It is every marketer’s wishful thinking to be able to understand the exact needs of every prospect and position offerings accordingly. With literally an explosion in the amount of consumer data that is being generated every second, deep insights can be derived into individual consumer behavior. Deep Learning, which is a subset of machine learning, makes personalised marketing-at-scale, a present day reality. The usage of artificial intelligence has already started re-inventing marketing and analytics.
How AI is reinventing marketing?
A good marketing ROI is the top priority of every CEO. Everyday, tons of data points are data gathered from leads, prospects, and customers. With data flowing in from websites, mobile apps, CRMs, marketing automation systems, social media, and even IoT devices, etc., companies now have more data than before. Once paired with the power of AI, this big data can create substantial value for any business. The value addition can be in the form of building enhanced capability to forecast sales, understanding customers in a more comprehensive manner backed by strong data points, identifying up-selling and cross-selling opportunities that traditional BI tools were unable to track, thereby enhancing overall customer experience, service, and ROI.
So to sum up, AI is reinventing marketing by:
- Engaging customers with precision
- Leveraging superior customer insights
- Maximizing the overall marketing ROI
Most features, benefits, and values of AI in marketing essentially revolve around these three broad categories.
A Few Initial Use Cases of AI in Marketing
AI ushered in a number of innovations in traditional digital marketing. AI led innovations can be spotted in the existing marketing tools. And at the same time a lot of new AI-powered tools got introduced into the marketing technology stack. Let’s take a look at some of the initial use cases of AI in marketing
Content Creation and Content Curation
AI has the potential to curate and as well as create massive amounts of quality content. AI tools can further be used to proofread content and then disseminate it in front of the right people on the right platforms at scale with few errors, if at all. Platforms such as Quill, Wordsmith, Articoolo, WordAI, etc. are thriving on machine learning to deliver targeted content. Many of these platforms deploy advanced natural language generation (NLG) to create meaningful content from data.
AI In Digital Ads
Artificial intelligence will very soon disrupt the way businesses advertise. The new AI enabled Ads would entail placing personalised ads in front of an extremely segmented target audience based on complex algorithms and big data. Although some form of segmentation and targeting are taken care of in today’s traditional digital marketing too, it is based on limited data sets often derived from traditional data sources such as customer touch points, CRMs, Website traffic, etc. With AI, personalisation would hit the next level. Brands today are beginning to use a number of artificial intelligence platforms and tools to intelligently identify and segment audiences, build ad creative, test variations, improve campaign performance, and optimize ad spend. AI-powered ad tools detect patterns in advertising data and predict what changes to campaigns will improve performance against a specific KPI. All this happens instantaneously, often in real time and at a massive scale. Analysing, testing, and iterating campaigns used to take weeks earlier.
Smart chatbots can be spotted on numerous websites now. They excel at not just answering customers’ frequently asked questions but, with RPA at the backend, these smart bots are doing much more. Customers expect easy processes, minimum wait-time, and self-service options and faster turnaround time wherever possible. Chatbots perform exactly these tasks in a more efficient, reliable and secure way. They are available 24/7 and they understand multiple languages. And lastly, they can be accessed through customer’s most preferred platforms such as Facebook messenger, WhatsApp or even through voice assistants such as Alexa and Google Assistant.
Behavior Analysis And Predictive Analytics
Today, there are so many data sets available for marketers that they alone can’t possibly even try to analyse them all. That’s where Artificial Intelligence comes in. And unless a sizeable, if not the entire, datasets from all sources are analysed, predictive analytics can’t be accurate. Common examples of predictive analytics would be the product recommendations on Amazon or a movie recommendation on Netflix, price optimisation through key insight into the impact of price change on revenue, creation of ads based on demographic segmentation and past consumer behaviour and trends, predictive lead-scoring by B2B companies to improve their lead conversion rates, etc. These examples are just to scratch the surface a little bit. It’s hard to exactly quantify the impact of AI on behaviour analysis and predictive analytics.
Some More instances of AI in marketing
So far, we discussed some of the most basic and early use cases of AI in marketing and analytics. Here are some more quick examples of how AI is helping streamline many other key processes of marketing.
- More accurately forecasting call volumes to call centers, enhancing customer satisfaction (C-SAT), and reducing the stress of high call volumes on agents.
- Using text mining to read through (and classify) thousands of responses to an open-ended question in a customer survey
- Allowing advertisers to test out newer ad platforms and optimize targeting. At present, Google controls roughly 40% of the U.S. digital ad market, followed by Facebook with about 20%. It’s possible for businesses to run more cost-effective advertising that gives them the best ROI. Algorithms can optimise ad bidding and find the best cost per acquisition ads for businesses.
- By analysing hundreds of data points about a single user (including location, demographics, device, interaction with the website, etc.), AI can display the best-fitting content. This leads to a much better customer experience for all the website visitors
- Designing hyper-personalised dynamic email campaigns based on previous website interactions, previously read blog articles and content, time spent on a page, wish lists, interest of similar visitors, previous interactions with branded emails, etc.
Although the above bullet points give just a glimpse of the degree of penetration AI can have into marketing, in reality AI can impact every aspect of marketing in a very positive way.
The Next Phase of Marketing & Analytics Evolution with AI
The following picture nicely illustrates the evolution of marketing and marketing analytics with time.
The first major paradigm shift in marketing came about with the advent of digital marketing around the year 2000 when marketers began to reach out to their online audience with persona-based segmentation and more personalised targeting. CRMs gave way to marketing automation platforms. Gradually social media marketing became the part of the marketing mix and this created a massive inflow of unstructured data for the marketers. New analytics tools too surfaced to capture social media data. Tools for social listening and analytics became a key component of the marketing technology stack. By 2020, Artificial intelligence became the backbone of existing technologies. For example, normal chatbots got replaced by smart AI based chatbots. With the help of AI, Marketing is becoming more and more data driven. Insights are being drawn from data lakes and not just data warehouses. 360 degree prospect profiles are being generated with the help of a massive pool of data available for each prospect. And a hyper-personalised campaign has replaced or is in the process of replacing what we called personalised campaigns till now.
AI is reinventing the depth of Analytics which in turn is reinventing marketing
With the help of big data analytics, companies can engage with their customers meaningfully throughout the buying journey in an unprecedented manner. In an online retail model, for example, machine learning can help retailers identify patterns of casual browsers and differentiate themselves from serious buyers. This insight generated with machine learning and advanced analytics, can allow the retailer to run hyper-personalised campaigns for a set of buyers out of the millions of casual browsers. Out of the millions who visited a retailer’s website, marketers can narrow it down to a small target group by identifying which stage each buyer is in, scoring their level of interest based on their overall interactions. Layering on geolocation capabilities, and smart chatbots make the buyer journey even more smooth for the customer. At the end, it’s the retailer who gains the much needed competitive advantage over their peers who are still counting on older business intelligence tools based on structured data.
AI is proving to be a powerful tool in enabling innovation by creating efficiencies and competitive advantage while opening up human resources to focus on more rewarding and higher-value strategic work. Big data, together with machine learning will not only make it easy for marketers to get more out of their marketing efforts but immensely augment their return on investment.
Sameer Narkar is the Founder & Chief Software Architect at Konnect Insights. Although a techie at heart, Sameer has a flair for story-telling and often wears multiple hats as a marketer, business developer and customer experience. He is been the brains behind Konnect Insights, which is the most preferred social listening and analytics platform.Tags: AI, Alexa, Analytics, Artificial Intelligence, Chatbots, Content Marketing, Digital Advertising, Facebook, Google Assistant, Marketing, Netflix, NLG, Personalization, Whatsapp