Future of Influence

Once, reaching the point of purchase was a simple and linear journey. A consumer saw a product or service, wanted it, bought it.

Now, the path is fragmented and research-intensive. It spans the discovery, comparison, and evaluation phases of the consumer journey that increasingly determine which brands even make it onto consumers’ consideration lists. It even encompasses the purchase phase, as bots and apps are increasingly able to close the deal for consumers.

To win in this environment, brands must be discoverable, desirable, and trusted across the touchpoints that matter to consumers; touchpoints that vary according to the stage of the journey and by the moment of demand (for example, if the purchase is planned or an impulse). Consider, too, the growing percentage of research-led consumers who look beyond the brand’s own marketing touchpoints and engage with a large ecosystem of digital touchpoints for advice, guidance, and validation. These include social media influencers and large language model (LLM) apps such as ChatGPT.

How are consumer purchase pathways evolving? Where do consumers place their trust? And how can marketers deploy social-media- and LLM-related approaches such as generative engine optimization (GEO) and answer engine optimization (AEO) to ensure that their brand touchpoints are a primary source of information and insight for consumers? How do they use not only brand assets but also paid touchpoints and influencers as well as earned reach and return and word of mouth to increase their influence across the journey?

If your question is why marketers should conquer these new approaches, consider the payoff for one multibrand retailer that has already embraced GEO and AEO, as shown in Exhibit 1.

Meet the New Consumer

According to a BCG Global Consumer Radar survey, approximately 50% of consumers approach their purchase without a predetermined brand preference. This comprises consumers who are impulse shoppers as well as the 43% of consumers who want to research and compare their options. This percentage tends to increase at certain times of year, such as back-to-school or holiday seasons, when consumers are looking for ideas, comparing products and brands, finding places to buy, and so on. Overall, we expect that the percentage of research-led journeys will continue to grow as barriers to extensive research fall, making the research-led consumer journey a critical path to purchasing. (See the sidebar “Methodology.”)

In addition, the proliferation of digital channels has dramatically expanded the number of touchpoints consumer-researchers encounter. Historically, consumers interacted with about 5 touchpoints; today, they may encounter more than 15. Many consumers use both online and offline touchpoints. Exhibit 2 shows how digital channels are increasingly dominant, outperforming traditional media in both information discovery and purchase conversion (by 9 and 13 percentage points, respectively).

It’s a pretty universal shift. Research-led journeys span demographics, geographies, and industries:

• It’s no surprise that more than 50% of Gen-Zers’ and millennials’ journeys are research-led, but so are 42% of the journeys undertaken by consumers aged 45 or older.
• Research-led journeys are the norm for more than 40% of consumers around the world.
• Even routine consumer goods categories are affected: 50% of journeys in apparel, 38% in food and beverages, and 36% in skin and hair care, for example.

For marketers, these trends present opportunities. When consumers start their journey without a brand preference (and about 50% of journeys start that way), marketers have an opening to guide them to their brands. This is most likely in the automotive, household appliance, and mobile tech categories. And by understanding the information these consumer-researchers seek, and how, marketers can develop AI-enabled playbooks to facilitate and personalize the journey to entice consumers.

Consumer-Researchers’ Reference Points and Trusted Advisers

With AI and LLM research rapidly emerging as trusted advisers, social media platforms and GenAI search experiences are now critical touchpoints influencing research-led consumers’ decision making. A reliance on research is more likely in complex and higher-risk categories and less likely in habitual or low-involvement purchases. (See Exhibit 3.)

Social media has become a “must win” channel. Once a means of communication, it’s now a primary research channel for consumers. With 97% of internet users active on social platforms at least monthly, social media plays a direct role in the discovery and evaluation phases. In 22% of research-led consumer journeys, this channel is used to gather information.

More importantly, social interactions are strongly linked to final purchase decisions: depending on the category, 90% of consumers who engage with social media rank it among their five most influential touchpoints, significantly ahead of traditional media channels, such as TV advertising.

This influence spans categories, from consumer packaged goods to discretionary purchases, and extends across age groups.

For brands, the implication is clear: social media is now a central battleground for consumer influence, from discovery to purchase, and it is expected to become an increasingly important purchase channel as well. A presence on social media is essential.

AI and LLM research are rapidly emerging as trusted advisers. The share of consumers using LLM research has more than doubled in the past few years, according to BCG Consumer Radar research. Already, 13% of research-led consumers interact with GenAI tools before making a purchase, a level approaching that of traditional TV advertising. Further, 85% of consumers who interact with GenAI tools across categories rank them among their top five most influential touchpoints in the purchase decision process, on par with social media and significantly ahead of traditional media. More importantly, 60% of consumers say they trust AI-generated results, citing clarity, objectivity, and personalized responses as key advantages. (See the sidebar “The Drivers of Consumer Trust.”)

