There’s no doubt that Artificial Intelligence is the next frontier of marketing. While it’s catching up to differentiate your business from the competition, the future of marketing is already here. Already companies like Google and Facebook make heavy use of AI tools to simplify their ad planning process and generate more revenue for their customers.
1. AI has been making big in-roads in customer service
There are a lot of in-roads that AI can make in customer service. AI can help in-house customer service and understand when a customer is at their wit’s end. AI can also understand the emotional state of the customer and use that to help in a customer service call. In the context of customer service, AI can help with the following. AI algorithms can uncover customer due to context sensitive variables like: how do you like your coffee? If you do not like your coffee, you probably do not like your coffee. But this sentiment has two implications If you like your coffee you will order only the best quality coffee as they usually are. If you do not like your coffee, you likely will order a cup of coffee that you do not really need or one that is really low quality. Customers who do not like their coffee often have low-worth such as their drinking habits or lack of enjoyment in life. They are not special and the service they receive could be given by an 80 year old woman. The simple insight here is that service detection based on context triggers action. Customers can put a management buy/refusal on this. If a customer refuses to drink their coffee, the customer needs to be tuned up. The customer did not put in just a regular order. The customer order was the opposite of what is going on in the store and due to context, a manager needs to learn about the coffee behavior. If a customer does not put in 10% of what is going on in coffee shop, the team need to investigate further. They will follow the behavioral triggers and soon learn that customer orders have been cancelled or the coffee order was impossible. Until that point, the team need to make adjustments such as putting in extra time for coffee the order does not match because customers react poorly and not intrinsically knowing the customer such as previous drinks order.
2. Machine learning and NLP are two ways to implement AI for business
Machine learning and natural language processing are two ways to implement artificial intelligence (AI) for business. Machine learning is a technology that can identify patterns in data and then use that data to make predictions. It’s increasingly being used to improve customer service. For example, machine learning can determine which email customers are most likely to open based on previous interactions with your brand.Natural language processing identifies the meaning of words in text. Then the processing is translated into URL rules so that those words appear as search terms by your customer service tech. Whether already implemented or in the planning phase, here are three tips to deploy machine learning and natural language techniques for faster customer service campaigns: To enhance your bandwith, embed machine learning in eCommerce applications that process orders. To do this effectively, create virtual machine learning environments or virtual cloud environments and run business logic in them instead of locally, as this will reduce latency and therefore increase the speed of processing orders. The ability to run ML will make your eCommerce tech more responsive, create less manual data entry, and allow for better notifications before and after a purchase. To implement these ideas, pay close attention to how many steps it takes to bring a new feature online, versus how intimidating and time-consuming it really is. Begin by creating the digital storefront. Once you’ve established the digital counterpart to your physical one, modify how the website displays the offerings for that digital store. For example, if you already have a physical store, you may be fine adding a new product category on the website, but it’s a lot simpler to add one now than it is to add the category then create an outdoor wear shop in the middle of nowhere your first month. Once you’ve finished with the digital storefront, customize how your automated call-center operates. For example, automated call centers serve as “buy now” or “put your name on it” buttons for online retailers. Their primary responsibility is to move your customer service requests online and automate customer service responses using the businesses’ API.
