How "Cognitive Computing in Marketing" can accelerate the economy

Mercerism and the Empathy Box

In Philip K. Dick’s novel, Do Androids Dream of Electric Sheep, SFPD bounty hunter Rick Deckard hunts downs five Nexus Six Androids that escaped from Mars. In 1982, the book was adapted into the well-known movie, Blade Runner – and was directed by the cinematic genius of Ridley Scott. Harrison Ford portrayed the main protagonist, Rick Deckard. Throughout the book, we see the influence of a religion called Mercerism. Mercerism as a religion promotes empathy between followers, through a process called fusion. By using the Empathy Box, followers can reach out and fuse with another follower so that they can feel one another’s struggles or success. 

In times of a public health crisis, empathy is particularly vital. Today, brands require to use the Empathy Box to fuse with their customers, instead of resorting to product push strategies. This will help them gravitate towards empathetic communication and help connect with customers on a much deeper level. The following industry observations and academic works, reflect how synthesising marketing content, customer behavioural patterns and contextual data across digital channels, can deliver immersive and unique experiences to each buyer:

• Between March and July this year, TSA observed an 80% decline in US Air Travelers compared to 2019. In an effort to revive the dwindling travel and hospitality industry, a study by Persado (pioneers in marketing persuasion technology), showed that customers engage mostly with empathy and trust-based emotions of intimacy, safety, and gratitude – while being reassured as they consider their purchases.
• In an article by Scott S. Grisby on AI for Advanced Human-Machine Symbiosis, AI has been called a cognitive prosthetic – technology that combines large data sets, algorithms and powerful computing to produce outcomes that mimic human thinking, but at speeds and volumes we can’t match. 
• And, lastly in my article on next-gen telecoms capitalising the crisis by monetising the subscription economy – I talked about data being the new oil for marketing, and how data is used to generate insights that can help reduce costs, improve efficiency, increase revenues, improve effectiveness, and enhance customer service.

Introducing "Cognitive computing" – a technology based on AI and signal processing that offers a wide range of capabilities including study of the human brain and how it works, machine learning and reasoning, neural networks, and perceptive technologies like Natural Language Processing (NLP), Speech Recognition, Visual Scene Analysis and Dialog generation – to help computer systems simulate human thought processes.

The importance of this is technology is highlighted as McKinsey reports, that in 2020 (illustrated in Exhibit-1), AI applications are expected to have up to $2.6 trillion worth of business impact in sales and marketing alone.

<Exhibit-1: McKinsey's view on potential value unlocked by AI apps in 2020>

So, let us understand how cognitive computing can help brand marketeers “fuse with their consumers” during these turbulent times and bring business-levels back and over pre-COVID levels to avoid a 2008-like recession.

Cognitive Computing + Marketing = Magic

In this section, I will discuss four cognitive computing applications, three of which were part of my Silicon Valley software-configuration based innovation lab and customer experience centre, where we designed tailored AI-driven marketing solutions for North American telecom service providers.

To understand the application of these tools – let us analyse a typical customer journey, that begins with the prospect using a mix of digital channels and interaction touch-points along their journey (you may refer to my previous article for a sample model):

1. Dynamic Campaign Personalisation

In the “get to know” part of the journey, making the customer brand-aware and building a list of potential options is key. Applying target audience segmentation, dynamic content assembly, progressive profiling and dynamic pricing – are ways for the customer to get personalised omni-channel campaigns. Wylei’s cognitive computing solutions allow for 50%+ lift in engagement & conversion per campaign as depicted below in Exhibit-2.

<Exhibit-2: Wylei’s Smart Campaign Management Solution>

2. Brand Voice Augmentation

While in the “buying” part of the journey, it’s essential for marketeers to change their brand-voice from salesperson to empathetic and understanding friend. This can be achieved by plugging your content into an algorithm, using IBM’s Tone Analyzer capability as illustrated in Exhibit-3. The IBM Watson-powered cognitive computing solution analyses emotions and tones in what customers write online, like tweets or reviews, and assigns a numerical score to five traits – expressiveness, formality, sociability, empathy, and emotion – helping marketeers predict whether their customers are happy, sad, confident, and more.

