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October 10, 2019 | Divya Khare
Blog / 3 Ways how Retail Companies can improve average Customer LTV using AI driven Marketing
Customer Life Time Value (CLTV) is the estimated entire profit that will be earned from a customer in their lifetime. The main reasons why it’s important is because :
Once a company knows who are it’s higher paying customers and how much can it spend on them – it becomes a process of optimization to transfer more of your incoming revenue towards these higher value customers. It becomes even more important for retailers to focus more on increasing average LTV across market segments because transactions are many, involve many different items, and customer frequency and spending is volatile throughout the year.
To handle the large volumes of customers , product types and geo-locations, Retailers are now aggressively moving towards AI driven marketing tactics to drive more revenue for their business. Here is a list of the 3 ways of how we are helping our Retail clients improve their average CLTV :
Not every customer is the same and the faster you start treating them differently – the faster you will see your engagement and revenue going up. With proper segmentation, you can ensure you are delivering personalized and contextual messages to your customers – whether via email or SMS. In fact, Mailchimp’s latest user data showed that segmented campaigns get 14.37% more opens and 64.78% more clicks than non-segmented campaigns.
However, The majority of customer segmentation retailers are doing today is based on broad filtering, using tools like spreadsheets. This time-consuming process often involves some guesswork, which, along with using an unsuitable tool and poor data skills, can lead to human error.
Whether customer segmentation is absent or poorly formulated – several problems occur. First, customers receive promotions for products and services that they do not want. This results in increased volumes of deleted communications, or worse, a customer unsubscribes from the marketing list. To combat these issues marketers may reduce the frequency of marketing communications, thus opening a whole new set of problems (less sales).
With AI driven dynamic segmentation, your brand can accurately identify the segments which have a higher LTV value and are most likely to convert – helping you focus marketing investments for a positive ROI. AI algorithms, analyze every individual user—the habits, likes and dislikes, and previous activity of existing customers and leads—and draw conclusions quickly to engage with them on a personal level.
As human beings we’ve been trained with hundreds of years of retail pricing training to react pleasantly to a deal that makes us feel special or like we’re beating the system. It works especially well for increasing both customer acquisition and the bottom line for Retailers. The main reason behind is that a department or e-commerce store isn’t looking for a recurring purchase. They’re looking to get you through the door to pull the purchase trigger as quickly and efficiently as possible. While they make appear counterproductive at first – as offering discounts will essentially lower the cart value of the customer, they work very well if they are applicable for larger cart sizes , or product bundles – convincing the customers to buy larger volumes at once.
With AI based predictive marketing, you can send personalized discount codes and coupons to people who are waiting right on the edge – pushing them to make a purchase. Moreover, AI Technologies like voice assistants and chatbots can go beyond offering new platforms for bringing coupons to customers. Using AI, retailers are able to learn about their users’ behaviour and help inform business decision makers about which products would be strategically best to provide discounts based on individual cases, which will maximise the effectiveness of coupon usage. Currently, shoppers are missing out on over 300 billion coupons. For instance, in 2015, out of 321 billion issued coupons, only 2.5 billion were redeemed.
AI can identify a propensity from the user it’s interacting with towards a specific product and create deals based on complex supply algorithms that can encourage the sale of a lesser performing product. For example, if a clothing company’s customer service AI acknowledges a customer’s interest in a pair of shoes, it has the potential to instantly detect a similarly coloured belt that’s not reaching sales targets and offer an overall discount if both products are bought together. Having such a technology gives Retailers the capability of instantly generating a bespoke code for said customer to use in the checkout process.
Customers now have an unprecedented number of ways to engage with companies, from traditional channels to an ever-growing array of digital modes. However, these channels are not mutually exclusive, your customers are on all of them – and to increase LTV across segments – you need to ensure that you are present in the channels that matter, delivering one seamless experience.
You need to embark on an omnichannel transformation—one that views touchpoints not in isolation but as part of a seamless customer journey. And since customer journeys aren’t simple and linear but a series of handoffs between traditional and digital channels that can vary significantly by customer type, an effective strategy requires an in-depth understanding of what customers truly want. This is where AI comes into play – helping you understand where each of your customers are spending their time and figuring out how to best target them on each of the channels they are present in – whether it’s digital or physical.
Artificial intelligence can help create a fuller picture of your customers by connecting the dots across their devices and revealing how their needs change with each. AI can also quickly identify patterns among customers to provide insight into the best way to approach them at each stage of the customer journey.