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Ask 10 marketers to tell you how they calculate customer lifetime value (CLV), and you'll likely 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 a top 3 priority for all retailers today. Especially as consumer shopping habits have shifted during the pandemic, 75% tried new brands, meaning 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 their share of mind and wallet to their competitors. But how can retailers enhance CLV?
How To Enhance Customer Lifetime Value
Many technologies and new processes can support increased CLV, one of which is dynamic offers through customer loyalty programs. Powerful personalization capabilities, combined with individualized customer data, empower brands to connect with consumers via dynamic, personalized offers more easily than ever before.
There are many ways to assess the impact of loyalty programs, but Customer Lifetime Value (CLV) is the foundational metric savvy marketers are using to determine whether programs are driving increased revenue. Keeping existing customers engaged is much more efficient -- and profitable -- than trying to attract new ones. By evaluating and tracking against this metric, retailers can determine how well they're keeping existing customers engaged and ensuring they don't lose interest or lapse. Since dynamic offers are a key lever in driving ongoing consumer conversions, they are a critical way to improve CLV for any brand.
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.
For example, if someone visits their local Starbucks twice a week and spends $5 each visit, getting that person to spend $7 per purchase by adding that scone can have a significant impact on the amount of money they spend over the course of their lifetime. In a year alone, the amount they spend based on a $2-per-visit increase would take them from approximately $520 in annual spending to $728. Over the course of five years, that's an additional $1,040 they might not have otherwise spent. Now imagine that increase for Starbucks' nearly 20 million loyalty members.
Growing CLV with Both Transactional and Non-transactional Objectives
All brands have business objectives they'll focus on for a campaign – deeper digital engagement, for example, or reducing churn – which are the tactical steps that increase overall CLV. Some of those objectives are:
- Spend more - Increase the average order value for each visit;
- Increase frequency - Increase the number of visits per consumer in a specific time frame;
- Omnichannel engagement - Increase adoption of digital channels for store-only customers and in-store visits for digital-only customers;
- Elevate onboarding - Enhance new customer onboarding to generate additional activity during the first 60 to 90 days;
- Reduce churn - Identify customers who are at risk of attrition and reduce churn;
- Exclusive brands - Generate engagement with brands available only through that retailer;
- Category exploration - Extend buying behavior to multiple product categories; and
- New product launches - Generate excitement and increase purchases of new products.
For these objectives, brands can require that a customer take an action to receive a reward, whether it's transactional or non-transactional. For example, a consumer might be asked to download the mobile app and fill out their preferences in order to receive a reward – a coupon or points in the loyalty program. Then the next offer from the brand may require a purchase to receive a reward.
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.
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.
How to Sustain & Grow CLV with Technology
Increasing 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.
Moreover, offers need to be moved into the 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.
Again, 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 within their customer loyalty programs 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.
If you’re interested in learning more about how to make customer personalization and customer loyalty programs scaleable and successful, download Formation’s Free Guide on Modernizing Loyalty Offers for Digital Consumers -> Here
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