CHAPTER 10

Artificial Intelligence-Based Decision Making Applied in Marketing and Sales in Third World Countries

Abel Kinoti Meru, Felix Musau, and Mary Wanjiru Kinoti

Introduction

Techonomy is quickly changing the way of doing business worldwide, both sectoralwise and within functional areas including business-to-­business, business-to-consumer, or at person-to-person level with profound positive implications. This is especially true in third world countries, resulting in a paradigm shift in the provision of goods and services. The so dubbed everyone-to-everyone (E2E) economy is driven by business ecosystems that are collaborative and seamless, user-specific, symbiotic, and cognitive (Glass, Haller, Marshall, and Yoragupipati 2017). The E2E economy shifts the focus from institutional centricity to user centricity (Cheung, Marshall, and McCarty 2017) determined by the artificial intelligence (AI) technology. This is the cumulative effect of massive automation of government processes, public and private firm activities, the economic sector, coupled with use of cognitive analytics and AI over the last decade or so. This scenario is giving hope to billions of people in the third world economies, who previously relied on discrete or none existing business processes.

While traditional digital automation processes boosted storage, retrieval, and application of information data sources, cognitive analytics as per Abercombie, Ezry, Goehring, Marshall, and Nakayoma (2017) sifts through data to unlock meaning and make recommendations, since machines can understand unstructured information such as imagery, natural language, and sounds from books and social media among others. For instance, cognitive technology enables a machine to demonstrate human like intelligence, utilizing big data to make informed decisions, offer suggestions or direct action compared to use of programming codes (McKinsey Global Institute (MGI) 2017a).

This MGI (2017a) paper notes further that AI enables machines to identify multifaceted patterns, synthesize information, predict, and draw resolutions, which were previously the domain of human beings. Globally, tech giants like Amazon, Apple, Baidu, and Google are leading in the development of AI, with instances of outright buying or working with AI start-ups and keenly eyeing to cash in using artificial systems/agents. AI, if harnessed as a new factor of production, will definitely enhance labor productivity and sustain double-digit economic growth rates in many parts of the world (Purdy and Daugherty 2016), including third world countries.

Again, it is imperative to note that key foundation technologies of AI like use of cloud, mobile, and Internet of things (IoT) are positively correlated to the diffusion and adoption of AI technologies globally. The MGI (2017a) report rightly observes that AI adoption is correlated to the level of digitization. For instance, within the transport and logistics spectrum, which carries a huge burden in third world countries, it is observed that by 2030 new automotive technologies like car sharing, autonomous driving, and integrated transport transit infrastructure will be the norm (MGI 2017b).

AI has ripple effects on an enterprise’s functional activities commencing with the back office (finance, human resources, IT, and procurement), through the middle office (innovation, production/operations, product development, risk management, and supply chain management) to the front office comprising mainly of customer service, marketing, and sales (Abercombie et al. 2017) activities, which is the focus of this chapter, with emphasis on third world countries. The rest of the chapter provides an overview of AI-based decision-making systems applied to marketing, sales, and customer service, and then the implications of AI in marketing and sales in third world countries are presented, followed by conclusion and recommendations for policy implications.

The AI-Based Decision-Making Systems Applied in Marketing and Sales

The highest AI growth sectors are in high tech and telecommunications, automotive and assembly, and financial services; then the middle sectors include resources and utilities, media and entertainment, consumer packaged goods, transport and logistics, retail, and professional services, while the lower adopters are in the education and health sectors (MGI 2017a). eMarketer (2015) analysis of digital advertising earnings found out that 55 percent of the revenues were driven by programmatic marketing activities due to speed of data processing and machine learning techniques, with projected increases of up to 63 percent.

Though there is no well-delineated subfields of AI globally, this chapter has adopted five sets of Al technology systems development areas: (1) computer vision, (2) natural language that is used to process external information, (3) machine learning (including deep learning) that learns from information provided, (4) robotics and autonomous vehicles, and (5) virtual agents that act on information (MGI 2017a). The report further shows that machine learning had attracted the highest investment of 60 percent in 2016, followed by computer vision at 30 percent. There were insignificant investments in natural language, smart robotics and autonomous vehicles, and virtual agents.

