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 customer insights into smart 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 personalization engine 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 machine learning in marketing

The Ethics Of Using Machine Learning In Marketing: What You Need To Know

In today’s competitive landscape, personalisation has become the watchword in marketing. In order to treat each customer as a unique individual with their own preferences and interests, we must learn from individual customers’ behaviour rather than aggregate this understanding at a segment or population level. We want our marketing decision models to make decisions not just based on statements like “53% of our customers bought Men’s Formal Wear in the past week”, but on statements like “Customer XYZ has shown a preference for Casual Wear over the past year, but has purchased Formal Wear on his last visit.” And this is exactly where machine learning in marketing takes centre stage.

Understanding The Context

Machine Learning is a highly popular branch of AI that involves constructing data-driven models to comprehend or simulate human decision-making. (FYI, much of the recent popularity of AI has to do with a class of algorithms under machine learning called deep neural networks.) Machine learning models are widely used in modelling and predicting customer behaviour. The importance of machine learning in marketing can be observed in the fact that large, state-of-the-art recommender systems in popular apps and websites like YouTube and Netflix are often powered by ML. To be more accurate, it is possible due to the application of a personalisation engine.

However, it is important to remember that this is made possible by data-driven models. So it follows that the more data you feed into these models, the better they get. But the term “more data” doesn’t always mean one thing. It could mean having data about a larger number of customers, thereby making sure that the models learn from a more diverse variety of customer behaviours, and therefore get better.

While we are on the topic of machine learning and what it can do for the field of marketing, how about reading The Benefits of AI in Marketing: Increased Efficiency and Personalisation for insights on that subject?

The Ethical Aspect of Machine Learning In Marketing

An alternative manner of viewing the data-driven system of ML is by considering that it could also be interpreted as having more in-depth data about each customer. And this interpretation is particularly relevant when you think about how you intuitively think about personalisation. The intuitive, one-line pitch on personalisation is: I know my customer inside-out, and am therefore able to recommend the very thing he/she needs, at the right time.

However, this notion of a deep understanding of customers raises several ethical considerations. The quest to gather more detailed data about each customer nudges us closer to a critical question: what data is ethically ours to use and learn from, and what isn’t?

It’s worth noting that data collection and learning from it aren’t inherently problematic. For instance, if a customer purchases several blue shirts over a period, it’s reasonable to infer that they favour that colour. Where it gets murky is when the emphasis on personalisation begins to infringe upon the boundaries of personal data privacy.

To simplify, any data that characterise a customer’s interaction with a brand—such as purchase history or browsing behaviour—is considered fair game. On the other hand, any data that describes the customer personally and hasn’t been obtained with explicit permission should be off-limits. Moreover, any practice that denies customer access to a brand’s products or services based on their characteristics is unacceptable.

Ethical Machine Learning in Marketing—A Path Forward

All the ethical notions discussed thus far are neither surprising nor for that matter, unregulated. There exist legal safeguards to dictate what brands can and cannot do with data. However, in day-to-day operations, it’s easy to assume that if some data is available, it’s okay to use it. Hence, when applying machine learning in marketing, it’s vital to adhere to ethical standards and respect personal boundaries.

You might also benefit from reading our article on the Power of Customer Insights: Unlocking Hidden Opportunities for Your Business.

a conversation between a man & a chatbot integrated with ai in marketing.

The Benefits Of AI In Marketing: Increased Efficiency & Personalization

Marketing is an umbrella term, encompassing an array of smaller activities. Some of these require sheer creativity, while others demand diligence and a knack for making sense of extensive data. The focus of our discussion will be on the latter and how the integration of AI in marketing can revolutionize these data-driven tasks.

Harnessing AI for Personalization at Scale

A prime example of data-intensive marketing activities includes personalizing a customer’s app or web experience and making tailored product recommendations. With traditional methods, these tasks require significant time and effort. However, AI has the potential to perform these tasks at scale and adapt swiftly to the latest available information on consumer behaviour. This is one of the most evident benefits of AI in marketing – the ability to personalize experiences with an unprecedented level of detail and speed with the use of a personalization engine.

The Science and Engineering of AI in Marketing

These AI functionalities have been around for some time. Still, packaging them into a single, comprehensive solution that considers data volume and sparsity, as well as the intricacies of regular maintenance and updates, is resource-intensive. This is precisely where AI-powered products and solutions make a significant difference.

