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Average is one of the biggest enemy of analysts!

Why do I say so?

The amount of reporting which happens on averages is astonishingly high. Sadly, working with averages never reveals actionable insights until broken down in segments. Let’s go through a typical example to demonstrate what I am saying:

Let us assume that you head Customer Management division of Credit cards for a bank. Two metric of immense importance to you are:

Monthly spend people do on their credit cards – Indicates usage of credit card for customers

How much of their credit limit are customers utilizing? – Increasing trend might mean increasing risk or higher satisfaction. Decreasing trend might mean the other way.

You look at the following report and feel that everything is under control. You reach a quick conclusion that there is no problem in continuing your engagement as they run today:

In a practical scenario, these metrics would be an aggregate across various cards, but for simplicity, lets say that there are 2 kinds of cards:

Card A: Aimed towards people with good credit history. They will tend to have higher credit limits, lower risk and hence lower lending interest rates.

Card X: Aimed towards people new to Credit or people with bad Credit history. These will have lower Credit limits, higher risk and hence higher lending interest rates.

Again, just to simplify, lets assume that you have an even mix. The minute you split the metrics by segments, a different story emerges:

As you can see, what is actually happening is very different from what you would interpret from Average metrics. Actually, usage of cards with your low risk customers is on decline where as on the high risk customers is on increase – might be a scary situation!

[stextbox id=”section”]What is segmentation?[/stextbox]

Segmentation is a process of breaking a group of entities (Parent group) into multiple groups of entities (Child group) so that entities in child group have higher homogeneity with in its entities.

Following is a simple example of customer segmentation for a bank basis their age:

In this case you take a single group (customers of bank) and segment them in 5 child groups (basis their age). Incorporating this segmentation in your analysis can then drive various insights and ultimately actions in interest of your business like:

Are customers buying right kind of products?

What are the opportunities to sell an additional product to the customer? If the person became a customer as “Young Professional“, has the need changed as he is now a “Married Professional“

What kind of marketing channels would appeal to which kind of customers? How much to spend in each channel?

General guideline to create the child groups is that they should be “Heterogeneous with other groups, but homogeneous with in group“.

[stextbox id=”section”]How to create a segmentation?[/stextbox]

While there are multiple techniques to create a segmentation, the focus of this post is not on technical knowledge. I’ll layout the process used to create a segmentation and keep the technical details for a later point. This will enable you to create and implement a segmentation, even if it is not the best technically. You can obviously learn more details about the techniques and apply them in conjunction with the process mentioned here:

Step 1: Define the purpose of the segmentation. How do you want to use this segmentation? Is it for new customer acquisition? Managing a portfolio of existing customers? or Reducing credit exposure to reduce charge-offs? Every segmentation is created for a purpose. Until this purpose is clear, you will not be able to create a good segmentation.

Step 2: Identify the most critical parameters (variables) which influence the purpose of the segmentation. List them in order of their importance. Now, there are multiple statistical techniques like Clustering, Decision tree which help you do this. If you don’t know these, use your business knowledge and understanding to come out with the list. For example, if you want to create a segmentation of products and focus on products which are most profitable, most critical parameters would be Cost and Revenue. If the problem is related to identifying best talent, the variables would be skill and motivation.

Step 3: Once these variables are identified, you need to identify the granularity and threshold for creating segments. Again, these can come from the technique developed, but business knowledge could be deployed equally well. As a general guidance, you should have 2 – 3 levels for each important variable identified. However, it depends on complexity of problem, ability of your systems and openness of your business to adapt a segmentation. Some of the simple ways to decide threshold could be:

High / Medium / Low with numerical thresholds

0 / 1 for binary output

Vintage / Age of customers

Step 4: Assign customers to each of the cells and see if there is a fair distribution. If not, tweak the thresholds or variables. Perform step 2, 3 and 4 iteratively till you create a fair distribution.

Step 5: Include this segmentation in your analysis and analyze at segment level (and not at macro level!)

[stextbox id=”section”]Example of creating Segmentation:[/stextbox]

Let us say that you want to create HR strategy to identify which employees should be engaged in what manner so that you are able to reduce attrition and offer what the employee actually wants.

Define purpose – Already mentioned in the statement above

Identify critical parameters – Some of the variables which come up in mind are skill, motivation, vintage, department, education etc. Let us say that basis past experience, we know that skill and motivation are most important parameters. Also, for sake of simplicity we just select 2 variables. Taking additional variables will increase the complexity, but can be done if it adds value.

