8 Ways Artificial Intelligence Can Assist You With Web Analytics
AI is being utilized in web analytics to improve accuracy, save time, and give vast amounts of in-depth data. But what exactly do we know of AI, and how can it improve web analytics? Sam Molony, part of the marketing team at Mailshake, will discuss how AI is used in web analytics and the emerging trends you should know. You’ll also learn eight ways you can use AI to assist your web analytics analysis.
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part of the marketing team at Mailshake
You analyze website data to understand user behavior better and optimize web pages for user satisfaction through web analytics. Web analytics has four main stages:
- data collection;
- data processing;
- key performance indicator development;
- online strategy formation.
You can collect data through log file analysis or page tagging. Then, you can quickly form metrics for the collected data by creating ratios out of the counts you obtained from your web server. Some vital metrics you should notice include unique visitors, bounce rate, exit rate, and pages per session during your web analysis.
Your next steps depend on your objectives, so you need to figure out where you want to be and how fast you want to be there. Then, you can implement changes to that effect.
You can see how tedious it would be if you had to go through the entire web analytics process depending solely on good business acumen. The processing stage, for instance, can be tricky since you’d have to analyze Java codes and then collate them into meaningful statistics.
Luckily, AI can efficiently work through the web analytics process once it is taught. It can benchmark your website’s performance and track your customer behavior using Key Performance Indicators (KPIs) infused with your business model. There are several ways in which AI can assist in web analytics. Let’s take a look at a few.
According to the Gartner Analytic Ascendancy model, web analytics has evolved from descriptive analytics through diagnostic analytics to predictive analytics thanks to AI.
AI systems can analyze data sources at a go and proffer conclusive predictions from them. That allows site owners to predict the outcomes of certain products, customer preferences, and the best marketing channels to use.
For instance, Hopper — a travel company — utilizes AI to predict the price of flight tickets for users who look to book tickets to certain areas. The picture shows the process.
After Hopper’s welcome home screen, you have to select your preferred dates and routes for the flight. Upon selection, Hopper’s bunny informs you of the current ticket price and gives you projections of how the prices might change over time.
Surprisingly, Hopper has reported a 95% prediction accuracy since implementing AI into their analytics. This rate has heightened customer trust in Hopper services as they save an average of $50 on flight tickets.
You can, therefore, use AI to get conclusive predictions that boost your company’s prospects and push you ahead of your competitors.
For instance, AI can project the pros and cons of launching a specific landing page. It can also analyze competitor data like pricing changes and content strategy and predict which competitor moves can mean trouble for your business. You can then make adjustments based on those predictions and stay ahead in your niche.
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AI can unify data from multiple streams and analyze it. AI’s scale and speed allow it to pick out threads of customer data, even hidden ones, group them into identifiable sections so that you can easily compare and draw constructive conclusions.
IBM Watson, for example, provides users with AI tools that unify product or service feedback from multiple sources into one platform. Applying this to web analytics, you can bring your website data together on one channel. That will enable AI to learn the patterns of visitors and project the likelihood of a prospect becoming a full-fledged customer.
When you have that type of data, you’ll know where you should focus your marketing efforts.
Did you know that 91% of customers are more inclined to purchase products or services from businesses that personalize their recommendations? Customized recommendations give your visitors a unique experience, increasing their engagement and your website’s conversion rates.
AI combs through customer web data across platforms and analyzes it to present you with relevant suggestions for your customers. You can exact these suggestions to give your customers a personalized experience.
Alternatively, you can teach an AI system to automatically implement specific suggestions that offer a better customer experience without reviewing them. Many brands such as Thread already use AI this way. Thread asks customers to answer questionnaires on their preferred style. The Thread AI then uses these to make personal recommendations for them. Here’s a look at their questionnaire.
The customers can then vote up or down depending on the recommendations they like. That allows the AI to learn their preferences at once. This is particularly powerful when used with techniques such as contextual advertising, where your site displays ads based on customer behavior.
According to TechRepublic, medium-sized companies receive about 200,000 security issues in a day, ranging from forgotten passwords to phishing and trojan horses. Security issues can be difficult to handle. Thankfully, an AI system can solve these security events.
AI automates data analysis and system assessments, resulting in a faster process of spotting system vulnerabilities before a cybercriminal does. A security team can then fix the problem before an attack occurs.
Alternatively, you can use AI to reduce alert fatigue and help your security team meander through security alerts much efficiently. According to Fire Eye, 35% of security analysts ignore alerts when their queues are full.
