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Customer segmentation is a necessary part of the marketing strategy for any industry. Demographics like age, gender, and location provide easy wins for companies looking to personalize their marketing materials. In the next few years, the tools that we use for will give every company a greater understanding of every customer on an individual level. These tools will quickly become the norm for any digital business looking to improve their data analysis. segmentation Still, there are some common misunderstandings about the practice on the whole. Before diving in head-first, consider these five factors: Customer behavior is just as important as customer details Effective segmentation digs deeper. It utilizes customer behavior, not just your run-of-the-mill data. What actions are customers taking once they hit your website? Do their actions resemble those of other customers? Does there seem to be a trend? We do see segmentation by customer actions, but few brands dive into it as deeply as they should. Many companies sort customers based on who abandons their cart on e-commerce sites. In these cases, companies might offer a discount or reach out to ask if they had any questions about the product. But, what if you segmented that group even further? Further segments could include those for customers who have never entered their credit card, customers whose credit card has been denied, or customers who have never entered a single detail after adding a product to their cart. By tracking and sorting customers based on their behavior on your site, you can better inform your marketing materials and custom-craft your messages for each customer type. You can then design your landing pages to target specific customer types. Many landing page builders, such as , let you incorporate segmentation into your landing pages and other marketing materials. Unbounce Automation and machine learning are inherent parts of effective segmentation A big reason so few brands haven’t used segmentation to its full potential is because sorting through all that data can be tedious. It can take days to sift through data by hand and properly categorize each person to ensure your assessments are accurate. And accuracy is important here: you wouldn’t want to send out customer emails only to find that you have miscalculated or missed a data point. Automation and machine learning have re-shaped digital marketing and segmentation in particular. Tools like provide hyper-targeting that examines the customer journey and then automatically optimizes your marketing materials for specific customer types, helping you interact with customers on a more personal level. Optimove’s software Micro-segmentation builds relationships Customers love brands that understand them. That’s why it’s so important to speak to their pain points in every piece of marketing that we create. Customers want to know that companies understand their needs, pains, and desires. They also want to be assured that the product you offer will solve their relevant problems. Micro-segmentation is about sorting your customers into more specific categories. In typical segmentation, you might have customer segments based on who lives in Denver, who has a job title of Vice President, or who is above the age of 50. An example of a micro-segment would be a segment that includes all 3–50+ year old VPs who live in Denver. According to an , 78% of customers stated that they’re more likely to buy from a company that sends them more targeted offers. Building that initial trust is incredibly important — customers who have been buying from a company for 30 or more months than they did on their first purchase. Infosys survey spend 67% more per order Micro-segmentation gives you the opportunity to speak to their biggest concerns and win their trust by helping them to address them. Segment research should inform product development At its core, segmentation begins with learning more about your customers. The more you know about your customers, the more you can tailor your marketing to their unique problems, preferences, and desires. Startups often to find a market that needs solutions. But once a company is sufficiently settled within their market and earning a healthy profit, they often shift their focus to other areas of their business. pivot While no established business is going to do a full-fledged pivot in the same way that a startup would, many could benefit from more customer data involvement in product development. Segmentation data should go beyond marketing; it should also be used to inform product development. Great customer data is the most powerful tool that companies have available to them, but too many continue to operate based on perception rather than data-backed facts. Customer journeys and segments are not static Another big mistake that brands make when segmenting is that once a customer has been placed in a segment, their analysis stops there. These customers will forever remain in that segment — even if additional data is collected in the future. Customer segments can and should change over time. Today’s customers may be in a completely different place in life than they had been a year prior. Continually working to correctly categorize your customer types will make all aspects of your marketing more effective. Personalization is the future Deep segmentation of your audience gives you more opportunities for personalization. It allows you to gain a deeper analysis of each customer and their specific situation and to tailor your sales and marketing efforts specifically to them. While segmentation itself isn’t a new concept in digital marketing, the tools that we have available have allowed for micro-segmentation to become affordable for companies of all sizes.

