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Why SaaS Product Management Is Crucial

Product management plays a major role in defining UI/UX, product usability, features, and vis-à-vis the success of a SaaS business. Project managers often lead the baton to strive and enhance user experience, the product development team follows scrum to achieve milestones, and the design team iterates solutions and makes changes if needed. All such departments collaborate to deliver the product on time.

All such efforts are clubbed to provide a cohesive product experience to customers, making way for product management.

Product management involves making multiple iterations of SaaS products, which is responsible for increasing the project’s scope. One research found that a skilled product manager may boost profitability by as much as 34%

In all, product management and customer-led product development are crucial to the success of SaaS businesses when product-led growth (PLG) is a must for SaaS businesses.

What does SaaS Product Management Include?

Product management is a complex process that includes the product operations’ who, what, when, where, and what. The project manager gets into the nitty-gritty using a product management platform to get all possible answers that enhance the product. 

SaaS businesses must ensure their employees are pulling in the same direction as that product manager’s vision. 

Typically, it involves —

1. Leveraging market study: Successful product management aims to make the product market fit and meet customer requirements. Therefore, it includes marketing research based on the SaaS business audience and refining products that enhance UX.

2. Brainstorming product ideas: A SaaS product needs a vision that defines the product. Project management involves discussing ideas that open ways to achieve them, assigning responsibilities, and offering scope for innovation.

Once done, the product manager oversees idea management, where the team brainstorms the new product (or features of an existing one) based on the idea. If there are existing ideas, the focus would be on improving them.

3. Building bonds with partners: Some SaaS companies have customers who work with them to use their software on a large scale. The scope of product management here involves collaborating directly with such groups and building strong ties for recurring business.

4. Creating a SaaS product strategy: This involves planning and segmenting the development phases based on the product’s complexity and breadth. The product update tasks will follow epics, user stories, and features in a secure iterative development methodology.

5. Enhancing collaboration: Plenty of agile SaaS organizations work collaboratively to design and implement strategies for early delivery. Therefore, product management also includes constant refinements in collaboration across different departments.

Why is SaaS Product Management Crucial?

A report by Allied Market Research estimates that the worldwide SaaS market will expand from its 2020 valuation of $121.33 billion to reach $702.19 billion by 2030. This means a compound annual growth rate (CAGR) of 18.82% between 2021 and 2030. Therefore, a product management strategy is necessary to improve operations in an increasingly challenging business environment. 

Product management is a pivotal part of the SaaS business as it helps provide a better customer experience, ensure a sustainable growth plan, and integrate sales and B2B SaaS content marketing with product support to take the business a notch above. The marketing element would also include creating effective communication copy for different media. It may require a business to hire a professional content writer who can create engaging content for its audience. Once done, comprehensive product management involving experts across verticals can drive desired results.

In all, here are some of the reasons that serve testament to why product management is important.

1. Match customer expectations

Getting customer input and implementing feedback is key to successful product management. This ensures innovation and creativity, which keeps SaaS businesses ahead of the curve. Therefore, product management can help tap into user expectations and help design goods with the consumer in mind, thus offering them unmatched services. 

Product management helps the manager to find a product market fit by understanding customers’ requirements using market research, cohort analysis, creating a user persona, etc. Once identified, everyone implements them into the company’s products or services.

Businesses primarily use project management while launching new software or refining an existing one to monitor performance. Moreover, it helps collect and implement feedback while paving the way for future research and development.

2. Leverage data for product strategy

Both quantitative and qualitative data play a role in good product management. Begin with measuring the product’s usability which helps improve SaaS product metrics (more on it later).

With data at their disposal, project managers can make better product-related judgments and provide stronger proof for those choices. Data-driven project management makes way for performance measurement, tracking the number of relevant contacts a product manager has with clients each week, driving productivity, etc.

3. Creating a product roadmap

SaaS businesses yearn for a long product lifecycle. Therefore, maintaining a market share demands a well-defined strategy. Product managers draw up a detailed plan for the product area to free up resources for R&D, facilitating goal completion.

Such groundwork helps create a product roadmap — including customer feedback, leveraging available resources, defining product update timelines, etc. This ensures effective resource utilization while catering to customer expectations.

4. Intelligent product management improves its conception and development

Prioritizing customer satisfaction requires SaaS businesses to consider in-depth details of their users at every stage of development. Therefore, knowing how users feel after using the product is crucial since it impacts their loyalty to the brand.

If managing a portfolio of products, B2B SaaS businesses may leverage such connections when a product is dwindling in the life cycle.

5. Improves sales strategies

Every SaaS business thrives on effective sales techniques. It is the key to expanding the customer base and selling more products.

Pivoting resources to product management enables the sales department to streamline and become more productive as it gets a stable platform to build new strategies. 

Sales teams drive efforts based on the collected data about customer demands, behavior, and trends. Such data can help develop sales arguments that matter to your audience. 

This will help the sales team create meaningful connections with prospective users and simplify their buying decision.

6. Tracking for metrics

The most important aspect of product management is metric tracking. It is necessary to analyze what happens after the product is launched in the market. 

Here are some key metrics that SaaS product management considers — 

  • Monthly recurring income or other financial parameters to determine monthly revenue.
  • User engagement indicators like the percentage of users who stick around after an initial trial period.
  • The qualitative metric involves gauging product success based on the frequency of user interaction.
  • User satisfaction indicators, such as the net promoter score, quantify the proportion of happy consumers who would suggest the product to others.
  • Key stats indicating the percentage of consumers canceling their subscriptions after a certain time frame are known as “customer churn rate.”
  • Determine the cost per acquisition, where businesses may see how much it costs to bring in new customers.
  • Measure customer lifetime value (LTV) to determine how much a customer is worth to a SaaS business over their engagement.

