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AI in Mobile Development

How hard is it to develop an AI app? – In the realm of AI, it is a constant journey and not a destination. Indeed, AI developers and experts are on a mission of solving the most complex problem – human behaviour. They are on a path to study patterns and produce results that a human being would most likely exhibit.

In the making of all of this fabulous innovation, what kind of challenges does an AI developer face? What are the hindrances in their role? Does AI Development manager approach in a responsible manner? To answer many such question lets dive deep into some of the stories of AI development.

‘AI – Opportunities’ in mobile app development

AI is kind of magic wand to its innovators, true to its nature of being complex it hosts a bunch of opportunities’ for developers to explore the world….

Voice Enablement Helps in understanding customer better and delivering the best

How often have you called up customer care to complain when the internet is not working or DTH not working? The first thing they ask you is – what kind of problem are you facing? While at times the problem is simple, many times the executives try to know the exact steps to reach a particular problem. While manually saying click this, click that could help, voice recognition or voice enablement allows developers in identifying the exact process that was followed.

As the user says OK Google on his phone, followed by instruction check new emails or the weather or the best deal for iPhone, it helps developers in understanding the behaviour of the customers. The kind of apps they use most, what are the instructions provided, what kind of instructions not working. The voice input also helps in understanding customers expectations from an app. I remember when my nephew instructed Google Home “You are useless,” the answer came in was I am sorry to disappoint you, and I would let my engineers know about it.”

Simplifying Complex needs

The most exciting opportunity for an AI app developer is about streamlining complex processes and workflows. Well, indeed otherwise how would the language translation work out? Or how could a chatbot help in resolving human beings technical problems? Or could you fathom of any human being going through thousands of lines of log to look for something suspicious? Or how about commanding Voice assistant to locate the best restaurant near you serving Mediterranean food?

All these are the needs to structure and present data in the simplified form. Thanks to AI app developer.

‘AI – Challenges’ in Mobile App Development

Well, the aim is to simplify lives but what are the challenges faced by developers?

No Standards tools and languages

While Google has launched some of the projects like Teachable Machine and Google AI tools to let users experience how AI works, it is still a challenge for developers to start off. In fact, Quora is flooded with queries like what are the languages or software used to develop an AI app. Many firms use Python due to the benefits it offers but has its limitations like weak in mobile programming and enterprises desktop shops. Similar is the case for other software languages like – Prolog, JAVA, C++ and LISP programming languages for artificial intelligence research

Lots of data create confusion

However, it’s the data that helps in creating the best AI app; the irony is that its also in a massive amount at times challenging to segregate and structure. With big data buzz and data tracking now a trend, developers at times face a hurdle in putting the data sets in a meaningful way.

The new availability and advancement of AI and ML are causing a revolutionary shift in the way that developers, businesses, and users think about intelligent interactions within mobile applications.

 

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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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