Today’s business is ruled by data and data-driven understanding. How you understand the data and interpret the data into business decisions has a direct impact on your business conversion and growth. For a more precise understanding of data, today we have artificial intelligence (AI) and Machine Learning (ML) technologies on our side. No doubt, these technologies that mimic human reasoning can positively transform businesses and their strategies.
We need to understand the impact of AI and ML technologies have in shaping our understanding and capability to interpret data.
Any business understands the importance of communicating with customers individually. Yes, thanks to the very nature of digital interfaces that opened up the tremendous scope of individual preferences and choices, your business communication must take into account the preferences of individual customers. The increasing importance of addressing individual choices for business conversion has forced many companies to focus on data-driven personalization measures.
Not only the large businesses but also the startups and small businesses increasingly understand the importance of having access to the relevant data for meeting the needs of visitors. AI can dig the available user data deeper and fetch out relevant patterns and insights that can be further utilized for data-driven decision making personalization. AI can also help to scale up such personalization efforts for every individual user.
A superb example of how AI can allow personalization in business operations can be found in the case of Starbucks. The global coffee chain brand designed 400,000 different types of emails created based on the data of individual preferences, tastes, and choices. Such well crafted personalized communication can help brands to create more engaging communication and conversation for business brands. The brand actually AI to decipher the volumes of data corresponding to customer preferences and choices.
When it comes to smaller businesses and little startups, such as AI-based data collection and data-centric personalization may be a little expensive. But small businesses can embrace similar approaches to create very specific data-oriented marketing campaigns with short duration to boost business conversion and customer engagement. Such AI-powered data-driven campaigns can also help to lift the brand image of any company.
For the B2B segment, business conversion highly depends on generating new leads. The B2B companies also need to depend heavily on tracking contact data and reaching out to them effectively through lead generation funnel. Most marketers agree to the humongous range of challenges B2B-based businesses face in doing this. This is where AI can play a great role in streamlining the process of lead generation through intelligent automation.
Artificial Intelligence (AI) powered lead generation and contact tracking solutions have the capability to make an analysis of the customer base along with important trends and emerging patterns. These trends, patterns, anomalies, characteristics, and various attributes can deliver important insights for optimizing websites and web apps. Thanks to AI-based optimization insights a website can venture to use better programming language, tools, features, and UI elements to generate more leads.
On the other hand, AI-based business data analysis can work hand in hand with big data analytics. This sophisticated and highly incisive approach to data utilization can easily help to discover ideal customers for a business. The interactions of users on web pages and corresponding data can be analyzed by B2B brands with the help of AI tools to produce the most relevant as well as actionable insights.
To make things easier for the businesses, AI, and machine learning technology for such analytical activities are now spotted in most of the leading analytics solutions across the spectrum. Simple Google Analytics can also offer highly result-oriented and precision-driven reports. Such technologies can easily know about the shortcomings and loopholes behind the decreasing motivation of traffic and readings of business conversion fallout.
There are also great tools like Finteza that uses AI technology for monitoring website traffic on a continuous basis besides checking other crucial issues and irregularities. These tools can also improve your data security since by detecting bad traffic they automatically point out the vulnerabilities in the web app.
Poor web traffic often results in DDoS attacks, manipulation of website cookies, and hackers or malicious programs impersonating computer bots. An AI-based lead generation solution can also reduce these security vulnerabilities.
AI optimizes the scope of personalization in a data-driven manner and that is portrayed as the principal useless of AI in dealing with data. But AI is also highly effective in optimizing the web design and improving the user experience (UX).
AI achieves this optimization and improvement by analyzing user behavior and interaction data and user feedback. Machine learning programs particularly can play a very effective role in learning from user behavior and adjusting various interactive elements accordingly.
AI and ML programs running behind the scene basically collect a lot of data corresponding to real user behavior so that real-time feedback about shortcomings and improvement needs can be communicated to the business owners. An ML-based program can also bring instant tweaks to the UX attributes for better engagement.
Another important thing in this respect that needs to be explained is the great role of AI in improving the efficiency of A/B tests. In the A/B testing process the AI and machine learning can deliver the most important insights about user demands and preferences to take further enhancement measures for UI and UX.
The most important aspect of AI in making an impact over A/B testing is that it leaves no scope for vague assessment or guessing. The data-driven insights guiding the A/B testing is more possible now as website cookies provide clear insights concerning user behavior.
Based on such insights the landing pages can reduce form fields as per user interest and preferences.
Biometrics data corresponding to direct interactions with a web app can help developers and marketers with a lot of actionable insights. There are many advanced online services right now available in the market that can help to understand and decipher website data.
Biometrics data coupled up with AI and machine learning technology opened up new possibilities for improved user experience.
Among these available services for data interpretation mostly take the help of a combination of both artificial intelligence and machine learning. These sophisticated solutions can easily track the eye movements of the users.
In addition, some of these services can also track facial expressions to assess user responses in different contexts. These services can extract the most organic kind of user data and generate the most valuable insights that can be used for UX design and performance optimization of websites.
As the trends stand, from this year onward the AI and ML-based data analytics and data-centric optimization of business apps will have more dominance. Thanks to these two technologies, there will be the least guesswork for all design, development, and optimization decisions.