Artificial Intelligence (AI) marketing technologies

Artificial intelligence (AI) is manifesting itself within a variety of different Marketing technologies, bridging the gap between vast and complex data sets and the execution and delivery of marketing campaigns and initiatives. The growing stream of data has made it impractical for humans to analyse and process the available data. The introduction of AI has not only made this possible, but it is also relieving the pressure on marketing teams, allowing them to focus their efforts on the more intuitive and creative activities.
The evolution of technology and data has made it possible for marketers to form a more precise image of their target audience. Providing insight into their behaviours and motivators, better than ever before. Marketers have access to an increasing number of key metrics and parameters giving them the ability to make informed decisions based on intelligent insights. All of this is made possible by the advances in artificial intelligence (AI) marketing technologies.
In this article we look at the variety of different ways that AI technologies are supporting and influencing the Marketing, Sales and Business Development teams within an organisation.


What is Artificial Intelligence?

The term Artificial intelligence (AI) has been around since the late 50’s. It was first coined by John McCarthy during the Dartmouth Artificial Intelligence (AI) Conference in 1956, a summer research project that took place in New Hampshire. The conference was the largest formal gathering on the subject and brought together leading research specialist from various disciplines including language simulation, sensory inputs, neuron nets, complexity theory and more.

The topic of AI was new to the majority of these magnificent minds and had never before been discussed at such a formal event. It was here that these great minds witnessed the birth of Artificial Intelligence and from which the foundations of AI were laid.

Today, there are a variety of definitions for Artificial Intelligence, the Oxford Dictionary defines AI as:
“The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages”

Artificial Intelligence for Marketing takes this to a new level, our focus shifts to how we can utilise such technologies and applications to influence smarter decision making and deliver optimal results on marketing efforts. We can use these technologies to glean greater insight into our customer data, anticipate and predict future purchase behaviour, tailoring their journey to improve their overall customer experience.

Data analysis: making sense of it all

Big data today is about more than just the core contact information, companies have the ability to track engagement in a variety of different ways including: activity and behavioural data, email and web interactions, social media profiles, engagement and sentient, career changes and so much more. With so much information at our disposal, it is no longer feasible to expect a human to review, process, analyse and draw intelligent insights from this information, there is simply too much to digest and make sense of and asking this of your employees can only lead to frustration and a lack of satisfaction in their work.

The internet is one of the most fundamental parts in today’s modern business, it has transformed the way that businesses interact with, and understand, their consumers. Websites are a 24/7 shop window with the ability to capture and categorise data like never before and with so much data at our disposal, Customer Engagement and CRM platforms have had to evolve in order to catalogue and store this increasing stream of data.

The increased level of intelligent insight, coupled with more effective use of data is crucial to improving the customer experience, a critical factor of any marketing strategy. It doesn’t stop here though. AI is helping to identify trends in consumer activity, spot repeat behaviours, observe responses and reactions and collate this information to provide sales and marketing professional with intelligent predictions and observations across the data set. With this information at their disposal, digital marketers are able to explore and develop more targeted and personalised customer journeys that lead to a better return on their marketing efforts.

Social listening, insights and sentiment analysis

With 3.2 billion users worldwide, social media is currently one of the most prevalent online activities. With users spending an average of over 2 hours on social media everyday it is, without a doubt, one of the most important channels for consumers and businesses alike. But with so many users and the vast array of different social media channels out there, how can companies cut through the noise and turn this into actionable insight?

Social insight tools such as AI for Market Insights from Microsoft provide AI-powered insights allowing companies to gain a competitive advantage by learning about and responding to relevant conversations. Such tools grant companies the ability to explore sentiment analysis on social conversations. It provides an understanding of the attitudes towards a companies brand, product/services as well as their competitors. It doesn’t stop here though. The built-in AI technology within these marketing solutions can also be used to spot purchase intent. It does this by analysing the behaviours and language consumers use to talk about a product. It identifies spikes in consumer interests and detects issues derived from Microsoft’s proprietary web search data.

Marketers can create news feeds that fit in and around specific search terms. They can also choose the methods in which this information is shared with them. Notifications can be setup within the tools alerting you changes in sentiment, trends and topics of discussion. Likewise, daily or weekly digest emails containing summary information can be pushed via email. All of this can be tailored to meet the unique requirements of the business.

Chatbots and conversational agents

In recent years there has been a huge change in the way that consumers research and purchase products and services. With the internet at our disposal, prospects can browse, review, critique and research products to their heart’s content. Organisations are seeing a significant reduction in the number of lead capture forms being completed, whether these be for gated content or enquiry. Prospects are increasingly less willing to disclose contact information in order to obtain content.  In fact, 81% of technology buyers refused to complete gated content forms in order to access online content.

So how are companies responding to this?

Research suggests that two-thirds of buyers stated that real-time messaging was their preferred method of communication with a business, though not all of these conversations are via telephone. Introducing, the chatbot.

