Trending March 2024 # Sideswype Mixes The Mechanics Of ‘Threes’ And The Match # Suggested April 2024 # Top 6 Popular

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When you look at the picture above, your first thought may be, “this looks like another clone of Threes.” I know that I thought so when I took my first glance at the game. Although the basic mechanics are the same, the game is not a clone.

SideSwype is a match-three game that doesn’t act like it. Unlike traditional gem matching puzzles, players don’t have nearly as much control over what matches are made or when…

Design

The board is made up of a five-by-five grid. The 20 squares are populated with a handful of blocks in two different colors. Players match the colors by swiping the screen either up, down, left or right. When at least three blocks match, they explode in a simple confetti animation.

Your game’s score is displayed at the top of the screen. Every time you make a match, a list of that move’s statistics appears on the screen. You will see the total score you received for that particular move, plus the individual line item score. For example, if you made a match in a single move with a Sywpe Score of 22 points, you’d see that you received 10 points for the number of blocks you matched, 10 points for the number of matches made, and two points for the bonuses received.

In addition to two solid colored blocks, there are some blocks that have either the number four or five inside. These blocks will not count toward a match unless the amount of blocks that are matched is equal to the number.

For example, if a green four appears on the screen, it will stay on the board until it is matched with at least four green blocks in a row or column.

When you successfully make a match with a number in it, it produces a special bonus block. The diamond will destroy adjacent blocks when used in a match and bombs will destroy every block on the screen with the same color when used in a match.

Gameplay

If you’ve ever played Threes (or its free clone, 2048), you will have a basic understanding of the mechanics of this game. It is important to note that, while similar in movements, this is not a copycat of any of the number adding block games.

When the game begins, eight colored blocks randomly appear on the grid. You can slide all blocks at once up, down, left or right. Every block will move to the furthest possible spot on the grid. For example, if you swipe upward and there are three blocks at the very top, one in the middle, and one at the bottom, and are all in different columns, they will all end up in the same row at the end of that move.

The goal is to make as many matches as possible to increase your score. You can keep playing and keep matching, as long as there is at least one open square on the grid. If all squares are full and no matches can be made, the game ends.

Every time you swipe across the screen in any direction, a new set of blocks appears. At first, you will get three new blocks in random places each time. As the game progresses, more blocks appear at once. I got to five new blocks at a time, but it is possible that the game goes even higher.

When a block with a four or five inside of it appears, you must match it with that number of blocks. You can still use the color to match other blocks of threes, but it will remain on the screen until it is matched with four or five blocks in one move.

For example, if three blue blocks can be matched and one of them has a four inside it, the two solid colored blocks will disappear, but the one with the number in it will remain in play.

When you successfully match a number block, your reward will be a power up. The diamond power up destroys adjacent blocks, while the bomb destroys every block on the grid with the same color. These bonus items can be very helpful for getting rid of number blocks (especially fives) that you are having a hard time clearing off of the board.

At first, the only available color scheme is blue with brown. However, after you score a certain number of points, other themes will unlock. I’m not exactly sure what score you must reach. However, my highest score was 2,390 and I managed to unlock all six color schemes.

The Good

I like that this game isn’t a clone of Threes. It mixes the match-three genre with the mechanics of the new trend in grid swiping games. It is easy to pick up and play without being excruciatingly challenging.

The game will always continue where you left off. So, if you are playing it while in line at the store and suddenly remembered that you forgot the milk, you can put the game away and pick up right were you left off when you get back into line.

The Bad

There doesn’t seem to be any way to play the game strategically. The block preview shows you what colors will appear, but not where they will end up. My highest score was achieved when I tried playing the game swiftly instead of tactically. I simple swiped one direction or another without thinking about the outcome and ended up scoring nearly three times as much as when I was actually planning my moves.

Value

SideSwype costs $1.99. In an era of knockoff clones of Threes that are free to download, you might think this game is too expensive. However, it is priced according to the market average for the genre. The replay value is high since no two games are ever the same and the whole point is to see whether you can beat your high score.

