SOLUS AI Achieves SOC 2 Compliance: Your Data, Our Priority

At SOLUS AI, we are deeply committed to safeguarding our customer’s privacy, viewing it as both an ethical imperative and a compliance requirement. We utilise advanced technologies and stringent security measures to protect sensitive customer data, fostering transparency and control.

We are pleased to announce that SOLUS AI has successfully achieved SOC 2 compliance. This significant milestone reflects our unwavering commitment to the security, availability, and confidentiality of the data entrusted to us by our customers.

SOC 2 compliance underscores our dedication to maintaining robust controls and stringent security measures in our operations, ensuring that our clients can have full confidence in the protection of their data. We believe this achievement further solidifies our position as a trusted partner in the realm of artificial intelligence and data analytics.

What is SOC 2 compliance?

SOC 2, which stands for Service Organization Control 2, is a framework for assessing and ensuring the security, availability, processing integrity, confidentiality, and privacy of customer data in service organisations.

It is a set of standards and guidelines developed by the American Institute of Certified Public Accountants (AICPA) to evaluate how well a company manages and protects customer data. SOC 2 compliance involves a thorough audit and assessment of an organisation’s internal controls and processes related to data security and privacy.

Achieving SOC 2 compliance demonstrates a company’s commitment to data security and privacy, which can be crucial for businesses that handle sensitive customer information, such as cloud service providers, data canters, and other service organisations. It helps build trust with customers, partners, and stakeholders by showing that the organisation has implemented strong controls to protect data from unauthorised access, disclosure, or breaches.

SOLUS is committing to protecting data with SOC 2 certification.

We are aligning ourselves with following the five trust service principles.  These principles are designed to assess an organisation’s ability to protect customer data and ensure the reliability and security of its systems and services.

1.     Security

The Security principle assesses the effectiveness of an organisation’s controls and measures to protect against unauthorised access, both physical and logical. This includes safeguarding data, equipment, and facilities from threats and vulnerabilities.

2.     Availability

The Availability principle evaluates the organisation’s ability to ensure that its systems and services are available and operational when needed to meet its commitments to customers. This principle focuses on minimising downtime and disruptions.

3.     Processing integrity

The Processing Integrity principle assesses whether the organisation’s systems and processes are accurate, complete, and reliable in delivering the intended results. It ensures that data is processed correctly and that errors are appropriately addressed.

4.     Confidentiality

The Confidentiality principle evaluates the controls and measures in place to protect sensitive information from unauthorised access and disclosure. It includes assessing how the organisation classifies and restricts access to confidential data.

5.     Privacy

The Privacy principle assesses the organization’s controls and practices related to the collection, use, retention, and disposal of personal information in accordance with its privacy policies and compliance obligations. It focuses on protecting individuals’ privacy rights.

What does this mean for our customers?

We at SOLUS AI are taking this step to signify our commitment to ensuring the privacy and security of customer information. We recognize the importance of instilling confidence in our customers regarding their data, which is why we are delighted to achieve this certification.

  • We utilise an automated GRC platform, guided by our third-party compliance vendor, to effectively manage compliance with all three standards.
  • Our security posture undergoes regular assessments and adjustments to align with these standards.
  • We’ve centralised all compliance-related documents and tasks for SOC 2 on the platform.
  • Our organisation has established and enforces Information Security policies to adhere to these protocols.
  • We’ve integrated information security training as a mandatory component of the onboarding process for new team members.
  • We’ve implemented a proven framework to identify and address potential issues in real time, ensuring proactive mitigation efforts.

This commitment also signifies that we are actively safeguarding any data under our care. As our valued customers, you can have peace of mind, knowing that your data is securely handled and protected.

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 | 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!

A man measuring incremental revenue

Incremental Revenue can transform your Business – if you get it right

In the ever-evolving world of marketing measurement, multiple studies have shown that measuring incremental revenue, or lift, is the gold standard. However, achieving accurate and reliable revenue measurements for the same can be a daunting task. In this article, we will explore the concept of incremental revenue, its robustness as a measurement framework, the nuances involved in its measurement, strategies to increase it, and how to unlock its full potential to formulate smart shopping campaigns for your business.

Understanding Incremental Revenue

At its core, the measurement of incremental revenue is defined as establishing a control group (CG) and measuring the impact of interventions, such as email sends, on the target group (TG). By comparing the response of the TG to that of the CG, we can calculate the revenue generated. This approach is generally considered more robust than measuring conversion percentages or using pre/post approaches. Other methods are harder to defend due to various biases and confounding factors, but incremental revenue measurement provides a solid foundation.