Adoption is especially strong in complex and higher-risk categories such as travel and automotive, but usage is also growing in everyday categories like skin care and consumer goods.

For brands, the emergence of AI-driven discovery means that visibility in LLM-generated responses is becoming a new and necessary form of digital shelf space. But visibility alone is not enough. As AI tools compare alternatives in real time and consumers grow more discerning about which sources to trust, brands will be judged not only on how they show up but on what they can credibly claim and consistently deliver.

This shift also forces a harder question for brand owners. When an AI agent can compare attributes across hundreds of options in seconds (or, in some categories, transact on a consumer’s behalf), and when consumers can move directly to lesser-known products and retailers without the brand name acting as a guide, what is the role of the brand itself? While algorithmic comparison gets sharper, a strong brand still encodes emotion, identity, aspiration, and instinct, all of which still shape what a consumer reaches for. A list of features and attributes cannot fully capture that.

Three imperatives will define the winners in the age of research-led consumer journeys: discoverability, desirability, and consumers’ trust in the brand and in the systems that increasingly stand between brand and buyer. Strong brands give consumers a reason to choose, give algorithms a clearer signal to recommend, and give the relationship a foundation that survives the next interface change.

How Brands Can Shape the Research-Led Journey

The prevalence of research-led journeys, combined with the rise of social and AI-powered discovery, requires a fundamental evolution in marketing strategy; one that optimizes human and AI inputs. Winning brands will focus on continuing to find fresh answers to five high-priority questions that will remain germane even as the technologies and trends evolve.

How can I keep tabs on consumers’ research and trends?

• Understand what matters. As more information becomes available and research complexity increases for consumers, prioritize the true drivers of choice and convey them clearly to consumers.
• Continuously monitor and optimize consumer questions, needs, and decision drivers across search, social, and emerging discovery.
• Any consumer voice you can access, through consumer insights, social listening, synthetic simulations, and more, should inform your prompt strategy so that your GEO/AEO performance will be measured against the highest-value prompts. Then, you can optimize, test, and learn, thereby building a continuous process that incorporates routine learning into an ever-improving prompt strategy.

How do I build a fast-acting, consumer-centric content engine?

• Ensure that your brand articulates its functional, technical, and emotional benefits to the consumer. Doing so is essential given that LLM models are very rational and critical.
• Make sure to translate insights into scalable, research-led, GEO- and AEO-optimized content (such as user-generated content, Q&A modules, and social assets) that helps consumers and AI agents understand, compare, and choose products and services across channels.
• Given the growing volume of information, structure content and prioritize key messages, delivered at the right place and right time via a fast and responsive content value chain (delivering new content in no more than a week, rather than the six months or more that has traditionally been required).

How do I establish an always-on activation engine on social?

• Reorient your activation engine to ensure relevant content is continuously present and discoverable across paid, organic, and creator ecosystems. Don’t rely only on campaign bursts.
• Make content that is visual, exciting, and emotive; that has the brand voice and empathy; and that creates desire. Bear in mind: social media influences not just humans but also bots.

How do I make my website LLM-ready?

• Think of your site as a distribution hub that will serve up content discovered not only through SEO fundamentals but also through GEO and AEO content fundamentals.
• Key changes are to refresh the site’s technical foundation (for example, by ensuring data is structured for machine readability without parsing) and constantly updating product information to feed into LLMs. Establishing technical best practices for today will help prepare you for the agent-to-agent journey of tomorrow.
• Content on your site must be factual, functional, technical, rational, and comprehensive.

What else should I do to meet consumers where they are now?

• Measure, test, and learn. Establish a clear measurement system that enables structured piloting, learning, and scaling of what works across content, channels, and markets. This is critical, to ensure that content and activation are driving incremental uplift as trends and LLM functionalities continue to evolve.
• Establish a consistent set of KPIs to monitor and understand impact, especially in newer touchpoints like AI engines.

For CMOs, the shift from a marketing-led to a research-led journey requires answering new questions: Which touchpoints actually move the needle for your category and your consumers (not in aggregate, but for your target audience in the moments that decide brand choice)? Where is your brand earning credibility in the eyes of both human researchers and the AI systems they now rely on? And once you are found, what is the brand consistently delivering that drives positive reviews and justifies being chosen again?

As research-led journeys become the norm and new digital gatekeepers emerge, brands must compete not only for consumer attention but also for credibility and differentiation across an expanding network of decision touchpoints. Those that succeed will be the ones that show up consistently, with the right message, where consumers (and, ultimately, agents) search, compare, and decide.

Acknowledgments

The authors thank their BCG colleagues Loubna Khennoufa, Surbhi Jain, and Janmejai Bhargava for contributions to this publication.