AI is, undoubtedly, a form of science, or as some may argue, a form of magic. However, the process of extracting business value from AI entails as much engineering as it does data science. To illustrate, it’s akin to the difference between understanding the laws of motion and thermodynamics and building a motorcycle.

Also, how about reading Solus Updates – The 4th Pillar, Predictive Scores For Targeting Customers for more insights on the latest on all things about customer targeting?

AI and Creativity: Breaking Familiar Patterns

Even for tasks that require a creative approach, individuals often resort to familiar patterns of behaviour and expression. This is where the latest generative AI models for text and images come into play, offering new possibilities. These cutting-edge AI tools enable marketing professionals to think beyond the known, to synthesize novel ways of engaging with their customers.

These models leverage what has been learned from vast amounts of data, which includes the creative output of countless others. It’s like having a limitless well of creativity and inspiration, all powered by AI in marketing. This ability to break out of the familiar and venture into the new is another immense benefit of AI in marketing.

Leveraging AI for Marketing Efficiency and Personalization

In conclusion, the inclusion of AI in marketing can revolutionize both data-intensive and creative tasks, driving increased efficiency and personalization. As we continue to witness rapid advancements in AI, we can expect even more innovative and effective ways for marketing professionals to connect with and understand customer insights. The future of marketing lies in AI, and it is up to us to harness its potential to its fullest.

a fictional image of multi-armed bandit

“What Works?” Are Multi-Armed Bandits The Answer?

It’s (always) a good time to double down on customer retention. The trouble is, while the ROI is high, it’s sometimes hard to figure out whether you’re doing the best you could. The question that comes up often is – what kinds of campaign mechanics are doing well? Is it multi-armed bandits? What should we do more of? What should we do less of? And this, I’ve often found, is not an easy question to answer. Here’s why:

Fearlessly Try Stuff

If you want to figure out what works, you need to have tried a whole lot of things and monitored them for a while. Does reaching out to small sets of customers with targeted messages work better than mass outreach with offers? Can’t say till you’ve tried (no it’s never as straightforward as “Yes, targeted works better!”). Does personalization help? Does sending it on Friday work better than on Wednesday? Does a 50% Off work better than BOGO? Many organizations just haven’t tried enough to be able to get good insights so the point is – keep trying stuff. If you haven’t been doing it already, start now. If all you’re doing is mass campaigns with an offer and no personalization you aren’t going to be able to optimize campaigns.

Benchmarks From “Out There”

Ever so often I get asked to help with “what’s the benchmark”, how well are we doing compared to our peers. The only real answer, I’m afraid, is: You are your peer. Do better than you did, and keep optimizing. Every firm operates in a different context. Even if they look similar, maybe their loyalty program enrollment criteria are different, maybe the number of outreaches per month they do is different, so many maybes… So don’t bother with what’s being curated and put out there in conferences and case studies, just keep doing better than your past self.

Why A/B Testing Isn’t The (Complete) Answer

This line of discussion often gets around to A/B testing. Nothing against it, but to me, you can’t have a winner-takes-it strategy when it comes to campaign mechanics. A/B testing is winner takes it all – you see which option worked better and then divert all weight there. Variant A has a % Off offer, Variant B has an Upgrade offer – if A does better than B, it doesn’t mean all comms should now go out with the % Off offer. It does however mean one should amplify the winner strategy and suppress the loser – till perhaps things change. And that’s the key point – things change, context changes, people change – so what they respond to will change, and if you shut the door on an option, you miss picking up a signal that could be vital.

Enter Multi-Armed Bandits

Multi-armed bandits are fascinating. The name originates from a Casino-beating strategy wherein you put some tokens in multiple slot machines and crank their arms (a Slot Machine = a one-armed bandit, stealer of your tokens. Many slot machines = Multi-armed bandits… you get the drift). You feed more tokens in the winning machine(s) and less in the losing ones, without diverting all your money to the winning one. This is where ML takes traditional A/B testing and amps it up – exploit winning campaign mechanics by diverting more comms there, and suppress losing campaign mechanics but don’t shut the door on them.

You might like to know more about our prompt generator & how it differs from the rest.

