Brief

Reimagining Marketing During Recessionary Times

How top brands are using marketing data and experimentation to stay connected to consumers.

By Justin Halim, Jessie Lin, Entis Sutisna, Boning Zhang, and Lakshmi Menon

Published on 七月 18, 2023
min read


At a Glance Consumers tend to respond to uncertain and recessionary economic environments in one of two ways: spending extensive time on product research before purchase, or making impulse decisions when standing in front of the shelf. Reactionary brands that lose customers in this critical period often rely heavily on traditional pricing and promotions to retain sales and tend to cut marketing budgets. Leading brands, on the other hand, use their data, test consumer behavior hypotheses across relevant media channels, and develop personalized communications to improve customer experience and drive customer retention. In recent years, consumers across the globe have cut back on spending against the backdrop of the global pandemic, with global retail sales down 2.9% in 2020. They shifted online, not only to purchase, but to research and engage with others before buying to stretch their purchase dollars. The current inflationary environment and recession will likely continue to fuel anxious customer sentiment. In such environments, brands often turn to reducing prices or offering promotions to attract new customers and retain existing customers. But these are not sustainable over the long term. Companies that view marketing spend and distribution as secondary often make large cuts to these areas in tough times. Instead, brands should seek to assess marketing spend and customer targeting using resources at hand and adapt marketing operations with a reimagined approach to stay connected with their customers. This can be accomplished by following three steps: (1) data collection and segmentation, (2) data activation via small-scale testing and validation of hypotheses, and (3) personalization. 1. Data collection and segmentation Brands can start by using existing customer data from various sources. Once an organization has collected data, it can enrich customer profiles with additional details to develop an in-depth understanding of customer behavior. The organization can then segment the profiles and use them for optimized targeting. Digital marketing uses in-house data (first-party, second-party, and third-party data) and external data from publisher ecosystems (e.g., Meta, Google, Amazon) to target customers. Today, many performance-driving strategies rely on retargeting customers using third-party data. However, with the advent of privacy laws and the deprecation of the cookie, brands are scrambling for ways to find and connect with their customers online. Several brands, including a cosmetics company Bain has worked with, have developed strategies around their first-party data (1PD) and are succeeding in the race to adapt to the increasingly privacy-conscious future while continuing to deliver personalized experiences for customers. Bain supports this approach by guiding brands in managing and using their 1PD to gain insights into their customers. A confectionary company based in India was heavily reliant on walled gardens for its customer data. Bain’s modern marketing capability, FRWD@Bain, enabled a 1PD-led transformation to reduce this reliance and help the company build its own data-driven marketing capability. Bain set up the infrastructure and process for 1PD collection and enrichment within the confectionary company’s existing system, collecting over 20 million 1PD records and developing a systematic test-and-learn framework to activate the data, resulting in 1.3 times the media effectiveness against the baseline. Bain’s Advanced Analytics Group used customer segmentation to help a retail company in Southeast Asia and an airline company decide where to play (which customer segments to focus on) and how to win (how to differentially tailor products, marketing, and operations for customer segments). One key insight we discovered in supporting the airline company was that the highest-spending customers were using their airline-affiliated credit card to continue engaging in the airline’s miles program during the pandemic, despite the fact that planes were grounded. This made them the most valuable segment through the pandemic, as they continued to earn points for their next flights while grocery shopping or refueling their cars. In another example, the retail company’s segmentation highlighted how shoppers with the highest spend per visit also had the fewest annual visits. We hypothesized that this was because these shoppers were promotion-driven and made the trip to physical stores only to restock on products. Therefore, we suggested that outreach to this segment be focused on the value these shoppers get for their purchase dollars rather than on product benefits. 