The 5 Factors to Consider When Segmenting Your Customers

By now, most of us are accustomed to using CSS Custom Properties on projects. Since its introduction in 2017, CSS Custom Properties, also known as CSS variables, have optimised the code we work with and allowed engineers a finer degree of control when managing values within rules. CSS Custom Properties can be tricky to deal with and aren’t always as intuitive as one might hope. However, when used right, they can significantly reduce boilerplate code and keep things efficient, making our work more productive. While the approach below is not perfect, it works well when writing CSS for building projects. Why Use CSS Custom Properties As far as my experience goes, I can only think of three possible reasons to implement a CSS Variable: We want to use a given value in more than one place,


We know that a value will change in the future and want to reduce our efforts for refactoring,


We need to store dynamic values from JS execution. With this in mind, we can be more assertive about our use cases. Point 3 is less of a concern for this article, but points 1 and 2 are essential. Let's illustrate this using the familiar HTML button element. The Infamous Button Buttons are notoriously tricky to style consistently for such a simple HTML element. Perhaps it’s because they have several different states and applications, regardless - it's usually the first component we Frontend Engineers take on when it comes to a fresh build. Let's have a look at how we could write the style for a button using CSS Custom Properties: :root {
 --color-primary: purple;
 --color-text: black;
 --color-text-inverted: white;
 --color-border: gray;
}

.button {
 --button-bg: var(--color-primary);
 --button-color: var(--color-text-inverted);
 --button-border: var(--color-border);

 align-items: center;
 background-color: var(--button-bg);
 border: 1px solid var(--button-border);
 color: var(--button-color);
 display: inline-flex;
 line-height: 1;
} As far as this example goes, from a technical standpoint, it’s great. It’s clean and straightforward and would get a 👍 from me in a code review. But could we optimise it further? Well, yes. The var() function in CSS is pretty nifty…we’re used to using it in a somewhat one-dimensional way by just rendering out a value; however, it can be more dynamic. The way to do this is by using a fallback value. If we considered the above example, we could rewrite it like so: :root {
 --color-primary: purple;
 --color-text: black;
 --color-text-inverted: white;
 --color-border: gray;
}

.button {
  align-items: center;
  background-color: var(--button-bg, var(--color-primary));
  border: 2px solid var(--button-border, var(--color-border));
  color: var(--button-color, var(--color-text-inverted));
  cursor: pointer;
  display: inline-flex;
  line-height: 1;
  padding-block: 0.875rem;
  padding-inline: 1rem;
} This is far cleaner and will lead to the same outcomes as the previous example - with less boilerplate code. Overriding it in a variation makes each locally scoped CSS Variable (effectively) optional. We can easily now optionally define the variables defined in .button as separate variations: .button:where(.button-secondary) {
  --button-bg: orange;
  --button-border: teal;
}

.button:where(.button-tertiary) {
  --button-bg: teal;
  --button-border: orange;
} Although this technique is not revolutionary, we care how many lines of compressed text are heading over the wire. The less physical memory a CSS file takes up, the better, which ultimately translates to how many lines of CSS exist. In the first example, we’re looking at almost double the amount of code to achieve the same thing in our optimised example. This compound effect has an influential impact on performance and further improves readability and developer experience. If you'd like to play around a bit further, feel free to fork the following Code Pen: https://codepen.io/iamdainemawer/pen/NWmQZMz Support Since April 2017, according to Google Baseline, this feature is well-supported across all major browsers. If you're interested to learn more about Baseline, read my post about Google Baseline and Its Impact You can check for browser support by adding the following statement to your CSS stylesheets: @supports(--css: variable) {} Can I Use - CSS Nesting
CSS Specification - CSS Nesting Module Resources MDN - CSS Custom Properties
CSS Variables Polyfill By now, most of us are accustomed to using CSS Custom Properties on projects. Since its introduction in 2017, CSS Custom Properties, also known as CSS variables, have optimised the code we work with and allowed engineers a finer degree of control when managing values within rules. CSS Custom Properties can be tricky to deal with and aren’t always as intuitive as one might hope. However, when used right, they can significantly reduce boilerplate code and keep things efficient, making our work more productive. While the approach below is not perfect, it works well when writing CSS for building projects. Why Use CSS Custom Properties As far as my experience goes, I can only think of three possible reasons to implement a CSS Variable: We want to use a given value in more than one place, We know that a value will change in the future and want to reduce our efforts for refactoring, We need to store dynamic values from JS execution. We want to use a given value in more than one place, We want to use a given value in more than one place, We know that a value will change in the future and want to reduce our efforts for refactoring, We know that a value will change in the future and want to reduce our efforts for refactoring, We need to store dynamic values from JS execution. We need to store dynamic values from JS execution. With this in mind, we can be more assertive about our use cases. Point 3 is less of a concern for this article, but points 1 and 2 are essential. Let's illustrate this using the familiar HTML button element. The Infamous Button Buttons are notoriously tricky to style consistently for such a simple HTML element. Perhaps it’s because they have several different states and applications, regardless - it's usually the first component we Frontend Engineers take on when it comes to a fresh build. Let's have a look at how we could write the style for a button using CSS Custom Properties: :root {
 --color-primary: purple;
 --color-text: black;
 --color-text-inverted: white;
 --color-border: gray;
}