Granularity – Let us say we are able to classify both skill and motivation into High and Low using various techniques. This creates a simple segmentation as mentioned below:

If the distribution is skewed highly in one of the segments, we can change the threshold to define High and Low and re-create the segmentation.

Finally, you can now start analyzing employees to answer following questions:

Which kind of employees are having highest attrition? Is this HL / LH / LL or HH?

What is the average life span for each of these categories?

How many training each of these segments get in a year?

How many HH employees have been recognized for their work and contribution? What can be changed?

Hope this gives you a fair idea about creating and implementing a segmentation.

[stextbox id=”section”]A few additional notes:[/stextbox]

Before closing the article, would like to mention a few additional points to keep in mind:

Try and keep a fair volume distribution in various segments. If this does not happen, then you will end up analyzing on this data, which can result in wrong inferences.

Segmentation is always done as a means to achieve something. It can not be an objective in itself. So before starting any segmentation, always ask are you clear about the objective.

Techniques of segmentation help, but you can achieve more than 70% of results with a good business understanding.

So next time if you see any reporting happening at an overall level, STOP. STOP and think what you might be looking over and how can you improve this to bring out more actionable insights.

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The Growing Importance Of Virtualization Certification

Any IT professional who’s missed the buzz about virtualization might as well keep his head in the sand.

For the rest of the IT community it’s clear that talk about enterprise server virtualization adoption isn’t a matter of “if,” but “when.” So the question is whether certification in virtualization technology is a must-have.

With vendors like VMware, Citrix and now Microsoft in the virtualization certification game and the job market for IT professionals with virtualization skills sizzling, it would appear that many individuals would stand to benefit from sinking time and money into this specialized training. Red Hat offers Enterprise Linux Virtualization training for Red Hat Certified Technicians (RHCT) or individuals with equivalent knowledge.

What’s clear is that there’s no doubt that getting certified in virtualization technology matters.

“It just matters to some, not to everyone,” says Cushing Anderson, program vice president at IDC.

Fast-rising Market

But where virtualization is relevant to an IT professional’s career — such as storage, server management and PC management — certification can put them ahead of the curve. IDC projects that by 2011 the market for virtualization services will reach about $12 billion.

Silver is on the same page as Anderson when considering a certification in virtualization, noting that it depends on an individual’s career path and where they are on it.

“If you’re looking to get a job or move into a new area, certification can help. But certifications can be a mixed bag because once you’re in the door, employers aren’t as interested in certification versus whether you can do the job,” says Silver.

Jason Martin, vice president services at VMware, says that people who take the VMware Certified Professional (VCP) training should have some hands-on experience with virtualization already.

The vendor reports that it’s seeing a shift in demand for its VMware Certified Professional (VCP) on VMware Infrastructure 3 from the channel community to large enterprises.

“It’s becoming requisite training for IT staff who will install and manage VMware,” Martin says.

In fact, he expects that by year-end more corporate IT professionals than channel partners will pursue VCP education. The VCP allows IT professionals to demonstrate their virtual infrastructure expertise, according to Martin.

Microsoft’s Enters Game

Most recently upping the ante for virtualization experts is Microsoft, with the launch of its new virtualization products. The vendor also announced a road map for certified technical specialists in virtualization.

The vendor will offer four Microsoft Certified Technology Specialist (MCTS) certifications on virtualization, two are which are available now: Microsoft Desktop Optimization Pack, Configuring; and Windows Server 2008 Applications Infrastructure, Configuring. Available later this year will be: Windows Server 2008 Virtualization, Configuring; and System Center Virtual Machine Manager, Configuring.

The four certifications are designed to validate skills on the features and functionality of key Microsoft technology areas such as Window Server 2008: Hyper-V; System Center: Virtual Machine Manager; Terminal Service Virtualization; and, Application Virtualization, according to the company.

Industry experts warn that rather then getting caught up in the virtualization buzz, individuals should only consider undertaking a certification track if they’re interested in managing complex architectures.

“Virtualization is very technical. So while the technology may be hot, only pursue it if it’s your bliss,” says Anderson. “Otherwise, you’ll be a dull employee.”

Brief, Various Methods, And Importance

Introduction to Clustering Methods

Clustering methods, such as Hierarchical, Partitioning, Density-based, Model-based, and Grid-based models, assist in grouping data points into clusters. These techniques use various methods to determine the appropriate result for the problem. Clustering helps to group data points into similar categories, with each sub-category further divided to facilitate the exploration of query output.

