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The focus is on the product

The reason?

Great products trigger word-of-mouth effect which is the most reliable and also the cheapest marketing channel. Therefore, keep in mind that the 4Ps of marketing are still alive and well.

What about SaaS pre-launch stage when you don’t have a product ready yet? I’m here to share our experience and best practices.

Make use of the market research

SaaS marketing starts long before the product gets launched. When you make first steps with your next big thing, it’s a good idea to begin with a detailed research. The first task is to identify the buyer persona. At Chanty we’ve created a number of surveys asking about communication challenges people experience, problems that team chats solve for them and so on. It helped us create an image of the target audience we are building our product for.

This kind of analysis helps make a decision when you shape your product and answer many questions:

Is there a free niche you can occupy?

Which product features should your product include to stay competitive?

What marketing channels can you rely on?

And the most important:

What’s your competitive edge?

You can also use marketing hooks that help spread the word. E.g. Slack claims to kill emails. This statement certainly stands out and attracts attention. Groove helpdesk software positions themselves as a “breath of fresh air”. At Chanty we’ve chosen to go with artificial intelligence – the 2023 trend that resonates with our tech savvy audience.

Test your marketing assets

Marketing and design have to go hand in hand to achieve results. What you think as an amazing piece of design or a line of text may be not perceived by your target audience in the same way. How do you know what your buyer persona actually likes?

The Next step is to compare the results and see if one of the versions of your marketing assets performs significantly better for your target audience.

Design a remarkable landing page

A landing page is all you have when the product isn’t ready yet. While there’s no problem with having a simple one-page website, in the beginning, it must be nevertheless crystal clear and user-friendly as you will never have a second chance to make the first impression. Responsive design that a user can easily open from a mobile device goes without saying.

Remember, Nike doesn’t sell sports shoes, they sell motivation for courage and success. The content of your product landing page should reflect the values you are delivering with your software, so don’t start off with listing your “unique” features. Big brands sell value, not functionality.

Content marketing is still king

There’s no excuse for not creating content today when digital marketing has turned into a non-stop content creating machine. Starting a blog is a must, moreover, you have to spend more time promoting your content than actually writing it. Keep conversions in mind when working on your articles. It means looking for the right high-intent keywords to attract visitors looking for your product or answers to the questions that your product solves.

The quality of the blog posts goes without saying. You have literally no chance to be noticed in the ocean of valuable in-depth content if you don’t pay attention to the quality. Therefore, I always recommend not to hire freelancers to write content for you. Content becomes useful and valuable only when you put your heart and soul into it. My experience tells me it’s usually hard to expect this from a freelancer.

We’ve started looking for beta testers long before our actual beta test. On the one hand, more time gives you an opportunity to attract more emails. On the other hand, when you contact those you’ve found half a year ago once your app finally enters its beta, they barely remember you or your app. Therefore, I’d recommend to strike a balance and spend one or two months actively promoting your product to get the precious emails of early adopters. Here are some helpful sources that got us the majority of the emails:

Direct outreach

We’ve tried to contact people directly on LinkedIn, Twitter and Facebook. As a result, LinkedIn brought us the largest number of early adopters.

Beta platforms (Beta List, Beta Bound, Beta Family, etc)

I should highlight that Beta List was the most effective bringing us twice as many emails compared to other beta platforms altogether.

App review content

It’s hard to overestimate the role of content marketing and SEO when it comes to SaaS promotion. We’ve developed a number of articles reviewing our main competitors that continue to bring us traffic and conversions.

Key takeaways

No matter where your marketing activities take you, always keep your product a priority. The overall success of your SaaS depends on whether or not your customers will enjoy using it. If they do, you will be rewarded that viral effect you’ve been craving for. At the pre-launch stage, when there’s no product ready, design your website to get the most of it and not to spoil the first impression.

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Best 5 Saas Product Development Ideas For Startups

It is difficult to start a truly innovative company. Some many steps and procedures must be followed. It is not easy to set up a business because they require participation, determination, and investment. Remote work is essential for company success and profitability.

In 2023, the SaaS industry was worth $105 billion, and by 2023, it is estimated to be worth $141 billion.

This remarkable increase is a result of SaaS’ dominance within the computer software industry and a sign that it will continue to be a major player in the future.