The Nuances of Measurement

Measuring incremental revenue involves addressing several nuances. Firstly, determining the duration for which the CG is held out is crucial. It should be long enough to capture the full impact of interventions without introducing excessive time-based biases. Secondly, it is essential to differentiate between short-term and long-term impacts. Some interventions may lead to immediate revenue gains, while others might have a delayed effect. Separating these impacts enables a better understanding of the true revenue generated. Finally, organisations must decide whether to measure it by messaging channel or overall impact. Both approaches have their merits and should align with specific business growth strategies.

Increasing Incremental Revenue

To maximise incremental revenue growth, it is crucial to explore what truly works. Often, strategies that generate higher absolute profits may not exhibit the highest percentage lift. Additionally, high conversion rates do not necessarily equate to good revenue. It is possible for high TG conversions to mirror high CG conversions, limiting the true incremental gains. By analysing segments, campaign mechanics, timing, channels, and other factors, businesses can develop a comprehensive strategy to maximise their respective potential.

You might also want to read about The Benefits of AI in Marketing: Increased Efficiency and Personalization.

Unlocking the Potential

Realising the full potential of incremental revenue starts with a well-defined measurement framework. This framework should outline the processes for establishing control groups, implementing interventions, and accurately tracking and reporting results. Investing in suitable tools and practices that facilitate CG holdouts and enable robust reporting is crucial. Furthermore, organisations must foster a culture where the team obsesses over it and continually seeks opportunities to improve it. By incorporating a data-driven approach and actively experimenting with different interventions, businesses can unlock the untapped potential of this gold standard.

Reading about the Benefits Of Customer Life Cycle Management: How It Can Improve Your Business can also prove helpful in certain avenues.

Measuring incremental revenue is undeniably challenging, but it provides the most robust framework for assessing the effectiveness of customer engagement efforts. By employing a carefully constructed measurement plan, organisations can leverage it to gain valuable insights into the impact of their marketing initiatives. By understanding the nuances involved, developing effective strategies, and investing in measurement frameworks and practices, businesses can propel their growth and success. Incremental revenue is not just a metric; it is a powerful tool that can transform the way organisations approach CRM and customer engagement.

In conclusion, harnessing the power of Solus AI’s machine learning-based recommendation systems is a game-changer for businesses looking to maximize their incremental revenue. With cutting-edge algorithms and advanced machine learning techniques, Solus AI empowers companies to deliver personalized and targeted recommendations to their customers.

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.

How can LLMs work with product recommenders?

How can LLMs work with product recommenders?

The first question obviously is, can LLMs work as product recommenders themselves?

The short answer is: not really, no. Recommender systems develop an understanding of customer engagement with products (purchases, cart/wish list addition, browsing) and use it to recommend products to them. Strategies to do so include finding customer lookalikes, using co-purchase patterns, trending products, content-based filtering etc. However, all these methods are necessarily trained on data about what customers do.

LLMs, on the other hand, are trained on very large corpora of public domain text data; their purpose is to understand and generate natural language at a level comparable to humans. What this means is that, once you’ve decided what to recommend to your customers, LLMs can help you wrap the product recommendations within an appropriate selling story.

Pro tip: You must be aware of the benefits of customer lifecycle management & how you can use predictive modelling software for better targeting your customers & enhance customer lifecycle.

Customers don’t approach purchases the same way every time. For instance, if you’re looking for a restaurant to visit, sometimes you want to try something completely new, sometimes a different restaurant that serves your favourite cuisine, and sometimes you just want to go back to your favourite place once again. It’s the same with selling stories for product recommendations.

You can, of course, tell your customers: “Hey, we’ve got some recommendations especially curated for you!” or you can filter these curated recommendations through various lenses, like: “Hey, we know you love our Men’s wear collection, so here are some recommendations for you” or “Have you tried out our new line of beverages?” or “You’d like the usual once again, sir? Very good”.

Here’s where LLMs come into the picture. Since they are capable of producing very good, human-readable messages, they can write your marketing copy for you in all of these cases. You could even use something like Midjourney to work out what your creative image needs to look like, in case you want to embed these in an email.

But here’s where it gets even more interesting. Suppose you want to run a standard marketing campaign to give a 20% discount on all items in a particular category, say Men’s wear. Rather than send the same message to everyone, you could break your target audience down into two groups: people who have bought men’s wear before, and people who haven’t. You could then ask an LLM to come up with marketing copy, based on your original message, to personalize the message to these two groups. The following is an actual interaction with ChatGPT to accomplish the above task.