3. 4 ways that AI can make your company more efficient
Artificial Intelligence is already having an impact on our lives, and it’s only going to be getting more and more advanced. In particular, AI is making things faster and more efficient. We can see this with services like Amazon’s Alexa and Google Home, which are voice activated devices that have the ability to recognise voice commands and respond to them. If you need to phone someone urgently, you can pull up Google Home and ask Alexa to send a text to 999. If you need to buy something, you can ask Google Assistant to do it for you. You can use voice commands to make use of Google’s Deep Brain Learning. And, it’s easy to imagine that voice assistant services is only going to grow in popularity over the coming months and years. With that in mind, I wanted to start building an assistant that doesn’t focus so much on automation and conditioning as it does being genuinely useful. The main idea behind a ‘Benevolent AI’ voice assistant is that it should emulate the type of human interaction that it sees and be motivated to really help you. Intelligent assistants like Siri, Alexa, Cortana, and Google Home are largely focused on communicating with you, using text and voice to get the job done. Generally, unless they are asked to do something incredibly difficult (such as finding a lost item), they usually respond with something along the lines of ‘Ok Google’ or ‘Hey, me and my colleague need to go to the toilet, what’s the time?’ Their success or failure rests on their ability to persuade you that they are genuinely interested in what you need. It’s arguable that Siri and Alexa developed their character over time, but generally they do appear to have an air of superiority to them that doesn’t quite translate to success at the tasks they are trying to do. It’s for this reason that other startups are starting to compete by developing assistants that are more approachable and approachable assistants are generally easier to buy into.
4. 3 ways to integrate AI into your marketing strategy
The best way to integrate AI into your marketing strategy is to make it a feature of your customer experience. When people interact with your product, they should feel like they’re interacting with a human. AI should be a byproduct of a human experience. In 2018, the target audience for AI-powered marketing was composed of 75% B2B companies and 25% CMOs.By 2027, the industry expects that this percentage will reach 80% – though it may take longer than that to see the intended benefits. Established businesses will probably have a better time supporting the growth of AI, as they already have the infrastructure in place to gather, process, and act on data in an efficient and cost-effective manner. AI can address long standing processes that traditionally required too many investment and resource-effort steps, allowing businesses to effect change more easily. AI is also a great way to make your marketing more relevant to your audience. It will allow you to tap into customer insights and personalize your marketing, whereas appropriate contextual data is where AI excels. With the rapid pace at which AI is developing, the industries that will make the most forward progress in AI-powered marketing are those which already have a significant number of their systems in place. The most prominent among these are education, healthcare, transportation, security, energy, and finance. Some of the most visible leaders in the fields of education, healthcare, and transportation have already transitioned their platforms to be fully- or partially-automated and are tackling strict data privacy requirements. And for those in the financial services space, AI has already been adopted to the most advanced levels of a banking organization. These include algorithmic trading, robo-calls and automated broker recommendations, and quantitative machine learning. It’s worth noting that smart-contract technology is still in its early stages, and it requires strict regulatory frameworks to operate effectively.
You can use the power of AI to streamline your business processes by using machine learning and natural language processing technologies such as machine translation and text analytics to help you improve customer service, boost efficiency, and innovate new products and services. We touched on the cost-per-customer and customer lifetime value metrics in our previous articles to help you make better business decisions. This article will cover in-depth the analytics-driven key performance indicators (KPIs) with examples to illustrate how machine learning can help you optimize your business processes to become more efficient while ensuring equally excellent customer service. Let’s take a look… Data is the lifeblood of any business, and no one can deny that. You can extract valuable insights from customer data with things like: There are plenty of analytical technologies that can help enterprises extract data from their customers’ data and extract actionable insights. And you can even automate many of the tasks associated with data analytics. But most businesses still rely on manual processes that are costly and resource-consuming. Machine learning (ML) is an advanced technique, which we have already touched on extensively during our articles on Deep Learning and Natural Language Processing technologies. Machine learning systems attempt to learn patterns from data to help companies make intelligent decisions based on what they see. It enables data-driven predictions that reveal patterns that cannot be spotted by traditional methods. One of the advantages of using machine learning is that it helps businesses make decisions based on data, i.e. without humans inferring or guessing, which can avoid errors, discover patterns that cannot be deduced from data, and thus make decisions that companies couldn’t have done before. Electronic data processing (EDP) systems help streamline the data flow within an organization. An EDP system simplifies data gathering and makes it easier for data scientists to create machine learning models while automating repetitive data processing tasks. For example, these systems help manage e-learning and enable users to find information from spreadsheets or content online.