Conclusion

I enjoyed this game, even though I don’t need strategy to play it. It is a great game for picking up and playing when you only have a few minutes. You will not lose your game if you have to stop playing to answer the phone or respond to an email from your boss. If you like match-three games, you should give it a try. This game is available on the iPhone, iPad, and iPod touch for $1.99. Download it in the App Store today.

Related Apps

The biggest comparison is to Threes, and the more popular, newer title, 2048. However, this is not a clone or a ripoff. It has similar mechanics, but is a very different game.

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Bet Wisely: Predicting The Scoreline Of A Football Match Using Poisson Distribution

“The biggest religion in the world is not even a religion.” – Fernando Torres

Spanish footballing giant Sevilla FC together with FC Bengaluru United, one of India’s most exciting football teams have launched a Football Hackathon – Data-Driven Player Performance Assessment. This Hackathon will be a unique opportunity to effectively use data science in the space of professional football scouting and player performance analysis and enhancement and are excited to have you on the journey.

Introduction

Football is loved by all and its beauty lies in its Unpredictable nature. One thing which is strongly associated with this game is its fans, brooding and debating before a game over who will win the game. And some fans even go to the limit of speculating the scoreline before the match. So let`s try to answer some of these questions logically.

Getting to know Poisson

Suppose your friend says that on average 2 goals happen per game, well, is he right? If right then what are the actual chances of seeing two goals in a match? Here comes to our rescue Poisson distribution helps us to find the probability of observing ‘n’ events (read ‘n’ goals) in a fixed time period given that we provide it with the expectation of events occurring (average events per time period). Let`s see it mathematically once

(where λ = average events per time period)

Chances of Scoring

Now let`s answer some questions with this equation, but first we need data, so for this I downloaded the International football results from 1872 to 2023 data from Kaggle. A sample of our dataset is shown below.

Let`s start with finding the average goals we can expect within 90 minutes.

Python Code:



Now putting this expectation in Poissson Distribution formula let`s see what are the actual chances of seeing 3 goals in a match.

Wow, only a mere 22% chance. Let`s plot the probabilities of the no. of goals in a match to get a better picture.

Now from this, we can calculate the probability of seeing ‘x ‘or fewer number of goals simply by adding the probabilities of ‘x’ and the numbers which are less than ‘x’.And by just subtracting this from 1 we can get the probability of seeing more than ‘x’ goals in a match. Let`s plot this too.

The wait is over…

Now suppose you have an impatient friend who does not want to sit for the whole game. And he comes to you during a match and asks how much time does he has to wait to see a goal. Woah, that`s a tough question right, but worry not, ask him to sit through 10000 games and note the time between each goal. Just kidding, obviously, he would freak out. Actually I simulated 10000 matches and found out the average time.

The most likely waiting time is 2 minutes. But wait this not actually what I was looking for, I want the average time that I have to wait to see a goal if I start watching the game at a random time. For that, I will take 10000 instances, where each instance is watching 10000 games and calculating the average waiting time between goals in that 10000 games and reporting us. Finally, I will be plotting those 10000 reports from each of my instances and find out the expected average waiting time.

It looks like we have to wait for 33 minutes approx. However we may have to wait for more, this is a classic Waiting Time Paradox.

Predicting the scoreline

Finally, let`s do the question with which we started and the most exciting question that who will win and what will be the scoreline to be precise.

For this, I will use the history between two teams (let them consider as the home team and away team) and take the average_home_score as the expected goals for the home team and average_away_score as the expected goals for the away team and predict the scoreline using Poisson distribution. In case the teams have fewer encounters between them, we will consider a few factors

HS = Mean of home goals scored by the home team throughout history.

AS = Mean of away goals scored by away team throughout history.

HC = Mean of goals conceded in home matches by the home team.

AC = Mean of goals conceded in away matches by the away team.

