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Five Ways to Leverage Artificial Intelligence in Web Marketing to Grow Your Business

Five Ways to Leverage Artificial Intelligence in Web Marketing to Grow Your Business
August 24, 2018 Simon Hopes

There’s been a rising interest among web marketing professionals and Search Engine Optimisation – SEO experts about Artificial Intelligence (AI) and how it can be harnessed to increase revenues.

The challenge businesses face when running an online advertising campaign is that they have very little idea about who watches their ads or clicks on them. For instance, if they are running Facebook Ads with a click-through rate of 1% it also means that 99% of their audience isn’t clicking on the ads for reasons known only to them. This leads to a high cost per acquisition and subsequently huge amounts of marketing losses for brands.

Improving Ad Targeting

Artificial Intelligence is helping businesses in improving their ad targeting by reaching out to the right audience at the right time and the right location. AI systems such as IBM’s Watson and Microsoft Azure can comb through vast reams of data such as email clicks, interests, age, location, websites visited shopping habits as well as comments and conversations on social media. Based on this data, customised algorithms can be created to suggest the most relevant audience for displaying ads.

These insights can be used to create incredibly accurate 360-degree profiles of existing and future customers ensuring that you only target consumers who already have the propensity to buy from you.

Generating Quality Leads Using Chatbots

Chatbots can be used for a variety of purposes in marketing, the most important of which is to get excellent quality leads. Chatbots are hosted live on websites and powered by a virtual agent engine.

Natural language processing which can be combined with artificial intelligence enables a chatbot app to comprehend text messages or human speech, including discerning intent. The chatbot can be programmed to understand and respond to online queries and requests. They can also collect pre-sales documents from prospects and gather leads with high buyer intent.

In the simplest application, a sentiment analyzer will tell you if the opinion about your brand or product is positive negative or neutral. With the help of Artificial Intelligence, you can customise a chatbot conversation to help the chatbots respond to a user sentiment. This method results in a more human-like smart artificial intelligence solution which can respond in the most appropriate manner based on the emotions people display in a written chat conversation.

Identifying Opportunities for Improvement

UX experts and web marketing professionals in Sydney and elsewhere can also use this tool and find out what products and features are missing the mark by analysing negative emotions and product reviews. Depending on which machine learning model one uses, the efficiency of the analysis varies from 90 to 95%.

For example, the deep forest decision tree model, which is based on neural networks, is an innovative machine learning model which claims to have the highest accuracy in sentiment analysis

Predicting Quality of Leads

Another use of AI is in predictive analytics. The basic principle on which it works is that a customised system is set up to collect and cleans data. It enables web marketing experts to identify different patterns and predicts the occurrences of unknown future events during campaigns.

The quality of leads can be improved through predictive analytics using predictive lead scoring. An algorithm is created using information such as lead field, behavioural data, social information and demographics. The lead scoring algorithm then creates a formula which automatically buckets your leads so that you can easily identify the most qualified ones. This will help you get much higher conversion rates and reduce your cost of client acquisition.

Accurate Customer Segmentation

Accurate customer segmentation is important to be able to deliver the right messages to intended customers in a timely manner.

Conventional segmentation practices have several shortcomings. For one, they fail to provide sufficient detail which can be acted upon, they aren’t updated to take into account real-time changes in customer behaviour, lack precision and cannot tailor messages to specific groups.

Dynamic predictive analytics is used for customer segmentation and enables web marketing experts to send customised messages to each segment as per their behavioural traits. Through dynamic predictive  based segmentation, businesses can accurately segment customers based on their propensity to take specific action.

Customers are grouped based on the most relevant common predictive characteristics. These are identified for each segment. Segments are dynamic in nature and their characteristics change in real-time as new-data is obtained from customer interactions.  Advances in artificial intelligence have brought this closer to reality, representing a significantly higher level of relevancy within each customer interaction.

Big Data and social media have helped web marketing experts understand  customers better than they have ever been able to do before. Artificial intelligence will draw businesses and customers closer together in the near future. With customers having a better understanding of products or services and businesses being able to deliver relevant messages in real-time to segmented target audiences, Artificial Intelligence is set to become a driving force in the growth of businesses all over the world.