Understanding Customer Behaviour To Provide Positive Lifetime Customer Experience

In a digital world where customer-centricity, personalisation, and customer experience separate the winners from the losers, it’s no coincidence that a few companies thrive. It will soon become more challenging to compete in any industry for those who can’t keep up.

According to this research, businesses that can leverage customer behaviour data to generate behavioural insights exceed their competition by 85% in sales growth and more than 25% in gross margin.

And yet, it’s surprising how most companies still only take advantage of a mere portion of the behavioural data readily available. You don’t have to be a market giant to benefit from the power of behavioural customer data and analytics, and take your business to the next level.

In this post, we show you how customer behaviour data can be applied to drive results, and why every business should adopt this approach.

3 Ways To Dissect your Data Points And Understand Customer Behaviour

From customer acquisition to customer retention and loyalty, wherever the opportunities or challenges may hide, innovative businesses leverage customer behaviour data to bring them to light.

1. Leverage Customer Behaviour Data to Increase Customer Acquisition

To understand why behavioural data is such an efficient advantage for customer acquisition, we should first take a quick look at some of the top difficulties marketers encounter today.

The complexity of the Modern Customer Journey

The modern customer journey is incredibly intricate. The journey to purchasing usually involves various touchpoints across multiple channels, over an extended period. It can usually take weeks, months, or in some cases even years, to make a purchase decision.

This report by Salesforce Research showed that 67% of marketers indicated that developing a consistent customer journey across all touchpoints and channels is crucial to achieving their marketing strategy goals.

For most marketing leaders, 51% of campaign messages are still identically broadcasted from one channel to the next.

They also struggle to effectively adjust teams and strategies with the customer journey, indicating difficulties such as:

• Lacking a single view of the customer
• Fragmented data sources
• Budgetary constraints

Increased Customer Demand for Personalisation

The customer journey is intricate, time-spanning and multi-channelled, with many touchpoints, and is also driven by the individual consumer. A buyer’s motivations, goals, values and requirements can differ entirely from those that travel along the same path and all of them require distinct personalisation.

These statistics from the same report emphasise the extent of customers’ demand for personalisation:

• 52% of consumers are likely to change brands if a company doesn’t personalise communications
• 65% of business buyers are more inclined to change brands they don’t customise communications for their consumers

More touchpoints can mean more challenges, but it also means more chances to reveal actionable insights.

As customers follow various journey paths to reach their unique goals, you can observe performance and estimate the journey’s success or failure. Over time, patterns in behavioural data emerge that may explain those outcomes.

2. Apply Customer Behaviour Data to Boost Customer Retention

According to this research:

• 67% of customers state bad experiences as a reason for churn
• Only 1 out of 26 disgruntled customers complain.
• 91% of those unhappy non-complainers simply leave

Of the few that do complain, by the time the company addresses it, it’s often already too late.

As an example, Netflix also uses customer behaviour data to:

• Make decisions on what content to produce and license
• Prevent churn
• Improve customer acquisition

As a result of these efforts, Netflix:

• Has significantly reduced their churn rate
• Has increased the lifetime value of their customers
• Has more on customer acquisition
• Has acquired less new customers to backfill those that do churn.
• Has saved the company $1Billion a year.

The key takeaway here is that you can’t depend on your customers to warn you to correctly measure customer experience and satisfaction, or foresee churn and retention.

You can, nevertheless, usually recognise the red flags through a customer’s behaviours and, with the right analytics, ensure signs of distress are picked up on your company’s radar at an early stage, so there is still time to be proactive. Actions speak louder than words, especially when disgruntled customers often rarely complain.

Not receiving sufficient value from a product or service can be another cause of churn that customers often won’t raise a red flag about. That can be tricky to identify, especially without customer behaviour analytics and segmentation.

Most customers do not complain when they are experiencing negative encounters with brands. Likewise, you can’t depend on customers to proactively tell you when it’s the right time to market something.

3. Leverage Customer Behaviour Data to Encourage Customer Growth and Expansion

Customers can have a higher possibility of converting on particular cross-sell, up-sell, or repeat purchase exposure at certain times, but they might not realise that you market something they want, and often don’t even understand exactly what it is they want themselves.

Of course, the answer is to propose the right offers to the right customers at the right times, not reaching everyone at all times.

How Can Data Tell Which Offers to Present to Which Customers and When

The key to determining which offers to present to which customers and when can be also found in customer behaviour data. When it comes to up-sell and cross-sell, there aren’t many companies that do this better than Amazon.

“Customers Who Bought ________ Also Bought ________”

Regardless of your company size or industry, the same concept behind Amazon’s notorious “customers who (blank) also (blank)” suggestion call-to-actions can be implemented to recognise cross-sell and up-sell possibilities with machine learning and predictive analytics.

For its product suggestions, Amazon’s suggestion algorithm that is responsible for driving 35% of its revenue, utilises customer behaviour data like:

• The user’s purchase history
• The products in their shopping cart
• The items they’ve ranked and preferred
• What other buyers have examined and purchased

But, according to your business, there are various possible behavioural data points and references that can be applied to this.

It’s Just as Necessary to Learn Which Customers Not to Target

When developing behavioural customer segments for cross-selling, up-selling and repeat purchase possibilities, customer satisfaction is another critical determinant to analyse.

If a customer has recently had unpleasant experiences with your brand or hasn’t been receiving enough value from the products they’ve already bought, this probably wouldn’t be the right time to present a cross-sell or up-sell offer.

For the customer who never has shown past behaviour that indicates the possibility of a cross-sell, up-sell or repeat purchase, not only can the probability of conversion disappear after a negative experience, but continuing with an offer at this point can lead to even more damage.

Taking advantage of customer behaviour data and analytics, a consumer who has faced negative experiences recently can be recognised and led to a low-satisfaction customer segment which is momentarily suspended from viewing certain promotional offers. This way, they can be reached with other retention-focused actions to win back their support and enhance their satisfaction.

If those retention actions are successful, the consumer might re-qualify for a cross-sell proposal, at a much more convenient interval.

The Time is Now

Following the lead of Netflix, Amazon, and Google, the most prosperous enterprises now and in the future will be those that have the best appropriate insights that incorporate each customer’s entire experience with their brand.

Your customer’s behaviours can show valuable data about your customers, your business, and the connection between the two that you can’t find anywhere else. You just need to be smart enough to focus on capturing, interpreting, and operating on customer behaviour data.

Have you dissected your data points to understand your customers’ behaviour and drive results at your business? We’d love to hear about your experiences!