Explain Clustering Methods.

Hadoop, Data Science, Statistics & others

Hierarchical methods

Partitioning methods

Density-based

Model-based clustering

Grid-based model

Here is an overview of the techniques used in data mining and artificial intelligence.

1. Hierarchical Method

This method creates a cluster by partitioning both top-down and bottom-up. Both these approaches produce dendrograms that make connectivity between them. The dendrogram is a tree-like format that keeps the sequence of merged clusters. Hierarchical methods have multiple partitions concerning similarity levels. Agglomerative hierarchical clustering and divisive hierarchical clustering divide the data into clusters. These methods create a cluster tree through merging and splitting techniques. Agglomerative clustering merges clusters, while divisive clustering separates them.

Agglomerative clustering involves:-

They were initially taking all the data points and considering them as individual clusters starting from a top-down manner. Analysts merge these clusters until they obtain the desired results.

The following two similar clusters are grouped to form a huge single cluster.

Again calculating proximity in the huge cluster and merging the similar clusters.

The final step involves merging all the yielded clusters at each stage to form a final single cluster.

2. Partitioning Method

The main goal of partition is relocation. They relocate partitions by shifting from one cluster to another, which makes an initial partitioning. It divides ‘n’ data objects into ‘k’ numbers of clusters. This partitional method is preferred more than a hierarchical model in pattern recognition.

The following criteria are set to satisfy the techniques:

Each cluster should have one object.

Each data object belongs to a single cluster.

The most commonly used Partition techniques are the K-mean Algorithm. They divide into ‘K’ clusters represented by centroids. Then, each cluster center is calculated as a mean of that cluster, and the R function visualizes the result.

This algorithm has the following steps:

Selecting K objects randomly from the data set and forming the initial centers (centroids)

Next, assign Euclidean distance between the objects and the mean center.

Assigning a mean value for each individual cluster.

Centroid update steps for each ‘k’ Cluster.

3. Density Model 4. Model-Based Clustering

This model combines two or three clusters together from the data distribution. The basic idea behind this model is to divide data into two groups based on the probability model (Multivariate normal distributions). In this model, we assign each group as concepts or classes and define each component using a density function. We use Maximum Likelihood estimation to find the parameters to fit the mixture distribution. We model each cluster ‘K’ using a Gaussian distribution with a mean vector µk and a covariance vector £k, each having two parameters.

5. Grid-Based Model

The approach considers objects to be space-driven by partitioning the space into a finite number of cells to form a grid. Then, the approach applies the clustering technique with the help of the grid for faster processing, which typically depends on cells rather than objects.

The steps involved are:

Creation of grid structure

Cell density is calculated for each cell

Applying a sorting mechanism to their densities.

Searching cluster centers and traversal on neighbor cells to repeat the process.

Importance of Clustering Methods

Having clustering methods helps restart the local search procedure and removes the inefficiency. In addition, clustering helps to determine the internal structure of the data.

This clustering method has been used for model analysis and vector region of attraction.

Clustering helps in understanding the natural grouping in a dataset. They aim to make sense of partitioning the data into some logical groupings.

Clustering quality depends on the methods and the identification of hidden patterns.

They play a wide role in applications like marketing economic research and weblogs to identify similarity measures, Image processing, and spatial research.

They are used in outlier detections to detect credit card fraudulence.

Conclusion

Experts regard clustering as a universal task that involves formulating optimization problems to address various issues. It plays vital importance in the field of data mining and data analysis. We have seen different clustering methods that divide the data set depending on the requirements. Researchers mainly rely on traditional techniques such as K-means and hierarchical models for their studies. They apply cluster areas in high-dimensional states, which presents a potential area for future research.

Frequently Asked Questions (FAQs)

Answer: Several types of clustering methods exist, including hierarchical clustering, k-means clustering, density-based clustering, and model-based clustering. Each method has its strengths and weaknesses, and the choice of method depends on the data’s characteristics and the analysis’s goals.

Answer: Clustering can help identify patterns and relationships in data that may not be apparent from simple visual inspection. It can also segment customers or products for targeted marketing, identify anomalies or outliers in data, and reduce the dimensionality of large datasets.

Q3 What are the limitations of clustering?

Answer: Clustering can be sensitive to the choice of distance metric or similarity measure, and the number of clusters can be difficult to determine. The clustering results can also be highly dependent on the quality of the input data and the assumptions underlying the clustering method.