For instance, the retailing sector utilizes machine learning and robotics mainly in promotion, assortments, and supply chain, enabling smarter decisions and real-time forecasting (MGI 2017a). Cheung et al. (2017) further observe that AI has deeply changed consumer-centric enterprises by creating personalized customer experiences, in areas like personal care (Procter & Gamble’s Olay brand analysis of mobile phone ­digital ­selfies to offer skin solutions) and food (Campbell soup uses location-based personalized recipes) among others. Also, wholesalers and retailers use AI to predict customer behaviors, analyze fast-moving goods, and integrate front-end and back-end office operations.

Evolution of AI in marketing and sales is creating opportunities for growth in public and private enterprises. Te, Tsai-Fong, and ­Chieh-Heng (2016) point emerging fields in AI and marketing, sales, and customer service such as marketing solutions, virtual customer service agents, ­automated marketing and sales, virtual assistants, and marketing decision making. AI in marketing can help process big data to identify ­target ­customers, utilize multichannel for marketing campaigns, ­conduct ­powerful research for market positioning, identify pattern of high-­conversion propensities, and improve accuracy in reporting marketing activities ­(Abercombie et al. 2017).

Further, use of AI in sales function can eliminate front office/customer-­facing services, improve key account management, enhance cross-sell and upsell prospects, and improve efficiency in lead time management ­(Abercombie et al. 2017). Zhou (2017) observes that AI can augment customer service experience first through front-end AI-powered bots like chatbox, automated responses to basic customer queries, and the drastic reduction of customer service cycle time. Second, the AI-assisted human agent or human loop supports human customer service representative through AI technology like the case of text and voice inquiries, where the AI platform initiates the response or vents callers and the customer care human representative makes the final reply.

The AI-Based Decision-Making Systems Applied in Marketing and Sales by the Association Of Southeast Asian Nations (ASEAN)

AI continues to impact all markets globally particularly in the marketing and sales field even with inherent differences in infrastructure and systems in third world countries. The MGI (2017) paper observes that AI investment in ASEAN had reached US$ 2.6 billion in 2016 driven by wide-ranging technologies such as natural language processing ­(Bindez, Myammar; kata.ai, Indonesia; and FPT, Vietnam), machine learning (CloudSek, India, and Runngaru, Indonesia), and image recognition (Sero, Vietnam), led by high tech, telecommunication, and financial ­services industries.

The ASEAN telecommunication firms are using analytics to predict customer behavior, upsell or cross-sell, and offer mobile banking, insurance, and loans. For instance, Dataspark (Singtel) collects and analyzes shoppers’ information, and Eureka (Indosat) concentrates on digital marketing for retailers and offers credit rating services to banks (MGI 2017a). Further, from the paper, the financial sector has over 300 FinTech start-ups offering payments, micro lending, and wealth management, and big firms such as Hong Leong Bank (Malaysia) are using IBM Watson to decipher customer voices over the phone. Digibank (Singapore) utilizes virtual assistant to respond to customer queries, and CompareAsiaGroup uses machine learning to match customer needs with financial, telecom, and utilities requirements in five ASEAN countries.

Within the manufacturing sector industry 4.0, digital transformation of the sector is driven by IoT, AI, robotics, and 3D printing thereby enabling motionless management of factory floors, value chain, seamless flow of information leading to real-time decisions, and production efficiency (MGI 2017a). For instance, in China, Alibaba has made inroads connecting cars to the Internet and slowly moving on to introduce cloud-based AI services aimed at health care and manufacturing sectors (­Daugherty 2017b). According to the McKinsey Global Institute (2017a) paper, future prosperity in China will be determined by the rate of adoption of AI technologies to accelerate economic growth. This situation is replicated in most other ASEAN and Middle East countries, but the phase in Africa is certainly lower.

