In December 2022, Loris's executive team considered their go-to-market strategy. Loris was an artificial intelligence (AI) software startup for the customer service industry with two products on the market: 1) Agent Assist which provided customer service agents (CSAs) with empathetic, on-brand responses to text-based live chat (live chat) conversations, and 2) Insights which provided customer experience (CX) leaders with CSA performance and customer satisfaction (CSAT) data. Loris was also developing a third product: Automated Quality Assurance (AQA) which analyzed quality assurance in customers' email and live chat conversations. The Loris team faced challenges to growth with prospective clients cutting costs through laying off their CX leaders and automating customer conversations through chatbots including the recently-released ChatGPT generative AI chatbot. To increase marketplace traction in preparation for raising a Series B round, the Loris team was reevaluating two aspects of its go-to-market strategy. First was sales approach: Loris previously used a sales-led growth model with robust marketing and sales teams, but had begun experimenting with product-led growth (PLG) which focused on developing exceptional products so that word of mouth would drive quick and exponential sales. Loris's PLG efforts had little success, though, and the team wondered if they should continue with PLG, revert to sales-led growth, or pursue pay-for-performance where clients only paid for Loris products upon Loris's achieving agreed-upon revenue or cost savings. Second, was product strategy: Loris had been offering Agent Assist and Insights as a bundled suite, but was considering using one of those products or AQA as a foot-in-the-door approach to cross-sell and upsell other products. Which sales and product strategy would help Loris grow, especially given the threat from ChatGPT which both raised awareness of AI tools like Loris and served as competition to Loris?
Advanced analytics can help companies solve a host of management problems, including those related to marketing, sales, and supply-chain operations, which can lead to a sustainable competitive advantage. But as more data becomes available and advanced analytics are further refined, managers may struggle with when, where, and how much to incorporate machines into their business analytics, and to what extent they should bring their own judgment to bear when making data-driven decisions. In general, humans are better at decisions involving intuition and ambiguity resolution. Machines are far superior at decisions requiring deduction, granularity, and scalability. How can you find the right balance? There are three common approaches to analytics: descriptive, where decisions are made mainly by humans; predictive, which combines aspects of the other two; and prescriptive, which usually means autonomous management by machines. This article describes when and how to use each approach and examines the trade-offs and limitations. Although the focus is on marketing and sales, the principles may be applied more broadly.
T.V. Narendran, CEO of Tata Steel, India's oldest steel manufacturing firm, had taken concrete business and cultural transformation steps to future-ready the firm since taking over in 2013. He had deleveraged and instilled financial discipline, acquired new businesses, entered new segments and adjacent businesses, and launched digital transformation, agility, safety, and sustainability programs. At the heart of his transformation program lay the digitalization drive. Now, he turned his attention to the firm's burgeoning B2C business. In 2018, the firm had launched an e-commerce platform to sell products directly to retail customers. Now he needed to decide whether to open up this platform to non-Tata group third-party suppliers of ancillary home construction products, like cement, paint, tiles, and other home fittings. Would this create a unique one-stop shop for construction-related goods, or take Tata Steel further away from its core mission? He and his team needed to decide fast.
Bajaj Finance, India's largest consumer finance firm with $20.9 billion of assets across 50.5 million customers, is on a journey to transform itself from a traditional firm that sells loans and other financial products through brick-and-mortar outlets to an omnipresent firm that offers customers a seamless experience across the physical and online world. Can a traditional offline firm with limited experience in the digital arena embrace a new customer-focused way of working? Can it win against a consumer tech giant like Amazon, which aspires to be the "earth's most customer-centric company"?
During the early part of the 2021 Covid-19 pandemic, Hometown Foods, a large seller of flour-based products, thrived as consumers hoarded baked goods and took up baking to pass the time and find comfort. Then, amid growing shortages in commodities, a vaccine arrived, businesses began to re-open, and consumers benefited from federal relief aid. This perfect storm of high demand amid stock shortages generated the highest inflation in 13 years. Commodities accounting for a large percentage of its products, Hometown had to decide whether to increase prices, and if so by how much. Although the industry norm was to wait for the number #1 player in each product category to increase price first, with escalating ingredient costs significantly reducing Hometown's margin and profit, lack of action could result in serious financial distress. A decision to move first would entail several more decisions. Should Hometown price for cost or elasticity, employ component or blended pricing? Given pandemic-induced volatility, how should it prioritize quantitative pricing calculations relative to qualitative considerations? It would also need to anticipate reactions from competitors and stakeholders including its sales force, retailers, and consumers as well as investors and lenders.