.button {
 --button-bg: var(--color-primary);
 --button-color: var(--color-text-inverted);
 --button-border: var(--color-border);

 align-items: center;
 background-color: var(--button-bg);
 border: 1px solid var(--button-border);
 color: var(--button-color);
 display: inline-flex;
 line-height: 1;
} :root {
 --color-primary: purple;
 --color-text: black;
 --color-text-inverted: white;
 --color-border: gray;
}

.button {
 --button-bg: var(--color-primary);
 --button-color: var(--color-text-inverted);
 --button-border: var(--color-border);

 align-items: center;
 background-color: var(--button-bg);
 border: 1px solid var(--button-border);
 color: var(--button-color);
 display: inline-flex;
 line-height: 1;
} As far as this example goes, from a technical standpoint, it’s great. It’s clean and straightforward and would get a 👍 from me in a code review. But could we optimise it further? Well, yes. The var() function in CSS is pretty nifty…we’re used to using it in a somewhat one-dimensional way by just rendering out a value; however, it can be more dynamic. The way to do this is by using a fallback value . var() fallback value If we considered the above example, we could rewrite it like so: :root {
 --color-primary: purple;
 --color-text: black;
 --color-text-inverted: white;
 --color-border: gray;
}

.button {
  align-items: center;
  background-color: var(--button-bg, var(--color-primary));
  border: 2px solid var(--button-border, var(--color-border));
  color: var(--button-color, var(--color-text-inverted));
  cursor: pointer;
  display: inline-flex;
  line-height: 1;
  padding-block: 0.875rem;
  padding-inline: 1rem;
} :root {
 --color-primary: purple;
 --color-text: black;
 --color-text-inverted: white;
 --color-border: gray;
}

.button {
  align-items: center;
  background-color: var(--button-bg, var(--color-primary));
  border: 2px solid var(--button-border, var(--color-border));
  color: var(--button-color, var(--color-text-inverted));
  cursor: pointer;
  display: inline-flex;
  line-height: 1;
  padding-block: 0.875rem;
  padding-inline: 1rem;
} This is far cleaner and will lead to the same outcomes as the previous example - with less boilerplate code. Overriding it in a variation makes each locally scoped CSS Variable (effectively) optional. We can easily now optionally define the variables defined in .button as separate variations: .button:where(.button-secondary) {
  --button-bg: orange;
  --button-border: teal;
}

.button:where(.button-tertiary) {
  --button-bg: teal;
  --button-border: orange;
} .button:where(.button-secondary) {
  --button-bg: orange;
  --button-border: teal;
}

