Big data, behavioral analytics, predictive modelling, data segmentation — these words are practically a part of our lives now, and every marketing professional is well-versed in them. But are we genuinely tapping into the potential of harnessing the power of these to understand our customers and make informed business decisions?
The truth is there is a science behind every sale and interaction. Customers are complex and unpredictable, but they’re not random. They respond to you, your products and services, and their environment in predictable ways. Through predictive analytics, you can gain access to critical behavioral information on 90-95 per cent of your customers whom you never hear from directly.
Businesses simply collecting and storing business information isn’t enough. You need to analyze it to turn it into usable intelligence and then put that intelligence to work to inform everything from product creation and development to marketing strategy.
A customer behaviour analysis is an in-depth examination of how customers interact with the company. It’s looking at every step of the customer journey and gathering relevant insights into what drives consumer behaviour using qualitative and quantitative methods.
Predictive analytics aids customer retention efforts by allowing marketers to understand customers’ needs better. It assists marketers in understanding consumer behaviours and trends and planning campaigns accordingly.
Losing customers
Research shows that 74 per cent of consumers are likely to make purchases, relying on their experiences alone. On the other hand, when a site is poorly curated for customers, 48 per cent of them exit without buying from it. And after having a single unsatisfied experience with a brand they love, 32 per cent of customers stop buying from it completely. By tracking your customers’ journey, any company can comprehend the emotion behind their purchase decisions and grab growth opportunities.
However, it must be perfectly integrated into your marketing stack to maximize your return on investment. Let’s take a look at a few scenarios:
- A retailer can use predictive behaviour models to determine which customers are likely to purchase specific products or services at certain times of the year or during specific seasons.
- A bank can use predictive behaviour models to assess which customers are likely to fail on their loans and acquire insurance policies for them; this helps the bank identify high-risk consumers before they become an issue for the bank and its other clients.
- A hospital might utilize predictive behaviour models to anticipate which patients are more likely than others to develop specific illnesses or disorders based on their lifestyle choices (e.g. smoking habits) or genetic proclivity to certain diseases.
- A hospital might utilize predictive behaviour models to anticipate which patients are more likely than others to develop specific illnesses or disorders based on their lifestyle choices (e.g. smoking habits) or genetic proclivity to certain diseases.
Every company’s digital marketing solutions should be data-led. A custom framework rooted in data, research and behaviour analysis that begins and ends with users’ needs over time will lead to a deep understanding of the dynamics of different audiences and market segments.
These tools, methods and techniques connect us with real human users - their needs, expectations and accurate insights, which form the core of customer experience while aligning with the business’ KPIs and the end-users expectations.
As the saying goes, ‘data without analytics is just noise’, and predictive customer data analysis is the present and the future. In 2022 there will be a 40 per cent increase in digital customer service interactions. By 2023, industry experts have projected Machine Learning (ML) and Artificial Intelligence (AI) based automation of nearly 40 per cent of all customer interactions.
Start incorporating data-driven decisions across your marketing strategies and platforms - and watch your business grow.