Distinct Software (disguised name), a global enterprise software company, is at an important point in its growth trajectory where the luster of its mantra of "grow and win at any cost" has dimmed with increasing competition and margin pressures. To help navigate its sales organization through this difficult phase and improve sales productivity, the firm has hired Sam Chadwick, a digitally savvy industry veteran as its new Global Sales Head. Chadwick would like to use the power of AI to get the job done but is expecting stiff resistance from his sales organization. Chadwick thinks his best shot to demonstrate the power of AI is to use it to predict the chances of winning three specific deals as detailed in the case. Students can use the accompanying large-scale dataset of the firm's win/loss analysis to develop AI models for predicting probability of winning deals and provide guidance to the firm on whether or not to pursue each of the three specific deals.
During economic slowdowns, consulting, law, and accounting firms often start offering services and taking on clients they really shouldn't, just to keep the lights on. This path is perilous. If a firm's practices have a diffuse mix of clients and unclear strategic positioning, it will weaken the firm's market profile and lead to internal conflicts, especially about the organization's future direction. This article presents two tools that professional service firms can use to manage their client mix and optimize their strategic position. The first is the practice spectrum. All practices fall on a continuum of sophistication that ranges from "commodity" to "rocket science." Any position on this spectrum can be profitable, though the forces driving profits change as you move along it--as do the capabilities and skills required. Successful practices understand their true position on the spectrum and know which performance levers to pull. The second tool is the client portfolio matrix, which separates clients into four categories on the basis of cost to serve and willingness to pay. Rather than spreading clients across all four, firms should target their acquisition efforts and follow different relationship strategies for each type of client.
This brief case describes the rise of so-called digital natives (also called born-in-digital) in the 2000s and 2010s that successfully grew without a sales force. The case highlights the emergence of business-to-business Internet and cloud-based companies and their decisions regarding sales strategy based on different customer bases. The case discusses the firms Atlassian, Basecamp, and Slack.
Launched as a private brand in 1980 to counter the increasingly brand-conscious consumer in Japan, MUJI offered beautifully designed, fairly priced, no-frills quality goods. The once modest private label brand with 40 products had expanded significantly by 2019 to more than 7,000 products with more than half its 975 stores outside its home market in Japan. It had even expanded into the service industry, opening hotels. President Matsuzaki of Ryohin Keikaku, MUJI's operating company, was charged with reorganizing the product portfolio and prioritizing new initiatives, tasks complicated by the absence of a clear definition of "MUJI-ness," the meaning of which had always been intentionally left open.
Launched in 1981 as an "all occasion" sparkling water brand, LaCroix Sparkling Water has had a number of ups and downs as a brand. After being purchased by National Beverage in 1996, the brand was re-positioned as a new, colorful, fun alternative to the other sparkling water players at the time. As time passed; however, the brand was faced with internal turmoil as well as shifting customer expectations. How did LaCroix react to change both within the company and within the industry?
Launched in 1981 as an "all occasion" sparkling water brand, LaCroix Sparkling Water has had a number of ups and downs as a brand. After being purchased by National Beverage in 1996, the brand was re-positioned as a new, colorful, fun alternative to the other sparkling water players at the time. As time passed; however, the brand was faced with internal turmoil as well as shifting customer expectations. How did LaCroix react to change both within the company and within the industry?
Twenty-four-year old Ritesh Agarwal, founder and CEO of India-based online, hotel branding network OYO Rooms, has tackled the issue of unreliability in India's highly-fragmented budget hotel industry. In 2018, OYO branded 8,500 properties across 200 cities and managed to capture almost 1.5% of India's budget hotel market. Ritesh believes that in the process, OYO has honed technological skills and infrastructural capabilities that can transform the company from being a technology player to a hotel developer. OYO now aspires to convert corporate spaces and homes into accommodation spaces, and to make its mark worldwide.
Soon after being named regional managing partner for Ernst & Young (EY) China in September 2009, Albert Ng reflects on the enormity of challenges facing EY China. Despite EY Global's commitment to the China practice, EY China's growth agenda has been reversed, post global financial crisis. The smallest of the Big Four global accounting firms in China, EY China's reputation has weakened in the market, morale of its professionals has plummeted, and it faces the threat of a major lawsuit. Ng is musing how to address all these challenges confronting EY China.
The case outlines how regional managing partner (RMP) Albert Ng steered Ernst & Young (EY) China through a period of significant growth from 2009, when it was the smallest of the Big Four firms in China, to 2017, by when it had become the second largest firm. Partners worldwide felt a sense of shared achievement, since EY Global had come together with China leadership to invest in building a strong China practice that was integral to all of EY. But Ng was not resting content. he believed that EY China was at a critical juncture. Chinese economic growth, which had partially fueled EY China's dramatic rise, was slowing down, just as competition was intensifying. Was EY China prepared to face the challenges that lay ahead?