The “now” economy focuses on immediate fulfillment of consumer demands. It is a rapidly evolving facet of modern business, mostly suported by millenials. In this economy, consumers expect goods and services to be available and delivered quickly, often in a matter of hours or even minutes. This concept has profoundly influenced various industries, including retail, entertainment, and services, and continues to evolve with the advent of new technologies, mobile apps and platforms. This blog post delves into the key characteristics of the “now” economy (1), examines the challenges it faces (2), and discusses the crucial role of data scientists in navigating this fast-paced environment (3).
1. Key Features: Instant Access, Personalized Services
The cornerstone of the “now” economy is providing consumers with what they want, exactly when they want it. Hence, immediacy and personalization reflect a shift towards a more responsive, consumer-driven market.
Instant gratification. This concept has had a significant impact on a wide range of industries. In fintech, for instance, companies like PayPal and Venmo have revolutionized financial transactions by offering instant money transfers, making financial interactions quick and seamless. In retail, Amazon‘s one-day delivery and Prime services epitomize the “now” ethos, catering to the consumer’s desire for rapid fulfillment. In transportation, companies like Uber and Lyft have disrupted the traditional taxi industry by offering on-the-spot transportation services that can be ordered and paid for through a mobile app. Food delivery services like DoorDash have made it easier to get delivered food from local restaurants and have it delivered in less than an hour to your doorstep.
On-demand services. The entertainment industry, led by platforms like Netflix and Spotify, provides on-demand content, tailored to individual tastes and available at a moment’s notice. The now economy has also created new opportunities for entrepreneurs and businesses to capitalize on this trend. For example, companies like Postmates and Instacart offer on-demand delivery services for a variety of products, while healthcare startups like Doctor on Demand offer on-demand virtual medical consultations.
2. Key Challenges: Maintaining Quality, Expectations and Data
However, the “now” economy faces its own set of challenges, chief among them being the maintenance of service quality and reliability amidst the rush for instant gratification. Additionally, the vast amount of data generated and processed in the “now” economy raises concerns about data management, privacy, and security.
Balancing speed with quality. The pressure to deliver quickly can sometimes lead to compromises in product quality or customer service. For example, in the food delivery industry, services like DoorDash or Uber Eats must balance the need for speed with the quality of food delivery, ensuring items arrive in good condition. Conversely, in the fintech sector, particularly in payment processing, the balance is between speed and security. Contactless payments, offered by platforms like Apple Pay or Google Wallet, provide a swift transaction method. However, they also introduce risks like contactless payment fraud, where unauthorized transactions can occur without the user’s knowledge. This necessitates fintech companies to implement robust security measures without compromising the ease and speed of transactions.
Managing consumer expectations. Another challenge is managing consumer expectations; the demand for immediate service can escalate to unrealistic levels, putting strain on businesses and workers alike. This is evident in gig economy platforms like Uber or Lyft where drivers face immense pressure to meet tight delivery schedules and high demand, especially during peak hours or in congested areas. This can lead to stressful working conditions, potentially impacting service quality and worker well-being.
Managing Data Overload. Great marketing challenges await in the future. Consumers are buying products with a thumb swipe, expecting them on their doorstep within days, hours or even minutes. This is blurring the lines across retail, advertising and brand positioning. Businesses now have to navigate a complex landscape where advertising strategies need to adapt to a fast-paced, multi-channel consumer journey. The challenge lies in executing effective marketing campaigns and accurately measuring their impact in this dynamic environment.
Managing privacy laws. With the enforcement of privacy laws like GDPR in Europe and CCPA in California, creating a comprehensive profile of consumers for marketing purposes has become more challenging. Consumers are increasingly using privacy-focused browsing options and disabling tracking on mobile devices. This change impacts digital marketers who previously relied on data from cookies and online tracking to understand consumer behavior and preferences. Marketers now need to find new, privacy-compliant ways to gather consumer insights and tailor their marketing strategies.
3. Data Scientists: Speed, Sustainability
Data scientists must therefore navigate the delicate balance between speed, accuracy, value and an ethical use of data to propel the “now” economy forward responsibly.
Scaling demand growth. In this fast-paced landscape, data scientists are instrumental in ensuring that businesses can sustainably meet consumer demands. They harness big data to optimize logistics and supply chains, ensuring that the promise of immediacy is met without compromising operational efficiency or service quality. They are tasked with creating algorithms that not only respond quickly to customer demands but also anticipate future needs. A notable achievement is the development of predictive algorithms by companies like Square, which balance transaction speed with effective risk management. Data scientists at Amazon also utilize predictive algorithms to anticipate customer orders and manage inventory, streamlining the delivery process. On the other hand, a negative example is seen in the challenges faced by gig economy platforms like Uber, where data-driven algorithms for optimizing ride routes and times can sometimes lead to driver fatigue and safety concerns.
Daily actionable data. Short-term demand drives short-term sales targets which drive the need for short-term decisions, and hence a need for instantaneous data. Luckily, these needs grew proportionaly with the insights that can be extracted from modern data structures. To provide answers, data scientists increasingly rely on monitoring social media, tracking passive search trends and retrieving daily advertising campaigns performance across all media channels, touchpoints and sales. They provide a wider context of an individual’s worldview, beliefs or habits. So they can understand why people do what they do in order to either influence their consumption or control the quality of their services. Platforms like PayPal and Robinhood utilize such data to inform their marketing strategies and financial product offerings, ensuring relevance and immediacy. However, the accuracy of these rapidly sourced data points is critical, as misleading information can lead to poor strategic decisions.
Multichannel marketing. The “now” economy does not accept technology boundaries. Marketing tools need to provide reach across multiple channels, often at a single point in time. A new generation of marketing data scientists is now powering marketing technology (martech) platforms. They provide marketers and sales representatives with a suite of tools and insights that enable interconnected and quick campaign decisions making to ensure a cohesive and effective customer outreach and investment return. All digital banks use martech to tailor their multi-channel marketing strategies across social media, email, and app notifications.
Focus on customer value. Companies today must adopt a mindset that prioritizes customer value, emphasizing the delivery of personalized products and services. For example, fintech startups like Chime focus on delivering customer-centric banking experiences, emphasizing fee-free services and user-friendly interfaces. The startup also offers features like early direct deposit, where users can access their paycheck up to two days early, enhancing the convenience factor. By focusing on these customer-oriented features, Chime has built a strong trust relationship with its user base. They have positioned themselves as a financial partner rather than just a service provider.
Ethical data use. However,ethical challenges persist, particularly in AI and machine learning applications. Data scientists should ensure ethical use of consumer data and avoid biases in AI-driven recommendations. An example of this is in credit scoring algorithms, where biases can inadvertently creep in, leading to discriminatory lending practices. Companies like ZestFinance, have addressed this by developing a more transparent AI model for credit scoring baptised ZAML Fair™. This involved rigorous testing and continuous monitoring of the models to identify any inadvertent biases and to validate that the credit decisions were socially fair and compliant with social regulations. They also began emphasizing the importance of “explainable AI” in lending, to ensure that decisions made by their algorithms could be understood and justified in human terms.
Explore more
Art Weinstein (2020) Creating Superior Customer Value in the Now Economy
Roxanne Jiang (2022) Is MarTech The New FinTech: A Guide To Marketing Technology