As per chúng tôi the SaaS market will cost $55 billion in the United States alone by 2026.

Start-ups in this industry are also possible.

This blog will focus on top SaaS business ideas to make 2023 profitable.

Let’s discuss this briefly before we get into the details.

Top Benefits of SaaS Product Development

Below are some benefits of SaaS product development!

Flexibility – The user agrees to pay monthly for SaaS services by adopting the offering. This allows businesses to budget more accurately. End users can unsubscribe at any time from SaaS services.

Agility – is a company’s ability to adapt to industry changes. SaaS is a low-cost industry that increases operational efficiency. You can save time in developing and testing your SaaS products.

Scalability – It gives you the ability to grow your business and your startup simultaneously. SaaS innovation allows for tactical resource allocation due to the flexibility offered by cloud computing. SaaS solutions offer enhanced flexibility on demand, which can reduce the costs associated with your product.

Greater availability – Access the application via cloud storage from any computer, mobile device, or laptop with an Internet connection. The data that was loaded will not disappear if the device is lost or stolen.

Also read: 10 Best Chrome Extensions For 2023

Best 5 SaaS Product Development Ideas for Startups

Let’s now get to work identifying which SaaS applications will be most popular in 2023.

#1. SaaS CRM software

Customers enjoy a quick setup, low initial investment, and easy Internet connectivity through SaaS-based CRMs. SaaS CRM systems are great for small businesses. They offer automated upgrades and support, as well multi-level subscription plans. This type of software automates not only customer management, but also covers most needs of local small businesses: keeping track of finances, inventories, payroll, and analytics. For more details, check out Orderry. This is a SaaS solution for service companies that helps automate routine and streamline business processes.

#2. SaaS-based AI apps

Artificial intelligence is already a hot topic. You can improve productivity and efficiency by developing an AI-based SaaS platform. This will allow you to optimize business processes, automate repetitive tasks, and enhance the capabilities of potential customers. Businesses may find artificial intelligence and machine learning extremely beneficial, which could help to elevate the SaaS platform.

#3. SaaS-Driven real estate software

Also read: 10 Best Android Development Tools that Every Developer should know

#4. Telehealth

Telehealth is a way for people to access medical care from faraway locations. Telehealth SaaS allows professionals to communicate online with patients. Telemedicine is a very lucrative industry that is in good shape due to rising demand. This SaaS model allows remote patient monitoring and mobile healthcare.

#5. SaaS driven video editing software

Here are some additional SaaS product ideas to help you develop a profitable product.

Take a look at these ideas!

Blockchain-based SaaS app

SaaS-based team collaboration software

SaaS-based accounting software

SaaS-based eLearning app

Content planning software

SaaS-Driven employees management software

Live video streaming software

Do Network File Systems Pre

Introduction

In a networked computing environment, file systems allow users to access and manage files across different computers and storage devices. Network file systems (NFS) are a type of file system that enables remote file access and sharing between machines over a network. In NFS, a client machine can access files stored on a remote server as if they were on its local file system. One common question that arises regarding network file systems is whether they pre-fetch data to improve performance. In this article, we will explore concept of pre-fetching in network file systems and provide examples of how it works.

What is Pre-fetching in Network File Systems?

Pre-fetching is a technique used to improve performance of file systems by anticipating data that a user might access and proactively loading it into memory. This approach is based on observation that data access patterns are often predictable, and certain files or blocks of data are likely to be accessed soon after others. By loading this data into memory before it is requested, file system can reduce latency of file access operations and improve overall system performance.

Pre-fetching in network file systems works by caching frequently accessed files or blocks of data on client machine’s local file system. When a file is opened, file system first checks if requested data is already present in cache. If it is, data can be quickly retrieved from cache without need to access remote server. If data is not in cache, file system will fetch it from server and also pre-fetch additional data that it predicts will be accessed soon.

Examples of Pre-fetching in Network File Systems

Pre-fetching is a technique that is used in a variety of network file systems. Here are some examples −

NFSv4 NFSv4 is a popular network file system used in many enterprise environments. It includes a feature called layout hints, which allows clients to provide hints to server about which files or portions of files are likely to be accessed soon. server can then use this information to pre-fetch data and make it available to client before it is requested.

