over time to uncover deeper insights.  For example, a time series model could examine hotel bookings over the past several years to estimate reservation levels over the next 12 months, which can help inform staffing, pricing and operational decisions.

Ready to explore the benefits of customer acquisition modelling?  Talk to the team at Datamine now.

The benefits of predictive modelling include:

• More effective future preparation

• Actionable insights informed by historical data

• Ability to quantify and assess potential risks and opportunities

• Improved visibility of past events and future potential

• Versatile tool for problem-solving and business challenges

Want to see the benefits of predictive modelling in your business? Talk to the Datamine team now.