The Big Reset: Navigating Data-Driven Marketing in the Next Normal
Nelinia (Nel) Varenas, MBA
Co-founder & Executive Advisor with U.S. Manufacturing Strategic Value+ Solutions | Certified ISO 9001 QMS Auditor | Six Sigma Black Belt (candidate) | FP&A SME | Marketing Guru | AI & Automations Nerd | Author | Speaker
The recent report by McKinsey, "The Big Reset: Data-driven Marketing in the Next Normal," underscores how businesses must adapt their marketing strategies in light of shifting consumer behaviors and a changing economic landscape. McKinsey's findings highlight the challenges marketers face in updating their models to remain effective while providing actionable insights for capitalizing on new opportunities.
Understanding the New Challenges in Precision Marketing
In the report, McKinsey notes that precision-marketing models traditionally rely on established behavioral patterns to predict customer actions. For instance, an algorithm might recognize that customers visiting a store's website multiple times within a set period are more likely to make a purchase. This data-driven approach allows marketers to focus their efforts on the most profitable customer segments, optimizing conversion rates and increasing returns on investment (McKinsey & Company).
However, the pandemic has disrupted these once-reliable patterns, invalidating many of the previously established data models. External factors, such as customer mobility restrictions, have introduced new variables that marketers must now consider. McKinsey emphasizes that behavioral changes are more challenging to discern and predict in this environment. For example, a drop in store visits may be due to various factors: the customer's preference for online shopping, physical store access restrictions, or concerns over health and safety.
The implication here is significant. To keep pace with these changes, marketers must integrate a broader set of data sources into their models. As noted in the McKinsey report, some companies have started pulling in data at more granular levels, such as city blocks, to identify trends and adapt their marketing efforts accordingly. This approach requires a willingness to explore new data points, refine models, and continuously iterate based on real-time feedback (McKinsey & Company).
Adapting Strategies with New Data
McKinsey provides a valuable example of how tapping into new data sources can lead to substantial business outcomes. A retail chain, seeking to understand its customer shifts during the pandemic, incorporated cell phone data to track competitor traffic changes. This analysis revealed that new customers were transitioning from more expensive specialty stores, while existing customers leaving the chain were opting for cheaper, larger-format competitors. Armed with this insight, the retailer was able to tailor its marketing campaigns, promoting higher-end products to new customers and offering discounts to retain price-sensitive customers. These data-driven strategies led to more targeted marketing efforts and an increase in customer retention and acquisition (McKinsey & Company).
This strategy emphasizes the importance of combining in-house customer data with external data sources to create a holistic view of market dynamics. Companies that ignore these new data sources risk losing touch with their customers’ evolving preferences and behaviors, leading to missed revenue opportunities.
Investing in AI and Technology That Learns at Scale
McKinsey's report also underscores the growing necessity for technology that learns at scale. AI has become an essential tool for marketers, enabling them to quickly read and interpret consumer intent signals. Unlike traditional analytics, which may take days to extract actionable insights, AI can analyze and adjust strategies within minutes, responding to real-time shifts in consumer behavior.
A notable example shared in the McKinsey report is of a consumer services company that leveraged AI to evaluate campaign responses at the core base statistical area (CBSA) level. This AI-powered analysis revealed that their marketing campaign was highly effective in specific niches with similar economic and epidemiological profiles. By integrating AI-driven insights into their marketing engine, the company was able to continually adjust its targeting logic, thereby increasing its campaign's effectiveness (McKinsey & Company).
Overcoming the Barriers to Agile Marketing
The report points out two major challenges marketers face in adopting agile marketing practices: budget cuts and the shift to remote work. Many companies have seen their marketing budgets slashed, forcing them to make tough choices about where to allocate their limited resources. Agile marketing is particularly effective when teams can test and iterate in quick sprints. However, the move to remote work has disrupted traditional workflows, making it harder for marketing teams to maintain the rapid pace of testing and iteration needed for agile operations.
领英推荐
McKinsey recommends that companies find ways to reallocate their marketing budgets to invest in technology and analytics. Some organizations have successfully identified areas of unproductive spending, such as event sponsorships and outdated programmatic display advertising, and redirected those funds toward data-driven marketing programs. By investing in AI and agile practices, companies can continue to adapt their strategies to the rapidly changing market environment (McKinsey & Company).
Implications of McKinsey's Findings
The McKinsey report presents a critical message for marketers: traditional data models and marketing strategies are no longer sufficient in the current landscape. The pandemic has accelerated shifts in consumer behavior, and businesses must be proactive in updating their data models, leveraging new data sources, and adopting AI technologies to keep up.
The implications are clear. Businesses that fail to adapt risk falling behind, as their marketing efforts will not align with the new consumer behaviors. Conversely, companies that embrace data-driven marketing with agility, continuously refine their models, and integrate AI into their marketing engines are more likely to gain a competitive advantage. McKinsey emphasizes that the key to success lies in recognizing that data-driven marketing is not a static process but a dynamic one that requires constant attention, refinement, and investment.
Additional Resources for Further Research
By incorporating these resources and the insights from McKinsey's report, marketers can better navigate the evolving landscape of data-driven marketing and stay ahead of consumer behavior shifts.
We Can Help
We can guide you through the dynamics of your data-driven marketing and provide expertise. If your company needs help with internally communicating and marketing to build alignment to leverage the new norm, contact us for a free discovery session. In either case, schedule a strategic meeting with us to start developing an agile marketing plan.