This article originally appeared on Forbes.
Since 2015 and the rise of e-commerce hyper scalers, we have witnessed a fundamental shift in consumer behavior, one that has dramatically accelerated due to the COVID-19 pandemic, according to a Bain & Company. Consumers today are increasingly making buying decisions on the values that they hold dear– health and wellness, sustainability, safety, ethically sourced materials – instead of factors like price and convenience.
As businesses look for new ways to reach, engage and serve consumers, the delivery of timely, tailored and relevant experiences have become key. While this shift represents significant transformation challenges for companies, it also represents an enormous opportunity to drive the next wave of growth in consumer industries.
For over 75 years, industries have organized themselves around the concept of the “two moments of truth” – get a consumer into a store to purchase a product, and influence them to use the product. And hopefully, the cycle repeats. In this linear path to purchase with touchpoints that focused on getting consumers to complete transactions in a store, the retailers, consumer products companies and wholesalers in the value chain had well-defined and complementary roles, and all stakeholders favored economies of scale.
This is no longer the case. Today, companies are reaching consumers directly in “moments of opportunity”, requiring them to have a more holistic understanding of consumer desires. This model favors economies of speed – companies that can spot moments of consumer opportunity and organize themselves the fastest to orchestrate the delivery of experiences and products, will win.
To respond effectively to these behavior shifts, consumer products companies need to differentiate themselves as a ‘category of one’, where their business is so unique that there are no direct competitors, and these companies need to make innovative and data-centric decisions.
One example that illustrates this dramatic shift comes from a Google study. One participant spent 73 days and made 250 touchpoints (several blogs, merchant websites, local retailers, product reviews on YouTube) before buying a single pair of jeans. Indicative of today’s consumers, this consumer is engaged with and desires to be inspired by brands through viewing multiple options before making a selection.
This example demonstrates the changes businesses have to make to succeed going forward. We expect that soon, 50% of consumer products industry growth will occur through a direct business model. Consumer-facing companies will need to build compelling consumer experiences based on:
- Consistent master and consumer data across all channels to enable a consistent and meaningful experience
- Full visibility of the entire value network, from sourcing and partner networks to all consumer touchpoints, both physical and virtual, and actual consumption
- Live access to, and use of, both structured and unstructured data to assess demand drivers and market dynamics in real time
- Scaled, quantitative and qualitative analysis of consumer perception, sentiment and feedback to deliver a unified, personalized user experience
Key Technologies & Innovations
So how can companies transform and adapt quickly to meet these new expectations and needs while reaping benefits in productivity, efficiency, personalization, and profitability? With intelligent technologies that can quickly increase consumer-centricity.
Advanced Analytics & Machine Learning (or AI)
With advanced analytics and AI, companies can now mine massive data volumes with high granularity and speed to uncover connections and insights that would be otherwise impossible. The right analytics selection can bring augmentation that helps businesses find the right insights the moment they need it and AI algorithms can guide business users in finding correlations in the data. Companies that have already adopted these technologies are seeing significant performance improvements from accelerating and optimizing decision-making, allocating resources, and efficiently scaling operations.
For consumers, powerful AI-enabled algorithms could personalize experiences by inspiring, guiding and educating consumers. Consumers will reward companies that offer personalized shopping experiences, in fact, 71% of consumers express some level of frustration when their shopping experience is impersonal. Companies are now able to leverage AI to create dynamic consumer profiles that enable them to personalize campaigns in real-time, adapt to consumer context and needs while also helping to ensure data security and consumer privacy at scale.
AutoML – Modeling and Analytics
More startups are developing solutions that help companies make sense of their data without having to hire a dedicated data science team. This could become the norm in the enterprise software-as-a-service (SaaS) stack in the next five years. Data scientists will continue to be critical, but these technologies will help to scale their effectiveness, making their capabilities more accessible to everyday users, or “citizen data scientists”.
Data Marketplaces and Exchanges
Data marketplaces and exchanges represent a data-as-a-service (DaaS) that we see startups capitalizing on to monetize different sets of data. As with e-commerce platforms where end consumers can buy all kinds of goods, enterprises will soon be able to shop for specific sets of data to feed their algorithms. Companies are already beginning to explore opportunities to anonymize and monetize their own data, adding to and/or creating their own data marketplaces and exchanges to enable new revenue streams.
The Way Forward
In an increasingly interconnected world where data volumes are increasing exponentially every day, managing and maintaining, let alone understanding, all data volumes are now effectively impossible without technology. Businesses need to acknowledge that experiences need to be contextual to history and changing social signals as consumers evolve with age and core values transform.
The challenge is the amount of computational power, and the corresponding costs, associated with running real-time analysis. Increasingly, startups are focused on canvassing massive data sets to help companies with trends detection and analysis using advanced AI solutions which help companies cut through the noise and support decision-making processes. The output generated can be then plugged into their demand and forecasting processes to gain competitive advantage.
Mark Osborn, Roosi Magi and Miroslav Dimitrov also contributed to this story.