AFS Andrew File System (AFS) is a distributed file system that also includes pre-fetching capabilities. AFS pre-fetches data based on access patterns of individual users and groups, as well as overall behavior of system. It also includes a feature called Venus Fetch Agent, which can pre-fetch data in background without disrupting other system operations.

CIFS/SMB Common Internet File System (CIFS) and Server Message Block (SMB) are network file systems used in Microsoft Windows environments. These file systems include a pre-fetching feature called read-ahead, which allows client to pre-fetch data that it expects to be accessed soon. read-ahead feature can be tuned to adjust amount of data that is pre-fetched and degree of aggressiveness used.

Benefits and Challenges of Pre-fetching in Network File Systems

Pre-fetching can provide significant benefits for network file systems. By proactively loading data into memory, pre-fetching can reduce latency of file access operations and improve overall performance of system. This can be particularly important for large files or for operations that involve many small files. Pre-fetching can also help to reduce load on network by reducing number of requests that need to be sent to server.

However, pre-fetching can also pose some challenges for network file systems. For example, pre-fetching requires additional resources, such as memory and CPU cycles, which can affect overall system performance. Pre-fetching can also be difficult to tune, as effectiveness of pre-fetching depends on specific access patterns of individual users and system as a whole. In addition, pre-fetching can lead to increased network traffic, particularly in situations where pre-fetched data is not actually needed. This can be particularly problematic in low-bandwidth or high-latency network environments, where cost of sending unnecessary data over network can be high.

Another potential challenge with pre-fetching is that it can lead to stale data in cache. If data is pre-fetched but never actually accessed, it can remain in cache for an extended period of time, potentially leading to inconsistencies between cache and server. This can be particularly problematic for applications that require up-to-date data, such as databases or content management systems.

Here are some additional points to consider regarding pre-fetching in network file systems

Pre-fetching can also be useful for reducing load on network. By pre-fetching data on client machine, number of requests that need to be sent to server can be reduced, resulting in less network traffic.

Pre-fetching can be particularly beneficial in situations where network latency is high. In these situations, time it takes to fetch data from server can be significant, and pre-fetching can help to mitigate this delay by ensuring that data is already in memory when it is needed.

To use pre-fetching effectively, it is important to carefully analyze access patterns of individual users and system as a whole. This analysis can help to identify files or portions of files that are most likely to be accessed and can inform pre-fetching algorithm.

Pre-fetching can be challenging to tune, particularly in dynamic environments where access patterns are constantly changing. In these situations, it may be necessary to continuously monitor system and adjust pre-fetching algorithm to ensure optimal performance.

It is important to carefully manage pre-fetching cache to avoid stale data. This can be done by setting appropriate cache expiration times and by periodically checking cache for data that is no longer needed.

Conclusion

Pre-fetching is a powerful technique for improving performance of network file systems. By proactively loading data into memory, pre-fetching can reduce latency of file access operations and improve overall system performance. However, pre-fetching can also pose challenges, particularly in low-bandwidth or high-latency network environments. To use pre-fetching effectively, it is important to carefully tune pre-fetching algorithm to specific access patterns of individual users and system as a whole. With careful tuning and management, pre-fetching can be an effective tool for improving performance of network file systems.

Visualizing Product Relationships In A Market Basket Analysis

Last week had been very hectic. I had slogged more than 100 hours to come out with an awesome recommender based on market basket analysis.

“Now was the time to shine!” I thought, just before the meeting with stakeholders was about to start. I had prepared a good presentation and was feeling confident about the work. Thirty minutes into the presentation, I was trying my level best to explain lift, support and confidence in an imaginary 3d plane to the stakeholders.

Guess what – they were not impressed, they found the technique too complex. The meeting ended up with the key stakeholder saying “Can you create something simpler and more intuitive?”

This is when I went back to a drawing board and came out with this technique to visualize and explain market basket analysis in very simple visualization. This was the core thought behind this technique:

Algorithm used in Text mining can be leveraged to create relationship plots in a Market basket analysis.

