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Introduction to Serverless Python

A framework called Serverless makes it easier to deploy apps in a server-independent environment. Such services are typically created using programming languages, and Python is frequently just at the top of a list for generating such web-based programs. With a growing community of plug-ins and a set of features that provides numerous cloud vendors in conjunction with AWS Lambda, the Serverless Framework is a very well-leading company. The Serverless Framework is a beautiful place for a Python developer to start. Real-time processing demands and the dynamic allocation of servers are both made possible by a serverless system.

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This post will offer instructions on how to create a Serverless Python function as well as guides on Python and Serverless.

Serverless Python Requirements

In a deployment architecture known as serverless, the deployer does not explicitly provide servers. Instead, code is performed in response to developer-defined events, such as when a new line is added to a file, or an HTTP POST request is made to an API.

The method of developing a Serverless Python function is straightforward and needs Node and NPM.

To run, we require :

Step 1:

Set up the project in step one. Install NPM and Node first:

Code:

npm install -g serverless

Output:

Step 2:

Step 3: Let’s Create a New Project with sls. The code should be given as:

Code:

mkdir ~/my-serverless-project cd ~/my-serverless-project sls create -n my-serverless-project -t aws-python3

My-serverless-project is the name of the directory over here we have established, and sls create was used to create the project. With the option -t aws-python3, we additionally added a template. Several templates that are included with Serverless give lots of certain logical defaults in chúng tôi  Specifying the Python 3.6 AWS template in this example.

Step 4: Deploy a Function

Code:

def handler(event, context): return {"message": "hi buddies"}

Using the serverless CLI, we can design a template for our Lambda layer that we will call lambda-layer. The chúng tôi file essentially includes the program which will execute on AWS Lambda. The chúng tôi file provides all the settings required for the service deployment as well as a definition of the Lambda function or level.

Code:

service: layer-numpy frameworkVersion: '2' provider: name: aws runtime: python3.8 lambdaHashingVersion: 20231221 plugins: - serverless-python-requirements Serverless Python Function

Step 1: The installation requires

Code:

$npm install --save serverless-package-python-functions

For packaging, Python Lambda functions only with the requirements they require; use the Serverless Framework plugin. If we have a function, add the information to serverless so that the Serverless Framework is aware of it. Replace the functions section in serverless with the following.

Code:

functions: hello: handler: hello.handler

To deploy, give the following command:

sls deploy

Serverless Python Examples

When does it make sense to use Serverless?

It’s like when using Web App Backends, Scheduled Tasks, Data Processing, and the Internet Of Things.

Step 1:

The Process Starts with Serverless through the process of starting a new project, selecting the project type (Node.js, Python, or Other), giving the project a name, and choosing whether we wish to activate additional Serverless features (requiring a Serverless account). The initial process will generate once finished.

Step 2:

As discussed in the last section. yml is generated as the next step. Here is an opening serverless file produced by the procedure as an illustration:

Code:

service: app provider: name: aws runtime: python 4.2 functions: hello: handler: handler.hello

Take account of the service’s name in the file; we can change it to conform to the chosen naming rules for the functions. We would default have the name we gave it when we created the project. Additionally, the Python runtime allows us to view the AWS Lambda provider information. Finding the related ARN is important once our Lambda layer has been deployed because it will be used as a reference in the definition of our Lambda function.

Step 3:

Direct deployment to AWS Lambda and Serverless testing. Serverless uses the serverless. yml file’s requirements to deploy directly to Lambda.

Incorporating huge dependency into a Lambda Layer is yet another approach to managing things. Add the layer option to the setup via simple means. Lambda option is selected through AWS Console.

Step 5:  To configure step events, we have to create a Skeleton.

Step 6: Testing

Additionally, testing is a valid HTTP function. However, to do this, an API Gateway trigger must be added to the AWS Lambda Console.

The above is for when we use any PostgreSQL. But the Lambda function doesn’t want to trigger it. The below Screenshot shows the lambda documentation invoking functions.

Note: Developers and businesses are building and deploying applications to meet serverless cloud architecture as the opportunity. Python is the preferred language for creating these applications since it is user-friendly and has a vast library support structure. Utilizing the benefits of serverless architecture is made easier by building serverless Python applications.

