8 min read
Customer behaviour analytics helps you understand how customers interact with your business. It also tells you what factors influence their decisions and actions.
When we’re talking about ‘customer behaviour’, we’re not talking about who shops with you. Instead, we’re talking about how customers interact with your business. As a result, a customer behaviour analysis model assesses factors like how frequently customers shop, which products they prefer, and how they perceive your marketing, sales, and customer service.
Although demographic factors like age, gender, and occupation are still a vital part of any form of audience analysis, customer behaviour analytics helps you understand how customers interact with your company through both qualitative and quantitative observation. By focusing on ‘how’ your customers interact with you rather than ‘who’ they are, you can gain an insight into the different variables that influence your audience.
By understanding these details, your business can effectively and persuasively communicate with your customers at the right time and in the right place. Using the information you’ve gathered, you can then create personalised customer experiences that increase customer loyalty.
What is customer behaviour analytics?
Before we begin to assess the many benefits of conducting a customer behaviour analysis, we first need to take a look at what the process actually involves.
In essence, this form of analysis is a qualitative and quantitative observation of how customers interact with your company at every stage of the customer journey. It then provides insights into the factors driving customer behaviour.
In a customer behaviour analysis model, customers are first segmented into buyer personas based on their common characteristics. Then, each group is observed at every stage of the customer journey.
Due to this, the process can tell you a lot about the motives, priorities, and decision-making methods that are under consideration during each buyer’s journey.
The data required for customer behaviour analysis
Remember, each customer’s journey is different. Thanks to this, a number of metrics and data sources are utilised when performing the analysis. These include:
- Demographic data, such as gender, age, and location (this still covers the ‘who’ aspect of your customers)
- Response rates from previous marketing campaigns customers
- The campaign and channel that originally converted each customer
- Information about previous purchases and their purchase frequency
- Lifetime customer value
- Average order value
- Customer support information, including whether any issues have been resolved
- Discounts used by customers and whether each customer is involved in any loyalty schemes
- Abandoned cart information, including total number of abandoned carts, products abandoned, and the total value of abandoned carts
- Interactions with digital channels such as social media
Customer behaviour analysis impact
The goal of customer behaviour analysis is to uncover ways that your business can better meet the expectations of your customers and increase your conversion rate.
However, there are a number of ways that conducting a customer behaviour analysis can help your business. This is because customer behaviour analytics gives you an insight into which of your customers bought what, when, and via which channel. It also helps you to learn about why customers acted in a specific way, i.e. why they came to your website or why they lapsed. This subsequently offers you a competitive advantage and means that you’re better equipped to make data-based marketing decisions that help you steer your customers' journeys towards desired outcomes.
On top of this, customer behaviour analytics metrics make it possible for you to predict the behaviour of your current customers. Using the insights you’ve garnered from the analysis, you can improve several aspects of your operations, from inventory planning to marketing activity. By accurately anticipating buyer behaviour, you can make relevant and targeted offers at each stage of the customer lifecycle.
With this in mind, let’s take a look at exactly how running a customer behaviour analysis can help your business at each stage of the customer lifecycle, from acquisition right through to engagement and retention.
By determining the long-term value of your individual customers, you can identify and target high-value customer segments.
By understanding the behaviours and preferences of these ideal customers (including customer behaviour analysis metrics such as the channels they respond to and their favourite products), you can determine the right way to attract more customers like them and keep high-value customers engaged. You can do this by running targeted marketing campaigns, smart loyalty programs, and personalised promotions.
On top of this, conducting a customer behaviour analysis can also help you understand which of your customers are most profitable, what their preferred channel is, and when the best time to reach them is.
When you understand patterns in customer behaviour, you can then generate personalised next-best, cross-sell, and up-sell offers.
After all, certain individual customers may have a higher likelihood of converting on particular cross-sell, up-sell, or repeat purchase offers. However, these customers might not know that you have something they want, and they might not even know exactly what it is they want themselves. Thankfully, with the help of a customer behaviour analysis, you can introduce the right offer to the right customers at the right time.
In addition to this, analysing various customer behaviour analysis metrics can also help you in the long term. This is because it can allow you to understand seasonal buying patterns, how likely customers are to make repeat purchases, how long they tend to go between purchases, and more.
According to research by Esteban Kolsky, 67% of customers report bad experiences as a reason for churn. However, only 1 out of 26 unhappy customers actually complain to the company involved.
