Not just to sanction your loan application and craft the ideal buying recommendation for you on Amazon, artificial intelligence’s latest applications in marketing is letting online businesses optimize their campaigns almost real time based on feedback or sentiment analysis tools. But one of the greatest blessings of AI has been with regards to targeting audiences better and generation of quality leads. In other words, predictive analysis is helping improve conversion rates and thereby reduce marketing costs, solving the biggest and most expensive problem for digital marketers which was-poor audience targeting. Here we take a look at why marketers need to get on with AI as soon as possible and why indeed according to a study by PwC, 72% marketers believed, adapting AI was a ‘business advantage’ and those who have not adapted, may already have fallen behind the curve.
AI in marketing helps you answer these basic questions better:
- On discovering your product, how likely is a consumer to buy it?
- How does your consumer feel about the product? Is he liking it, not liking it, common reasons for either negative or positive behaviour.
- Can you map the psychographics like interests, habits, preferences, propensities, age etc.
One of the most widely discussed advantages of artificial intelligence in digital marketing has been its skill to leverage customer data and applying machine learning to predict customer behavior, thereby improving the customer experience right from searching for the right goods to buying them and then having them come back to the brand.
Better Audience Targeting
This partly answers our first question-What is the consumer of your ad buying? Before the advent of AI, digital marketers would launch a campaign and would not really know much about who was watching those ads and how were these ads being received. The ads campaigns would suffer from poor click through rates or poor quality lead generation. For example, with reference to Facebook ads, you are often shown ads of products that you recently purchased or of hotel reservations when you have just booked a hotel and this means total loss of the ad spend. Here’s where Big Data has revolutionized audience targeting. Big data is the application of machine learning to aggregate and augment large sets of data with minimal human interference. The outcome is, this helped marketers reach the right audiences at the right time.
How does this work? Let us consider an example.
Artificial intelligence systems like IBM’s Watson or Microsoft’s Azure have algorithms that allow the system to sift through reams of data to bring out meaningful psychographs based on parameters like interests, email clicks, age, location, shopping habits, preferences among other things.
High Quality Results
Another major problem that has been facing marketing teams is the poor quality of lead generation. You may run a campaign for a product for men working at mid management levels but the leads you get may be from interns or fresh graduates, which may not be of any relevance to you. The customized algorithms of the AI systems mentioned above scores audiences based on machine learning through lead scoring tools, there by segregating them to give you a clearer picture of the quality of your leads. This then makes it easier to optimize your campaigns almost real time to maximize sales or sales targeting.
How does AI help lead generation?
Artificial intelligence is the process of creating systems that enable machines to understand, learn from patterns and interact with the environment to make decisions much the same way as humans would. In other words, we are preparing our systems to not just respond to the customers in the most efficient way that would enhance their interactive experience with the brand but also help in large scale data collection and analysis of the patterns that enhances audience targeting and lead selection. AI systems are advanced enough now to identify major themes and patterns in consumer behavior by way of interpreting natural language, read sentiment, and draw inferences from customer’s interactions on social media platforms like Twitter, Reddit, Instagram or Facebook or email or on ecommerce sites like Amazon and Flipkart. All of this makes lead analysis a fairly comprehensive task based on logical algorithm that can significantly improve the media buying experience.
Simplifying and Automating The Process Of Data Collection and Response
A simple example is the runaway success of viral agent engines or chat bots run by AI, installed by many brands on their websites. The algorithms use machine learning models to allow the chat bots to interpret human language and respond accordingly on real time basis. These AI bots can process information available all over the net to come up with the most well analyzed response. The chat bots can take in queries, discern intent, predict behavior, help deepen psychographic information collected, sometimes more efficiently than humans. Similarly, AI driven sentiment analyzers can give you a real time feedback about the brand, campaigns, customers’ buying experience, retention of the customer or even potential buyers. This helps brands to work on the negative information about product features that are not working with the customers. At a time when some industries like e-commerce sites are playing for the margins, employing AI can significantly impact the output of marketing divisions. But how accurate are they? The efficiency of these systems, based on the machine learning models that they use, can range from 90-95%. For example, the latest in marketing is the Deep Forest Decision tree machine learning model that has been working magic in sentiment analysis and claims to have the highest accuracy in this area.