One Class, One Day: The History Of Rock And Roll

Chuck Berry’s music had a message; his performances had the famous duck walk. Photo courtesy of Bradford Timeline

Class by class, lecture by lecture, question asked by question answered, an education is built. This is one of a series of visits to one class, on one day, in search of those building blocks at BU.

Ohio radio DJ Alan Freed was the opening act for a revolution when he dubbed the pulsing, edgy music he was playing in the early 1950s “rock and roll.” He stunned one performer familiar with the term as a euphemism for sex. “I can’t believe you said that on the air,” he told Freed.

The anecdote is one of many that William McKeen drops like doo-wops in his summer term medley of music and social history, The History of Rock and Roll, covering R&R’s roots from its pioneering performers to 1970. This course sways to both the classic songs McKeen plays and the stories he tells to humanize the genre’s icons.

Elvis, of course, is among the pivotal figures covered in the course. Yet he was an “interpretive” artist of others’ music, McKeen told students during a class focused on the man he calls rock and roll’s first great creative artist—Chuck Berry. Berry, he noted, wrote his own songs, made his on-stage “duck walk” famous, and became more than just a performer in the process. McKeen stressed the lyricism of “rock and roll’s first poet” and “the economy of his language. He can tell you an awful lot with a little.” Nadine, for example, describes a beautiful woman walking like a wayward summer breeze toward a coffee-colored Cadillac. “You can kind of see her as she sashays away,” McKeen said.

Berry’s lyrics fried bigger fish than merely describing cars and women. Brown-Eyed Handsome Man was actually code for “brown-skinned,” the song becoming an early anthem of pride for people of color, McKeen told his students. Too Much Monkey Business is “kind of a serious song” about a disaffected Army vet reduced to pumping gas after serving in a war. Perhaps most poignant is Memphis, about a six-year-old whose parents’ breakup leaves her waving good-bye to her father with hurry home drops on her cheeks.

McKeen described the genesis of Berry’s duck walk (he invented it while working as a barber to make people laugh) and of his first hit (he took a blues tune titled Ida Red to a record company exec, who found it too slow and told him to “goose it.” Berry came back with Maybellene.) He noted that for all Berry’s pioneering music, his only number-one hit was a 1972 novelty song about his sex organ, My Ding-a-Ling. According to McKeen, Berry never intended the song for release. “He was furious, until he started getting the royalty checks. Then he got over it.”

The main lesson McKeen wants students to learn is “the role that rock ’n’ roll played in introducing black America to white America. Because radio didn’t obey Jim Crow laws, it took music into places it normally wouldn’t be heard.”

These morsels are a highlight of the class for Nate Fisher (CFA’13). McKeen “knows so much about every story that influenced every event,” he said. Fisher wanted a summer class that could fulfill his liberal arts requirement, and “this is perfect. Music is my favorite thing in life.” He has long idolized Berry, thumped out Bo Diddley’s trademark beat on the table when McKeen asked if anyone knew it, and also accepted the professor’s invitation to do a pitch-perfect impression of Little Richard’s stratospherically high Wooooooo!

This enthusiasm, Fisher admitted, probably makes him unusual for his generation, to whom the 1950s might as well be the Middle Ages. (One quarter of the class hails from Metropolitan College’s Evergreen program for learners 58 and older.) “There are probably not a lot of kids my age listening to old rock and roll,” Fisher said. “But kids interested in music? Definitely.” And even nonfans appreciate the music and music-makers the class studies in rock’s later period, he said. “The Beatles, the Stones, Led Zeppelin—they’re never going to not be listened to.”

Anyway, old rockers never die; they just release children’s albums later in life, as Little Richard did with his 1992 CD Shake It All About, familiar to some in Fisher’s generation. As the man said, rock and roll is here to stay.

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The Importance Of Diversification In Seo Strategy

Therefore, it’s important to diversify and not keep all your optimization eggs in one basket – there’s a good chance that basket is one algorithmic update away from being vaporized.

Relying on a singular strategy is dangerous because that strategy can quickly become obsolete, but it’s also dicey because you run the risk of over-optimizing.

Over-optimization of a specific tactic can signal manipulation to search engines, which can result in devaluations or even manual penalties.

Overuse of a specific link anchor text is a prime example of how you can run into trouble with over-saturation; a natural anchor text distribution features a diverse range of anchor texts.