Market basket is a widely used analytical tool in retail industry. However, retail industry use it extensively, this is no way an indication that the usage is limited to retail industry. Various X-sell strategies in different industries can be made using a market basket analysis. There is a good amount of content available in the web world on the theory behind market basket analysis but I have hardly seen any articles on how to visualize market basket analysis . In this article, I will leverage some algorithm of text mining to get such visual plots.

Some basic Definitions

Support : Support is simply the probability of an event to occur. If we have an event to buy a product A, Support(A) is simple the number of transactions which includes A divided by total number of transactions.

Confidence : Confidence is essentially the conditional probability of an event A happening given that B has happened.

For more detailed definition refer to our last article (last post).

Importing the dataset

The first part of any analysis is to bring in the dataset. I am using a dummy data to demonstrate this application. The data has details of 12k transactions. Each transaction has 3 products. Following is the code to import the transaction data stored in a CSV file.

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txn_data<-read.csv("Retail_Data.csv") summary(txn_data)

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transaction_id Prod1 Prod2 Prod3

Max. : 112000

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As you can observe, each transaction has all 3 products. Product 1 takes only A,B,C and D. Product 2 takes E,F and G. Product 3 takes H and I. All the three products are mutually exclusive.

Creating an “item-transaction” Matrix

This is a concept, I learned in text mining. But it very well fits into this application as well. We will first create a matrix with flags on each product. In total we have 9 products, hence we generate 9 vectors to capture these flags. Here is the code to generate the 9 vectors and joining them to form item document matrix.

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#Initializing vectors A <- numeric(0) B <- numeric(0) C <- numeric(0) D <- numeric(0) E <- numeric(0) F <- numeric(0) G <- numeric(0) H <- numeric(0) I <- numeric(0) #Preparing the flag metrics for ( i in 1:nrow(txn_data)) { if (txn_data$Prod1[i] == "A") A[i] <- 1 else A[i]<-0 if (txn_data$Prod1[i] == "B") B[i] <- 1 else B[i]<-0 if (txn_data$Prod1[i] == "C") C[i] <- 1 else C[i]<-0 if (txn_data$Prod1[i] == "D") D[i] <- 1 else D[i]<-0 if (txn_data$Prod2[i] == "E") E[i] <- 1 else E[i]<-0 if (txn_data$Prod2[i] == "F") F[i] <- 1 else F[i]<-0 if (txn_data$Prod2[i] == "G") G[i] <- 1 else G[i]<-0 if (txn_data$Prod3[i] == "H") H[i] <- 1 else H[i]<-0 if (txn_data$Prod3[i] == "I") I[i] <- 1 else I[i]<-0 } chúng tôi <- rbind(A,B,C,D,E,F,G,H,I)

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Creating plots using igraph library

Once we have the transactions-item matrix, it is time to create an item-item correlation matrix. I have done this using a simple mathematical formulation. We multiple the transaction-item matrix with its own transpose to get item-item correlation matrix. In this matrix, the number on diagonal gives an indication of Support whereas all other numbers give the confidence. We use both these numbers to build a relationship plot. Following is the code to build the matrix and the plot.

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#Creating the relationship matrix termMatrix <- chúng tôi %*% t(final.mat) #Creating the graphs library(igraph) # build a graph from the above matrix g <- graph.adjacency(termMatrix, weighted=T, mode = "undirected") # remove loops g <- simplify(g) # set labels and degrees of vertices V(g)$label <- V(g)$name V(g)$degree <- degree(g) # set seed to make the layout reproducible set.seed(3952) layout1 <- layout.fruchterman.reingold(g) plot(g, layout=layout1) plot(g, layout=layout.kamada.kawai) tkplot(g, layout=layout.kamada.kawai)

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As of now we have not incorporated the strength of confidence or the support to plot this graph. Something to observe in this plot is that products like A and B are not connected. This is simply because they never co-exist together in any transaction. This plot can be use to visualize the negative lift items. Such items should not be placed near each other. The next step is to incorporate the support of each product in the visual plot.

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V(g)$label.cex <- 2.2 * V(g)$degree / max(V(g)$degree)+ .2 V(g)$label.color <- rgb(0, 0, .2, .8) V(g)$frame.color <- NA egam <- (log(E(g)$weight)+0.2) / max(log(E(g)$weight)+0.2)

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Here, we have incorporated the support of each product. As you can see H and I form the biggest letters and A,B,C and D the smallest. This is an indication of higher and lower support. You can validate these inferences from the initial frequency distribution. The next step is to incorporate the confidence as well in the relationship line width.