Today, researchers have developed an AI system that uses a notification delay technique to decrease the number of alerts health care providers receive and filter the most critical alerts from the bunch. The system goes a step further to detect false alerts so that dedicated personnel can look into them without stressing caregivers out.
You can implement this AI algorithm on your website to detect urgent security concerns and push them up the priority list so your team can work on them first.
Around 60 percent of customers are more drawn to businesses that provide an image relevant to their searches in a local search query. That implies you can increase your conversion rate by pulling up images relevant to your customers’ needs.
AI can help with that. You can program an AI system to detect images your customers interact with and show them when prospects visit your website. That way, the AI system can grab your prospect’s attention, and you can take it from there.
On the other hand, you can task an AI system to determine the images your visitors least react with and replace them with high-performing images to boost your website conversions.
Pinterest, for instance, uses AI to determine images its users like and provide related images. If a user pins a denim top, the AI algorithm combs their system for images with visual similarities and brings them up to the user.
SEO rises and falls on data and analytics. You’ll need to keep observing, learning, and tweaking your website to ensure that your web pages are constantly on the top of SERPs results. There are a couple of ways that AI can improve your website. Here are a few:
- opportunity discovery;
- content creation;
- content optimization;
- optimization for voice search.
By combining digital tools and SEO, you can construct a robust SEO strategy to get recognition and a loyal audience. For instance, if you use these tools and discover that a significant portion of your potential audience doesn’t click through to your website because the content isn’t tagged properly, you can take the necessary steps to rectify it.
You can also employ AI to split-test your meta titles and descriptions to increase your search traffic by 54 percent. In a typical A/B meta title testing, you would categorize various URLs into two groups — a keyword in one group and the other without a keyword.
You would then run split tests to assess the impact of the keyword in the two groups like Etsy did, as seen in the image below.
You can also see from Etsy’s test that there can be many variations in a split test. These variations demonstrate the efficacy of potential changes an organization wants to make. So, typically, with more variations, you get more efficiency.
Split-testing many variations manually, however, brings about the cumulative alpha error. The cumulative alpha error is when more variants in your test increase the probability of false-positive results.
You can prevent that by integrating AI into split tests. AI can handle large sets of data variations with a low margin of error. Bidalgo, for instance, has introduced Creative Artificial Intelligence, which allows advertisers to learn more about their clients without having to manually split-test their campaigns.
Bidalgo’s Artificial Intelligence tool evaluates many aspects of the ad depending on the entire client journey. It also compares the effectiveness of various commercials, messaging, and graphics.
Advertisers may be flying blind in many cases because even if they notice a pattern, they have no idea why it is occurring. Artificial Intelligence, on the other hand, can assist advertisers in understanding both what and why they are doing so, as well as allowing them to break down certain variables.
Customer churn is the rate at which customers discontinue consuming a company’s services or goods after some time.
Because customer churn can hurt your business and reputation, it is one of the most critical metrics you should track. It costs five times more to acquire new customers than to retain old ones, which means that you need to do whatever you can to keep your current clients satisfied.
You can use tools that leverage AI to predict customer churn and devise strategies to reduce churn. For example, you can send timely emails to a person that exhibits the characteristics of someone who will stop using your tool.
AI can detect patterns and learn without explicit programming. You can use this system to target online behavior indicators that signal diminishing consumer satisfaction.
Here’s an example of a customer churn prediction model:
The AI system combs churned customer journey history to recognize patterns and determine whether or not your existing customers are happy with your services. The model then creates suggestions for proactive re-engagement.
Alternatively, you can utilize existing customer churn prediction tools to burn through your customer churn. Churnly is one of these tools.
Churnly’s AI identifies patterns in customers’ journeys and determines which factors most likely lead to churn. Their AI then suggests ways by which you can prevent churn and consequently increase customer lifetime value.
One customer service agent can only reasonably handle one customer service problem at a time. AI enables customer service teams to accomplish more, improving the customer experience.
Chatbots, for example, can respond to many questions at the same time. This is a lifesaver for companies with overburdened call centers and long wait times. A live representative can still intervene and resolve the problem if a query is too complicated for a chatbot to answer.
Here’s a chatbot example from Hipmunk.
The bot determines where the visitor is traveling based on their location and then delivers travel deals from that area.
It doesn’t end there. AI can study these customer interactions and predict when a prospect on your website might need help.
AI systems can go a step further and provide pop-up suggestions that offer solutions to these problems before the visitor even asks the question. It’s a great way to push visitors to make a purchase and increase conversion rates.