In today’s world of digital transformation, data is an essential commodity that directly fuels growth and success. And with an abundance of data now available, businesses that harness the power of data analytics to make better decisions will have a competitive advantage over those that don’t. For example:
- Data-driven businesses are 58% more likely to beat revenue goals1
- Data-driven businesses are three times more likely to report a significant improvement in decision-making2
There is a wide range of data analytics techniques businesses can deploy in order to improve efficiency, ensure growth and obtain a competitive advantage. Amongst them, predictive analytics is one of the most advanced and potentially advantageous.
In this article, we’ll explore predictive analytics and how it works, focusing on one of its most powerful use cases – marketing.
What is predictive analytics?
While it may sound complex, predictive analytics is relatively simple. It’s about using current and historical data to accurately predict future events, outcomes, trends, and behaviour.
These predictions are generated using a combination of statistics, predictive modelling, artificial intelligence (AI), and machine learning (ML), which analyse patterns and trends in data to predict possible future outcomes.
As a result, predictive analytics is often deployed to help organisations navigate during terms of uncertainty. This includes difficult economic times where consumer confidence drops, as predictive analytics can help forecast demand and identify where investments need to be made.
How does predictive analytics work?
Predictive analytics requires a good deal of planning, input, and expertise to yield results. Let’s take a look at some of the steps businesses can take to turn raw data into actionable insights about the future.
#1 Understand your goals
Before we even talk about data, it’s important to set the foundation for what you want to achieve. Understanding the business goals or issues you want to work towards is a critical first step upon which to build. In marketing terms, for example, underlying goals are likely to include:
- Understanding how customer behaviour may change in the future
- Boosting revenue through cross-selling, upselling, and optimised pricing
- Launching smarter, more effective marketing campaigns
#2 Develop a plan to collect the right data
Once you know what you want to achieve, it’s time to start looking at the data you’ll need to realise those goals. In many cases, customer data can be spread across a wide range of systems and platforms, so understanding what you have and where is key.
Remember, predictive analytics often requires extensive work with large data sets. That’s why it’s crucial to ensure that you have the ability to collect and analyse sufficient marketing data to accurately predict outcomes.
Businesses should also consider broadening their insights with third-party data. For example, performance data from third-party retailer sites and additional industry data can help you obtain a better understanding of what benchmarks should be, how competitors are performing and the current state of the industry.
#3 Analyse the data you have collected
Once you have collected the data you need, it’s time to analyse it. In order to make accurate and relevant predictions that ensure well-informed decision-making processes moving forward, predictive analytics needs data that is:
- Clean
- Complete
- In a suitable format
#4 Create a predictive model
Predictive modelling is a core function in the predictive analytics process. Data scientists build them using algorithms and statistical models and then ‘train’ them using subsets of data. Once they are proven to work effectively, they can be applied to full data sets to generate insights.
Examples of predictive models include:
- Decision trees
- Neural networks
- Linear regression
- Time-series analysis
- Cluster models
Building a predictive model is a complex process requiring a great deal of expertise. A defective or poorly trained model will generate inaccurate predictions, which could lead to disastrous outcomes.
#5 Use data for actionable insights
This is where the magic happens. Once you’ve outlined your goals, ensured that your data is relevant and clean, and built a predictive model that works, it’s time to put it all into action. Now you can use your predictive insights to guide your decision-making and give you a competitive edge.
Predictive analytics in marketing
So what is predictive analytics in marketing? Put simply, it’s the process of using customer data to make predictions about the future that help marketing teams become more effective and intentional in their decision-making.
In an ultra-competitive world, predictive analytics helps businesses to decode past buying habits, enabling them to project future buying habits. Armed with this information, marketers can make smarter, better-informed decisions, allowing them to:
- Preempt future demand, behaviour, and trends
- Create targeted, well-informed marketing campaigns
- Maximise their resources
- Engage existing customers and acquire new ones
- Outmanoeuvre the competition
- Identify when and where to target customers
Predicting future trends and behaviour with a high level of certainty brings huge advantages for marketing teams. Without the power of data, decisions around what to market, how, and to whom are essentially best guesses.
Uses of predictive analytics in marketing
Now we know what predictive analytics means within a marketing context, let’s take a look at some of the ways predictive analytics can be used in order to transform marketing operations and improve outcomes:
- Analysing and forecasting seasonal behaviour: Customers have different needs and preferences at different times of the year. Having a clear, data-driven understanding of seasonal behaviour allows you to plan for spikes in demand, focus marketing efforts in the right areas, and maximise revenue.
- Targeting the most profitable products to customers: Not all sales are equal. Some products have a higher profit margin than others. With predictive analytics, you can boost revenue by targeting customers with the most profitable products and services, helping you to squeeze more out of each sale.
- Conduct ‘what if’ scenarios: What if demand for your product dries up? What if customer behaviour shifts significantly in the coming years? What if a new competitor takes a large chunk of your market share? What if you decide to target a completely new customer segment next quarter? Predictive analytics allows you to answer questions like this by exploring different future scenarios and their likelihood, helping you to create long-term plans that are flexible and effective.
- Developing more effective marketing strategies: By leveraging the power of predictive analytics, you’ll have a better understanding of what customers want and how your offering lines up. You’ll be able to understand different customer segments and the potential value they bring, while pinpointing opportunities for upselling and cross-selling. All of this will help you design and deliver marketing campaigns that are smarter and more effective.
- Prioritising key customers: Predictive analytics can give you a clear picture of your customer’s potential lifetime value, helping you focus your marketing efforts on the ones most likely to bring repeat sales over time.
The benefits of predictive analytics within your marketing team
The ability to understand and plan for future events and trends before they happen is a real game-changer. Predictive analytics offers numerous benefits across your business, and particularly in marketing, such as:
- Enhanced performance due to data-driven decision-making
- Increased lead generation and conversion
- Improved funnel efficiency
- The ability to build long-term plans that are flexible and futureproof
- Greater ROI on marketing campaigns
- An improved customer experience
- A better understanding of your customers and what drives their behaviour
Get the support you need to implement predictive analytics
The benefits we’ve outlined in this article aren’t guaranteed. Predictive analytics is a serious endeavour that requires the right skills, expertise, and systems to get right. If you don’t already have the skills and resources to implement predictive analytics in-house, you have two options:
- Close the skills gap by hiring fresh talent or upskilling current staff — both of which are expensive and time-consuming processes
- Work with outside professionals who can guide you towards your goals with maximum efficiency and minimum hassle
In our experience, it pays to go with the latter option. As one of the UK’s leading data analytics agencies, here at Ipsos Jarmany we can provide the tools and expertise you need to implement a predictive analytics marketing strategy that yields outstanding results.
We use Azure Machine Learning and Python to deliver rapid and accurate marketing analytics results that help some of the biggest companies in the world make smarter, better-informed decisions. If you’d like to join them in harnessing the power of predictive analytics, get in touch with us today.