Wrapping up

Many B2B SaaS businesses are recognizing the significance of product management. Ultimately, it helps get an edge over the competition, ensures operational efficiency, and provides a better user experience.

Efficient product management helps drive better revenue, redefine advertising messages, update product features, enhance customer support and improve finances. It works as a central repository for product and market data. 

Running a successful SaaS business needs considering a range of factors, including users, the industry, technology, rivals, platforms, press, experts, market dynamics, etc. And that’s where product managers can create a difference. They run the show by keeping all activities in tandem.

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Developer’s Guide to Building AI Chatbot

Chatbots are ruling the marketing automation game. 

Studies show that more than 71% of businesses are planning to adopt chatbots in their businesses. 

And although they are increasingly gaining popularity because of their capabilities of streamlining core business processes, it is still their easy buildup that takes the cake. 

However, to truly leverage the chatbot technology, knowing the basics isn’t enough. 

Especially for developers. 

Because there isn’t just a single way of building chatbots. You either build them by a rule-based approach or an AI approach. 

In the former approach, the developer writes the rules for the system. Whereas with an AI approach, a massive amount of streaming data is used and the chatbot learns with each interaction. 

For an AI chatbot, there are several steps involved in between that the developers need to pay heed to. 

You also need to understand the technicalities of NLP (Natural Language Processing) engines, implementing design, adding integrations, and testing the chatbot to ensure accuracy. 

Let’s take a look at this process in detail. 

Steps to Building an AI Chatbot

Conversational Design

Before proceeding with the design, outline the goals and motives of the chatbot. Ask yourself: 

  • Why are you required to deploy a chatbot? 
  • What is the end goal of the chatbot?
  • What NLP engine are you going to require?
  • What will the flow look like? 

Answers to these questions will help you outline the conversational flow of your chatbot. They will help you understand how the chatbot will represent your brand, what tone you should adopt, what fonts you should use, and what personality to give to the chatbot. 

What makes conversational design challenging with AI chatbots is that you’re not simply making a decision tree on a bot-builder. In the case of NLP engines, you’ll have to start by defining the intents, entities, and responses. It will require you to brainstorm and get creative. 

Some of the top NLP engines in the market are:

  • Dialogflow
  • Luis
  • IBM Watson Assistant 
  • Amazon Lex
  • Wit.ai

The components of an NLP engine include: 

Intents

‘Intent’ in an AI chatbot is the core problem of the user. For example, for a movie theater, some of the most common questions that a visitor might have are related to timing, location, and pricing. These form the different intents for a movie theater chatbot. 

Entities

Entities comprise the objects of the conversation. It’s what breaks the intent to extract specific pieces of information from users. For example, the entities in our example would be the name of the movies and the intent would be the timing. 

Responses

This is the response to the different intents and the output that is aimed to satisfy the user intent. For example, what will be the answer to the question “What is the movie timing for X?”, or “What is the ticket price for the movie Y?”. 

Based on your customer queries, you’ll have to identify multiple intents and add responses to each one of them. This is also where you would want to add flavor to your bot conversations. Although the replies will be automated for every user, you can ensure that the copy isn’t bland. 

You can address every user by their name and also take the help of a content writer to draft a compelling chatbot script that engages the user. 

You can also make use of the marketing data to understand the ideal visitor and decide if your bot should be formal or informal, quirky or professional, etc. These factors will ultimately give your bot a personality that resonates with the overall brand image. 

Chatbot Development 

If the NLP engines are the brain behind the chatbot, the chatbot platforms are the body. This is the process where you’ll actually be integrating the NLP inputs on a no-code bot-builder. 

You can also build a chatbot framework using programming languages. But building it on a platform will save you the hassle of coding and hasten the process. 

To build an AI chatbot on a chatbot platform, you’ll need to ensure that the chatbot pricing plan provides integration with NLP engines like Dialogflow or IBM Watson. Some of the top examples of no-code bot-builders are:

  • WotNot
  • Landbot
  • Ada
  • Ubisend 

With a visual drag-and-drop interface, You can start by developing a rule-based flow for simple questions. You can then add an NLP integration by calling an API to the respective NLP engine.

Chatbot Testing

One of the most distinguished qualities of an AI chatbot is that it will constantly learn. The engine is such that it may or may not get the responses right in the first go. It will self-learn with each interaction. This makes testing a very essential part of AI chatbot development where you can keep training the bot to improve its accuracy and fix errors. 

To improve the chatbot programming skills to better understand the customer intent, you’ll need more training data to input values that chatbots need to process. Check all the manner in which users are asking the questions that the chatbot isn’t processing. You can use this data to ensure that your chatbot provides answers in every scenario. 

You can also test the chatbot via RPA, security testing, and UFT testing or leverage tools like: 

  • Botium
  • Zypnos
  • TestMyBot

After testing the bot, remember to implement the changes in the bot. You may come across various new training phases that ultimately have the same intent. You’ll have to include them in the bot to enhance the efficiency of AI conversations. 

Conclusion

AI chatbots are all about nailing the intricacies of human conversations and replicating them in a bot. The development of an AI chatbot becomes much easier if you have a holistic outlook of how people interact and how you can teach your AI to do so. 

Once you get that right, you have a range of options using which you can build an AI chatbot. Just take into account your business objectives and possess the basic technical know-how of constructing a chatbot and you’re good to go.

The post Developer’s Guide to Building AI Chatbot appeared first on noupe.


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