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E(g)$color <- rgb(.5, .5, 0, egam) E(g)$width <- egam # plot the graph in layout1 plot(g, layout=layout1) tkplot(g, layout=layout.kamada.kawai)

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The final plot makes the entire story clear. We have already seen that H and I have the highest support. Now it is also clear that E-I , I-F and H-F have a high confidence as well. Hence, if a customer buys a product F there is a high propensity that he will also buy product H and I. Hence, following are the rules which we can infer from this analysis :

1. If a customer buys E, he has a high propensity to also buy I.

2.If a customer buys F, he has a high propensity to also buy I.

3. If a customer buys F, he has a high propensity to also buy H.

4. If a customer buys I, there is very small that he will also buy H.

The arrangement of items should flow from these rules in order to maximize the sales.

End Notes

Graphical representation of market basket analysis makes the interpretation of the entire puzzle of “probabilities/conditional probability/lift above random events” much simpler than a tabular format. This simplification can be more appreciated when we have a large number of transactions and product list. In case of large lists, we can simply find out using the dimension of product sign and width of the line connecting them to infer out simple rules which otherwise were buried in a matrix of complex probabilities.

Have you ever visualized relationships in a market basket analysis? If you did, what algorithm did you use? Did you find the article useful? Did this article solve any of your existing dilemma?

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Five Seo Tips For Product Pages On Ecommerce Websites

Aimed at capturing a greater share of higher converting ‘long tail’ traffic

It’s surprising how often ecommerce sites neglect this dimension of SEO.

People tend to be distracted by the high volume head and middle keyword terms and forget to pay attention to the crucial keywords pertaining to the long tail.

Many sites succeed in capturing a fair amount of long tail traffic without paying particular attention to it. Nevertheless, it’s still important to capture as much of long tail traffic as possible because long tail traffic converts better than head term traffic.

Tip 1 – Duplicate product content

Serve products on a single URL whenever possible.

A single URL may not be achievable because products might occupy multiple categories, giving them alternative URL directory paths. For example;

A diesel denim shirt would be best served as

instead of sitting in alternative categories where duplication can occur, such as

and

In this scenario, it’s best to use canonical tags (that’s another post in itself).

A duplicate content problem may also occur if there are multiple variants of the same product; for instance, when a product comes in different sizes and/or colours. A couple of options to combat this problem are:

Pros– Easier to implement, lessens duplicate content riskCons – Products aren’t as optimal for variant specific search terms

Serve on unique URLs – a specific page per product variant allows you to optimise for each colour, or other product variant. This method still carries a duplicate content risk because products are still too similar despite some differences, such as colour and size.However, you can still avoid triggering duplicate content filters by producing a unique product description for each variation to more strongly differentiate each page.Pros– More optimal product pagesCons – Difficult to administer / come up with unique descriptions

Tip 2 - Product naming conventions

Make sure SEO is ingrained in the build and considered by everyone who updates the website. It is particularly important to include the admin people who add new products to the database.

It is common practice for product names to be re-employed in titles, headings and other key SEO areas, so product names must be created with search users in mind.

For example, a dress colour may be described as ‘cobalt’, a description unlikely to reflect most of the search terms searchers use when trying to find a ‘blue dress’. Changing the dress colour to ‘cobalt blue’ is likely to support optimisation of all sorts of relevant long tail search terms.

Instil a set of characteristics that must form part of the product title; an example from ‘fashion’ might look like this;

Tip 3 – Product schema

Mark-up your products correctly using the schema found here.

You can also include review schema and breadcrumb schema.

Tip 4 – Product templates

Apply the following best practice elements to your product template:

Unique Title

Unique Description

Breadcrumbs

Optimal heading

Friendly optimised URL’s

Unique product descriptions

Optimised alt tags on images

Tip 5 - XML sitemaps

Ecommerce sites generally have a large number of products, and therefore URL’s, within the site. It is vital to get the products indexed to capture the long tail. So, for SEO it is strongly recommended ecommerce sites submit all product URLs using XML sitemaps.