These simple variations in product recommendations allow you to take a generic category promotion and make them a lot more personalized and engaging for a better customer experience. The effort involved in getting these variations done simply boils down to telling ChatGPT what to do. You can even provide it with sample messages from your brand and ask it to mimic your preferred tonality.

In conclusion, the collaboration between Large Language Models (LLMs) and product recommenders brings a new dimension to the realm of personalization & machine learning in marketing. While LLMs themselves may not function as product recommenders in the traditional sense, their remarkable capability to generate human-readable content adds a layer of sophistication to the presentation of product recommendations.

By leveraging the strengths of both LLMs and product recommenders, businesses can craft smart shopping campaigns around their offerings, tailoring the messaging to different customer preferences and engagement patterns. Whether it’s suggesting new items, highlighting favoured collections, or introducing special promotions, LLMs have the potential to infuse creativity and personalization into marketing campaigns.

Moreover, the marriage of LLMs with customer data enhances the customer lifecycle journey by creating distinct marketing copies for diverse audience segments, effectively turning a generic promotion into a collection of individualized messages. Harnessing the power of LLMs for product recommendation marketing not only streamlines the process of message creation but also empowers brands to establish deeper connections with their customers by resonating with their unique interests and purchasing behaviours.

Personalisation For Retail

How Personalisation For Retail Can Enhance Customer Loyalty

In the competitive landscape of retail, businesses are constantly searching for ways to stand out and forge lasting connections with their customers. One powerful tool that has emerged in recent years is personalisation. It has revolutionised the retail industry by tailoring experiences to individual preferences and needs, thus creating a unique opportunity to enhance customer loyalty. In this article, we will explore the significance of personalisation for retail and how it can foster long-term customer relationships.

Understanding Personalisation For Retail

Personalisation for retail refers to the practice of leveraging customer insights and data to deliver tailored experiences, recommendations, and offers. It involves analysing customer behaviour, preferences, and purchase history to provide relevant and customised interactions across various touchpoints, including websites, emails, mobile apps, and physical stores. By harnessing the power of data and using established machine learning methods such as reinforcement learning, retailers can create more meaningful engagements, anticipating customer needs and exceeding expectations.

You might also like to read about Solus Updates – The 4th Pillar, Predictive Scores For Targeting Customers.

The Link Between Personalisation And Customer Loyalty

Personalisation for retail plays a crucial role in cultivating customer loyalty. When customers feel understood and valued, they are more likely to develop a deeper connection with a brand. By delivering personalised experiences, retailers can demonstrate that they genuinely care about their customer’s needs and preferences, fostering a sense of loyalty and trust. The way this manifests is like communication sent – for instance saying “We know your preferences, hence we’re sending you this recommendation” works a lot better than “Here’s an offer for you”. This works especially well over some time where the impact of staying relevant compounds.

Enhancing Customer Loyalty Through Personalisation Techniques

Tailored Recommendations

By analysing customer data, retailers can provide relevant product recommendations based on previous purchases, browsing behaviour, and demographic information. This personal touch enhances the shopping experience, making it easier for customers to discover new products they are likely to be interested in. Having the help of a good machine learning recommendation system can prove useful in this regard.

Customised Communication

Personalised emails, notifications, and messages create a sense of exclusivity and relevance. By addressing customers by name, acknowledging their previous interactions, and providing targeted offers, retailers can create a personalised dialogue that encourages repeat purchases and loyalty.

Dynamic Website Experiences

Personalisation for retail extends to online platforms as well. By utilising the help of something like data-driven intelligent campaigns & customer insights, retailers can customise website content, product recommendations, and promotions based on individual preferences. This ensures a more engaging and personalised browsing experience, increasing the likelihood of conversion.

Overcoming Challenges and Ethical Considerations

While personalisation offers significant benefits, it is essential to navigating potential challenges and ethical considerations. Respecting customer privacy and data security is paramount. Retailers must be transparent about the data they collect and how it is used, ensuring compliance with relevant regulations. Additionally, maintaining a balance between personalisation and intrusive practices is crucial to avoid alienating customers.

Best Practices for Implementing Personalisation Strategies

Data Collection and Analysis

Implement robust data collection methods to capture customer information, including preferences, browsing history, and purchase behaviour. Leverage advanced analytics tools to gain valuable insights and identify patterns that can inform personalisation strategies.