So, the Home team’s expected score will be calculated as (HS + AC) / 2

So, the Away team’s expected score will be calculated as  (AS + HC) / 2

Wait, the expected score is not the predicted score. The expected score is the average number of goals we expect them to score in a game between them.

code:

def PredictScore(): home_team = input("Enter Home Team: ") ht = (''.join(home_team.split())).lower() away_team = input("Enter Away Team: ") at = (''.join(away_team.split())).lower() avg_home_score = data[(data.home_team ==ht) & (data.away_team ==at)].home_score.mean() avg_away_score = data[(data.home_team ==ht) & (data.away_team ==at)].away_score.mean() home_goal = int(stats.mode(np.random.poisson(avg_home_score,100000))[0]) away_goal = int(stats.mode(np.random.poisson(avg_away_score,100000))[0]) else: avg_home_goal_conceded = data[(data.home_team ==ht)].away_score.mean() avg_away_goal_scored = data[(data.away_team ==at)].away_score.mean() away_goal = int(stats.mode(np.random.poisson(1/2*(avg_home_goal_conceded+avg_away_goal_scored),100000))[0]) avg_away_goal_conceded = data[(data.home_team ==at)].home_score.mean() avg_home_goal_scored = data[(data.away_team ==ht)].home_score.mean() home_goal = int(stats.mode(np.random.poisson(1/2*(avg_away_goal_conceded+avg_home_goal_scored),100000))[0]) avg_total_score = int(stats.mode( np.random.poisson((data[(data.home_team==ht) &    (data.away_team==at)].total_goals.mean()),100000))[0]) print(f'Expected total goals are {avg_total_score}') print(f'They have played {len(data[(data.home_team ==ht) & (data.away_team ==at)])} matches') print(f'The scoreline is {home_team} {home_goal}:{away_goal} {away_team}')

Let`s try with Brazil as the home team and Mexico as the away team.

code:

PredictScore()

Poisson Distribution gives us a prediction of Brazil winning with a 2-0 scoreline. I searched the net and found that the last match between them was played on 2 Jul 2023 and the scoreline says Brazil won by 2-0. Well, I got lucky, you may not.

Conclusion

If you want to explore further no worry, here is my code. Furthermore, this is just a basic way of predicting the game, nowadays classification algorithms are used to predict the outcome and regression algorithms to predict the scoreline. But That`s the topic for another day, till then have fun playing with this. Adios!

Related

The Rise Of Diamond Semiconductors And The Future Of Tech

Diamonds are still a girl’s best friend, but in today’s technology-driven world, they have become so much more than eye candy and have increasingly been used as a replacement for silicon.

With any industry, it’s all about supply and demand. So what are the demands that have prompted this diversification both in diamond use as well as in diamond creation?

The limitations of silicon-based technology is one of the biggest factors in the rise of diamond-based technology. The element silicon has been the primary semiconductor in electronics for over half a century. Unfortunately, silicon semiconductors come with a few key issues. Firstly, there’s the issue of heat. Silicon semiconductors require a great deal of heat management which in turn results in major energy waste. Secondlythe size and speed of electronic devices are limited by the performance capabilities of silicon. At this point, it is difficult – if not impossible – to create smaller or faster devices while still relying on silicon semiconductors.

The Power of Diamonds as Semiconductors

Diamonds have always been powerful. But these ones aren’t powerful as in “you must be filthy rich to be able to afford something like this,” powerful, but more as in “wow these can really help change the future” powerful. Diamond semiconductors are the answer to the issues of heat and cooling, as well as size and efficiency. Diamonds can withstand greater heat while still providing superior performance; what heat is generated is more easily and efficiently cooled. Electronic devices that rely on a diamond semiconductors can be made faster and smaller, thanks to diamond’s higher voltage tolerance and ability to provide 1 million times more electrical current than silicon counterparts.

The Potential for Supercomputer Utility

Consumer-driven, mass-marketed electronics aren’t the only devices that could see great technological leaps thanks to a diamonds-for-silicon replacement. Diamonds could also enable scientists to create supercomputers with greater storage and greater power. These quantum computers would be capable of solving complex problems that are out of reach for current technology.

Of course, semiconductors and supercomputers are only two of the non-aesthetic uses for diamonds. As one of the hardest minerals on Earth, diamonds are becoming more and more useful for processing other materials, either by cutting, grinding or polishing. Other tools and materials such as windows, surgical instruments, blades and phonograph needles all have diamond-reliant variations.

