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Analysing the ‘digital body language’ of customers
This topic is designed to help you uncover actionable insights by analysing customer behaviour. By the end of this topic, you’ll understand how to track, interpret, and leverage behavioural data to create high-impact marketing strategies.
Let’s introduce you to Rachel, a digital marketing analyst at a leading e-commerce company. She used to think understanding customers was simply about tracking purchases and page views, until she discovered the rich world of digital body language and behavioural patterns. This new understanding transformed her approach to customer analysis.
Did you know
63% of B2C consumers and 76% of B2B customers expect brands to know and understand their unique needs and expectations. – Salesforce
Just as a skilled salesperson reads body language in a physical store, noting whether a customer lingers at certain displays or quickly passes by, digital body language reveals how customers behave online. Every scroll, click, and pause tells a story.
“I remember the day it clicked for me,” Rachel shares. “We had a customer who spent 45 minutes comparing two similar products, repeatedly switching between tabs. Instead of seeing this as indecision, we recognised it as thorough research behaviour, characteristic of our high value customers who make confident, long lasting purchase decisions.”
Digital body language manifests in various ways, for example, mouse movements reveal hesitation or interest. When a customer hovers over the “Buy Now” button but doesn’t click, it might indicate price sensitivity or the need for more information. Scroll depth shows engagement levels.
Rachel noticed that customers who read 80% of product descriptions were 3 times more likely to purchase than those who only skimmed the first paragraph. Session time and frequency paint a picture of purchase readiness. Multiple visits to the same product page over several days often signal an imminent purchase decision.
A real world example
A real world example of this is Amazon. They excel in reading and responding to digital body language, using customer interactions to personalise experiences and boost engagement. By analysing browsing patterns, search queries, and purchase history, Amazon tailors recommendations to suit the buyer and their possible needs.
This deep understanding creates a seamless shopping experience, fostering trust and loyalty. By mastering digital body language, Amazon turns casual browsers into repeat customers, setting the standard for personalised engagement and growth.
Did you know
Selling to an existing customer has a probability rate of 60-70%, but selling to a new customer is only 5-20%. – Invesp
RFM (Recency, Frequency, Monetary)
Rachel’s team started noticing interesting patterns in customer behaviour. They used RFM (Recency, Frequency, Monetary) analysis to categorise customers.
- Recency measures how recently a customer has interacted with your business, such as visiting your website, making a purchase, or engaging with your content.
- Frequency measures how often a customer engages with your brand within a specific period. This can include purchases, visits, or interactions on digital platforms.
- Monetary measures how much money a customer spends with your business over a certain period.
By combining Recency, Frequency, and Monetary scores, businesses can segment customers effectively and tailor strategies to engage them more meaningfully.
This analysis revealed something fascinating.
Customers who browsed the website during their lunch break, between 12 and 1 pm, were 40% more likely to complete their purchase after work between the hours of 6 and 8 pm. This insight helped Rachel’s team time their reminder emails perfectly.
Predictive analytics uses historical data to forecast future behaviour. Rachel explains: “We stopped asking ‘What did our customers do?’ and started asking ‘What will they do next?’”
Using Machine Learning algorithms such as, Amazon Web Services (AWS) and Amazon SageMaker, they identified patterns that indicated a customer might be ready to purchase.
Increased website visits show more time spent on comparison pages, higher engagement with email content and multiple items added to their wishlist. These indicators helped predict customer behaviour, allowing for more targeted and timely marketing interventions.
Customer behaviour analysis isn’t just about what people buy, it’s about understanding their journey. Rachel discovered that customers who used the site search function typically followed one of three patterns:
- The Direct Searcher who knows exactly what they want and uses specific search terms.
- The Browser who uses broad terms and explores multiple categories and
- The Researcher who combines multiple search terms and reads reviews extensively.
Understanding each pattern helped Rachel develop tailored strategies for support and engagement.
One of Rachel’s most significant insights came from studying cart abandonment patterns. “We noticed that customers who abandoned carts during the shipping information page weren’t necessarily lost sales, they were often comparison shopping and would return within 48 hours to complete the purchase.”
This understanding led to a revised remarketing strategy:
- Day 1: Send a gentle reminder
- Day 2: Offer free shipping
- Day 3: Provide a small discount
- Day 4: Showcase alternative products.
The result showed a 25% increase in recovered abandoned carts.
Rachel’s team turned their behavioural insights into actionable strategies which included website optimisation, where they redesigned product pages based on typical user scanning patterns, placing key information where eyes naturally land. Content timing where they aligned email sends with peak engagement periods identified through behavioural analysis. Finally, with personalised journeys, they created dynamic user experiences that adapted based on behavioural signals.
Practical steps to get started
Define your goals
Are you trying to improve conversion rates, reduce churn, or increase repeat purchases?
- Start with clear objectives.
- Use tools like Google Analytics, heatmaps, and customer surveys to collect information across touchpoints.
- Look for recurring behaviours, trends, or anomalies in your data.
- Use A/B testing to see how changes like altering a CTA or redesigning a landing page impact behaviour.
Customer behaviour analysis that’s done well, is a game-changer in digital marketing. It empowers you to go beyond guessing and start making informed decisions that truly resonate with your audience. Correctly executed, it transforms your marketing from a series of campaigns into a journey of meaningful connections.
Remember, every click, scroll, and interaction tells a story. It’s up to you to listen and act!
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