Conclusion

Comparable to how web frameworks manage typical web development activities, serverless libraries and platforms strive to provide repeatable code that performs time-consuming or repetitive operations. So far, we’ve given everyone a brief overview of the Serverless Framework and demonstrated how to utilize it to create a serverless Python application.

Recommended Articles

This has been a guide to Serverless Python. Here we have discussed the Introduction, functions, requirements, and examples with code implementation. You can also go through our other suggested articles to learn more –

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How To Build A Future

It’s been a little over six months since Open AI launched ChatGPT and rang in the next age of AI.

Since then:

Google has introduced Search Generative Experience (or SGE), an AI-powered search beta.

ChatGPT

added powerful search capabilities with the Bing plugin.

Google launched and improved its own AI chatbot, Bard.

Everyone talks about AI all the time. It’s easy to get tired of it.

But everyone talks about it because it has massive potential and shows utility already.

Lately, though, I’ve noticed the discussions have cooled down a bit.

Not completely, as in “frozen,” but “cooled” because we now understand AI’s capabilities much better after the initial hype phase. We can better place how good AI tools are today.

May was the first month the number of searches for “chat gpt” dropped (-14.2%, according to Similarweb), but total traffic to chúng tôi is still growing. Mobile traffic seems down, but remember, ChatGPT launched a mobile app.

Many SEO pros remember how CNET published AI content with wrong information in it.

But chúng tôi and Bankrate published AI content as well.

And at the end of the day, the AI content on these three sites performed just like human-generated content. The PR disaster was big because CNET didn’t fact-check content before it went out.

But that’s precisely the point: We’re realizing we’re not yet at the point where AI content can be published cold. AI can only create dirty drafts, show us exciting angles, and remove writer’s block (maybe some editing steps like fixing bad grammar). For now.

We can see where the road is heading: upward. New models with exponentially higher numbers of training parameters are sprouting up everywhere.

Notice the logarithmic scale; we’re seeing a wave of AI tools that edit, summarize, and create content of all formats.

It’s inevitable for us to head towards a future where AI lives in every piece of software and fundamentally changes marketing.

Even the biggest AI critics acknowledge that we’re heading towards a future where AI is table stakes.

Everyone – yes, everyone – in the tech space is currently thinking about three questions:

What will the AI future look like?

How is it going to affect us?

What can we do about it?

Let me attempt to answer these three questions for SEO:

The Implications Of Search Generative Experience (SGE)

Recommendations for future-proofing your business don’t make sense without understanding the implications of SGE.

Keep in mind SGE is still in beta. The public version that’s supposed to launch in December might look very different, and we don’t have any data about traffic from SGE yet.

All statements in this piece are assumptions based on what I see in the beta. That said, I spent a lot of time with SGE, and with search engines over the last 13 years.

SGE will likely drive less traffic to most sites ranking for a keyword compared to the current version of search, simply because SGE gives the answer to a query away and could drive searchers deeper into a conversation with Bard instead of to websites.

Just like users didn’t abandon Google for Bing when it became the first to show AI answers, users have built trust and habits with certain websites over the years.

Pre-AI, Google struggled to answer longtail queries well. Large language models (LLMs) and generative AI solve that problem.

They match results with classic web search results to reduce hallucination and fact-check AI answers, which works especially well for informative queries. As a result, companies that monetize traffic volume across all stages of the user journey will be hit the hardest: publishers and affiliates.

Retailers, direct-to-customer (DTC) brands, and local businesses might have it even harder since Google jumps straight to a list of products/businesses unless the search query contains a question (other than “best”).

Together with GoogleTest to connect the Google Merchant Center directly to your checkout, Google is likely building a marketplace to compete head-on with Amazon.

I don’t assume Google wants to take that risk and might stay out of them completely or until it finds a better solution. Even though we’ve seen examples of SGE answering sensitive YMYL questions in the beta, I’m less confident it will do so in the public version.

Since SGE looks and works so differently from the current version of Search, the data we work with in SEO won’t be as useful anymore.

There is a chance that Google will update GSC with SGE-related data or provide us with a new tool altogether. But if not, we’ll miss a ton of ranking data that helps us reverse-engineer how results come together right now.

How Businesses Can Set Themselves Up For Success

Better solutions will surface once SGE comes out of beta, and we all have more time to digest its impact and final version. Until then, we can make assumptions about future-proofing based on what we see so far.

