Manoj Dawane founded an advertising technology (AdTech) start-up in Delhi, India in 2020 that provided digital consumer behaviour intelligence for targeted digital advertising without using third-party internet cookies. His patented technology solved the issue of personal data privacy created by cookie-based targeting. His unique competitive advantage would not last long and he faced three dilemmas. First, to define a clear business model that would be a balance between the core competency of the company and the emerging market opportunities. Second, to identify an alliance partner for a faster growth. Third, to redefine his go-to-market strategy for seven times growth in two years, as demanded by investors.

This article explores the role of artificial intelligence (AI) in aiding personalized engagement marketing-an approach to create, communicate, and deliver personalized offerings to customers. It proposes that consumers are ready for a new journey in which AI is a tool for endless options and information that are narrowed and curated in a personalized way. It also provides predictions for managers regarding the AI-driven environment on branding and customer management practices in both developed and developing countries.

In September 2017, the managing director of Summit Maritime Pvt. Limited, a marine consulting firm and luxury boat manufacturer in Kochi, India, faced a dilemma. He needed to relocate a production facility and set up a service factory, where boats would be both manufactured and displayed. The managing director needed to decide on the optimal location for the new facility by considering multiple factors. He also needed to design a preliminary layout for the new facility. How should he decide on the optimal facility location and layout to restructure his growing business operations and ensure the company’s future?

In September 2017, the managing director of Summit Maritime Pvt. Limited, a marine consulting firm and luxury boat manufacturer in Kochi, India, faced a dilemma. He needed to relocate a production facility and set up a service factory, where boats would be both manufactured and displayed. The managing director needed to decide on the optimal location for the new facility by considering multiple factors. He also needed to design a preliminary layout for the new facility. How should he decide on the optimal facility location and layout to restructure his growing business operations and ensure the company's future?

Sunrise Power, a first-generation mid-sized power and mining company in India, was considering geographical diversification in the African continent. While many African nations were rich in resources, they often lagged in economic indicators, and global companies hesitated to invest in infrastructure due to limited risk appetite. However, this left an opportunity for mid-sized firms such as Sunrise Power, so long as they could attain regulatory support and ensure high returns. Sunrise Power needed to evaluate the complexities in identifying the right market in Southern Africa. This included examining indicators like population, gross domestic product, energy demand forecasts, and electrification capacity. The firm also had to identify the critical success factors and assess the risks in the strategy planning process. Finally, it needed to design an organizational structure for its African venture so as to realize the benefits of diversification.

Sunrise Power, a first-generation mid-sized power and mining company in India, was considering geographical diversification in the African continent. While many African nations were rich in resources, they often lagged in economic indicators, and global companies hesitated to invest in infrastructure due to limited risk appetite. However, this left an opportunity for mid-sized firms such as Sunrise Power, so long as they could attain regulatory support and ensure high returns. Sunrise Power needed to evaluate the complexities in identifying the right market in Southern Africa. This included examining indicators like population, gross domestic product, energy demand forecasts, and electrification capacity. The firm also had to identify the critical success factors and assess the risks in the strategy planning process. Finally, it needed to design an organizational structure for its African venture so as to realize the benefits of diversification.

That some companies are choosing to invest in better-trained and more service-oriented workforces should be no surprise. With increasing competition, technological advances and globalization, many companies, especially those selling services, have come to realize that employee expenditures are more than a cost: Employees are the face of the business and sources of innovation and organizational knowledge. They interact with customers at every touch point and create lasting brand impressions. They personify the company's service philosophy and are expected to live by its culture and values. While the products and services many companies offer can appear quite similar on the surface, exceptional service can be a competitive advantage. Competing through service is only possible when the organization treats its employees as a valuable resource. Well-known service-focused companies, including Whole Foods Market, Starbucks, Marriott International and Southwest Airlines, have long invested in initiatives focused on maintaining a holistic framework of making both their customers and their employees happy. Herb Kelleher, the founder and chairman emeritus of Southwest, summarized this philosophy well when he said, "You put your employees first, and if you take care of them, then they will take good care of you, and then your customers will come back, and your shareholders will like that, so it's really a unity." Howard Schultz, the CEO of Starbucks, echoed this view: "[Employees] are the true ambassadors of our brand, the real merchants of romance and theater, and as such the primary catalysts for delighting customers." For the past two decades, employee engagement has been a topic of interest both in the academic literature and among managers.