Longevity and Faster Production Time

The increased technological demand for diamonds also has ties to the growing manmade diamond industry. Lab-grown diamonds take far less time to create than their natural counterparts, while still providing the durability and thermal conductivity found in Earth-grown diamonds. Chemical Vapor Deposition (CVD) stacks carbon atoms on top of a small diamond seed, creating precious gems in mere months instead of millennia. In addition to fast-tracking the diamond formation process, CVD diamonds are also free of the negative stigma of blood – or conflict resource – diamonds.

Silicon is still the base for a majority of the tech on the market today, and that may never completely change. Not every man, woman or child has need of a quantum computer. However, as the lure of “smaller and faster” maintains a constant influence in tech marketing, it may be that the amount of diamond-based consumer electronics will rise as silicon’s limits are found – and found wanting. The era of diamond-driven technology has only just begun and will far surpass early dreams of quantum computers and semiconductors before it draws to a close.

The Blessing And Curse Of The Social Media

Introduction

Social media has much older roots than you would think. When the first computer was built in 1940, scientists and engineers started to design and build networks, which led to the creation of the internet.

Social Media apps on a smartphone

There are several social media networking platforms available nowadays. Social media platforms like Twitter, Facebook, Snapchat, Instagram, etc. have become addictions for every one of us.

The good of social media

The positive impacts of social media are so much more if people use it positively. Let’s have a look at some well-known positive outcomes of social media that are useful in daily life.

Better and Speedy Communication − On the phone, simply saying what you intend to say, then hanging up, is not okay because it is considered insulting to do so. However, on social media, you can leave direct msg for any purpose without any pleasantries before and after. Moreover, Social media is renowned for generating fresh debates. You can share your opinion on any issue with your friends and relatives on social media.

Fastest way of News − People did not know what was happening in the world before the development of Social Media technology. Now people have access to current events and can keep themselves updated. You can rely on this platform to provide you with accurate information and facts.

Helpful in finding jobs − To understand more about job applicants, it is now common practice for businesses to go through their social media profiles. In this way, it aids in deciding whether or not a person is appropriate for a given position. Additionally, job seekers may deliberately design their social media postings to make them seem like the perfect fit for any firm. They can showcase their pursuits, successes, ideas, principles, and more! This can improve their chances of landing a job.

The bad and ugly sidex of social media

Addiction towards social media is one of the major side effects of social media usage

Privacy loss − It’s quite evident that privacy and the internet don’t get along, whether it’s due to social media apps or websites that store and sell your personal information, i.e. NSA incident, which involved improper access by the government to private data including Skype calls, emails, and more. This is a significant privacy warning.

Unhealthy Sleep patterns − One of the most common negative impacts of social media is sleep deprivation. Teens who are using social networking sites more frequently had poorer sleeping habits, according to research. Along with Sleep deprivation, there are some other problems such as headaches, eye strain, and social withdrawal, experienced by people who are addicted to social media. It’s producing more sadness than happiness.

Scams − On social media, there have been several reports of frauds and scams where victims were manipulated into believing someone and making a purchase only to discover afterwards that it was bogus all along. It is very difficult to distinguish between influencers and bloggers who give genuine involvement and those who are simply bright fraudsters since there are so many of them on Facebook and Instagram.

Conclusion

If used appropriately and cautiously, social media can be a great way to interact with citizens when face-to-face meetings are not possible. However, t is indeed essential to be aware of the negative consequences of social media if you want to avoid being drowned in the online space. Limit your screen time and wander back into the physical world.

Finally, social networking is more of a curse than a benefit, especially for teens. We still have time to turn things around for the better, but we need to act quickly before our style of living is utterly altered.

Frequently Asked Questions

Q1. How many people use social media all over the world?

Ans. According to the data for 2023, there are a total of 3.81 billion people use social media.

Q2. Which court resolves social media crime in India?

Ans. Cyber Appellate Tribunal Established Under S-58 Of The IT Act, 2000 resolves social media crime.

Q3. What is phishing?

Ans. Phishing is a type of cybercrime in which attackers uses any method to reach the target in order to get the passwords, PIN, or other essential information for the theft of data and money.

Quick Notes On The Basics Of Python And The Numpy Library

This article was published as a part of the Data Science Blogathon.     