1. Build Optionality With Direct Traffic

Direct traffic is the strongest sign of popularity – often the best-converting traffic – and it can provide optionality when organic traffic breaks away.

There are several ways to build direct traffic:

A great experience with your product: outstanding customer service, quick onboarding, and high engagement.

Strong positioning and messaging.

Advertising.

Content.

2. Source New Ways To Learn What Your Customers Are Searching For

Search volume is a curse and a blessing. It has been flawed for a long time.

Rather run experiments in paid search to see how well a keyword could convert.

Since it’s unclear whether we’ll still get demand metrics like search volume or visibility metrics like keyword ranks to learn what users want and what works in SEO, we need other ways to source what customers are looking for.

One of the best is talking to customers about their user and search journeys.

For example, someone in the market for a mattress might have specific needs like reducing back pain. So, they might search for “best mattress for back pain,” “best mattress for back pain and sleep apnea,” or “can a mattress help with back pain.”

If you can’t talk to customers and prospects directly, query sales or support call transcripts with AI (tools like Humata allow you to do that).

3. Create Content Machines Cannot Replicate

Two things AI cannot replicate are experience and expertise. The latter might be doable for AI at some point later down the road, but not for now.

Even if machines could replicate experiences at some points, humans might be more interested in the experiences of other humans.

Today, you can think about what keywords and topics have a high intent for experiences. Travel guides are an obvious example, but even in product reviews or when describing a problem, you can emphasize the experience.

Who writes the content is already important but might become even more critical. Some authors are so deeply connected to a topic that no generic machine could replace them.

Think about Dr. Andrew Huberman writing about supplements, Tim Ferriss about self-improvement, or Henry Kissinger about diplomacy.

Companies need to ask themselves how they can bring the best author for their topics on board – and maybe monopolize their content. I wouldn’t be shocked if companies exclusively license some experts’ content down the road.

4. Find The Right Angle

Topics have different angles, like pros and cons, differences between similar topics, or who they’re for.

Since SGE highlights specific angles about topics (we don’t know why and the logic behind these angles might get more sophisticated), companies must get good at either covering all angles in their content or efficiently finding out which one Google prefers for a target keyword, and adjusting content accordingly.

Angles are different from sub-topics. They’re a view on the main topic and its nested subtopics. Companies should work on including angles in their content today.

5. Accelerate Your Work With AI

Experts working at integrators – companies that have to create content themselves to drive organic traffic – can create first drafts within minutes and spend more time editing and giving a piece their personal touch and expertise.

They can then use AI to challenge the article, find new angles, and remove gaps. They might even use good prompts to refresh and tune content on a regular basis.

Experts at aggregators – companies that leverage UGC or product inventory to scale SEO – can develop clever prompts to fill thousands or millions of pages with little pieces of information based on their own or public data. Data from APIs can be contextualized and presented in a much better way.

Conclusion: AI Goes Both Ways

The rise of AI came slowly and then quickly.

Now, we’re reaching “cruising altitude” and starting to better understand where AI is effective and where it is not.

I speak to many in-house and agency teams.

When I ask them how often they use AI, I see a lot of blank stares. Many have never tried it. That’s a grave mistake.

Complete abstinence guarantees that you build opinions based on hearsay and headline reading.

The best way to future-proof your business is by staying engaged with AI, pushing the boundaries, and trying new things.

More resources: 

Featured Image: Andrey Suslov/Shutterstock

Introduction To Aggregation Functions In Apache Spark

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

Introduction

Aggregating is the process of getting some data together and it is considered an important concept in big data analytics. You need to define a key or grouping in aggregation. You can also define an aggregation function that specifies how the transformations will be performed among the columns. If you give multiple values as input, the aggregation function will generate one result for each group. Spark’s aggregation capabilities are sophisticated and mature, with a variety of different use cases and possibilities. Aggregations are generally used to get the summary of the data. You can count, add and also find the product of the data. Using Spark, you can aggregate any kind of value into a set, list, etc. We will see this in “Aggregating to Complex Types”.

We have some categories in aggregations.

Simple Aggregations

The simplest grouping is to get a summary of a given data frame by using an aggregation function in a select statement.

Grouping Aggregations

A “group by” allows you to specify more than one keys or aggregation function to transform the columns.

Window functions

A “window” provides the functionality to specify one or more keys also one or more aggregation functions to transform the value columns. However, the input rows to the aggregation function are somewhat related to the current row.