Segmentation/ Segment-of-One

It is important to have customer segmentation into meaningful sections based on demographics, purchase behaviour, and preferences. This allows for more targeted and relevant personalisation efforts, increasing the likelihood of success. Eventually – get to individual-level decision-making, true N=1 marketing.

Real-Time Personalisation

Embrace real-time personalisation to deliver timely and contextually relevant experiences. By leveraging automation and AI-driven technologies, retailers can respond to customer actions instantly and run campaigns for personalised recommendations and offers at the moment.

Continuous Optimisation

Personalisation strategies should be an ongoing process of learning and improvement. Regularly evaluate the effectiveness of your personalisation efforts and make adjustments based on customer feedback and evolving trends. Here too a part of the optimization can be automated and left to learning algorithms that will amplify what’s working while suppressing what’s not working.

So Does Personalisation For Retail Work?

Classic CRM metrics like repeat and retention indicate the effectiveness of better personalisation and relevance. We find that better personalisation can impact repeat metrics to the tune of 20-30% (X% Repeat goes up to 1.2X the repeat rate). Retention metrics which are longer-term impact metrics lag but can see a 10-15% improvement within 12 months. The impact of these improved metrics for a 1 Mn customer base is in the range of an additional 6-10% revenue over a year, coming at a high ROI as the cost of running personalisation is a fraction of the incremental revenue generated.


Personalisation has emerged as a powerful tool for fostering customer experience and loyalty in today’s competitive retail landscape. It allows for a more engaging and relevant shopping experience, demonstrating a genuine understanding of customer needs. However, it is crucial to approach this with a balance of ethical considerations and best practices to ensure customer trust and loyalty. By harnessing the potential of personalisation for retail, businesses can differentiate themselves and create a loyal customer base that keeps returning.

Rise in incrementality measurement

Challenging Nuances Of The Incrementality Conundrum

Incrementality has been hailed as the gold standard in measurement for marketing. In the BCG-Meta “Measure to Grow,” report measuring incrementality is seen as the ideal for measuring the impact of interventions and spending, even though the ease of use is lower than click-based metrics. However, despite its significance, there are nuances to its measurement that can make it challenging for business users to extract the right insights. In this article, we will explore the intricacies of measuring incrementality and shed light on some key considerations that marketers should keep in mind.

How Incrementality Is Measured

To measure incrementality, a target group (TG) is identified to receive a specific intervention, such as an email campaign, while a control group (CG) is withheld from the intervention. By comparing the conversion rates of the TG and CG, the delta between the two can be calculated as the incremental conversion. This incremental conversion, when multiplied by the target base and revenue per conversion, provides a measure of the incremental revenue generated.

Incrementality Measurement Table

You might also like to read about The Ethics of Using Machine Learning in Marketing: What You Need to Know.

Some Challenging Nuances of Incrementality Measurement

Short-Term vs. Long-Term

It can be conducted in the short term or the long term. Short-term control groups are held out specifically for a campaign, while long-term control groups are held out for an extended period, typically ranging from six months to a year. Both approaches offer valuable insights. Short-term control groups help evaluate the impact of withholding a specific communication, while long-term control groups provide insights into the cumulative effect of not receiving a series of communications. While both approaches have their merits, long-term control groups often offer more actionable insights.

Handling “Unsuccessful” Campaigns

Occasionally, smart shopping campaigns may not yield the desired results, with the control group outperforming the target group. In such cases, convention dictates labelling the campaign as “unsuccessful” and setting the incremental revenue to zero. Reporting negative impacts at the campaign level is counterproductive since it does not provide meaningful information. Instead, considering the campaign’s impact as neutral helps maintain its integrity.

Granularity of Measurement

The level of granularity chosen for measurement can significantly impact the calculated lift. Whether measured at a campaign level per day, campaign level per month, or segment level per month, the decision affects the resulting incremental revenue. A more granular approach, such as measuring at a trigger level per day, tends to yield higher incremental revenue values. However, it is essential to strike a balance between granularity and practicality, as excessively granular measurements may become unwieldy and time-consuming.

Non-Comparative Nature

When assessing incrementality, it is crucial to recognize that trigger-level lift and segment-level lift may not align when measured separately for the same period. Different data cuts, such as triggers, segments, revenue centres, or periods, can influence the calculated incremental revenue. This can lead to complex discussions as stakeholders attempt to reconcile incrementality numbers that may not precisely match.

Reading about the Benefits Of Customer Life Cycle Management: How It Can Improve Your Business can also be quite helpful too.

