How to use AI & data science to understand customer lifetime value
With customer acquisition costs continuing to rise, companies have to maximize Customer Lifetime Value (CLTV), to increase the amount of profit each customer will deliver a business throughout their lifetime.
Importantly, companies who invest more in nurturing existing shoppers than they do in acquiring new ones, typically get a far higher ROI on their marketing spend. According to research done by Frederick Reichheld of Bain & Company, increasing customer retention rates by 5% increases profits by 25% to 95%.
Despite the research and stats supporting this, it’s impossible to ignore that customer loyalty in this increasingly saturated consumer world is on the decline.
The Erosion of Loyalty
In the post-war years, businesses did not need to focus on actively retaining customers. Brand loyalty was robust and customers typically remained faithful to that one product or service they knew and loved. However, the intense growth in channels during the 1980s forced companies to think carefully about personalization and differentiation due to consumers’ growing resentment of mass marketing.
With the number of buying choices available to customers, it became crucial for marketers to target customers with more care and attention to their needs and preferences. In fact, a 2018 study showed that 52% of consumers would abandon businesses who fail to personalize their engagement.
Differentiate to Thrive
With consumer attention spans at an all time low, marketers have to ensure their content works even harder for them to attract and engage the wandering eye of the target customer.
The number of channels available is a double-edged sword. While the vast landscape of connection means that there are more opportunities to reach customers than ever before, all too frequently marketers do not properly understand the customer journey, thus weakening the overall efforts.
The volume of customer data also causes confusion and inertia. Inevitably, without the proper tools to measure and analyse this data, marketers do not know where consumers prefer to interact and what content they appreciate. What is worse, the consumers of tomorrow are ahead of the curve and are increasingly less forgiving of brands who target them without attention to detail.
Understand the Customer Journey to Increase CLTV
The customer journey can only be fully understood through precise data-driven methods. There are two tools in particular that deliver powerful results:
Multi-touch attribution (MTA) tells marketers exactly what channels are engaging customers, leading to sales and attracting new high-value clients. By measuring the precise impact of every touchpoint in a customer’s journey, marketers can eliminate poorly performing channels, reinvesting the capital into those that are working.
Businesses must know exactly who their high-value customers are. It is now possible through granular analysis to calculate how much it costs to convert a lead into a customer. By then factoring in how much profit the customer goes on to generate, you gain a comprehensive view of which customers are helping to provide a high CLTV.
Take the Next Step
Increasing CLTV can be challenging for many marketers. Working with data science experts like those at Fospha, brands can implement groundbreaking ways of understanding their customer data, optimizing their campaigns and driving competitive advantage. What is more, Fospha’s agile ‘Start Small & Grow’ approach means that a business can start to see vast improvements within just 30 days.
If you think your business might benefit from data-driven solutions to understanding your customer data, then please do not hesitate to get in touch with us at firstname.lastname@example.org