All these aggregations in Spark are implemented via built-in functions.

In this article, I am going to discuss simple aggregations.

Prerequisites

Here, I am using Apache Spark 3.0.3 version and Hadoop 2.7 version. It can be downloaded here.

I am also using Eclipse Scala IDE. You can download it here.

I am using a CSV data file. You can find it on the github page.

The data set contains the following columns.

station_id, name, lat, long, dockcount, landmark, and installation.

This is bike station data.

Importing Functions

I am importing all functions here because aggregation is all about using aggregate functions and window functions.

This can be done by using

import org.apache.spark.sql.functions._

Now I am reading the data file into a data frame.

Simple Aggregations

Now, we are ready to do some aggregations. Let’s start with the simplest one.

The simplest form of aggregation is to summarize the complete data frame and it is going to give you a single row in the result. For example, you can count the number of records in this data frame and it will return you a single row with the count of records.

Now, we start with the data frame and use the select() method and apply the count function. You can also hive alias to the summary column. You can also add one more summary column for the sum of the dockcount column. You can also compute the average. We also have countDistinct() function. Here, I am counting the unique values of the landmark column. The countDistinct() will give the number of the unique landmark in this data frame. There is another thing called approx_count_distinct(). When we give countDistinct(), it will group the distinct values and count them. What happens when we have a huge dataset with millions of rows. The countDistinct() function will take time. In that case, we can use approx_count_distinct() which will return an approximate count. It is not 100% accurate. We can use this when speed is more important than accuracy. When you want to get the sum of a distinct set of values, you can use the sumDistinct() function.

be implemented like this.

df.select( count("*").as("Count *"), sum("dockcount").alias("Total Dock"), avg("dockcount").alias("avg dock"), countDistinct("landmark").alias("landmark count"), approx_count_distinct("station_id").alias("app station"), sumDistinct("station_id").alias("station_id") ).show()

The select method will return a new data frame and you can show it.

Let me run this.

The output will be as follows.

So, as expected, we summarized the whole data frame and got one single row in the result.

Great!

We have many other aggregation functions like first() and last() where you can get the first and last values in a data frame. We can get the minimum and maximum values using min() and max() functions respectively.

This can be done in Scala like this.

df.select( first("station_id").alias("first"), last("station_id").alias("last"), min("dockcount").alias("min"), max("dockcount").alias("max") ).show()

When we execute this, we will get the following output.

Now, I am going to use selectExpr() where we can pass the SQL like expressions.

df.selectExpr( "mean(dockcount) as mean_count" ).show()

Here, I am calculating the mean of the dockcount column.

The mean value is displayed.

Variance and Standard Deviation

Let’s look into other aggregate functions like variance and standard deviation. As we all know variance is the average of squared differences from the mean and standard deviation is the square root of variance.

They can be calculated by

df.select( var_pop("dockcount"), var_samp("dockcount"), stddev_pop("dockcount"), stddev_samp("dockcount") ).show()

And the output is

Skewness and Kurtosis

Skewness is the degree of distortion from the normal distribution. It may be positive or negative. Kurtosis is all about the tails of the distribution. It is used to find outliers in the data.

It can be identified by

df.select( skewness("dockcount"), kurtosis("dockcount") ).show()

The output is

Covariance and Correlation

Next, we will see about covariance and correlation. Covariance is the measure of how much two columns or features or variables vary from each other. Correlation is the measure of how much they are related to each other.

It can be calculated by

df.select( corr("station_id", "dockcount"), covar_samp("station_id", "dockcount"), covar_pop("station_id", "dockcount") ).show()

The output is

Aggregating to complex types

Next, we will see about aggregating to complex types. Suppose if you want to store a particular column in a list or if you need unique values of a column in a list, you can use collect_list() or collect_set(). collect_set() will store the unique values and collect_list() will contain all the elements.

