A fictional image of customer lifecycle management

Customer Life Cycle Management: The Impact It Can Have On Your Business

The business world has seen the rise of numerous paradigms to increase efficiency and profitability. Among these concepts, Customer Life Cycle Management (CLM) has proven to be paramount in recent years. By leveraging the power of new technologies and data analysis, Customer Life Cycle Management can significantly impact your business.

Evolution of Customer Life Cycle Management

Customer Life Cycle Management is not a newcomer to the business world. Yet, the automation tools that facilitate its processes, and the intelligence they add to it, are a recent development. As it has always been, CLM is data and discipline intensive. It thrives on historical data interpretation, understanding customers’ reactions to various nudges, and harnessing these intelligent campaigns & customer insights into smart shopping campaigns. Its intricate nature makes it an ideal candidate for application in reinforcement learning.

Navigating the Customer Journey with CLM

A key aspect of Customer Life Cycle Management is its role in guiding a customer throughout her journey with the brand. This journey can be broadly divided into four key stages:

  • New Customer Handholding and Onboarding: This initial stage helps familiarise the customer with the brand, its products or services, and its unique value proposition.
  • Active Customer Frequency Driving and Category Addition: CLM focuses on increasing the frequency of purchases or interactions while introducing new categories to the customer.
  • High-value Customer Nurturing and Churn Prevention: At this stage, CLM ensures the retention of high-value customers and reduces the chances of their migration to competitors.
  • Lost Customer Win-back: Finally, CLM aims to regain the business of customers who have stopped interacting or transacting with the brand.

Dissecting CLM Programs: Recurring Triggers and Personalisation

Each of these stages is subdivided into actionable campaigns like First to Repeat (FTR), Cross Sell, Frequency Driving, On the Brink (OTB) churn prevention, and Winback. These programs are built around recurring triggers, automatically fired for eligible customers daily. For instance, a customer who hasn’t transacted for 90 days might receive a message that the brand misses them and has an offer waiting.

What sets CLM campaigns apart is their focus on smaller customer sets and high levels of personalisation. A classic CLM message is specific, referencing the recency of purchase, the last category bought, and the outlet, and often includes a personalised recommendation.

Evaluating the Effectiveness of CLM Campaigns

From a conversion and lift perspective, CLM campaigns have proven highly effective. Despite contributing less in absolute dollar terms compared to mass or segmented marketing campaigns, they provide significant value due to their focused customer sets. Typically, CLM campaigns contribute 20-40% of the incremental sales generated, but with a conversion rate that’s usually 60-80% higher and yield numbers that are 3-5X of mass campaigns.

To increase efficiency & personalization, you might want to consider reading about the benefits of AI in marketing.

Crafting an Excellent CLM Program

The ingredients for a successful CLM program are:

  • Single View of the Customer (SVOC): A consolidated view of customer data, spanning dozens of variables, helps in target criteria setting or personalisation.
  • Machine Learning Models: These tools predict propensity scores, offer recommendations, and aid segmentation.
  • Campaign Blueprints: Established strategies and plans that define reasons to communicate with all CLM segments.
  • Target vs. Control Measurement: A method to assess the effectiveness of the program.

Solus is a leading machine learning-based recommendation system that was built to accommodate these crucial aspects of CLM, providing a streamlined solution for businesses to enhance their customer relations and ultimately, their bottom line.

In conclusion, Customer Life Cycle Management, driven by technology and data analytics, offers tremendous potential to businesses. With its high conversion rates, personalised campaigns, and strategic approach to customer engagement, CLM can significantly boost your business’s revenue and customer retention rates.

a fictional image of a man with an AI brain for prompt generator

Onto The ChatGPT Bandwagon With Our Prompt Generator

We’ve jumped onto the ChatGPT bandwagon with our Prompt Generator. (I’m using ChatGPT as a placeholder for all LLMs, so please don’t flame me for not mentioning all the other options!)

What did we solve for?

This was initiated with a simple problem statement – brands struggle with generating creativity for targeted or personalized campaigns. For instance, at the MarTech Summit in Singapore this week, a panel discussion that had Zalora, Citibank and the likes, stated that one of their main challenges to operationalizing the personalization engine is content and creativity. Quite unimaginable right – to be in a world where the ML Algorithm has become the “easy part”.

Knowing the problem statement well led to a fairly focused solution definition: Help brands use ChatGPT to generate creative personalized messages.

So we built a Prompt Generator with a very focused use case – content for SMS/ WhatsApp/ Email/ Notifications.

The SOLUS Prompt Generator

It’s at https://prompt.solus.ai | Free and instant Sign Up | in Beta | Desktop only!