Sometimes, all you want is to know what works

This is where we started, right? Multi-armed bandits can solve a lot, but they’re not great at actually spelling out what works and what doesn’t to you. Good old descriptive analytics does that just fine. In SOLUS we just released a new module to help analyze what campaign attributes work. Here’s how we’ve done it:

Campaign Intelligence in SOLUS AI

  • We’ve broken down all campaigns and triggers into attributes like Type/ Segment/ Timing/ Size/ Channel/ use of Personalization/ Offer type/ use of Recommendations etc
  • We measure them on Metrics of Yield (Incremental Rev per Outreach), Conversion %, Incremental Revenue $
  • We give you the ability to look at variables in isolation or in combination

A couple of no-context screen grabs below: You have to see these on your data, hence is no attempt to describe these charts:

One more where you see campaign attributes in combination:

This personalization engine from Solus AI is incredibly useful. Give it a look (or try), and if you’d like to know more about how we work with Multi-armed bandits to auto-optimize campaigns, contact us today!

photo of a man working on intelligence systems

Solus Intelligence Systems: Enhancing Performance And Efficiencypower of customer Insights

2022 has been an interesting time for folks in tech– and as you well know we’ve pivoted so much to being a tech-first organization that suddenly everything happening in tech impacts us too! We shifted to being product-first in 2020, and the roller coaster ride that followed is worthy of a small book – or lengthy blog post at the very least. But that’s for another day. Today I’d like to touch upon one of the three main pillars of Solus AI. For context, the three central systems of SOLUS that work together to create magic are: Recommendations | Smart Campaigns | Intelligence Systems

Intelligence Systems in Solus

Part of our entire pivot to being product-first meant that we had to go back to our “greatest hits” of analytics work done over the last decade and see if we could make them available to clients using Solus. But of course, better than before – configurable, always on, and far richer. One decision we did take however is to not create a dashboarding platform – we’re not trying to build a Tableau/ PowerBI, but we’re bringing in our experience of how one should look at CRM and campaign metrics, and making the best of that experience available.

I’m happy to share that the Solus Intelligence System module has matured beautifully over the last year. This is a quick recap of where we are, and where we’re headed with it.

You might like to read how the power of customer Insights helps unlock hidden opportunities for your business.

Customer Intelligence Systems Report

We’ve ramped up the Intelligence systems reports to over everything a CRM manager (or someone dealing with Retention or targeted campaigns) might need. We’re constantly adding to these reports, so while these are currently a set that would work for pretty much any industry, we’re next coming up with industry-specific reports too.

Campaign Performance Reports

In addition, the ability to track campaign performance has been greatly enhanced as well. We’ve focused on Incrementality measurement – which we all know is hard, but also the truest measure of impact, and what we’ve added is a whole lot of reporting that comments on what’s working, what’s the yield it’s generating, what consistently works etc.

A few things we’ve added to these reports that are small, but powerful:

  • The ability to filter out triggers where the Control Group is too small
  • The ability to switch between Trigger X Day and Trigger X Month level of granularity for Lift measurement
  • The ability to make response attribution Time relative rather than Day relative

 There’s so much more, and we’re truly helping clients see beyond clicks and conversions, the true impact of their customer-centric efforts.

Roadmap Stuff: Campaign Diagnostics (and soon enough – What Ifs) 

We aren’t stopping here, of course. A big ask has been – can we help diagnose what campaigns work/ don’t work better? Can we understand what aspect of the campaign works? So we’re now coming up with a campaign diagnostic that does exactly that. What campaign attribute works? The segment being targeted? The use of a personalization engine? The timing? The presence of an offer? The use of a Recommendation? And so much more.

The obvious next step from here is adding a What If, going Prescriptive – recommending campaign types that give you lift and incremental, as well as campaigns that give you coverage and absolute numbers. Answering the Q of – “What If we run these types of campaigns in the coming month”. Getting to goal-seeks “IF I want to attain X, what should I do?” Watch this space!

Looking Ahead

Building Solus has been an immense endeavour. No part of this is easy – CRM is complex, it’s quite literally a space in which everything that can go wrong, will. Personalization and scale are notoriously hard. If you think about it, however, we are driven by a not-complex goal to help brands derive the most value from customer intelligence systems. This translates into – uplifting how brands nudge customer behaviour, whether the context is brand initiated (you nudge them by sending the customer a message), or customer-initiated (the customer comes to your app/ website and we need to decide what to show and how to engage them). We’ve had to break and build like crazy this last year, and the coming year will no doubt be the same. 