Here is the implementation.

df.agg(collect_set("landmark"), collect_list("landmark")).show(false)

The output is

Complete Code

Here is the entire implementation.

import org.apache.spark.sql.functions._ import org.apache.spark.SparkContext import org.apache.spark.SparkConf object demo extends App{ val conf = new SparkConf().setAppName("Demo").setMaster("local[1]") val sc = new SparkContext(conf) val spark = org.apache.spark.sql.SparkSession.builder.master("local[1]").appName("Demo").getOrCreate; df.select( count("*").as("Count *"), sum("dockcount").alias("Total Dock"), avg("dockcount").alias("avg dock"), countDistinct("landmark").alias("landmark count"), approx_count_distinct("station_id").alias("app station"), sumDistinct("station_id").alias("station_id") ).show() df.select( first("station_id").alias("first"), last("station_id").alias("last"), min("dockcount").alias("min"), max("dockcount").alias("max") ).show() df.selectExpr( "mean(dockcount) as mean_count" ).show() df.select( var_pop("dockcount"), var_samp("dockcount"), stddev_pop("dockcount"), stddev_samp("dockcount") ).show() df.select( skewness("dockcount"), kurtosis("dockcount") ).show() df.select( corr("station_id", "dockcount"), covar_samp("station_id", "dockcount"), covar_pop("station_id", "dockcount") ).show() df.agg(collect_set("landmark"), collect_list("landmark")).show(false) } End notes

So, these are all simple aggregations. The simple aggregations will always give you a one-line summary. Sometimes, you may want a detailed summary. For example, if you want to combine two or more columns and apply aggregations there. It can be done simply by using Spark SQL. But you can do the same using data frame expressions also. It can be done by the concept of grouping aggregations. I will discuss grouping aggregations in another article. You can find it here.

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Related

How To Install Python 3 On Mac

Python is a popular programming language that is widely used by beginners and longtime developers alike. Modern Mac OS versions come with Python 2.7.x installed (or Python 2.6.1 if an older Mac OS X version), but many Python users may need to update Python in Mac OS to a newer version like Python 3.8.x or newer.

This article will discuss how to get an updated Python 3 installation on the Mac by covering two different ways to quickly and easily install Python 3 onto a Mac.

Note that we said install Python 3, not update to Python 3, because how this will work is installing Python 3 while simultaneously maintaining Python 2 on the Mac. This is essential because apparently some Mac apps rely on Python 2 support, so if you attempt to upgrade Python 2.x to Python 3.x in Mac OS you will eventually find that something is broken, perhaps critically so. With that in mind you should not attempt to update the existing preinstalled Python release on a Mac, instead you will just have a co-installation of Python 3 for full compatibility.

And yes, Python 3 and Python 2 can coexist on a Mac without any conflict, the usage commands will just be slightly different.

How to Install Python 3 in Mac OS

Perhaps the simplest way to install Python 3 is by using the Python package installer from python.org

Run the Python installer package and install Python 3 onto the Mac

Python 3.8.x requires about 100mb of disk space to install. Installation is quick, and you’ll have Python 3.x alongside Python 2.x on the Mac.

Once Python 3 is installed you will find a Python3 folder within the /Applications directory of your Mac. You’ll also find the simple IDE called IDLE within the /Applications/MacPython3/ directory, which basically gives you the same Python IDE you’d encounter if you ran ‘python3’ at the command prompt in Terminal.

You can also install Python 3.x on a Mac through Homebrew, which is my preferred method as a Homebrew user.

How to Install Python 3.x with Homebrew

Installing an updated version of Python 3.8 (as of time of writing) is super easy with HomeBrew. Of course you will need Homebrew installed on the Mac before you can use the Homebrew method, but if you’re interested in messing around with Python then Homebrew will probably appeal to you anyway.

We’re going to assume you already have Homebrew, if you don’t the you can read here how to install Homebrew on Mac OS.

To install the latest version of Python 3 using Homebrew, just issue the following command string:

brew install python3

Once the updated Python 3 has been installed on the Mac, you can run it with:

python3

Whether you install the updated Python 3 with the package installer or Homebrew, the default version of Python 2.7 that comes preinstalled with Mac OS and Mac OS X will still be installed, completely untouched, and can be run with the simple “python” command as always.

How to Check What Version of Python is Currently Installed in Mac OS

From the Terminal application, simply typing the following command will report back what version of Python is currently installed:

python --version

In MacOS, you will find it’s typically Python 2.7.x of some variation, either 2.7.4 or 2.7.10 or similar.

After you have installed Python with Homebrew or with the package installer, you can check the updated new version of Python with:

python3 --version

And as mentioned before, both installations of Python will coexist without conflict.