      “Champions are brilliant at the basics”

Quick Basics of python 1. What is an interpreter? 2. Difference between the virtual environment and the existing interpreter?

Ans: The difference is that if you use a virtual environment for your project and add/remove packages then it will only affect the virtual environment. If you use an existing interpreter then all changes will affect the system-wide interpreter and these changes will be available in all the projects that use that interpreter.

3. What is a pip?

Ans: Pip is a standard package management system used to install and manage the software packages written in python.

4. What are the various commands of pip?

Ans: Below are the various commands of pip to be run in the command prompt/terminal

5. What are variables?

Ans:  Variables are used to store information to be referenced and manipulated in a program. In python, we don’t need to explicitly mention the datatype during declaration. A string variable cannot be manipulated by mathematical actions.

6. What are the basic operations in python?

Ans: We have ‘+’ (addition) ;  ‘-‘ (subtraction) ; ‘*’ (multiplication);  ‘/’ (division); ‘%’ (modulus meaning the remainder of division) ; ‘**’ (power meaning ab i.e. a to the power of b);  ‘//’ (floor division meaning this will give the quotient of division without the decimal).

7. What are string indexing and slicing?

Ans: Index is the position of each character starting from 0. Slicing is getting the substring i.e. subset out of a string value or a word or sentence.

You slice your butter so that the chunks can be used for various purposes !! Right !! SAME IS APPLICABLE HERE AS WELL !! 

Suppose we have a string variable having ‘stay positive’ stored in it. The below picture shows the indexing of the elements in the string. 

Below is a code snippet having examples of indexing and slicing.

8. What is mutable and immutable property?

Ans: Mutability means you can change the values of an object after it is created, and the Immutable property of an object means it cannot be changed after it is created.

9. What are the different data structures of python?

Ans: Below are different types of data structures :

9.1) Lists : These are the data types that hold elements of different/same datatype together in a collection in sequential manner. These are enclosed in square brackets [ ]. Lists are mutable and indexable in nature, and also allow duplicates. There are many list methods like list.append(), list.pop(), list.reverse(),list.sort(),list.count(),list.insert(),list.remove() etc.  for performing various list operations; few of which is showed in the below code snippet.

NOTE:  Accessing and indexing elements in the list are the same as the Q7(indexing and slicing) topic explained above.

9.2) Tuples: These are similar to lists with two major differences i.e. (that is) Firstly they are enclosed within round brackets() and second they are immutable in nature.  There are two inbuilt methods that can be used on tuples index() and count(). Code snippet for same is mentioned below:

NOTE: Accessing and indexing elements in the tuples are the same as the Q7(indexing and slicing) explained above.

9.4) Dictionaries: Dictionaries in python is a data structure that stores the values against its keys. Basically key-value pair. It is enclosed within curly braces having key:value pair i.e. {key1:val1}

In the above example, ‘mydict’ is a dictionary that stores the number of students present in each class. So classA is key and 30 is its value. 

Dictionaries do not allow duplicate keys and are mutable in nature. Below is the code snippet having dictionary examples:

There are few more functions like get(keyname) that will return the value of that key, update(),popitem(), etc. . We can also have nested dictionaries,                                                            lists value for the key i.e. key1 : [1,2,3].

What is the use of dictionaries  ???

Well, there might be scenarios wherein you will have to count the number of occurrences of an item in a list, then you can easily compute this using dictionary. Another example is using a dictionary like a lookup file wherein you might have a set of static key-value pairs to refer to. Also, dictionaries are used in backend code while building APIs. Hence with dictionaries in place, many operations like I mentioned above become easier to deal with.

10. What are the various common libraries used in Data Science?

Ans: Common libraries are :

11. Why is Numpy required when we have python Lists? Since both do the same work of storing data in array form?

Ans: Absolutely, but Numpy is better since it takes less memory as compared to lists. Also, a Numpy array is faster than a list.

Now the question is HOW ??? Please follow the below code snippet showing the answer for the question of HOW  IT TAKES LESS MEMORY AND IS FASTER THAN LISTS??   

In the above code, we have compared the memory used by the list and the memory used by the Numpy array. The size of a single integer element in the list takes 28 bytes whereas a Numpy array takes only 4 bytes. This is because lists are python object which requires memory for pointers as well as value, but Numpy array does not have pointers that will point to the value. Hence IT TAKES LESS MEMORY.