Unreal Numbers

While incremental revenue can be quantified, isolating the specific transactions that contributed to it is challenging. Drill-down analyses, such as identifying incremental bills, customers, or products, are often not feasible. Incrementality focuses on the collective impact of interventions rather than pinpointing individual transactions. Therefore, it is essential to consider the overall incremental revenue rather than fixating on isolating specific components.

Impact of Intervention vs. Selection

Most incrementality metrics primarily measure the impact of interventions—targeted and relevant messaging over some time. However, it is crucial to distinguish between the impact of the intervention itself and the impact of selecting the right audience to target. If predictive modelling is used to identify the best customers for targeting, and the control group consists of similar high-potential customers, the lift observed is primarily attributed to the intervention rather than the selection process.


Understanding and leveraging incrementality effectively requires a thorough examination of its intricacies. Superficial analysis of reported incremental metrics may lead to confusing results upon deeper investigation or contextually inappropriate measurements that fail to provide true insights. At SOLUS, we have invested years in studying it and have incorporated practical and usable approaches into our methodology. Reach out to us to gain a comprehensive understanding of incrementality and lift measurement.

Customer Segmentation

Customer Segmentation As A Product

The concept of customer segmentation is a critical one in modern business strategy. In essence, it is the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing. Segmentation enables businesses to target specific customers with messages that are tailored to their needs and thus increase marketing efficacy. However, it’s worth exploring how this old as ages practice has evolved, and how it can now be viewed as a product in itself.

Customer Segmentation: An Overview

For as long as there has been business, there has been customer segmentation. Historically, it has been considered more of an art than a science, with businesses using intuition and experience rather than concrete data. However, with the rise of machine learning and data science, efforts have been made to transform this art into a more precise and accurate science.

Despite these attempts, traditional data science methods like clustering haven’t often taken off beyond the model build itself. The reason? These machine-learning customer segmentation models often fail to produce business-friendly, actionable segments, which leads us to the pivotal aspect of customer segmentation as a product.

Reading the Power of Customer Insights: Unlocking Hidden Opportunities for Your Business can prove helpful in unlocking ideas concerning customer segmentation.

Empowering Business Users Through Segmentation

If machine learning models are not yet able to create actionable segments, the best course of action is to empower business users with the ability to create useful segments themselves. To do this effectively, businesses need to make it easy for their teams to manipulate and explore the data.

Traditionally, this would involve running complex queries and waiting for segment counts – a time-consuming process and a significant bottleneck. To alleviate this issue, segmentation needs to follow a “train of thought” approach. In this process, business users can quickly identify the number of customers meeting certain criteria and then drill down further into that data.

The Three-Step Process of Segmentation

Creating customer segmentation as a product is a three-step process:

Step 1

Quantifying the size of a customer segment is the initial step in identifying its potential value. This allows businesses to get the most out of predictive modeling software for targeting customers of their preferred base by grasping the scope and significance of the segment.

Step 2

Determine the profile of each segment to provide deeper insights into customer behaviour and preferences. This can include metrics such as average frequency, value, favourite products, and other relevant attributes to help create a comprehensive understanding of each segment’s characteristics.

Step 3

Once the customer segments are identified and profiled, businesses must transform them into action-ready entities. This may involve initiating targeted smart shopping campaigns, personalised communications, or exporting data for further analysis or integration into other systems.

This clear, step-by-step process helps businesses create actionable segments that can drive marketing and sales efforts.

Segment Management And Refreshing

Once a segment is created, businesses must decide whether it is a one-off need or something that will be revisited often. This decision introduces two significant complications. Firstly, the segment has to be stored for future use, meaning it has to be easily searchable, categorised, and similar segments need to be grouped. Secondly, the labels indicating whether a customer belongs to a segment or not must be refreshed regularly. This is a process that requires careful consideration, especially in large businesses with high data volumes where refreshing segments daily can be a challenging task.

The Future of Customer Segmentation

Looking to the future, it’s entirely possible that AI could change the face of customer segmentation. Advanced pattern recognition could help identify useful segments within data without the need for human intervention. This raises intriguing opportunities and suggests that the original premise of “data science” clusters being less useful than segments may well get reversed in the future.

The concept of customer segmentation as a product is a fascinating one. It involves leveraging data in a user-friendly manner to create actionable segments and drive business growth strategies. With continuous advancements in AI, this approach could revolutionise how businesses understand and target their customer base, making the art of segmentation more scientific and precise than ever before.