It is a well known fact that attracting new customers to a retail business costs considerably more than engaging and retaining current ones – on average it’s between five and twenty five times more expensive. While this range is considerable , even on the lower end of the spectrum it shows there is still a real cost difference, highlighting the cost-saving benefits offocusing on retention.
Based on this, when any business is looking to measure its customer base it’s important to take into consideration the Customer Lifetime Value, or CLV. This metric encompasses the total revenue a business can expect to generate over the course of a relationship with a customer. The longer you retain a customer, typically the more they spend, so by that logic a retained customer is also more profitable than trying to market to new ones.
The big question is, how can we increase CLV by adding more value to customers’ experiences?
Understanding Customer Lifetime Value
Knowing the average CLV helps businesses understand the long-term value of their customer base. This means that rather than focusing on the present, and short term gains, this provides a longer-term strategy that can be used as a framework for innovation and growth.
By calculating CLV, companies can gain insights into how much revenue a single customer is likely to generate over time, allowing them to make informed decisions about how much to invest in acquiring and retaining customers. This understanding is crucial for guiding strategic decisions and marketing investments, ensuring resources are allocated efficiently to maximise returns. In fact, increasing customer retention rates by just 5% can boost profits by 25% to 95%. This is again a wide range, but even focusing on the more conservative side of these figures still illustrates a substantial profit increase, underscoring the importance of focusing on customer retention strategies.
In a pre-epos and ecommerce world, it was almost impossible to understand customer behaviour Now in a digital environment , incorporating elements such as CRM systems, digital receipts, and tracking, this has opened up transparency and visibility for retailers to use to their advantage. These can tell us the “what” – such as Harry bought this item, coming in from Google, on this date – but they often fail to show the “why”.
This is where Conversity can really transform the data into something even more valuable and actionable. If we know that Harry purchased this item on a particular date, after coming from Google, but also completed a Conversity-designed questionnaire. This means that we know that Harry answered a number of questions based on lifestyle, preferences and other factors, that uncovered motivations and behaviours behind that purchase. Whether on a singular example such as Harry, or aggregated across multiple purchases into pure data, these can become a powerful tool for understanding customer behaviour and making informed decisions for strategy and ongoing activity.
How Can Businesses Collect Information For CLV?
There are multiple ways that a retail brand can collect datasets that will go towards a more accurate CLV.
Loyalty Schemes
Many retailers now have customer loyalty schemes that encourage customers to make repeat purchases and generate points and other perks and rewards. This not only provides value to a customer, but also matches up any data that would have been collected anonymously with demographics, marketing activity and purchase history.
Feedback Gathering
Along with loyalty, there are also now multiple ways for customers to give feedback, or retailers to seek it. From email newsletter sign ups, that come with prizes for reviews, to in-store feedback collection points that request feedback on the fly, it can be a great way to gauge customer feelings and experiences. These do come with the caveat that the data collected may be impacted by a number of biases and demand characteristics, but looking at the general trends is typically a good indicator of overall customer satisfaction.
Purchase History
Purchase history can be tracked digitally very easily, because even if someone checks out as a guest, the data will still be aggregated with location, address, purchase details, etc. Even the use of platforms such as Shop via Shopify can bring together purchase data in a much more visible way than when it was predominantly cash based.
Tools Such As Conversity
Working with Conversity, retailers can create bespoke recommendation systems that can not only offer incredibly detailed and helpful recommendations to their customers based on smart tech and expert knowledge, but the data collected also reveals more specific preferences on both the individual and overall scale. A hypothetical example of this would be an online beauty store using this feature.
A new customer visits an online beauty store for the first time. To provide a personalised shopping experience, the website prompts the customer to fill out a short, smart questionnaire created in conjunction with Conversity. This questionnaire gathers information about the customer’s age, likes, and preferences without directly asking about specific products.
Based on the responses, Conversity’s system provides a few specific, highly relevant product recommendations, such as a particular lip balm suited to the customer’s preferences. These recommendations are backed by advanced algorithms and expert data from retail specialists.
The customer purchases the recommended lip balm, finding this small initial purchase to be an excellent match for their needs. Encouraged by this positive experience, the customer continues to use the beauty store’s personalised recommendations for further purchases. As they buy more products, the system tracks their buying patterns and preferences, continuously refining future recommendations.
On a larger scale, as more customers engage with the system, the aggregated data feeds into the store’s Customer Data Platform (CDP). This broader dataset allows the retailer to identify trends, optimise inventory, and refine marketing strategies to better serve their entire customer base. The insights gained help the retailer become more customer-centric, enhancing customer satisfaction and increasing CLV.
Tools and Technologies for Optimising CLV
Understanding and tracking CLV can be done using a range of tools. For any retailer that is looking to increase their overall CLV, will require a level of visibility of what it is currently doing and then use this to take actions such as personalisation, loyalty programmes and more to try and drive this up.
The first recommendation would be to install a comprehensive Customer Relationship Management system (CRM) that works with a B2C and D2C model. Some that do this are Zoho CRM, Pipedrive, and Freshsales, all which are either well-suited for B2C contexts, or have some functionality that can make them more B2C specific. These CRMs help manage customer interactions, track purchase history, and analyse customer data, enabling personalised marketing and improved customer service by providing insights into customer preferences and behaviour.
Next would be a CDP platform, which includes tools like BlueConic, Segment and Tealium. A CDP takes data from various sources to create a unified customer profile. This makes it a lot easier to process raw data, along with other data points to build more context for the customer overall and the CLV as a result.
Many retailers also use a third party for their loyalty schemes. Some well known tools are Smile.io and Yotpo. These allow for a much easier execution and management of these schemes, and help make them more enriching for the customer, while also facilitating a frictionless solution for the data it needs to collect.
Marketing automation platforms, particularly those integrated directly with CRMs, are also incredibly useful. This could newsletter distribution from MailChimp, or ones that automate social media outreach and community response. These tools can bring those real-time actions, such as engagement and click throughs, into the data pool, while also working to drive more sales at the same time.
There are many other tools as well, but even simple tools that are free to use, such as Google Analytics, can provide top level data that helps to drive a better user experience, track conversion and give information that can really help to create a higher CLV.
Finally, a tool such as Conversity’s highly personaliased questionnaires, can bring together the elements of automation and algorithms, with real-time data from customers, to help build a better idea of what is popular and what customers really want.
When you bring all of these elements together, you can really dig down into what the CLV is, and also test different approaches and methodology, while also having more than one datapoint to track the progress of these action. This means that any activity is transparent and easy to track, both in the short and long term to really increase CLV and retention.
The Power of Tailored Product Recommendations
Understanding the CLV is a very important part of retail, and should be standard practice for any brand in the modern age. There are multiple ways to track engagement and purchases, while also adding value to the customer at the same time. Having a customer-centric approach in the first instance, while also engaging and asking for feedback at a large scale, is one way of making sure that you are serving customers and also future proofing and retaining their custom.
The hyper-personalisation of retail is only going to increase, with consumers expecting tailored and relevant experiences as the standard and not a “nice-to-have”. Retailers, by using tools and strategies that focus on detailed customer insights and preferences, can not only meet but exceed customer expectations. This means that they will not only enhance customer satisfaction but will in turn increase the CLV, ensuring sustainable growth and long-term profitability. Embracing these practices now will position retailers at the forefront of the industry, ready to thrive in an increasingly competitive market.
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