You can split your sitemaps up into the different categories and then bring them all together using a sitemap index file.

Here are some useful links to help you create search engine friendly sitemaps.

Once you have created your sitemaps, upload them to your domain and submit using Google webmaster tools.

With thanks to Bloggers for use of the image.

Will India Become A Cybersecurity Product Hotspot In 2023?

Companies and governments all over the world are increasingly interested in investing in cyber resilience making India a cybersecurity hotspot

According to computer security experts, India is quickly becoming a talent hotspot for the global cybercrime sector, owing to delayed recruiting in the traditional software industry, the attraction of easy money and a lack of law enforcement. Hacking into computer networks and creating malware are among the services outsourced to cyber-mercenaries in India via underground markets. Botnets – hacker-controlled machines used to conduct crippling assaults and shut down websites – may be rented for as cheap as US$2 (Rs 125) per hour. After establishing itself as a worldwide hub for IT services and goods, India is on course to become the epicentre of the cybersecurity product industry, with increased occurrences of cyber assaults during the pandemic that is fuelling this expansion. According to a recent report by the Data Security Council of India (DSCI), the national industry body on data protection, the number of Indian cybersecurity product firms has increased to over 225 in 2023 from over 175 in 2023, with revenue increasing to over $1 billion in 2023 from $275 million in 2023, representing a CAGR of around 39%. Between 2023 and 2023, the number of people working in the Indian cybersecurity product industry increased by 25%, to around 18,000 people. Cisco, CrowdStrike, Lucideus, FireEye and Symantec, among others, have important R&D facilities in India, and indigenous cybersecurity businesses are experiencing growing business from both local and foreign firms as the epidemic pushes demand for cloud usage, remote working technologies and cost optimization. “It’s heartening to see that 63 percent of the [cybersecurity] systems studied have AI-ML capabilities, 78 percent are cloud-ready, and firms in specialty fields like Quantum and Blockchain are rethinking traditional cybersecurity stacks,” said DSCI CEO Rama Veda Shree. According to the DSCI, over 20% of cybersecurity companies were founded in the previous two years, with Bangalore, Mumbai/Pune, Delhi NCR, Hyderabad and Chennai serving as the most important hubs.While the total amount raised in the previous four years has been roughly US$490 million, fundraising has increased by 88 percent year over year from 2023 to 2023. As the complexity of cyber threats forces organizations to tighten IT security, Lucideus, a cybersecurity start-up headquartered in Palo Alto with R&D in Bengaluru, saw top-line growth of about 250 percent in the October-December quarter. According to Lucideus’ founders and CEO, India’s standing as a global engineering powerhouse, a strong national digital vision by politicians, and the existence of institutions with extensive research skills make the country a worldwide hub for cybersecurity R&D and a gateway to Asia.According to the research, India accounts for 63 percent of global cybersecurity product sales, with the United States coming in second with 16 percent. While the BFSI and IT industries provide the most revenue, healthcare, e-commerce and manufacturing are developing at a rapid rate in the aftermath of the epidemic. According to the research, India accounts for 63 percent of global cybersecurity product sales, with the United States coming in second with 16 percent. While the BFSI and IT industries provide the most revenue, healthcare, e-commerce and manufacturing are developing at a rapid rate in the aftermath of the epidemic. In terms of the predicted growth in the Indian cyber security products market, data protection and endpoint security would rise at a faster pace. The adoption of linked devices, bring your device (BYOD), and Internet of Things (IoT) technologies is expected to expand in the endpoint category. As more businesses utilize digital technology, network security tools are becoming increasingly important. The market for network security products in India is expected to grow at a CAGR of 15.3 percent from USD 257 million in 2023 to USD 394 million in 2023.Incident response and security testing services are projected to be the main drivers of demand in the Indian cyber security services industry, according to forecasts. Security consulting services, which comprise cyber security strategy planning, policy formulation, creating security architecture, and other services, are predicted to increase at a CAGR of 12.2 percent over the next three years, reaching a market value of USD 157 million by chúng tôi India, the market for security testing services, which is one of the fastest-growing, is expected to expand from USD 201 million in 2023 to USD 325 million in 2023. By 2023, the market for security testing services will be worth a million, with a CAGR of 17.4%.

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