Let’s see how it works:

If the prompt you use in ChatGPT is something as basic as:

“Generate an SMS marketing message for SOLUS, an Apparel brand”

You get something like:

If you use our prompt generator, the prompt evolves to something much more refined and the results can be something like:

 And it gets better. The email copy comes out (using a good prompt, again) looking like this:

Does this work across categories? It does – we’ve tried it for Apparel, QSR, Hospitality and Travel, Mutual Funds, and even Securities!

What about all the fears and reservations against ChatGPT?

I feel these are largely mitigated here. Writing copy for direct marketing has been a fading craft for a while. Creativity has become less attuned to the needs of relevance and personalization, so to use an LLM to generate a baseline creative that ticks off all the requisite boxes from a craft perspective are very valuable. Brands often need dozens of creative templates with variants for targeted messages – this means a factory output, which in turn means there’s a solid case to use AI.  Once ChatGPT generates a message one can iterate and add own tonality to taste – but 80% of the job has been done by the AI.

There’s no sharing of data. There are no biases in the training set of ChatGPT polluting decisions – because the use case is not decisions, it’s playing an otherwise cumbersome craft.

What’s the catch?

You still need to know what you want. In our prompt generator, you’ll need to give inputs of the Hook, the Tonality, what Personalization to use, whether you want a Follow-up message etc. I’ve been told this is intimidating, and many folks in marketing will not have answers to these Qs. Well, there’s always the option to leave these blank – but that’s also a bit of a shame. Whoever is generating your copy – Human or AI – needs a good brief!

Let us know, please.

Use the prompt generator, or have folks in your marketing team give it a spin. And let us know if it works for you, and how we can improve it.

Click here to try our prompt generator | Free and instant Sign Up | in Beta | Desktop only!

Smart Campaign Prioritization in Solus

Smart Campaign Prioritization in SOLUS

The most common mechanism for personalized engagement with a customer is through a marketing campaign. These campaigns can be broadly classified into two groups:

  • Customer Lifecycle Management (CLM) campaigns, which are sent on various relationship
    milestones such as the 3 rd anniversary of the first visit, upon the 10 th visit etc, and
  • Go-to-Market (GTM) campaigns, which typically target a large segment of customers with product recommendations, store/category promotions etc.

By its very nature, the second category above tends to target a large percentage of customers, and personalization is achieved through the use of customer-specific information in the messaging, use of recommender algorithms, variations in message tonality etc. However, the side effect of such mass-market campaigns is that, in any given week, there might be multiple campaigns that could be used to target the same customer. Which brings us to the central question in this note: which of these campaigns should we choose for which customer?

There are many ways to approach the prioritization problem. The obvious ones are:

  1. Set a pre-defined priority order for campaigns so that, if the same customer qualifies for two different campaigns, the higher priority one is chosen. This assumes sufficient domain knowledge to set the priority order on the part of the decision maker. While this might be true in general, it might be a challenge when the competing campaigns involve, for instance, different recommender stories (link to the article on recommender stories).
  2. Solve the problem at the design level, by determining more fine-grained targeting rules such that conflicts are avoided. This makes sense when there are only a few campaigns at any point in time, but also assumes that sufficient domain knowledge and care is employed in determining mutually exclusive targeting rules.
  3. Choose campaigns at random from the eligible ones for each customer. This works when you have no prior knowledge of what would work for whom, and want to test out all
    alternatives. This is essentially equivalent to an A/B test. However, one needs to determine how long a random choice is okay to do, and whether the conclusions drawn from the test will hold forever or will require further testing.

As you can see above, each of these techniques have their uses but also some significant
disadvantages. The Solus smart prioritization feature is designed to address these issues.

Solus is, at its heart, a data-driven self-learning product, and this philosophy applies here as well. It figures out what works and what doesn’t for each kind of customer, and uses this information to smartly prioritize campaigns. However, while doing so, it keeps in mind three things:

  1. What works today might not work tomorrow, so constant testing and learning is necessary.
  2. Data-driven approaches can only be driven as long as there is data. For instance, if there has been only one campaign sent to inactive customers in the past, there is no data to determine whether a different campaign might work better. This means that Solus needs to determine when and where it has enough data to be relatively more certain of the outcome and prioritize campaigns accordingly, and when to prioritize exploration.

This is why the key algorithm used to do smart prioritization is a contextual multi-armed bandit (CMAB) algorithm. In order to explain how this works, let us first understand how a multi-armed bandit problem is framed.

Imagine you’re at a casino in Las Vegas, and find a row of slot machines in front of you. Assume that each slot machine has a fixed but unknown probability of paying off, and each pull of the arm in the slot machine is independent of the previous pulls. Now, you have a bag of quarters to feed into these slot machines, and you don’t know which one to pick. How do you spend your money wisely? 

The trick is to start putting a few quarters in each machine, and keep doing it until you start seeing one or more of the machines paying off. When they do, put more quarters in those machines and less in the ones that haven’t paid off much. The more you see, the stronger your understanding of what might be a better bet, and the more money you put in there. The specifics of how to determine the allocation is where all the math comes in.