U.S. businesses spend $800 billion annually on sales force compensation and another $15 billion on sales training. Yet the backward-looking metrics they rely on (such as revenue generated) to gauge the impact of this spending provide limited insight into how a salesperson will do going forward and what types of training and incentives will be most effective. As a result, many companies misallocate sales force investments. The authors worked with data from a Fortune 500 B2B software, hardware, and services firm to develop a method for measuring reps' future profitability. The metric, salesperson future value (SFV), is the net present value of future cash flows from a salesperson's existing and prospective customers minus the costs of developing, motivating, and retaining the rep. The SFV analysis revealed that the firm had been overvaluing poor performers and undervaluing stars. Using the SFV calculations and data on each rep's prior training and incentives, the authors segmented reps according to whether they were motivated more by training or by various incentives. The firm then increased training for some reps and increased incentives for others, thus achieving an 8% increase in SFV across the sales force. The firm also increased its investments in high-SFV reps and reduced investments in low-SFV reps--a reallocation of resources that increased the firm's revenue by 4%.

Companies work hard to persuade their customers to buy additional products. But a sizable segment of cross-buying customers are unprofitable, and the more they buy, the more companies lose. It's important to identify and neutralize the impact of these profit-destroying customers.

With the growth of social media, influencing consumer preferences and purchase decisions through online social networks and word of mouth is an increasingly important part of every marketer's job. Companies such as Geico, Dell and eBay are adapting the traditional one-way advertising message and using it as a stepping-stone to begin a two-way dialogue with consumers via social media. Marketers know that theoretically, social media should be a powerful way to generate sustainable, positive word-of-mouth marketing. If marketers can only select the right social media platform, design the right message and engage the right users to spread that message, their campaign should be a success. But until now, that's been a big if. The authors propose a seven-step framework for success in social media marketing campaigns. Their framework involves identifying social media users who are not only influential but also particularly interested in the company's product or service category and then recruiting and incentivizing those influencers to talk about the company's product or service. The authors describe the implementation of their seven-step framework at Hokey Pokey Ice Cream Creations, an upscale ice-cream retailer with more than a dozen outlets across India. Hokey Pokey's social media campaign resulted in substantial increases in brand awareness, social media ROI and sales revenue growth rate for the company. The authors also explain three new metrics they developed for use in social media marketing campaigns: the Customer Influence Effect, which measures the influence a social media user has on other users in the network; the Stickiness Index, which helps identify social media users who actively discuss the company's product or service category; and Customer Influence Value, which helps measure the monetary gain or loss realized by a company in social marketing campaigns by accounting for an individual's influence on purchases by other customers and prospects.

This is an MIT Sloan Management Review article. Many companies see customers' product returns as a major inconvenience and an eroder of profits. But recent studies have begun illuminating the potential benefits of allowing customers to return products with impunity. This research finds that when a company has a lenient product-return policy, which allows customers to return almost any product at any time, customers are more willing to make other purchases, thereby raising the company's revenues from sales. The authors' own research extended these studies by exploring the trade-offs between the costs of product returns -particularly when customers deem such experiences satisfactory -and their long-term benefits to the company. Analyzing six years of purchase, product-return and marketing-communications data from "Company 1"-a large national catalog retailer that sells apparel and accessories -they confirmed that ignoring product return behavior, or even trying to discourage it directly by not marketing to customers who return products (such as by not sending them catalogs), would be a mistake. In fact, managers should embrace customers' product-return behavior and offer them a satisfactory experience. In a field experiment with a second catalog retailer, "Company 2,"which sells footwear, apparel and other accessories through the Internet and mail-order catalogs, the authors found that under a lenient product-return policy, customers' purchases, induced profits and referrals were greater than under a strict policy (which discourages and limits product returns). These measures could be raised even further through a catalog-mailing strategy that takes into account the expected future profits from each customer and the relationship between purchases and product-return behavior -i.e., through an optimal allocation strategy.