HOW NUMPY ARRAY  OPERATIONS ARE FASTER THAN LIST ?? 

Let us PROVE IT IN BELOW CODE SNIPPET

In the above code, we have computed the time taken by the addition of two lists each having 1 million records that took142.3 seconds whereas when we performed the same operation with the same number of records with two arrays, the computation took 0.0 seconds !!!!! WOW!!!!

HENCE PROVED! Numpy array is much faster than a list.

In real-time, we have a huge amount of data that needs to process and analyzed so as to get useful and strategic information out of the data. Hence Numpy arrays are better than a list.

12. Can we create and access the n-D(n-dimension) array using the Numpy library?

Ans: Definitely, this is one more key feature of the Numpy array. We can create an n-dimensional array using the array() method of Numpy by passing a list, tuple, or an array-like object.

In order to know the number of dimensions an array has, we have the “ndim” attribute of Numpy arrays.

We can also explicitly define the dimension for an array by using “ndmin” argument of the Numpy array() method.

There is a “dtype” property that will return the data type of array. Also, we can also define the data type of array by passing an argument of dtype to the array method

Below is the code snippet for the same

13. How to index, access, and  perform slicing on an n-D Numpy Array

These are the positions assigned internally in the n-D array. Keeping this in mind, an n-D array can be accessed, manipulated, etc.

Please follow the below code snippets to understand how to access and slice the n-dimensional arrays.

The above picture represents how the indexes are represented in an n-dimensional array. Using the indexes, we can access array elements and perform slicing.

SLICING:  The concept of slicing remains the same as mentioned in the above queries. The syntax for slicing is arrayName[startIndex:stopIndex:step(optional)]

Be it 1-D,2-D, or n-D, array slicing works the same.

The array examples used in the below code snippet are the same as in above eg i.e. oneD_array, TwoD_array, ThreeD_array. Please refer to the array declarations in the above code snippet.

14. What are various methods and attributes in Numpy?

Ans: Numpy has various attributes and methods that can tell you the size, shape of the array(rows X columns), change the shape(reshape), size of each element, datatypes, and many more. Few are listed in below code snippet:

We also have copy and view methods that duplicate an existing array. But these two methods do have a very major difference internally. Please find the below code snippet wherein the difference is shown in a practical way:

Numpy also has many more methods and attributes like :

and MANY MORE. You can go through all of them on the Numpy website. The ones that I have listed are the common ones that are frequently used.

ARRAY OPERATIONS : 

Very easy addition, subtraction, multiplication, division can be done between two arrays. Below is the code snippet for adding and subtracting. Others can also be done in the same manner viz (a*b),(a/b)

15. Numpy array has an AMAZING PROPERTY !!  What is that ??

Ans: Let us assume we have a 2-D array wherein I want to check if that every array element is greater than value 10. If yes, then replace them with True otherwise False. So in return, I will get TRUE FALSE MATRIX. Below is the code snippet:

Now, if I want the values of array ‘arr’ which is greater than 10 ??  This can be achieved in just a line as shown in the below code snippet:

Now we can also replace these with specific flag values like -1 or 0 or anything.

The code snippet is as follows:

So all those elements greater than 10 is replaced by -1

THAT IS ALL FOR THIS ARTICLE !!! 

ENDNOTES: 

I am sure, you, as a beginner must have found this article to be useful. All the necessary basics have been covered and I have tried to cover the concepts in detail with the practicals where most people find difficulty in understanding. Thank you for your time.

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

Related

The Importance Of Conversion Goals And The Stats That Support Them

Conversions can be tricky things to keep on top of, and all businesses are different, so how will you know where to set the bar?

Failing to prepare means you’re preparing to fail. Even if things are going well, it’s still important to carry some form of preparation, otherwise, how will you know when you’ve hit your targets?

When it comes to the wide world of driving conversions, most websites will agree that the more they leverage, the better. While this is, of course, completely true, marketing strategies are complex and often expensive entities that require constant monitoring and plenty of funding. Due to this, it’s important to set goals and targets as you go, so you’re fully aware of when you’ve achieved everything you’ve set out to do and can successfully close your campaign.