You can also find out where each version of python is installed with the ‘which’ or ‘whereis’ command:

Note that some aspects of Python are different in each version, and even features like the instant Python simple web server trick is different from version 2 to version 3. If you’re planning on using something longterm, or learning in general, you’ll be better off writing in Python 3.x rather than the older Python 2.x releases.

So now that you’ve got Python 3 installed, you’re ready to roll!

Learning Python, and Python Resources

If you’re new to Python and programming in general, there are a variety of great resources out there to get you started.

If you’re the type to enjoy learning from a book, some popular choices are the following (these are affiliate links to Amazon):

You can check out free online courses too, including these from MIT:

Or you can also explore the broad Python Wiki resources page here too.

TLDR: Don’t Update Python 2.x to Python 3.x, Just Install Python 3.x on the Mac

TLDR: Don’t update the preinstalled Python 2.x to Python 3.x, it will likely break something in doing so. Instead, just install and run the updated Python 3 separately.

Related

How To Build Trust And Boost Employee Engagement

Effective Ways of Building Trust and Improve Engagement

The current

Communicate Initiatively

Employees want to know the motive behind the engagement activities. Sometimes even if one thinks the individual has communicated enough, the person probably hasn’t. They should understand what, when, why, and how. Effective communication, from creating the engagement design to making it happen, reduces confusion, and helps them see its benefits. In return, it boosts engagement by creating a sense of confidence and camaraderie throughout the organisation.  

Put Everyone in the Right Position

It’s required to ensure one gets the right people on the bus and give them the right roles. That means all talent acquisition and retention methods must be aligned with meeting the organisation’s goals. Apart from considering technical skills, consider behavioural and soft skills as well when considering a candidate for a position. One must hire people with qualities the individual can build on, such as integrity, enthusiasm, a willingness to learn, a sense of humour, and a sincere interest in the business. Technical business can always be taught but changing one’s approach is more challenging.  

Provide Proper Training

Every leader should know that she will build a culture of trust and accountability and even enhance engagement by setting the team up for success. This indicates providing suitable training and development for the employees while removing hurdles. The leader can further a team’s engagement and trust levels by recognising each individual’s strengths and weaknesses and offering them training tailored to improve the strong points and tackle the weaker ones. It will show that the leader is thinking about employees on an individual basis, not just as part of the machine.  

Provide a Candid Timeline

Employees deserve to know what’s next to their engagement at the workplace. The leader should ensure providing them a timeline of the possible results to make this easier. While

Show Generosity without Commercial Goals

Employees want to be considered beyond commercial purposes. They should take an interest in their employees’ well-being and make efforts to improve their personal lives. If the leader looks at them for just business motives, it will be tough to keep them engaged in the long run. It is necessary to build trust by gradually being kind and accommodating and proving that monetary goals don’t just drive the leader.

The current business environment and the world are generally moving more rapidly than ever before. Worldwide organisations and companies are confronted with more change than most of them can manage. They are determined to grow fast with fewer resources. Managers are obliged to learn to excel by acting appropriately within their teams and simultaneously confronting organizational goals. Most leaders know that the employees are a company’s most important asset. That is accurate when the majority of the workforce is entirely working against the organisation. Today more than ever, companies count on the energy, commitment, and engagement of their employees to survive and flourish in the 21st century. Trust is difficult to be achieved, and it must be continuously re-earned by the employer. Employee engagement is not an exact science. The entire concept has been created on HR experience, positive psychology, and business models. These engage a company ‘s talent towards a productive culture of success. Here are ways how employers can build trust and improve engagement:Employees want to know the motive behind the engagement activities. Sometimes even if one thinks the individual has communicated enough, the person probably hasn’t. They should understand what, when, why, and how. Effective communication, from creating the engagement design to making it happen, reduces confusion, and helps them see its benefits. In return, it boosts engagement by creating a sense of confidence and camaraderie throughout the organisation.It’s required to ensure one gets the right people on the bus and give them the right roles. That means all talent acquisition and retention methods must be aligned with meeting the organisation’s goals. Apart from considering technical skills, consider behavioural and soft skills as well when considering a candidate for a position. One must hire people with qualities the individual can build on, such as integrity, enthusiasm, a willingness to learn, a sense of humour, and a sincere interest in the business. Technical business can always be taught but changing one’s approach is more challenging.Every leader should know that she will build a culture of trust and accountability and even enhance engagement by setting the team up for success. This indicates providing suitable training and development for the employees while removing hurdles. The leader can further a team’s engagement and trust levels by recognising each individual’s strengths and weaknesses and offering them training tailored to improve the strong points and tackle the weaker ones. It will show that the leader is thinking about employees on an individual basis, not just as part of the machine.Employees deserve to know what’s next to their engagement at the workplace. The leader should ensure providing them a timeline of the possible results to make this easier. While the leader expects employees to be open, the engagement activities should invest in employee growth and development. It could include providing the necessary tools and training or workshops to develop skills or even offering incentives. In short, if a leader offers them opportunities to grow and support them actively, it will increase their trust in the leader. But, if the leader isn’t timely in communicating what is earned, the faith that s/he has been working to build can be destroyed.Employees want to be considered beyond commercial purposes. They should take an interest in their employees’ well-being and make efforts to improve their personal lives. If the leader looks at them for just business motives, it will be tough to keep them engaged in the long run. It is necessary to build trust by gradually being kind and accommodating and proving that monetary goals don’t just drive the leader. The ways for building trust while boosting engagement aren’t complicated, but they must be prioritized. What works for an organisation might not work for others. Therefore, employers need to try and adopt methods that suit their business