2. Data activation via small-scale testing and validation of hypotheses Driven by an experimentation mindset, brands can develop hypotheses on drivers of sales, then prove or disprove hypotheses by testing marketing tactics with varying customer segments to determine key drivers of sales. Customer segmentation alone does not deliver value to the brand. But it does provide a peek into customer motivations and behavior, which should be validated through experimentation and in-market testing to truly understand how customers interact with the brand. The airline company’s marketing team sent offers as a guaranteed form of customer engagement and revenue. However, the team had a limited number of offers to work with and found itself struggling to provide new offers to customers to maintain engagement rates. We hypothesized that sending educational emails instead of offers to these existing customers would reinforce the benefits of the program. Keeping the brand fresh in customers’ minds would improve sales while reducing the need for curated offers. We identified members of the aforementioned high-spending customer segment who had stopped spending for three months and sent them communications regarding airline-affiliated credit card benefits to remind them to keep earning with the program. Within a month, we saw a nine-percentage-point uplift in revenue. The retail company’s marketing team, on the other hand, relied extensively on offline channels such as out-of-home spaces in stores to reach customers and demarcated digital channels primarily for customer engagement. We hypothesized that using digital channels to market to customers would increase offline sales, particularly from customers in the research phase and those in regions without stores. Our first test found a 14% uplift in sales revenue from users exposed to social media ads. Further, we found that targeting customers browsing near stores drove a 1.5 times higher conversion rate, with 50% of transactions occurring within seven days of customers seeing ads. This was likely due to optimal timing of the ads, reminding customers in the research phase to complete their purchases. Additionally, our second test found that targeting customers from regions without stores led to 18% higher average basket sizes than targeting customers in regions with stores. This was likely because these customers were restocking in bulk when they made the trip down to a store. 3. Path to personalization By embedding test insights and learnings into marketing communications, brands carve a path to personalization, delivering tailored experiences to customers at scale. These insights from tests are critical to personalization. However, they carry no weight if not applied to and scaled across customer segments. Brands may invest in advanced marketing technology, hire the best marketing analysts, or have exceptional customer understanding, but personalization truly starts once they become comfortable scaling test insights and key findings across their larger customer databases. For the airline company, we started small and selected lower-impact tactics such as email subject lines and social media creative assets. Then we ran simple A/B tests on them and measured their uplift on marketing metrics such as click-through rates and business performance metrics such as revenue. For example, we tested time of day and found that sending emails in the afternoon led to a higher email open rate than in the morning for the business flyer segment. This was likely because they spent their post-lunch lull periods on personal emails. We then scaled this learning across all business flyers in the customer database, given it was successful for our test group. While these small-scale tests were low effort, their impact when scaled was evident: 141 small-scale tests across eight weeks resulted in 30% higher email open rates, eight times higher click-through rates, and a 3% increase in members’ revenue over a three-month period. We went a step further with the airline company, when we found that sending reminders on program benefits to members on the verge of disengaging helped bring them back. This boosted member revenue and retention rates, prompting us to look into the full customer life cycle to find similar key moments to drive behavioral change. We identified 47 key moments in a typical customer’s life cycle (e.g., initial sign-up, earned first points, did not earn in a week); prioritized the moments when revenue was likely to decline; and then codeveloped relevant messaging for members in these moments to encourage them to continue to purchase. This set the airline company on the path to transforming its customer life cycle management from generic, one-size-fits-all messaging to tailored communications with the potential to drive program engagement and revenue. For the retail company, when initial tests across all customer segments showed that social media ads drove in-store sales, we scaled this learning across a subsequent campaign targeting existing customers only and found that the uplift in sales persisted, resulting in a 2.2% increase of in-store sales. This helped transform the insight into a golden principle to be applied across all customer campaigns, with social media earning its spot as a key media channel to drive offline sales revenue. Conclusion In times of economic uncertainty and a fast-changing technology landscape, we have seen nascent marketing teams daunted by the mammoth task of adapting their operations to stay relevant. But we have found the most successful organizations are those that operate with a strong and agile experimentation mindset and celebrate testing rigorously to move quickly from insights to impact.