A magnet attracting target customers

Solus Updates The 4th Pillar, Predictive Scores For Targeting Customers

We would like to share an exciting update with you on Solus about Predictive Scores For Targeting Customers. But first, some context:

Systems of Intelligence

We built Solus; a personalization engine to be a System of Intelligence. This concept has been around for a few years now. In our context, they sit between Systems of Record (Data sources) and Systems of Engagement (marketing systems) and do automated decision-making and optimization. Or at least assist the decision-making and optimization. Given our focus on customer value, we knew the decision-making had to revolve around the following:

  • Who do I target (The List)
  • What value proposition do I target them with (The offering or offer)
  • What message/ tonality/ cadence
  • When do I target them?
  • What call-to-action or Response devices do I use?

Do a good job on these, and the customer retention and engagement world is yours. You might find Unlocking Insights from your Customer data worth a read too for more strategies.

The Fourth Pillar; Predictive Scores for Targeting Customers

In SOLUS we focused on building Recommender and Smart Campaign systems in the early couple of years. Then, more recently came the Insights system. We now have the fourth pillar in place – the Predictive Scores system for targeting customers.

 Four Pillars of Solus

Predictive Scores

Predictive Scores for targeting customers have been the bread and butter of the analytics world for years. They’re the go-to project for any data sciences team wanting to help marketers get better ROI. What we’ve done, is make this out of the box, incredibly usable, and very accurate.

 The scores themselves will focus on familiar goals:

  • Who is likely to buy in the next N days (7/15/30 days, for instance)
  • Who amongst the new customers is likely to Repeat
  • Who amongst the customer base is likely to Churn
  • Who amongst the lapsed base is likely to be won back

We won’t stop here, of course. We’re next setting our sights on that most elusive of concepts – CLTV.

The models behind these are ML models – gradient boosting/ random forest for the most part – and have been fine-tuned to work quickly, and smartly. What often happens in data science projects is that there are folks that love building models, but putting them into regular production is much harder as that needs a different set of chops altogether. We’ve solved this:

  • The predictive scores for targeting customers are available at a customer level as ready-made variables to use in campaign selections
  • Ready segments are available (Top 30% by Likely to Buy etc.)
  • The segments can be combined with other segments for targeting (High Propensity + High ABV, for instance)
  • A ready reporting interface with gain charts and model performance
  • Configurations through a UI to set the refresh and calibration cycles etc.

This here’s a sample chart for the models that will be part of the UI:

A chart showing predictive scores for targeting customers

 The Impact

  • The obvious impact is that of time usually taken to get this nature of tool kit in place – many months of work get instantly taken care of.
  • The business impact comes in sharper targeting = better conversion rates
  • The Customer impact comes in better relevance = lower dissonance and better LTV

We’re looking forward to putting Predictive scores for targeting customers to work!

Two individuals with a graphic of bulb discussing hidden potential of Customer Insights

Unlocking Insights from your Customer data

It’s never been more important to understand your customers in a way that is useful for driving revenue for your business. All leaders need to know how to leverage customer insights and drive more revenue from returning customers, and the literature around why this is a better ROI than constantly acquiring (and losing) customers is legion. Here are a few tips on making this come to life:

An Insight into Customer Insights

While Customer insights appear to be all about understanding customers’ needs, behaviours, preferences etc, not all insights are equal. The first thing to figure out how far you’re going to get is to know the entitlement you begin with – the data you have. 

Our view is that the best stuff comes from the transaction and behavioural data, and less from profiles and demographics, for a couple of reasons:

Transactions and Behaviours are good data

These come from billing or online systems, are first-party data you own, and contain enough signals to make many of the decisions you need. If your goal is to figure out who’s going to do a Repeat transaction, there’s enough in the transactions and behaviours to go by, without waiting for Profile information.

Profile data = Infinite Procrastination

For many industries, waiting for profile data can be an infinite wait. You need to put in place systems to capture it, send out profile-update surveys, get front-line staff to ask Qs and so on. This can take a very long time to give you a half-decent fill rate in your customer database, which is time lost.  

