The Most Important Loyalty Metric - Customer Lifetime Value

The Most Important Loyalty Metric - Customer Lifetime Value

By Rob Fagnani, Head of Business Development and Operations

Ask 10 marketers to tell you how they calculate customer lifetime value (CLV) and it's likely that you’ll get 10 different answers. It's a tricky metric to assess, even though at its core it's a simple equation: CLV is the sum of each customer's total value of purchases over the course of their lifetime as a customer. However, the marketers that answer that question with confidence, no matter how many dependencies there are on other brand metrics, are the ones that are helping their brand thrive in the modern world of retail.

Increasing CLV should be at top 3 priority for all retailers today. Consumers shifted shopping habits during the pandemic, 75% tried new brands, so all brands are at risk of losing that core base of customers they’ve been dependent on. Add that to the sheer increase in the number of retail brands that exist today, and it's clear that customer loyalty is no longer a given. By tracking and focusing on CLV metrics, retail marketers can ensure they’re not losing share of mind and wallet to their competitors. But how can retailers increase CLV?

How To Increase Customer Lifetime Value

Many technologies and new processes can support increased CLV, one of which is dynamic offers. Powerful personalization capabilities, combined with individualized customer data, empower brands to connect with consumers via dynamic, personalized offers more easily than ever before. However, a critical element to enabling these relevant offers is good data, and many companies are struggling to get, and keep, their data in a good state.

Loyalty Programs: A Powerful Source of Customer Data

Today, most loyalty programs provide a useful proxy for having good customer data. Rewarding customers for loyalty means that brands must track data at an individual level. And, generally speaking, if companies have a loyalty program, they’re also likely to have their customer data in a reasonable state.

But why isn’t everybody doing personalized marketing and offers if they have the data?

Companies aren’t personalizing effectively because most of their data is managed with legacy systems and spreadsheets, and they haven't implemented solutions that both aggregate that data and then activate it. The sprawl associated with multiple buyer personas becomes too difficult to manage. Three personas creates three times the work, and modern companies can have hundreds – or thousands – of relevant profiles. Imagine the huge workload, as each granular profile is run through a marketing system. This kind of overhead limits speed, and failure to react quickly prevents companies from addressing individual customer profiles in a nuanced way.

Dynamic Offers: Make CLV Soar

Effective offer personalization requires automation. When personalized offers based on loyalty data are implemented effectively, with the latest technologies, CLV can soar.

By leveraging AI and data science to create tailored offers, retailers can address the needs of each customer’s journey, while reacting at a market-appropriate speed. If the average value of a given consumer purchase is $60, a retailer will want to see that average purchase rise to some $70 or $80 or more with each visit. This is where a more nuanced approach to offers – rather than gross discounting to all customers – comes in.

1. Utilize a Tiered Model

Consider a hypothetical clothing retailer and the average value of a sale. If Customer A spends $50 per visit, while Customer B spends $100, and Customer C spends $150 every time – a smart retailer would not offer a blanket discount for all customers. Instead, tiered offers, keyed to the spending level of each “A,” “B,” or “C” consumer, would make more sense, like offering a $50 discount at $100 in spend, while similarly offering discounts at higher spend levels, such as $150 and $200 and so on. An example of this would be Sephora’s beauty insider model.

Sephora_Tier_Ex

Source: Sephora

The makeup of each offer should integrate data around a customer’s frequency and amount of spend, as well as the items purchased. Gathering this data can be a massive undertaking, and automation alongside machine learning capabilities are key. But retailers that put the right processes and technology in place to capture customer data and begin to leverage it will gain an advantage. Those retailers can establish a baseline understanding upon which they can build offers. This understanding then leads them to develop new ways to increase spend and drive deeper brand interactions, “stretching” customers to become more active and frequent buyers, which creates correspondingly higher CLV.

2. Incorporate Product Affinity

In addition to tiered discounts, our hypothetical clothing retailer might also offer deal sweeteners based on product affinity. Someone who buys a shirt or a sweater might be offered a discount on an accompanying accessory, such as a scarf, mittens or perhaps a necktie, enabling the retailer to level up the purchaser to a higher dollar threshold, while extending the brand relationship. Data-driven, personalized offers will improve the purchaser’s overall experience and provide a “good deal,” while avoiding any appearance of a fire sale or deep discounting. It makes the retailer look tuned-in to its customer base, while also creating greater efficiencies with reward spend for the brand.

Within the scope of customized offers, it is important to adopt the above-mentioned techniques like spend-based reward amounts or to use some other key metric – like product affinity – to avoid overspending on advertising and/or over-discounting. Doing so drives engagement and simultaneously prevents customers from always expecting a deal, which helps preserve brand value and integrity. The practice of stretching customers with personal offers accelerates incremental revenue, ultimately increasing engagement and CLV.

How to Sustain CLV with Technology

As a practical matter, the ability to successfully “stretch” customers and increase sustainable CLV lies with technology. Given the scope and the scale of required data-gathering and processing tasks, as well as the challenges associated with offer creation, new tools are required.

Retail marketers should look for systems that can activate their first-party data, helping to create a richer understanding of customer wants and needs. Next, retailers must leverage automation to create data-driven and highly personalized offers at scale, alleviating the huge workloads associated with managing a large number of customer profiles and buyer personas.

What’s more, offers need to be moved into market quickly to keep pace with ever shifting market trends and consumer shopping habits. When offers are in-market, testing and fine tuning, enabled by AI and machine learning, are also crucial so brands can identify early indicators of those market shifts and adjust their campaigns accordingly. Continuous learning also enables retailers to optimize offers and enhance campaign performance over time, to increase CLV.

The adage that selling an existing client is always easier (and less expensive) than gaining a new one remains true, but many brands have not yet put modern technology to work in making this revenue stream more efficient. This is why brands that want to accelerate their growth need to focus on dynamic offers to better connect with customers without creating brand depreciation via too much “last resort” discounting. When marketers aim at the intersection of loyalty, personalization and CLV – and leverage technology for speed, scale and precision – they can truly begin to drive incremental revenue and greater long-term profits.


Technology now allows us to operate in a new space – one where companies can drive additional commerce through greater personalization and positively impact customer loyalty.

Read More About Customer Lifetime Value

How to Increase Customer LTV With a Loyalty Program