Have you ever wondered if we can accurately predict the future? Now, I’m not talking about psychic abilities, but rather taking a customer or consumer, or even a segment of them, and predicting how they will behave based on their previous actions.
This is what many businesses do to help gain a better understanding of general consumers and their own customers, and use those insights to evolve business, marketing, and product/service strategies to better serve their needs. So, the short answer to the title question is: yes. We can indeed use predictive analytics to achieve a better understanding of consumers - but only when used right.
Predictive analytics are used on a daily basis to spot future trends, opportunities and challenges; these insights are brilliant for future-proofing key strategies - but do they help us understand consumers better too?
Most organisations use predictive analytics on a daily basis to influence decision-making processes across the organisation. One tangible example of how predictive analytics helps both customers and businesses, is when energy companies apply it to predict how much energy you’re going to need and when, even if you’ve just moved into a new place.
What is Predictive Analytics?
Predictive analytics is a sector of data analytics that specialises in ‘predicting’ the future based on insights from current and historical data generated from consumer interactions. A very neat concept with a lot of potential for all industries.
Typically, data mining, machine learning, and statistical analysis tools are used to collate and sort this data from a variety of platforms that cover the many channels through which brands interact with customers and consumers, including but not limited to:
- Trawling social media for consumer reactions, trends, and opportunities - social media is a ready-made online platform that your customers and consumers use to interact with each other 24/7 - producing more data now in this pandemic than ever before as a sole means of communication.
- Trawling transcriptions of live chat customer service, or recorded data from customer service telephone lines - this is typically used to identify pain points and fix issues that occur during daily customer service interactions, but can also be used for predictive analytics purposes too.
- Using the IoT to gather passive data on app usage, frequently visited places, frequently googled requests, and a lot more besides to form a better picture with contextual insights that better inform the reasons behind previous consumer behaviour, and thus share insight into how consumers behave in the future when faced with similar circumstances.
Of course there are many more channels through which this data can be obtained. Through this, we can form an innate foundational understanding of both consumers and customers. There will always be surprises, but predictive analytics, when supplied with enough data, is one of the best chances businesses have of preparing themselves for potential future situations; future-proofing their strategies so that they can come through the other side stronger and intact.
Predictive Analytics: Tactics, Tools, and Techniques
Using predictive analytics to build up a better understanding of consumers is one of the best ways to future-proof strategies - but only if it’s used to inform rather than fully steer them.
In one of my previous blogs, I stressed the importance of understanding consumers outside of the ‘consumer’ context; those types of contextual insights are absolutely key in understanding consumer behaviour, as it points us to a reason why individual and collective consumers act the way they do, their biggest influences, and their most common reactions to both situations and circumstances. But there is a lot of contextual data that can be gathered about an individual consumer, so how are we meant to understand them all?
Because of the massive amount of data we produce on a daily basis, it becomes impossible for a research team to trawl through absolutely everything - this is where machine learning and automation comes into the picture. When programmed right, automated algorithms can easily pattern-spot and sort data into key themes ready for a researcher’s expertise; they can then use the statistical analysis tools of their choice that use data visualisation and modelling techniques to analyse the data and draw out key insights that help inform the decisions made across the organisation on a variety of different topics from product and service development, to the refinement of both business-wide and department-specific SMART objectives.
There area few different predictive analytics models that help with decision-making processes across organisations,, but these are the ones are the most applicable to enhancing consumer understanding:
- The Customer Segmentation Model helps researchers group consumers into segments based on whatever they desire, but the most effective for the organisation would be on purchasing behaviours or how they interact with brands.
- The Random Forest Algorithm, which is best used with large datasets. This is a model that aims to predict consumer behaviour based on historical data, and present the variety of possible outcomes in the form of a flowchart. It takes into account a considerable number of variables and anomalies that reduces the chance of bias, provides estimates for any missing data, and increases the chance of predictive success as long as the datasets are large and informative enough.
- Artificial Neural Network Models are an application of deep learning process that analyses a large variety of datasets for many purposes, such as natural language processing. They learn and model the relationships between nonlinear and complex inputs and outputs to reveal hidden patterns, relationships, and predictions to improve decisions.
Containing the large data sets generated from these and other models in a large data bank accessible to all departments in an organisation will ensure that the data and insights are used to the best of their ability in all decision-making processes, thus ensuring the best chance of business-wide understanding and insight activation.
Integrating Predictive Analytics for the Best Chance of True Consumer Understanding
These insights enabling consumer understanding can be used to full effect in all departments, from Marketing and Customer Service to Product Development and Operational Support, in organisations across all industries. So going back to the title question at hand, yes predictive analytics does lead to better consumer understanding when applied in the right way.
But as with any other tool and technique, predictive analytics can only take us so far in our quest to understand consumers. With predictive analytics focussing a lot on taking historical data to predict future behaviour, we forget that it’s only an estimate of what might happen rather than anything we can rely on with any great certainty on its own. It’s a good approximate roadmap for us to follow, but the landscape might change and evolve depending on the data that we get further down the line.
So the best way to make sure your understanding is as up to date and accurate as it can be using predictive analytics, the gathering and analysis of data must be continuous, and the predictions that we draw from it must be continuously re-informed, reinforced, and influenced by the streams of data and insights that we get from the data collection techniques outlined above.
To go one step further, integrating predictive analytics with other market research and behavioural science techniques to create a multi-pronged approach to data collection and analysis will allow any and all insights you generate to continuously inform each other, painting a bigger and more precise picture, and leading to a better chance of true consumer understanding.