However, the importance of diversification within SEO extends far beyond anchor text.

Here are some ways you can diversify your SEO efforts to earn visibility and organic traffic.

Diversifying Content Strategies

Content plays an integral role in SEO success, and it’s also an area where diversification is needed most.

Diversity within content starts with the audience – you need to diversify your content to reach all of your audience.

If you only focus on the people who are ready to buy from you, you’re going to miss a significant portion of your audience.

You need to create content that serves all stages of your marketing funnel, not just the bottom.

Your audience has different intents during different stages, and your content needs to match that specific intent to show up in relevant search results.

For example, some of the different types of content you should create to address your entire funnel include:

Top of the Funnel

Infographics

Blog posts

Podcasts

Webinars

Industry news coverage

Middle of the Funnel

Ebooks

Whitepapers

In-depth resources or guides

Checklists

Bottom of the Funnel

Case studies

Testimonials and referrals

Vendor comparisons

Product demos

FAQs

These are some examples of types of content that you can create to address every part of your funnel and all your audience.

To address every stage of your marketing funnel you need to create a variety of content types, but this is not the only reason to diversify content format. Content formatting can play a large role in search visibility and rankings.

When we think of “content,” we typically think of blog posts or written text. However, depending on the intent of a given query, text content may not rank well in the corresponding search results.

Closely examine the SERPs of any keywords or themes you’re targeting to better understand which types of pages Google wants to rank and diversify your content – with video, images, audio, etc. – to match the content formats currently ranking.

In fact, diversifying a single page to include multiple formats will give you the best chance to rank for a variety of queries while also giving readers options for digesting the information.

Offering diverse content formats strengthens a page in terms of search and user experience.

Finally, you need to diversify content topics to extend the topical authority of your website.

Focusing solely on one topic will make a strong connection between your site and that topic, but it could also mean pigeonholing yourself and missing out on other potentially lucrative opportunities.

For example, on our own Page One Power blog, we have discussed link building at length, which makes it easy to rank our pages for link building keywords.

However, this narrow focus has made it difficult to gain visibility as we work to expand our topics into broader SEO practices and philosophies.

It will take time to earn topical authority for these broader subjects, and perhaps a more diverse topic strategy would have served us better.

We’ve adjusted course accordingly, and have plans to build authority on-site relating to more diverse topics.

This is just one example that illustrates the importance of broader market research in informing marketing strategies.

As your company evolves to serve a broader market, your strategies and applications of market research must also evolve.

Diversification within your content will ensure you’re:

Addressing each stage of your marketing funnel.

Positioning your pages to be successful in search.

Expanding your website and brand’s topical authority in the eyes of search engines and visitors alike.

However, content and links are the driving forces in search rankings, and diversification in both of these areas is paramount to SEO success.

Diversifying Link Acquisition Strategies

Diversification within link building is equally important to diversifying content strategies, and it starts with tactics.

Diversifying your link acquisition tactics is especially important because leaning on a single tactic too heavily can quickly approach manipulation.

Essentially, any link building technique can be leveraged appropriately or overused in a spammy way, so you want to diversify your approach to avoid overemphasizing a singular strategy.

A diverse link acquisition plan is more sustainable and future-proof.

You should also diversify the types of link prospects you target.

You want to target relevant websites within your niche, but this doesn’t mean you should target only one specific type of site.

There should still be a wide range of websites to target that are also relevant to your brand or product or service.

For example, an infant care supplies company would be relevant to many mommy blogs, but there would hypothetically be opportunities to target other types of sites such as:

Parenting resources.

Niche-specific directories.

Local community pages.

Medical and health information sites.

Child development and child care websites.

Links from a wide array of websites will signal a natural and authoritative backlink profile to Google.

Finally, you should target a diverse set of pages on your website for links.

Not every page on your site will be link-worthy – so you’ll be forced to diversify to an extent.

That said, you need to make sure you’re securing links to a wide range of pages on your site to ensure you’re capturing as much of your available search opportunity as possible.

Earning links to a diverse group of pages on your site will cultivate a natural link profile and increase the authority and trust of your domain.

Recap

Diversification plays a major role in SEO success.

SEO changes and evolves quickly, meaning you’ll want to spread out your marketing eggs into multiple baskets.

A well-rounded, diverse SEO strategy includes:

Diverse content and webpages.

Wide range of topic coverage.

Diverse content formats, including multiple formats on the same page.

Diverse target audiences that cumulatively make up your entire marketing funnel.