The fundamental principle of predictive analysis is the ability to make sense out of data, that is, to identify patterns, remember them and predict events or behavior in the future. It is no more as simple as preening over historical searches, but going deeper into preferences, behavioral dispensations, age, and propensity to consume. Predictive analytical systems have algorithms for predictive lead scoring based on lead records, social information and behavioral data. For example, many omni-channel marketers or multi-channel marketers running campaigns across platforms use predictive marketing for simulation exercises before sending out the communication to millions. The simulation assesses the estimated outcome of the campaign, based on which it can recommend the changes in the communication which could be a change in the subject line of an email or change in the content or toggling around the Call To Action buttons among other things, for optimum results.
Predictive analysis has also resulted in real time optimization of brand campaigns based on the feedback coming in, through features like sentiment analysis. For example, if a campaign running on YouTube is not working out with customers, you can be informed by this input almost real time and make it better to suit the right audiences.
Doing Away With Irrelevant Content
There is nothing that puts a customer off like being bombarded with irrelevant content, which is set to be fixed by AI. Following the same rules of the game as discussed earlier, when marketers know how to reach the right audiences at the right time, and know what the customer is looking for and how likely is he/she to engage in buying, the marketers can then craft their content messaging in a way to encourage certain behavior, educate them on theme the customer is looking to know more about and thereby eventually convert and retain the audience. With machine learning, it is also possible to create template content for the targeted audiences that they find relevant and consumable, revolutionizing the content marketing space.
There are AI systems like PERSADO which help you optimise content by helping you with words and phrases that are more likely to work with a certain consumer and have him/her buy your service or product.
Based on your browsing data on multiple devices, systems will analyze millions of behavioral data points using probabilistic modeling to map a use on multiple devices and then suggest ways to personalize ads for device targeting where maximum return/engagement can be expected.
Let us look at this example of California based Drawbridge which uses AI and machine learning to enable brands to personalize ads based on data mined across multiple devices of a customer, and then tying it back to the buying behavior of the customer. For example, based on the information you were consuming on your laptop, the ads you see on your tablet would stand customized. Secondly, if you bought something on line or even offline, Drawbridge will let the advertiser know what ad campaign prompted the purchase.
Better and Faster Decision Making
With minimal human intervention, AI can take care of simple decision making actions that are based on logic. For example, chat bots are set up to take in representational responses from the customers on factors like, what product feature did the consumer like and which one did they not, was delivery on time, if the quality of the product good, bad or worse, or if the customer is likely to return or not. This can help design more consumer-centric campaigns.
An important point for marketers to know is how AI has fired up search engines to give the consumer exacting choices. This not only helps you find the right information on Google but also on various ecommerce sites and online businesses like Amazon, Flipkart or find the right movies on Netflix or movies you are most likely to watch. Big data solutions can help marketers be guided by the advanced search patterns thrown up by these AI systems. For example, technologies like Elasticsearch allow ecommerce stores to have search engines that are more nuanced than just matching keywords. There are softwares to detect commonly misspelled words and can correct them by context.
This helps automate the process of buying media by targeting specific audiences and demographics. In other words, programmed ads are placed using AI where the bidding for inventory is on a real time basis for either online display, mobile, or social media ads or even ads on TV.
But like with every scientific advancement, there are sections of people who continue to be skeptical about the scope of role that will be played by AI in marketing. Some say, AI at the end of the day can at best only be integrated into marketing campaigns and its utility will really still be driven by highly skilled resources for the most part. It will also rely on human intervention for creative use of the analysis in the best interest of the brand. Can AI driven campaigns be as creative as human designed campaigns? Or do they need to be? A question that only time can answer. Another issue that has been at the center of the debate around Big Data is that of privacy of the consumer. How will brands tackle this crucial question as they get prying around your desktops and mobile phones, to make you the best campaigns and recommendations like Netflix or Amazon have already been doing.