The AI-Based Decision-Making Systems Applied in Marketing and Sales in Africa

A similar pattern of application of AI globally in the key sectors of telecommunications, financial services, retail, transport, and logistics is evident in Africa, albeit on a smaller scale. This could be partly explained by the fact that AI is relatively young in the continent, poor infrastructure, and limited development of techonomy infrastructure and systems including skills and capabilities. Equally visible is the lack of government support to create an enabling environment such as policy framework, building public infrastructure and networks, and addressing cybersecurity issues.

There is also a general lack of preparedness and stakeholder involvement, thereby requiring private and international high tech and telecommunication firms to take a proactive role in the sector. All in all, the cost of doing business is prohibitive coupled with a dearth of AI-related skills and widespread illiteracy among the rural folks. It is also noteworthy that, although deeper inroads are visible, they are cluttered within the rural and urban areas and across regions.

Notable AI systems, although all are dependent on the telecommunication sector, include: M-Pesa and Pesapal (Kenya) in financial sector; in the retailing sector there is Jumia and Mall for Africa (Nigeria), SMSGH and Esoko (Ghana), and OLX (South Africa). In transport and logistics, Uber is present in a number of countries, Little Cab (Kenya), and Zebra and Jozibear (South Africa). In the agricultural sector, there is M-Farm (Kenya) and Farmerline (Ghana). A list is shown in Table 10.1.

Table 10.1 Selected AI-based decision-making systems applied in marketing and sales in Africa

Year	Digital innovation	Country	Functionality	AI capability	Source
2007	Safaricom-M-Pesa services	Kenya	Mobile money transfer/micro-­savings and credit	Machine learning	Mureithi (2017)/Bright (2017)
2008	Ushahidi	Kenya	Crisis management	Machine learning	Mureithi (2017)
2009	M-Farm	Kenya	Connect farmers and buyers	Machine learning	Osikakwan (2017), MFarm (2017)
2009	Pesapal	Kenya	Payment aggregation platform	Machine learning	Osikakwan (2017)
2015	Uber	Kenya	Integrated convenient travel and pay solutions	Machine learning	Biznews (2017)
2016	Little Cab	Kenya	Integrated convenient travel and pay solutions	Machine learning	Biznews (2017)
2012/2010	Jumia/Mall for Africa	Nigeria	Online shopping	Machine leaning	Osikakwan (2017)
2012/2004	Farmerline/Esoko	Ghana	e-Agriculture	Machine learning/­natural language	Osikakwan (2017)
	SMSGH	Ghana	Communication, content, commerce	Machine learning	Osikakwan (2017)
	OLX	South Africa	Online shopping	Machine learning	Osikakwan (2017)
2013	Uber	South Africa	Integrated convenient travel and pay solutions	Machine learning	Biznews (2017)
2016	Zebra cabs/Jozibear	South Africa	Integrated convenient travel and pay solutions	Machine learning	Biznews (2017)

Source: Author’s own from literature (2017).

From Table 10.1, it is clear that apart from the cash transfer services M-Pesa’s micro savings and credit services such as M-Shwari analyzes ­customers’ mobile usage, payment history, and credit rating among ­others to determine credit worthiness. Safaricom owns an innovation center to further develop an AI monetary platform to help analyze ­customer ­transaction history and to enable them make informed financial ­decisions (Bright 2017). Pesapal is an online and mobile payments system for ­individuals, businesses, and governments in Kenya (Pesapal 2017), akin to Safaricom but with limited scope.

Ushahidi gathers and analyzes information from disaster zones through SMS, E-mails, WhtatsApp, Webapp, and Twitter and shares simultaneously with disaster management units on a real-time basis. M-Farm was created in 2009 to mitigate agriculture-related risks, especially in the rural areas, and it enables farmers to source information easily, get inputs cheaply, and sell produce timely and competitively (Mfarm 2017), similar to Farmerline. Uber, an e-hailing taxi services, commenced operations in Kenya in January 2015, and since then numerous other apps are in operation including Taxify (Turkey) and rapid homegrown taxi e-hailing solutions like Little Ride, MaraMoja, and Dandia (Njanja 2016, p. 19). Little Ride is co-owned by Craft Silicon and Safaricom telecommunication firm, and comes with free wifi, mobile money payment mechanism, ride sharing, loyalty scheme, lady drivers, corporate taxi, and a feedback mechanism.

