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Create IOT products and solutions – Part 2

In my last article, I have talked about the challenges and oppurtunities of IOT industry. Now let’s talk about building an IOT product and  benefits of it in the market.

xl-2016-internet-of-things-1

How about building an IOT device?

Now let me also talk a bit about the process of building an IOT product. If you are thinking of building an air purifier, or a thermostat, or some smart lighting solutions for home, you are very likely to hit the first stumbling block as to how to go about the whole process. How to get a 3D design for the device, where to go for a prototype design, and how to get the electronics (the PCB part) done, and how to make the device talk and interact with various other devices like your mobile phone, etc.
 What you need is professional expertise in not one particular field, but many diverse fields. If you are a software engineer with some experience with coding, you will know writing software is not that difficult as all you need is a computer, and you could create wonders just sitting in home or office. Building a real, physical thing can be really tough & challenging. Not only it requires varied set of skill set, but also can cost much more to prototype, and test it out.

Steps to follow before going ahead

For the benefit of newbies to the field, I have listed down the steps generally followed in any IOT product development process.

  • Market Research
  • Conceptualization/Ideation
  • Design
  • Prototype (Schematic Design, Layout)
  • PCB Manufacturing
  • Procuring components & assembly of electronic circuitry
  • 3D printing of casing & outer facade of the product
  • Field Trials
  • Redesign & trials if needed
  • Marketing & Mass manufacturing

Loads of data is generated, but what to do with it?

Due to the large number of IOT devices around, it is quite as well expected that they will generate a huge volume of data. Question is how to make best use of the data captured, or how to make your device react to events triggered by actions of other users, or may be from the device owner himself through a mobile application.

Standards like the MQTT, AMQP, etc are the general protocols used for an IOT device or the cloud to communicate with each other. Both of them work on basic principle of publish/subscribe. The two parties subscribe to events, and whenever there is an update, or an occurrence of the event, the subscribing parties are notified.

Providers like Microsoft Azure, ABM, and AWS have all come up with their IOT platforms making it easy to monitor and control remote devices from click of a button. Being on the cloud, it gives IOT the ability to scale. The data being captured in the cloud can be analysed, and trends studied using Machine Learning algorithms and Artificial Intelligence.

Today it is possible to auto update the firmware of an IOT device without requiring any intervention from the customer.

How IOT will drive benefits for users?

Data generated from IOT devices are being continuously analysed and machine learning models are built to help in predictive analytics. Earlier emphasis was on preventive maintenance in industries, and anywhere else where machines were deployed. We used to ensure regular and timely checkups to ensure our machines are always in healthy state. But now with advancements in technology, based on the data captured, our machine learning prediction models can warn us in advance of a possible impending breakdown. A corrective action can be immediately triggered, and the machine is restored to good health much before breakdown.

emergency-notification-iot

Today IOT driven processes paves the way for improvements in existing processes leading to higher customer satisfaction & safety leading to better profits for businesses. Customers delight and an increasing affiliation are invaluable assets to any business, and when IOT is able to help the business achieve that, its relevance will never be in doubt. No wonder Gartner Research predicts there will be more than 20 billion IOT devices by the year 2020.
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10 Analytics Tools to Guide Data-Driven Design

Analytics are essential for informing website redesigns since they offer insightful data on user behavior, website performance, and areas that may be improved. Here is a list of frequently used analytics tools to guide data-driven design that can be applied at different stages of the website redesign process. 

Analytics Tools to Guide Data-Driven Design

1. Google Analytics:

Use case scenario: Website Audit, Research, Analysis, and Technical Assessment
Usage: Find popular sites, entry/exit points, and metrics related to user engagement by analyzing traffic sources, user demographics, and behavior flow. Recognize regions of friction or pain points by understanding user journeys. Evaluate the performance of your website, taking note of conversion rates, bounce rates, and page load times.

2. Hotjar:

Use case scenario: Research, Analysis, Heat Maps, User Experience Evaluation
Usage: Use session recordings, user surveys, and heatmaps to learn more about how people interact with the website. Determine the high and low engagement regions and any usability problems, including unclear navigation or form abandonment. Utilizing behavior analysis and feedback, ascertain the intentions and preferences of users.

3. Crazy Egg:
Use case scenario: Website Audit, Research, Analysis
Usage: Like Hotjar, with Crazy Egg, you can create heatmaps, scrollmaps, and clickmaps to show how users interact with the various website elements. Determine trends, patterns, and areas of interest in user behaviour. To evaluate various design aspects and gauge their effect on user engagement and conversions, utilize A/B testing functionalities.

4. SEMrush:

Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct keyword research to identify relevant search terms and phrases related to the website’s content and industry. Analyze competitor websites to understand their SEO strategies and identify opportunities for improvement. Monitor website rankings, backlinks, and organic traffic to track the effectiveness of SEO efforts.

5. Similarweb:
Use case
scenario: Research, Website Traffic, and Demography, Competitor Analysis
Usage: By offering insights into the traffic sources, audience demographics, and engagement metrics of competitors, Similarweb facilitates website redesigns. It influences marketing tactics, SEO optimization, content development, and decision-making processes by pointing out areas for growth and providing guidance. During the research and analysis stage, use Similarweb data to benchmark against competitors and guide design decisions.

6. Moz:
Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct website audits in order to find technical SEO problems like missing meta tags, duplicate content, and broken links. Keep an eye on a website’s indexability and crawlability to make sure search engines can access and comprehend its material. To find and reject backlinks that are spammy or of poor quality, use link analysis tools.

7. Ahrefs:
Use case scenario:
Research, Analysis, SEO Optimization

Usage: Examine the backlink profiles of your rivals to find any gaps in your own backlink portfolio and possible prospects for link-building. Examine the performance of your content to find the most popular pages and subjects that appeal to your target market. Track social media activity and brand mentions to gain insight into your online reputation and presence.

8. Google Search Console:

Use case scenario: Technical Assessment, SEO Optimization
Usage: Monitor website indexing status, crawl errors, and security issues reported by Google. Submit XML sitemaps and individual URLs for indexing. Identify and fix mobile usability issues, structured data errors, and manual actions that may affect search engine visibility.

9. Adobe Analytics:
Use case scenario:
Website Audit, Research, Analysis,
Usage: Track user interactions across multiple channels and touchpoints, including websites, mobile apps, and offline interactions. Segment users based on demographics, behavior, and lifecycle stage to personalize marketing efforts and improve user experience. Utilize advanced analytics features such as path analysis, cohort analysis, and predictive analytics to uncover actionable insights.

10. Google Trends:

Use case scenario: Content Strategy, Keyword Research, User Intent Analysis
Usage: For competitor analysis, user intent analysis, and keyword research, Google Trends is used in website redesigns. It helps in content strategy, seasonal planning, SEO optimization, and strategic decision-making. It directs the production of user-centric content, increasing traffic and engagement, by spotting trends and insights.

About the Author:

Vijendra is currently working as a Sr. UX Designer at Mantra Labs. He is passionate about UXR and Product Design.

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