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.

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.

a woman reviewing great customer experience

Customer Experience And The Cost Of Irrelevance

In today’s highly competitive business landscape, delivering exceptional customer experience is crucial for long-term success. Gone are the days when customers were content with generic interactions. Personalisation and tailored recommendations have become essential elements of a brand’s strategy to enhance customer satisfaction. Today we explore the significance of personalisation as a long-term strategy to improve customer experience and highlight the cost of irrelevance in customer communications.

Personalisation: A Long-Term Strategy for Improved Customer Experience

Effective personalisation goes beyond short-term tactics to boost conversion rates and drive incremental revenue. It encompasses a comprehensive approach that considers the individual needs, preferences, and behaviours of customers. By leveraging data and advanced analytics, brands can create personalised experiences and customer insights that resonate with them on a deeper level. Such experiences not only increase engagement and loyalty but also contribute to the overall perception of the brand.

The Desire for Individual Treatment

Customers no longer wish to be seen as just another face in the crowd. They crave individual attention and personalised interactions with brands. When customers feel valued and appreciated, they are more likely to respond positively and engage with the brand. However, the lack of relevance in outreach efforts often leaves customers feeling disappointed and ignored. Brands must strive to bridge this gap by focusing on delivering tailored messages that address the specific needs and interests of each customer.

The Cost of Irrelevance in Communication

Many brands fall into the trap of resorting to mass communications rather than investing time and effort into personalised approaches. Instead of leveraging the available tools like a machine learning-based recommendation system, they opt for hastily put-together messages, resulting in irrelevant and impersonal interactions with customers. This approach may yield short-term gains in terms of numbers, but it fails to consider the long-term consequences of irrelevance.

The Impact on Engagement Metrics

There is a tangible cost associated with being irrelevant in customer communications. Brands that over-communicate or deliver irrelevant content experience a significant drop in engagement metrics, such as open rates. When customers receive countless irrelevant messages, they become disengaged and lose interest in the brand’s communications. Such disengagement can lead to a decrease in open rates by as much as 50-60%. Ignoring half of your customer base can have a substantial negative impact over time.

The Challenges of Staying Relevant

Achieving and maintaining relevance is no easy task. It requires a combination of automation, machine learning models, disciplined processes, and a clear mandate from top-level management. Most importantly, it necessitates a mindset that embraces the idea of sacrificing short-term gains to achieve long-term success.

Embracing a Mindset of Continuous Improvement

To become relevant, brands must be willing to experiment and try new approaches. This means sending fewer but more targeted communications, tightening governance and rules, and investing in automation and other technologies like machine learning in marketing. While this approach may result in a temporary loss of business, it paves the way for enhanced customer delight, loyalty, and long-term gains.

A Journey of Incremental Gains

Relevance and improved customer experience are not achieved overnight. It is a continuous journey that requires ongoing investment in tools, data sciences, creative communication, and testing. Brands must commit to making incremental improvements and leveraging insights gained from customer interactions to fine-tune their strategies. By consistently adapting to changing customer needs and preferences, brands can differentiate themselves from their competitors and foster long-term customer relationships.

The cost of irrelevance in customer communications is significant, but its impact is often realised over time. Short-term gains obtained through mass communications and irrelevant outreach can lead to disengaged customers and decreased brand loyalty. By avoiding the allure of short-term gains and prioritising long-term success, brands can build lasting relationships with their customers and thrive in today’s competitive marketplace.

In conclusion, harnessing the power of predictive modelling software has become a game-changer in the realm of customer experience. By leveraging advanced algorithms and data analytics, businesses can now gain invaluable intelligent campaign insights into customer behavior, preferences, and needs like never before.

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 machine learning-based recommendation system.

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.

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

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.

In conclusion, harnessing the power of Solus AI’s campaigns for personalized recommendations, driven by cutting-edge technology, is a game-changer for any marketing strategy. By leveraging this intelligence system, businesses can unlock a plethora of benefits and enhance their marketing efforts like never before.

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 machine learning-based recommendations system.

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 intelligent campaigns & 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 & Efficiency

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 Systems | 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. 

In conclusion, if you’re looking to unlock the true potential of your business and drive long-term success, an intelligent campaign & customer insights solution is the key. With our powerful AI-driven platform, you can gain invaluable insights into your customers’ behaviors, preferences, and purchasing patterns. By harnessing these insights, you can create highly targeted and personalized campaigns that will not only increase repeat purchases but also foster customer loyalty.