.button:where(.button-tertiary) {
  --button-bg: teal;
  --button-border: orange;
} Although this technique is not revolutionary, we care how many lines of compressed text are heading over the wire. The less physical memory a CSS file takes up, the better, which ultimately translates to how many lines of CSS exist. In the first example, we’re looking at almost double the amount of code to achieve the same thing in our optimised example. This compound effect has an influential impact on performance and further improves readability and developer experience. If you'd like to play around a bit further, feel free to fork the following Code Pen: https://codepen.io/iamdainemawer/pen/NWmQZMz https://codepen.io/iamdainemawer/pen/NWmQZMz Support Since April 2017, according to Google Baseline , this feature is well-supported across all major browsers. If you're interested to learn more about Baseline, read my post about Google Baseline and Its Impact Google Baseline Google Baseline and Its Impact You can check for browser support by adding the following statement to your CSS stylesheets: @supports(--css: variable) {} @supports(--css: variable) {} Can I Use - CSS Nesting CSS Specification - CSS Nesting Module Can I Use - CSS Nesting Can I Use - CSS Nesting CSS Specification - CSS Nesting Module CSS Specification - CSS Nesting Module Resources MDN - CSS Custom Properties CSS Variables Polyfill MDN - CSS Custom Properties MDN - CSS Custom Properties CSS Variables Polyfill CSS Variables Polyfill

Using CSS Custom Properties To Optimize Styling Efficiency and Readability

Essential Entity Framework Core Tips: How to Optimize Performance, Streamline Queries, and More

This is paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Leah Chrestien, Czech Technical University in Prague; (2) Tomá˘s Pevný, Czech Technical University in Prague, and Gen Digital, Inc.; (3) Stefan Edelkamp, Czech Technical University in Prague; (4) Antonín Komenda, Czech Technical University in Prague. Table of Links Abstract & Introduction Preliminaries Conditions on strictly optimally efficient heuristic Loss functions Related Work Experiments Conclusion and References Appendix 7 Conclusion The motivation of this paper stemmed from the observation that even the cost-to-goal, considered to be an optimal heuristic, fails to guarantee a strictly optimally efficient search. Since a large body of existing research optimizes this quantity, we are effectively lost with respect to what should be optimized. To fill this gap, we have stated the necessary and sufficient conditions guaranteeing the forward search to be strictly optimally efficient. These conditions show that the absolute value of the heuristic is not important, but that the ranking of states in the Open list is what controls the efficiency. Ranking can be optimized by minimizing the ranking loss functions, but its concrete implementation needs to correspond to a variant of the forward search. In case of mismatch, the resulting heuristic can perform poorly, which has been demonstrated when the heuristic optimized for BGFS search was used with A* search. The other benefit of ranking losses is that from the point of view of statistical learning theory, they solve a simpler problem than ubiquitous regression in estimating the cost-to-goal. We do not question the existence of a strictly optimally efficient heuristic. Given our experiments, we believe that if the space of heuristic functions over which the loss is optimized is sufficiently rich, the result will be sufficiently close to the optimal for the needs of the search. 7.1 Acknowledgements This work has been supported by project numbers 22-32620S and 22-30043S from Czech Science Foundation and OP VVV project CZ.02.1.01/0.0/0.0/16_019/0000765 "Research Center for Informatics". This work has also received funding from the European Union’s Horizon Europe Research and Innovation program under the grant agreement TUPLES No 101070149. References [1] Forest Agostinelli, Stephen McAleer, Alexander Shmakov, and Pierre Baldi. Solving the rubik’s cube with deep reinforcement learning and search. Nature Machine Intelligence, 1(8):356–363, 2019. [2] Thomas Anthony, Zheng Tian, and David Barber. Thinking fast and slow with deep learning and tree search. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, editors, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 5360–5370, 2017. [3] Shahab Jabbari Arfaee, Sandra Zilles, and Robert C Holte. Learning heuristic functions for large state spaces. Artificial Intelligence, 175(16-17):2075–2098, 2011. [4] Jeff Bezanson, Alan Edelman, Stefan Karpinski, and Viral B Shah. Julia: A fresh approach to numerical computing. SIAM review, 59(1):65–98, 2017. [5] Mohak Bhardwaj, Sanjiban Choudhury, and Sebastian Scherer. Learning heuristic search via imitation. In Conference on Robot Learning, pages 271–280. PMLR, 2017. [6] Blai Bonet and Héctor Geffner. Planning as heuristic search. Artificial Intelligence. 2001 Jun; 129 (1-2): 5-33., 2001. [7] Vladimir Cherkassky, Xuhui Shao, Filip M Mulier, and Vladimir N Vapnik. Model complexity control for regression using vc generalization bounds. 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