Similar Profiles have vastly different behaviours

If you’re looking at predicting which of your churn base is likely to be won back, chances are there’s little commonality from a Profile perspective that will help. Gender/ Location/ Income etc don’t help much when it comes to predicting behaviour. They do help a LOT when it comes to getting the right tonality, but from a sheer utility perspective – our approach is to tap into transaction data first, and fast, before getting to profile information.

Customer Insights (that are worthwhile)

Of the many customer insights you might seek, we’ve found a few to be more immediately useful to drive business goals::

Lifecycle Behaviour

The simplest, the most insightful, and yet oft-overlooked. Lifecycle insights are simple metrics that tell you how healthy the customer base is. These help set goals and can drive the entire CRM effort. Sample metrics include:

Repeat behaviour: How many repeats, and within what time frame? What is the contribution of Repeat revenue in a month as against New revenue? 

Retention Metrics: A variant of Repeat, this typically looks at how many customers that transact in a year are retained in the subsequent year. 

Churn and Winback Metrics: How many customers are lost, after how long do we call them “churned”, and how many of them are won back? 

Stickiness and Stars

Insights into what makes for a loyal or “sticky” customer. What they buy, how often, how quickly etc. Loyal and Star customer profiling provides a wealth of insight into the ideal customer behaviour you’re chasing.

Product Adoption

Combining product adoption with lifecycle thinking leads to some great insights. Instead of just looking at what is the product mix being bought, one looks at what’s the mix at different stages of the lifecycle.

What’s the entry point, and also what was the entry point for those who went on to become Star customers? Which product seems to lend itself the most to repeat? How much is the category adoption amongst customers and what are the category associations? And finally, what seems to be most associated with customer churn?

Location/ Temporal

These often go hand in hand – insight into what makes a Location different, as well as what is the buying pattern over time bands – days of week/ month part/ season etc.

Here again, combing Location/ Time data with the Lifecycle often gives the best insight. Knowing that there is a sub-segment among your Stars that are weekday buyers is useful to craft targeted campaigns just for them.

Campaign and Offer Responsiveness

Finally – a great piece of insight to delve into is to know what nature of nudge works to get customers to buy. Does it work better to send personalized messages? Do recommendations work? Does day and time matter? What’s the right cool-off between messages? What tonality resonates?

Putting this to use

Do better Targeted Campaigns

Better, more targeted, hyper-personalized campaigns that get better conversions and Lift. The starting point to these is knowing your customer better.

Do better Mass Campaigns

Mass Campaigns can and should be smart campaigns. Yes, you may talk to the entire base but the offer and tonality may vary by segment. Timing can be picked, the offer can be tweaked.

Set better Goals

It takes insight to set good CRM goals – what Repeat/ Retention/ Churn Prevention and Winback goals you chase come from the customer insight you have.

Improve your ROAS

The ultimate goal is ROAS – get more revenue, at a lower cost. Which means better targeting without missing opportunities.

Align the Org. for Loyalty

This doesn’t mean a Loyalty Program. This means the always-on hunger for customer insight and how to put it to use to retain customers, better. Stay customer-obsessed, by being data-driven.

Why SOLUS AI?

We’re a System of Intelligence providing a personalization engine, built for Customer obsessed businesses. While at one end we operationalize Recommenders, Predictive Scores and Smart Campaigns, an equally large part of our value proposition is getting businesses closer to their customer data by giving them all the customer insight they need, on tap. 

These tools include:

  • Repeat customer base and revenue tracking. 
  • Bounce curve or Interpurchase cycle tracking.
  • Repeat Cohorts.
  • Glue number and Glue cohorts. (how sticky is the customer base)
  • Multiple Segmentation schemes with segment profiling.
  • Customer frequency distributions.
  • Segment movements over month/ quarter/ year.

SOLUS AI’s Campaign Insights revolve around Incrementality measurement and encompass:

  • Outreaches and channel split.
  • Incremental Revenue, Lift, TG vs. CG metrics.
  • CRM KPIs tracking such as Repeat, Retention, Winback etc.
  • Any vs. Hard Response. (bought the specific product)
  • Conversion Analysis – what was bought in the conversions. 
  • Attribution views – Last Touch vs. MTA
  • Yield reports that specify the ROI from campaigns.