How To Build Brand Trust Through Video Marketing

Building trust with customers makes them far more likely to buy your products and services and continue doing business with you over the long term. Established businesses succeed because they’ve built trust into their brand reputation and forged a genuine bond with their customers. 

Newer and smaller companies must also create this bond to show customers that they can trust you to provide a quality product or service, handle their payment data securely, and take care of any customer service issues. 

Here’s where video marketing can prove invaluable to your business. The intimacy of a video marketing campaign makes it uniquely well-suited to building trust. We’ll highlight effective trust-building strategies businesses can incorporate into their video marketing campaigns. 

Did You Know?

Video helps businesses emotionally connect with customers by fostering brand intimacy and creating shared bonds.

Tip

To become a better brand storyteller, detail struggles as well as successes, incorporate testimonials, and use compelling language to capture viewers’ interest.

Did You Know?

Generating video testimonials isn’t as straightforward as getting positive customer reviews online, but the power of video makes the effort worthwhile.

Tip

Brand video costs depend on your goals and budget. Professional talent and a production company will cost more than an in-house effort, but both can be effective.

Video marketing mistakes to avoid

Video is a powerful medium, so it’s crucial to get it right. Be sure to avoid these video marketing mistakes.

1. Don’t create videos without a clear goal.

You should establish an overall goal for your video marketing; within that, each video should have a specific goal. Decide how many videos you want to create for each objective, and set a topic for each. 

For the trust-building part of the customer journey, consider creating videos about the topics below:

The company’s history and founding

Company values

The company’s behind-the-scenes processes

Customer testimonials

Industry videos 

Videos that cover common questions 

To show authenticity, feature the business’s owners, managers and key employees on screen.

2. Don’t create videos without knowing your target audience.

If your company has been in business for a while, you should understand your customer base well. But if you’re new, research your audience and discover what interests them. 

For example, if your audience is full of people experienced with your product category, avoid videos on basic topics. If your audience contains mostly new users, create explainer videos. You can make video series for different customer personas to address each group’s needs.

Watch your direct competitors’ videos to discover topics that may appeal to your target audience. Additionally, ask your customers to share their problems or questions so you can address them in videos.

3. Don’t make your videos too long. 

Ideally, each video should be between one and two minutes long. According to Animoto, nearly 60 percent of viewers will watch a one-minute business video to the end, but their willingness to watch decreases with length. 

If you have very long videos, you’ll lose viewers’ attention — and they’ll be less likely to return for more video content. You can always make another video if you have more information to cover. Bite-sized video content tends to work best. 

4. Don’t neglect video SEO.

Like your online written content, video content should be part of your SEO strategy. Video can give your entire website an SEO uplift if done correctly because search engines value video. 

Here are some tips for optimizing your videos for SEO: 

Ensure the title and description contain your relevant keywords. 

Include captions and transcripts. 

Make your videos mobile-friendly.

Have a captivating thumbnail image.

Use relevant tags.

Did You Know?

According to a Wyzowl report, 87 percent of marketers say video boosts web traffic, and 82 percent say it increases the time visitors spend on a website.

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