If you work in Marketing, you have probably heard the term predictive analytics more than once. You may be familiar with it, or you dread to find out. But one thing is for sure; Predictive algorithms are the future of marketing measurement and optimisation. And the most sophisticated brands have already started implementing it into their marketing activities.
Find out how to use predictive analytics to boost your marketing effectiveness.
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What is predictive analytics in marketing?
Predictive analytics combines current and/or historical data, predictive modelling, and machine learning to forecast certain events and outcomes. Descriptive analytics looks into the past and helps you understand the drivers and outliers of your performance. On the other hand, predictive analytics aims to help you predict the future. Predictive analytics is used in :
Math, business management, sports, Insurance, policing, telecommunications, retail, travel, mobility, healthcare, child protection, pharmaceuticals, capacity planning, and marketing among others.
In marketing, they are used to forecast customer behaviour, trends, and the efficiency of a future campaign. The brands that adopt it can:
- Anticipate trends and trend shifts
- Get even more granular marketing insights
- Make better-informed decisions and wiser budget allocation
- Select a more effective channel mix for every campaign
- Expand their channel elasticities
- Increase their sales
How to Use Predictive Marketing to boost your Marketing Effectiveness
1. Predictive Analytics measurement models
One way to implement predictive marketing is through your marketing measurement models. Marketers have always used attribution modelling to measure their performance. From Marketing Mix Modelling (MMM) to Multi-touch Attribution (MTA) and Bayesian modelling, algorithms have always been a great asset in the hands of marketing teams. For predictive analytics, marketers rely primarily on:
Cluster models for audience segmentation
Clustering is a popular way of exploring data sets. It is often used in data mining to unveil possible groupings. Clustering modelling recognises clusters embedded in the data. A cluster is a collection of similar data in one or multiple ways. Typically members of a cluster are more like each other than members of a different cluster. This is why clustering can also serve to identify homogeneous groups on which to build predictive models.
Clustering models are different from predictive models as they have no target attributed to them. Clustering models uncover groupings (clusters) in the data. The model then assigns groupings IDs to data points. These clusters are usually used for audience segmentation based on past engagement, conversions, and demographic data. Naturally, audience segmentation can trigger customer behaviour predictions.
Propensity models to predict consumer behaviour
Propensity modelling is a set of approaches to forecasting the behaviour of a target audience by analysing their past behaviour. While propensity modelling is not a new concept, only the arrival of deep learning made it possible to unfold its full potential.
Nowadays, propensity models help identify the likelihood of a target audience performing a particular action. These actions may include clicking on a link, adding items to an online shopping basket, subscribing to a newsletter etc. This means more insights into the way certain groups will behave in terms of bounce, conversions, and clicks. Based on the estimations of propensity models, you can make informed speculations of the value each customer brings in real-time.
Recommendations Filtering to discover additional sales opportunities
A recommendation system is an information filtering system that seeks to predict the "rating" or "preference" a user would give to an item. These systems are usually found on platforms like streaming platforms or e-commerce websites.
In marketing, they rely on modelling that categorises consumers' preferences and generates forecasts of items that a specific user is prone to like. These systems can operate using a single input or multiple inputs within and across platforms like news, books and search queries. In that way, marketers can unlock more sales opportunities. These systems can operate using a single input or multiple inputs within and across platforms like news, books and search queries.
Predictive modelling is such an innovative field that Meta AI developed an open-source software named Prophet a few years ago to forecast using time series data. Prophet is a “forecasting procedure implemented in R and Python”. The model can be fit into applications and marketing attribution models to produce “forecasts for planning and goal setting”.
All the above have been proven to be powerful predictive techniques, but they certainly have certain shortcomings.
Most of the modelling techniques for predictive marketing rely on user-level data. As it is, the whole world sees the rise of a privacy-first era, where third-party cookies become less and less relevant.
Consequently, the accuracy of predictive models that rely on them drops dramatically. And Meta Prophet is innovative and reliable, but it requires fine-tuning and manual work to fit it into your own attribution modelling. Not only this, but the generated predictions are very limited to specific topics.
Objective Platform has the answer to these challenges. We drew inspiration from Meta’s Prophet to create our own forecast model that identifies trend shifts. Though it seeks to answer the same questions as Prophet, Objective Platform’s trend-break forecast model works in a very different way.
Our Machine Learning team led by Abbaan Nassar developed this algorithm to be faster, more efficient, and more accurate. As it uses far fewer parameters and identifies trend shifts in a more targeted way, it offers fully automated, fast, and accurate predictions. In addition, it is fully embedded in our Holistic Marketing Model and does not require any manual labour or implementation coding. And it is future-proof because it does not rely on user-level data only.
2. Predictive marketing tools
Modelling has been a significant aspect of predictive marketing, but the future lies in predictive analytics tools. Predictive analytics are usually offered to marketers in reports, open-source codes, and datasets. However, the insights provided are not actionable and come with certain limitations. Marketers have actually very limited control over the outcomes. And they can rarely adjust the objectives to make the predictions more accurate and relevant to their brand's dynamics.
Predictive marketing tools enable users to forecast the effectiveness of specific campaigns and budget allocations. Instead of mere insights, marketers can actually get recommendations and tweak the parameters to find the most effective way to execute a campaign. This puts the power in the hands of the marketers and makes the predictions accessible. The insights become actions and a brand can see the added value immediately.
Objective Platform brings you the most sophisticated marketing forecasting tools in the market. With Objective Platform you can forecast the performance of your campaigns and even the efficiency of you budget allocation. Not only this, but you can also get channel mix and budget recommendations for elevated ROI and lower CPC.
Media Scenario Planner
This feature allows you to create ‘what if’ scenarios; you define the campaign framework, the channel mix, the budget, and business dynamics. You can create media plans for one or more brands containing one or multiple products. This makes your predictions even more accurate and relevant to your business.
Once you determine the initial plan, you can put it to the test. Based on the predictions, you can tweak and optimise your marketing strategy to create the perfect plan. After you choose the most successful plan, you can execute it and compare the outcomes to the forecasting. By uploading the actual performance data, you make your model smarter for more accurate predictions each time.
Budget Allocation Tool
Marketing spend is an essential part of digital marketing. Besides optimising campaigns’ efficiency, marketers can now optimise their budget allocation. The budget allocation tool allows you to insert your preferred budget, or desired business outcome, to create the initial budget allocation.
The tool runs the plan through sophisticated algorithms, using the historical budget, conversion data, and channel elasticities. Use the outcomes to tweak the parameters and optimise your initial budget plan until you find the perfect one.
Predictive Analytics Overview
Predictive analytics has become very popular amongst marketers. The most sophisticated brands have already started implementing it. With descriptive analytics, marketers can look into the past to identify drivers and identifiers of their marketing performance. On the other hand, predictive analytics allows marketers to forecast their marketing performance, trends, and other events. The rise of Unified Marketing Measurement made predictive analytics even more accessible to marketers. Now marketing attribution goes beyond static insights.
The main benefits of using predictive analytics in marketing:
- Anticipate trends and trend shifts
- Get even more granular marketing insights
- Make better-informed decisions and wiser budget allocation
- Select a more effective channel mix for every campaign
- Expand their channel elasticities.
There are two ways to embed predictive marketing:
- predictive analytics measurement models
- predictive marketing tools
Objective Platform offers you both. We embedded a trends-break forecast algorithm into our Holistic Marketing Model to predict trends and events effectively and accurately. We also provide the most sophisticated predictive tools in the market to help you forecast your campaign and budget’s effectiveness.
Interested in Predictive Marketing Applications?
