Customer Lifetime Value has always been one of the most crucial metrics for e-commerce success. But retailers still find it a challenge to identify and apply them accurately to increase the company’s conversions and revenue.

The actual role of CLV is to keep acquisition costs down to improve overall e-commerce success. This metric focuses on the importance of customer retention over revenue growth to provide market differentiation. Customer lifetime value is the most critical factor in designing marketing strategies as it represents a customer’s value and loyalty over a defined period. The average annual customer profit needs to be multiplied by the average duration of customer retention to derive CLV.

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Focusing on CLV as a topline metric will have a significant impact on a retailer firm’s revenue generation. One of the UK based research highlighted that only 34% of marketers are aware of the term “customer lifetime value” and its related implications. CLV informs marketers about how to invest correctly in customer acquisition, followed by retention. CLV simplifies investment decision making by identifying the most sustainable investment decision. It helps mitigate costs by allocating the marketing budget appropriately.

CLV allows the segmentation of customers based on their value, providing a personalized experience. It helps marketers to focus on retaining customers by demonstrating their total value, to offer enhanced customer experience. But, CLV is a valuable learning process. It requires firms to think beyond individual sales or conversions, evaluating the entire customer journey.

The main challenge that firms face while assessing CLV is fragmented or siloed data. Data unification is critical for determining CLV for streamlining the whole customer acquisition and retention process. This is treated as a symptom of complex technology stacks, rapid company growth, or even a reflection of internal company culture. Without unified customer profile data, it’s impossible to get the desired result through CLV. This is because CLV models utilize machine learning methods to make predictions, and that’s not possible with siloed data. Data fragmentation gets worse as the customer makes purchases on multiple devices, making it difficult to glean meaningful insights from such disparate data.

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Another challenge is the lack of in-house expertise regarding data management. Companies have begun tracking CLV without qualified teams to analyze the data or produce actionable plans based on it. Once these issues have been addressed, firms can move on to actually leveraging CLV for:

  • Improved customer acquisition and retention
  • Prevent and reduce churn
  • Plan marketing budget and implement it smartly
  • Acquire higher value customers
  • Measure the performance of ads
  • Secure future VIP customers

For the retail industry, it’s not just about selling anymore; it’s about establishing a place for the customers to return and revisit. The biggest goal should be to convert first-time purchasers into repeat shoppers.

Retailers should focus on four e-commerce marketing pillars for success:

  • Delivering personalized customer experiences
  • Value segmentation
  • Automated email campaigns
  • Converting customers into VIPs

As AI becomes more vital in e-commerce marketing, the gap between retailers utilizing AI and those without it will grow wider. This will automatically wipe the smaller, less tech-savvy competition off.

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