As a result, it’s clear that your business cannot rely on its customers to raise any problems they have with your product or service. You certainly cannot use complaints data to gauge customer experience, satisfaction, or to predict churn and retention.
Instead, you need to actively analyse the behaviour of your customers in order to spot signs of trouble before these customers churn. Thankfully, behaviour patterns can be used to detect possible customer churn and generate next-best retention offers that keep these customers engaged.
Finally, customer behaviour analysis can also help you retain your best customers. It’s currently estimated that around half of all customers expect special recognition if they’re a ‘good customer’ that offers repeat business.
Even if they like your company and enjoy using your products, it’s likely that these people may start to look elsewhere if you don't acknowledge them and provide incentives. Thankfully, behaviour analysis can help your team reduce this customer churn by identifying good and bad customer traits. You can then focus on targeting customers with ‘good’ traits and actively maintain those relationships.
Further benefits of customer behaviour analysis
However, the benefits of conducting a customer behaviour analysis go well beyond these metrics. By properly conducting the analysis and then applying the results correctly, you will:
- Increase customer acquisition and conversion rates
- Lower your cost of acquisition
- Generate a larger average sale on initial purchases
- Increase the number of purchases per customer
- Generate larger order sizes on repeat purchases
- Increase the lifetime value of customers
- Improve customer retention rates (and lower levels of churn)
Customer behaviour analysis examples
When you leverage customer behaviour data correctly, your business can benefit from all of the advantages mentioned above. According to research cited by McKinsey, organisations that leverage customer behaviour data to generate behavioural insights outperform peers by 85% in sales growth and more than 25% in gross margin.
Proof of how vital customer behaviour analysis models are can be seen through two examples: Amazon and Netflix. Both of these multinational conglomerates have built their business empires around customer behaviour data and analytics, and have been hugely successful because of this.
These companies don’t simply focus on ‘who’ their customers are based on demographic data. Instead, they understand that knowing ‘what’ their customers do and ‘how’ they interact with the business is far more important. As a result, they know that quantitative insights revealed through their behaviours can paint a much more accurate picture of what their customers want and need.
But, how do they deliver what their customers need at the right time? Well, chiefly, they use customer behaviour data to deliver personalised experiences. With both businesses, this comes through the form of personal recommendations.
Netflix does this by telling customers which other shows they’d like to watch based on their previous viewing habits. Meanwhile, Amazon shows customers items that they may like to buy based on their previous purchases. These recommendations are fuelled almost entirely by customer behaviour data and studies show that they’re highly effective. For example:
Customer behaviour analysis tools
Using customer behaviour analysis tools, you can quickly and accurately predict customer value, personalise the customer experience, and improve customer retention rates.
When you use our powerful customer behaviour analysis tools, you can generate new insights about your customers. You can then use these insights to power high-performing campaigns. Overall, customer behaviour analysis tools allow you to:
- Segment all of your customer data, identify common traits, and improve engagement
- Identify patterns in transactional data, perform a profile analysis, or conduct predictive modelling
- Identify new business opportunities, such as geographic regions you’re yet to explore, or customers you’re not effectively reaching
- Predict the next purchase of your customers and use advanced modelling techniques to calculate the best next products to tempt customers
- Create bespoke audiences for your campaigns
Using our behaviour analysis tools, you can also perform a customer journey analysis. This means that you can identify each customer’s position during their customer journey and ensure that they’re always receiving carefully targeted and relevant communications.
On top of this, you can also use our tools for predictive analysis purposes. This means that you can run predictive models to support the decision-making process, identify significant characteristics about your customers by scoring your database, or profile your customers. This can also help you reduce churn. Remember, it’s much more expensive to acquire new customers than it is to retain your existing ones, so churn prevention is key. Thankfully, our customer behaviour analysis tools allow you to analyse multiple variables that can predict churn probability, allowing you to take appropriate measures to win back the customer.
Similarly, using Apteco Intelligence (AI), you can add extra insights and automated intelligence capabilities into your campaigns. This way, you can promote the most likely next purchase, adapt content choices, prioritise campaigns, and optimise to each individual’s preferences.
Finally, our tools can also be used for targeting and analysis. This means it’s easy to identify your most important customers, segment them along different criteria, or run a geoanalysis.
Witness the power of Apteco data intelligence first hand with a one-to-one demo. Book a demo for a time slot that suits you and your team now.