blogs Archives - Page 2 of 3

15 Sep

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

September 21, 2023
By Solus AI

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.

13 Sep

blogs

Smart Campaign Prioritization in SOLUS

November 21, 2023
By Solus AI

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:

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).
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.
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:

What works today might not work tomorrow, so constant testing and learning is necessary.
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).

04 Sep

blogs

The Power Of Personalisation For D2C Marketing

December 8, 2023
By Solus AI

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.

Conclusion

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.

09 Aug

blogs

How can LLMs work with product recommenders?

December 8, 2023
By Solus AI

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.

25 Jul

blogs

How Personalisation For Retail Can Enhance Customer Loyalty

December 8, 2023
By Solus AI

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.

Conclusion

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.

26 Jun

blogs

Challenging Nuances Of The Incrementality Conundrum

September 21, 2023
By Solus AI

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.

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.

Conclusion

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.

14 Jun

blogs

Customer Segmentation As A Product

September 21, 2023
By Solus AI

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.

02 Jun

blogs

Customer Experience And The Cost Of Irrelevance

September 21, 2023
By Solus AI

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.

19 May

blogs

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

September 21, 2023
By Solus AI

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 recommendation s, 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.

16 May

blogs

The Benefits Of AI In Marketing: Increased Efficiency & Personalization

September 21, 2023
By Solus AI

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.