<Exhibit-3: Tone Analyzer by IBM Watson>

3. Conversational Context Bots

The use of conversation automation bots through the customer’s purchasing lifecycle increases leads by 4x, increases revenue per user (RPU) by 20% and provides Next Best Action & Offer to help convert lead to sale, cross-sell and up-sell opportunities. ServiceNext’s cognitive computing solution for the Telecom industry comprises of the SnX Marketing, Guided Selling and Recommendation Bots, as illustrated in Exhibit-4, that apply the user’s conversational context (UCC) and over 3000+ indents to guide customer’s through the buying and support process. 

<Exhibit-4: Telecom Smart Bots with User Conversation Context technology>

4. Agent Performance Amplification

In most enterprise organisations, specific marketing teams usually silo their data, making it hard for the sales agents to track all their customers’ touch-points and understand their true buyer’s journey. Sales Agent performance and productivity uplift using cognitive computing, can provide actionable insights, data-driven coaching, performance driven micro-learnings, engagement and gamification. For example, AmplifAI, as illustrated in Exhibit-5, increases sales conversion by 44%, customer satisfaction (CSAT) by 27% and agent productivity by 9% by helping convert data into actionable outcomes.

<Exhibit-5: Converting data in actionable business outcomes>

Furthermore, as a reference – the Market AI Institute has listed Top 25 Use Cases for Marketing AI comprising of audience targeting, to content strategy, to search engine optimisation, media buying, email writing, predicting conversions and churn, and several other activities that marketeers perform every day – that will be intelligently automated using cognitive computing, to some degree in the near future.

Click here to read my latest article on Quantum Computing and how it adds scale, with “overparameterisation”, to Deep Learning algorithms used in Predictive AI.

Cognitive Computing with RPA

As covered in the four marketing cognitive computing examples above, you can see that this technology is highly-efficient in processing huge amounts of customer contextual data, and applying the information to structure and profile behavioural traits required to make accurate evidence-based predictions. Adopting Robotic Process Automation (RPA) to this, results in intelligent automation at scale and reports higher increases in revenue. So, while cognitive systems make it possible to provide contextual, and reliable information to the customers, RPA improves customer experience, making them satisfied and much more engaged with a business.

Here are three related topics for you to read – 

• Unlocking 5 Deep Learning AI apps with Quantum Computing
• Top 3 "Next-gen" Telecom Subscription Monetisation Opportunities
• Delivering AI-driven innovations in a post COVID-19 business boom

Looking ahead

AI technology and tools are constantly evolving as they are getting easier to access, providing smoother user experiences, and becoming a more natural fit into the every-day marketeer's life. As we wait for our world to return to normalcy – whether it is retail, telecom, travel and leisure, food delivery, online fashion, or media OTT, today, cognitive computing can help marketeers make the most relevant product or content suggestions to customers based on their past browsing, purchasing, or viewing activity. The products that I have listed above, are a subset of extensive applications and tools available to the industry. Marketing in a post-COVID world will require a more substantive orientation towards technology. If you are looking at the ways to grow your brand and meet the expectations of your customers – then you can reach out to me using the coordinates below.

Do you agree with the views expressed in this article? Have you designed or implemented any Digital Marketing tools using Cognitive Computing technology? Would you like to know more about the use cases discussed above? Please leave your comments and questions below, and feel free to share this post if you found it interesting and valuable.

Also, if you would like the citations for this content, then reach out.

– Ashish Kar

Author is a Chief Architect @PCCW Solutions, with 24+ years in the ICT industry and an innovation gameplanning coach. He has built a Silicon Valley innovation lab and designed several AI-driven Digital Marketing solutions for telecom and retail organisations. He can be reached on email at [email protected].