The colloquial term of a slot machine is “one-armed bandit” since it’s got a crank that looks like an arm and it takes your money. Hence the term “multi-armed bandit”. It is easy to see the analogy between this approach and some of the business problems you’re familiar with. Price testing, for instance, is a prime candidate. You don’t know which price works best, because you don’t know how much the demand might go down when you increase the price. So the best way to do it is to test, but multi-armed bandits allow you to do it in such a way that you quickly shift your focus to the price range that works better, thereby leaving less money on the table while running the test.

The contextual variant of this problem is one where each slot machine pays off with a probability
that depends on who you are. The equivalent in our context is: each campaign is a slot machine, and the likelihood of response to the campaign depends on who the customer is, i.e., what the customer-related variables (RFM, customer segment, favourites etc) are. By framing the prioritization problem as a CMAB problem, we are able to not just test and learn from customer responses, but also determine when more testing is required (e.g. when certain kinds of customers get new kinds of campaigns that they haven’t seen before).

personalisation for D2C

The Power Of Personalisation For D2C Marketing

In today’s digital landscape, direct-to-consumer (D2C) marketing has emerged as a powerful strategy for brands to establish a direct connection with their customers. The key to success in this competitive environment lies in delivering personalised experiences that resonate with individual consumers. In this article, we will explore the significance of personalisation for D2C marketing and how it can be leveraged to drive engagement, loyalty, and ultimately, business growth.

What is Personalisation For D2C Marketing

Personalisation in D2C marketing refers to tailoring marketing efforts, product recommendations, and offers to meet the unique needs and preferences of individual consumers. It goes beyond simply addressing customers by their first names or segmenting them based on general demographics. True personalisation involves leveraging data and insights to create meaningful, one-to-one interactions with consumers.

Leveraging Data for Personalisation For D2C Marketing

Data lies at the heart of personalisation for D2C marketing. Through advanced analytics and tracking tools, brands can gather valuable information about customer behaviour, preferences, and purchase history. This data can then be used to create intelligent customer insights, allowing marketers to understand their audience better and anticipate their needs.

To effectively leverage data for personalisation, brands need to invest in robust customer relationship management (CRM) systems. These systems can collect, organise, and analyse data from various touchpoints, such as websites, social media platforms, and email marketing campaigns. By gaining a comprehensive view of each customer’s journey, brands can deliver smart campaigns that have highly personalised experiences at every interaction.

Tailoring Product Recommendations and Offers

One of the most effective ways to implement personalisation for D2C marketing is by tailoring product recommendations and offers. By analysing customer data, brands can understand the preferences, purchase history, and browsing behaviour of individual customers. Armed with this knowledge, they can deliver relevant product recommendations that align with the customer’s interests and needs.

For example, a skincare brand can use a personalisation engine to suggest specific products based on a customer’s skin type, previous purchases, or even the climate of their location. By delivering personalised recommendations, brands not only prove customers with smart shopping campaigns but also increase the likelihood of conversion and repeat purchases.

Enhancing Customer Engagement and Loyalty

Personalisation in D2C marketing goes beyond transactional interactions. It creates opportunities for brands to foster meaningful connections with their customers, ultimately leading to increased engagement and loyalty.

Through personalised email marketing campaigns, brands can deliver tailored content and offers directly to their customers’ inboxes. By addressing customers by name and delivering relevant information based on their preferences, brands can build trust and strengthen the customer-brand relationship. Moreover, personalisation can be extended to social media interactions, where targeting customers through selective content and personalised messaging can be performed by brands.

Future of Personalisation For D2C Marketing

As technology continues to evolve, the future of personalisation for D2C marketing looks promising. Advancements in artificial intelligence and machine learning enable brands to gather and analyse vast amounts of data in real time, allowing for even more precise and timely personalization.

Chatbots and virtual assistants are becoming increasingly sophisticated, providing personalised recommendations and customer support. Augmented reality (AR) and virtual reality (VR) technologies offer immersive experiences, allowing customers to virtually try products before making a purchase decision.

Moreover, the rise of Internet of Things (IoT) devices enables brands to gather data from various touchpoints, including wearables and smart home devices. This interconnected ecosystem opens up new possibilities for personalisation for retail, allowing brands to deliver seamless, personalised experiences across different platforms and devices.


Personalisation for D2C marketing has become a powerful tool. By utilising data and advanced analytics, brands can tailor their marketing efforts to meet the unique needs and preferences of individual customers. This personalised approach allows brands to create meaningful connections, drive business growth, and provide exceptional customer experiences. The future of personalisation in D2C marketing holds great potential, as brands can leverage emerging technologies to stay ahead of consumer expectations and unlock new opportunities for personalisation. By incorporating personalisation into their strategies, brands can boost visibility, engagement, and loyalty, ultimately leading to long-term success in the competitive D2C marketplace.