The customers who buy the most from you are probably not your best marketers. What's more, your best marketers may be worth far more to your company than your most enthusiastic consumers. Those are the conclusions of professors Kumar and Petersen at the University of Connecticut and professor Leone at Ohio State University, who analyzed thousands of customers in research focused on a telecommunications company and a financial services firm. In this article, the authors present a straightforward tool that can be used to calculate both customer lifetime value (CLV), the worth of your customers' purchases, and customer referral value (CRV), the value of their referrals. Knowing both enables you to segment your customers into four constituent parts: those that buy a lot but are poor marketers (which they term Affluents); those that don't buy much but are very strong salespeople for your firm (Advocates); those that do both well (Champions); and those that do neither well (Misers). In a series of one-year experiments, the authors demonstrated the effectiveness of this segmentation approach. Offering purchasing incentives to Advocates, referral incentives to Affluents, and both to Misers, they were able to move significant proportions of all three into the Champions category. Both companies reaped returns on their marketing investments greater than 12-fold--more than double the normal marketing ROI for their industries. The power of this tool is its ability to help marketers decide where to focus their efforts. Rather than waste funds encouraging big spenders to spend slightly more while overlooking the power of customer evangelists who don't buy enough to seem important, you can reap much higher rewards by nudging big spenders to make referrals and urging enthusiastic proponents of your wares to buy a bit more.

Despite an abundance of data, most companies do a poor job of predicting the behavior of their customers. In fact, the authors' research suggests that even companies that take the greatest trouble over their predictions about whether a particular customer will buy a particular product are correct only around 55% of the time--a result that hardly justifies the costs of having a customer relationship management (CRM) system in the first place. Businesses usually conclude from studies like this that it's impossible to use the past to predict the future, so they revert to the timeworn marketing practice of inundating their customers with offers. But as the authors explain, the reason for the poor predictions is not any basic limitation of CRM systems or the predictive power of past behavior, but rather of the mathematical methods that companies use to interpret the data. The authors have developed a new way of predicting customer behavior, based on the work of the Nobel Prize-winning economist Daniel McFadden, that delivers vastly improved results. Indeed, the methodology increases the odds of successfully predicting a specific purchase by a specific customer at a specific time to about 85%, a number that will have a major impact on any company's marketing ROI. What's more, using this methodology, companies can increase revenues while reducing their frequency of customer contact--evidence that overcommunication with customers may actually damage a company's sales.

Companies spend billions of dollars on direct marketing, targeting individual customers with ever more accuracy. Yet despite the power of the myriad data collecting and analytical tools at their disposal, they're still having trouble optimizing their direct marketing investments. Many marketers try to minimize costs by pursuing only those customers who are cheap to find and cheap to keep. Others try to get the most customers they possibly can and keep all of them for as long as they can. But a customer need not be loyal to be highly profitable, and many loyal customers turn out to be highly unprofitable. Companies can get more out of direct marketing if they see it as a single system for generating profits than if they try to maximize performance measures at each stage of the process. This article describes a tool for doing just that. Called ARPRO (Allocating Resources for Profits), the tool is essentially a complex regression analysis that can estimate the impact of a company's direct marketing investments on the profitability of its customer pool. With data that companies already gather, the tool can show managers how much to spend on acquisition vs. retention and even what percentage of their funds they should allocate to the different direct marketing channels. Using the model, companies can easily see that even small deviations from the optimal levels of customer profitability are expensive. The tool can also show that finding the optimal balance between investments in acquisition and retention can be more important than finding the optimum amount to invest overall.

Who wouldn't want loyal customers? Surely they should cost less to serve, they'd be willing to pay more than other customers, and they'd actively market your company by word of mouth, right? Maybe not. Careful study of the relationship between customer loyalty and profits plumbed from 16,000 customers in four companies' databases tells a different story. The authors found no evidence to support any of these claims. What they did find was that the link between customers and profitability was more complicated because customers fall into four groups, not two. Simply put: Not all loyal customers are profitable, and not all profitable customers are loyal. Traditional tools for segmenting customers do a poor job of identifying that latter group, causing companies to chase expensively after initially profitable customers who hold little promise of future profits. The authors suggest an alternative approach, based on well-established "event-history modeling" techniques, that more accurately predicts future buying probabilities. Armed with such a tool, marketers can correctly identify which customers belong in which category and market accordingly. The challenge in managing customers who are profitable but disloyal--the "butterflies"--is to milk them for as much as you can while they're buying from you. A softly-softly approach is more appropriate for the profitable customers who are likely to stay loyal--your "true friends." As for highly loyal but not very profitable customers--the "barnacles"--you need to find out whether they have the potential to spend more than they currently do. And, of course, for the "strangers"--those who generate no loyalty and no profits--the answer is simple: Identify early and don't invest anything.