The chatbot framework dates back to the 1950’s when Alan Turing, a brilliant British mathematician, conceived that intelligent machines (or now applications) could be designed to convincingly imitate human intelligence, making them indistinguishable from a human throughout a text-only dialogue. This is now known as the Turing test.

Today’s definition

Chatbots, otherwise known as conversational agents, are AI-powered software applications, primarily found on websites and within standalone apps. They conduct conversations with the visitor/user via written text and voice control.

Since the 1950’s there have been various developments in and around this technology but the SmarterChild bot, developed in 2001 has pathed the way to modern chatbot technologies.

In today’s’ technology-driven world, the majority of us have engaged with a chatbot in some way, shape or form. Whether this encounter was perceived to be a positive or negative experience, recent research has found that 69% of consumers preferred this style of communication. Chatbots are revered for the speed of response and resolution, their 24/7 availability and the general ease of communication. Whilst they are most commonly found in customer service related spaces, the shift in buyer behaviour coupled with the demand for real-time response means that we are seeing an increase in the presence of AI chatbots within conversational marketing.

With machine learning at their core, bots are continuously improving themselves, becoming increasingly accurate the more they are used. They are able to respond to natural language queries and can respond to queries quickly and efficiently. Their continuous ‘learning mode’ means they are agile enough to adjust responses. For example, if they detect change in the buyer’s response they will adjust their response accordingly.

It’s important to note however that chatbots do have their limits. Companies looking to utilise chatbot technologies should consider their role and purpose. They are best suited to areas where the speed of response from a chatbot exceeds that of a human. Chatbots should complement and enhance the customer experience. Some of the most effective bot examples are known for their transparency, acknowledging their chatbot as a bot rather than as a human. They are designed to help guide conversation and provide prompts to help the user on their journey. Equally as important, bots must know where to stop! For example, if the user should reach a point where speaking to a human representative delivers a more favourable outcome.

Audience targeting & segmentation

Segmenting and targeting your communications is a key part of successful marketing execution today. The extent of this segmentation however, is limited to the data they hold on their audience. Data segmentation can be based on both geographic and demographic information. This might include age, gender, role, etc. or firmographic information held about the company they work for: size, turnover etc. Similarly, it is also possible to target consumers based on past purchase behaviours or lines of enquiry.

Audience segments can be static or dynamic. Microsoft’s Dynamics 365 for Marketing application allows users to build powerful segments based on a variety of different data, and is not limited to only that of the contact and/or company record. Dynamic segments can be built around a number of different parameters including behavioural metrics and lead scoring models. This enables marketers to initiate highly targeted and personalised customer journeys at the point in which recipients are most engaged. This, as we’ve established, leads to a better return on marketing efforts.

Microsoft’s Dynamics for Marketing application possesses another AI-powered feature within its segmentation capabilities: ‘segment boost’. The system interrogates your data to identify contacts who are “most likely to yield business returns”. This is because they resemble the engaged contacts within your current segment. This is repeated until the campaign reaches its target size or end date. Both of these parameters are within the users control.

Insights for Sales

Many of you will be familiar with the concept of ‘sales ready’ leads; that ideal list of customers ready to ‘sign on the dotted line’ the minute the sales person gets in touch. Well according to Forrester, there are a number of issues that are making it difficult for sales teams to obtain this information and companies looking to succeed need to consider the following:

Earlier in this article, we discussed the timely delivery of content to your segments. In this section, we are referring to the direct one-to-one contact between a sales person and a prospect. AI-driven intelligence is able to provide smart recommendations and insight to your sales team based on the wealth of information and intelligence it holds across your data. AI examines data about the individual, their company and their interactions with you. It collates this data from a variety of different channels including website traffic, telephone conversation and their social media interaction. Using this information the technology can identify next best actions and predict close dates. This information can be pushed directly to the relevant person to action immediately.

Predictive analytic insights

Organisations today are benefitting from predictive analytic AI technology in a multitude of different ways and across various departmental functions.

Within the manufacturing, warehousing and distribution industries, these technologies are providing actionable insights into asset and resource utilisation, identifying areas for improvement and spotting trends which allow companies make intelligent optimisations decisions and leverage savings on their operational costs.

Likewise, finance and credit control departments are harnessing predictive analytic applications to analyse payment data and spot trends to help predict late payers. Using this intelligence, organisations can streamline and refine their processes. This helps to speed up payment collection and reduce future risk of late payment. This area of AI has been fundamental to assisting businesses in the management of their credit collection process.

With the online retail market growing at such speed, predictive analytics is proving to be an essential tool within e-commerce, providing a deeper understanding of online customer behaviour. Each customer is unique, so too is their journey, making human analysis of this data an insurmountable process.
Predictive analytics allows the continuous analysis of client and customer purchase behaviour, identifying purchase timeframes, habits and behavioural patterns. Using this information, Marketers can optimise the customer journey, developing highly personalised and engaging experiences to help drive increased conversion.

For more information on how you can take advantage of these technologies then please get in touch with us today.

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