Online shopping is dominated by Jumia, found in Nigeria and a dozen African countries (Jumia 2017), while Esoko is found in Ghana. Mall for Africa connects African online retailers with their counterparts in the United States and Europe (Mall for Africa 2017).

Conclusion

As the global digital economy evolves fundamental changes especially in the field of marketing and sales will redefine tomorrow’s business operations. It is evident that AI will take central stage in bifurcating the human and machine interfaces. While the U.S. market has the first mover advantage, the strides made by China, ASEAN firms, and inroads by African companies cannot be taken for granted. Advances made by China, ­Singapore, India, Malaysia, Vietnam, Nigeria, Kenya, Ghana, and South Africa are worth paying attention to. Absolute transformation is being witnessed in the telecommunication, financial services, transport and logistics, agriculture, health, and media sectors in the third world countries.

Despite the gains, there are a couple of issues related to labor markets, regulatory framework, market dominance, cost of technology systems, and cybersecurity that beg answers. Already in the banking sector, banks are closing branches and laying off staff as a result of mobile money transfers. The same is observed in other sectors, so the question of employment will need to be carefully assessed since a large proportion of the population relies on wages and salaries, and they constitute a sizeable consumer market. Economies in the third world countries thrive mainly on taxes, but techonomy is borderless and creates a myriad of challenges with relevant tax authorities. Mobile money transfer, though somehow regulated by central banks, works mostly in the microlevel or even peer-to-peer lending, making it extremely difficult to regulate.

The growth and dominance of Uber hailing taxi services in different parts of the world left many players astounded. There are instances, where they thrived without legal approvals, stakeholder consultation, or an explanation of the business model, resulting in resistance, huge fines, and penalties. The cost of Internet and other related technology infrastructure seems a real threat to the growth and development of techonomy in the third world countries. Like any other public mega infrastructure, the cost should be borne absolutely by the governments. However, this has been left to profiteers at the expense of the taxpayers. Finally, the issue of cybersecurity is posing a great threat to the growth of the sector, since like in the banking industry there are incidences of massive fraud by hackers. Moreover, the issue of what happens if the system fails is disturbing.

Recommendations

It is evident that AI if properly harnessed will substantially contribute to the socioeconomic development of third world countries through innovative and inclusive marketing and sales models with the ability to serve marginalized communities. However, for this to happen, issues of the job market, regulatory framework, market dominance, cost of technology systems, and cybersecurity ought to be addressed once and for all. The issue of the effect of IT on the labor workforce, as discussed at length, has no tangible answers. But, what is imminent is that firms are getting leaner day by day. Since the interaction between human and machines will remain forever, there is need for equipping human beings with the requisite skills needed in the AI sector.

These skills should empower human beings to fully understand computer visioning, natural machine learning (including deep learning) robotics, autonomous vehicles, virtual agents, and industry 4.0 techniques (IoT and 3D printing). Third world governments need to play a fundamental active role in creating an enabling environment by enacting appropriate techonomy laws and policies, and building a pool of talents with requisite skills from universities, polytechnics, and AI incubators in all fields, located in various parts of the world. Governments will also need to develop regional AI protocols and agreements to guide virtual growth of the sector.

Finally, the cost of doing business in the techonomy era, if not properly controlled, could be life-threatening, simply because like air there is no life without Internet. It is difficult to communicate with others in far-flung and remote areas with no Internet. The people have fully embraced E-mails, WhatsApp, chatbox, and so on as means of communication. Therefore, governments in third world countries, needs to prioritize investments in information technology to grow the techonomy, besides addressing the increased need for embracing AI into marketing and sales.

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