Diverse link acquisition strategies.

Diverse link building tactics.

Varying types of link prospects.

Multiple target pages with internal linking to cover potential gaps.

Furthermore, you should work to diversify your entire marketing strategy beyond SEO. Relying on one channel can quickly get you into hot water.

In terms of SEO, this could be one ranking page that suddenly loses visibility due to an algorithmic update, but this issue extends into other marketing channels as well.

It’s always best to diversify the ways you reach your audience and tap into as many channels as possible.

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How To Use And Create Ssrs Parameters?

Definition of SSRS Parameter

It states that when a User can specify a particular number in the textbox, the SSRS Report Parameters will filter the report data based on the user-specified value. In other terms, Report Parameters allow the user to filter SSRS Reports automatically. The ability to offer a parameter with multiple values is one of the features of SQL Server Reporting Services.

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Introduction to SSRS Parameter

SQL Server Reporting Services (SSRS) uses parameters to make presentations more interactive. Parameters can be used for anything from query requirements to Tablix controls to regulating the appearance of entities on a report. They can also be generated from a set of constant values or the outcomes of a data set query. Any interactive reporting tool relies heavily on parameters.

The report’s parameters allow people to communicate with it. We can pass the parameters in four different ways.

Parameters that were requested

Parameters that have been cascaded

Parameters with multiple values

Parameters that have not been queried

How to Use SSRS Parameters?

Enabling the users to choose one, couple, many, or even all values from a list at execution is a terrific way to give them freedom. It even features a built-in method for quickly selecting and deselecting all possible options. Once we plan to publish a single report that several locations or departments can use, this is a common scenario that would profit from it.

The following are cases of report parameters:

To choose the report data by selecting required parameter values.

To Change Report Appearance – Using expression-based attributes, employ parameters to change report appearance, such as conditionally hiding report items and conditionally changing text color.

How to Create SSRS Parameter?

If a user copies or saves the report with the parameter shown in the header, the parameter choices taken at the beginning are maintained. This creates value to an account and makes it easier for others to grasp – especially if it’s a more detailed report with multiple parameters. Reports are easier to create using parameters, and the user experience is more functional and adaptable.

Let us make a parameter that takes a user-specified value from a textbox and uses it to filter the report data by Class. Then, when we select Add parameters, a Report Parameters Properties window will appear, wherein we must provide the parameters’ details, which are as follows.

Name: Supply a suitable parameter name.

Prompt: type a short message that will appear as a label in front of the text area.

Because the class name is a Text data type, leave the data type at default Text.

SSRS Parameter Add Reports

Here we shall create an example on Order Wise Sales Report

Step 1: Creating a Datasets

The Query list is given here:

Step 2: When we execute the query, the result is here. Here is the order details table:

To make this above table into a relational, we must write a question as below, and the preview list is given here:

Step 4: Let’s see a Step-by-step Implementation of Parameters in a Report.

Adding a table is given here:

Step 5: Adding Parameter values with available values. It’s difficult for users to recall which values are acceptable for a report; report developers frequently present a list of options to choose from. Go to the Known Values page for the parameter. As indicated in the screenshot, specific values tell the parameter to select a deal, and also, the option is set to None sometimes, which means that no list is presented.

Now when we preview the report, we can see a list of countries from which to choose, as shown below:

Multivalue SSRS Parameter

Within the parameter list, one can select several options. Both queried and non-queried reports can have multi-valued elements. The multi-value parameter allows us to pass one or more values to the information in addition to the input data. It also has a “Select All” feature for selecting all model parameters. In SSRS, we’ll now develop an example of a multi-value parameter.

We should first facilitate multiple values for a parameter to allow randomized, multiple-value selections in its drop-down menu.

-Select Allow multiple values from the drop-down menu.

Next, ensure that the “Allow multiple values” check option is selected in the parameter attributes.

To the query, add a WHERE clause like this: WHERE COUNTRY IN (@Country)

Several of the reports we produce in the future will also include more. Either the dataset will contain numerous predicates in the WHERE clause, or variables will be used to customize how the report appears.

Conclusion

We learned how to use parameters in various ways in this post, including a list of available values from a query, multiple selections, defaults, and much more. As a result, we’ve focused on the primary point: parametrized reports give us greater flexibility and improve the user experience. Multiple value parameters are a powerful resource in SSRS; utilizing one with an object filter is very simple if one knows how and where to implement the parameter values.