With this in mind, we can accurately contextualize the metrics that could help to drive your business. Let’s take a deeper look at the world of marketing from a numerical perspective, and see if data can help you to establish conversion goals that are right for your website.

The art of the conversion

As the table above illustrates, conversions and the rate at which they occur can differ wildly depending on lots of things – not least the industry that the website in question hails from. MarketingSherpa defines a ‘conversion’ as “the point at which a recipient of a marketing message performs a desired action.” this means that conversion is simply the name given to the act of getting someone to respond to your Call-To-Action.

This broad definition offers a degree of flexibility for website owners who are looking to set different goals alongside their respective Call-To-Actions and landing pages.

In the world of e-commerce, conversions can mean anything that encourages a prospective customer to successfully interact with the company. If somebody scans a QR code to receive a product voucher then this simple act can be interpreted as a successful conversion.

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More B2B-oriented businesses can have a more complex trail towards conversions that require visitors to interact with the business via email, engagements and on the website pages in a bid to raise the overall reputation of the company in question.

Because of the range of meanings and interpretations surrounding conversions in marketing, it’s logical for a business to set goals in order to turn relevant metrics into contextually appropriate charts of success and to ensure that all stakeholders are on the same page in an organizational sense.

It can be difficult setting the same goals for multiple members of staff, but luckily there are plenty of web analytics tools out there to help users on their path to success. 

Optimized pages

So, now we’re aware of the importance of conversion goals, let’s take a look at the numbers that will help to inform businesses of where to set such goals.

One of the largest drivers of conversions in marketing can be found in the form of landing pages. Amazingly only 52% of the companies that run landing pages actually bother running tests into their effectiveness. It’s also been found that businesses that use videos within their landing pages can boost their conversions by a seismic 86%.

The diversity of landing pages has proved to be limited, with only 48% of companies bothering to set up brand new landing pages for a new campaign. The approach of tweaking existing landing pages may save time but due to different campaigns requiring different kinds of language, it’s possible that audiences won’t be quite as impacted in the same way.

With this in mind, it’s important to anchor your goals to the number of hours you’re capable of investing in your campaigns. Advertising products and services will be effective, but without the right range of landing pages in place, the overall effect of campaigns will be mitigated by fewer conversions.

Putting in fewer hours doesn’t mean that you’re less committed to your marketing campaign – after all, small businesses may need members of staff positioned in more practical roles during their formative months – but it does call for more introspection when it comes to assessing what you want to take from your campaign.

Promoting conversions

Conversion rates are hard work at the best of times, and users can be put off by relatively low percentages of visitors completing conversions. Depending on your definition of conversion, rates typically range from around 1-3%. While these figures seem minuscule compared to the volume of interactions, impressions or traffic coming to your business, it’s important to note that if you set out to double your rate of conversions from 1% to 2% – that single digit of improvement would see your business double its sales revenue.

One of the most effective means of promoting conversions is by leveraging user-created content. Predominantly coming in the form of product reviews but carrying a few further-reaching applications, user-generated content can increase conversion rates by as much as 161%.

These figures are just as well because 68% of small businesses don’t have any form of documented or structured CRO strategy.

Target setting

Target setting when it comes to managing your conversion rates can be an excellent way of monitoring your progress and discovering which approaches yield more success.

There are hundreds of web analytics platforms out there that are designed to help users to establish their own business conversion goals in a way that monitors their progress automatically.

Google Analytics is a good tool to start with when it comes to tracking conversions.

Finteza is a good tool to set up conversion goals which are automatically updated whenever a specific conversion is registered.

If your goals are centered around customer queries and support, Intercom could be a great option to explore.

In a world that’s becoming increasingly competitive, where companies are investing astronomical sums into effective marketing campaigns to draw the attention of prospective customers, it pays to invest some time in establishing a clear range of conversion goals to help you to monitor your business’ success.

With the right range of metrics, users can establish achievable conversion goals and get incrementally notified whenever progress is made towards achieving said goal. In a world driven by metrics, it’s a great asset to keep track of where your website stands once in a while.

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