At a Glance

Consumers tend to respond to uncertain and recessionary economic environments in one of two ways: spending extensive time on product research before purchase, or making impulse decisions when standing in front of the shelf.
Reactionary brands that lose customers in this critical period often rely heavily on traditional pricing and promotions to retain sales and tend to cut marketing budgets.
Leading brands, on the other hand, use their data, test consumer behavior hypotheses across relevant media channels, and develop personalized communications to improve customer experience and drive customer retention.

In recent years, consumers across the globe have cut back on spending against the backdrop of the global pandemic, with global retail sales down 2.9% in 2020. They shifted online, not only to purchase, but to research and engage with others before buying to stretch their purchase dollars.

The current inflationary environment and recession will likely continue to fuel anxious customer sentiment. In such environments, brands often turn to reducing prices or offering promotions to attract new customers and retain existing customers. But these are not sustainable over the long term. Companies that view marketing spend and distribution as secondary often make large cuts to these areas in tough times.

Instead, brands should seek to assess marketing spend and customer targeting using resources at hand and adapt marketing operations with a reimagined approach to stay connected with their customers. This can be accomplished by following three steps: (1) data collection and segmentation, (2) data activation via small-scale testing and validation of hypotheses, and (3) personalization.

1. Data collection and segmentation

Brands can start by using existing customer data from various sources. Once an organization has collected data, it can enrich customer profiles with additional details to develop an in-depth understanding of customer behavior. The organization can then segment the profiles and use them for optimized targeting.

Digital marketing uses in-house data (first-party, second-party, and third-party data) and external data from publisher ecosystems (e.g., Meta, Google, Amazon) to target customers. Today, many performance-driving strategies rely on retargeting customers using third-party data. However, with the advent of privacy laws and the deprecation of the cookie, brands are scrambling for ways to find and connect with their customers online.

Several brands, including a cosmetics company Bain has worked with, have developed strategies around their first-party data (1PD) and are succeeding in the race to adapt to the increasingly privacy-conscious future while continuing to deliver personalized experiences for customers. Bain supports this approach by guiding brands in managing and using their 1PD to gain insights into their customers.

A confectionary company based in India was heavily reliant on walled gardens for its customer data. Bain’s modern marketing capability, FRWD@Bain, enabled a 1PD-led transformation to reduce this reliance and help the company build its own data-driven marketing capability. Bain set up the infrastructure and process for 1PD collection and enrichment within the confectionary company’s existing system, collecting over 20 million 1PD records and developing a systematic test-and-learn framework to activate the data, resulting in 1.3 times the media effectiveness against the baseline.

Bain’s Advanced Analytics Group used customer segmentation to help a retail company in Southeast Asia and an airline company decide where to play (which customer segments to focus on) and how to win (how to differentially tailor products, marketing, and operations for customer segments).

One key insight we discovered in supporting the airline company was that the highest-spending customers were using their airline-affiliated credit card to continue engaging in the airline’s miles program during the pandemic, despite the fact that planes were grounded. This made them the most valuable segment through the pandemic, as they continued to earn points for their next flights while grocery shopping or refueling their cars.

In another example, the retail company’s segmentation highlighted how shoppers with the highest spend per visit also had the fewest annual visits. We hypothesized that this was because these shoppers were promotion-driven and made the trip to physical stores only to restock on products. Therefore, we suggested that outreach to this segment be focused on the value these shoppers get for their purchase dollars rather than on product benefits.

2. Data activation via small-scale testing and validation of hypotheses

Driven by an experimentation mindset, brands can develop hypotheses on drivers of sales, then prove or disprove hypotheses by testing marketing tactics with varying customer segments to determine key drivers of sales.

Customer segmentation alone does not deliver value to the brand. But it does provide a peek into customer motivations and behavior, which should be validated through experimentation and in-market testing to truly understand how customers interact with the brand.

The airline company’s marketing team sent offers as a guaranteed form of customer engagement and revenue. However, the team had a limited number of offers to work with and found itself struggling to provide new offers to customers to maintain engagement rates. We hypothesized that sending educational emails instead of offers to these existing customers would reinforce the benefits of the program. Keeping the brand fresh in customers’ minds would improve sales while reducing the need for curated offers. We identified members of the aforementioned high-spending customer segment who had stopped spending for three months and sent them communications regarding airline-affiliated credit card benefits to remind them to keep earning with the program. Within a month, we saw a nine-percentage-point uplift in revenue.

The retail company’s marketing team, on the other hand, relied extensively on offline channels such as out-of-home spaces in stores to reach customers and demarcated digital channels primarily for customer engagement. We hypothesized that using digital channels to market to customers would increase offline sales, particularly from customers in the research phase and those in regions without stores.

Our first test found a 14% uplift in sales revenue from users exposed to social media ads. Further, we found that targeting customers browsing near stores drove a 1.5 times higher conversion rate, with 50% of transactions occurring within seven days of customers seeing ads. This was likely due to optimal timing of the ads, reminding customers in the research phase to complete their purchases.

Additionally, our second test found that targeting customers from regions without stores led to 18% higher average basket sizes than targeting customers in regions with stores. This was likely because these customers were restocking in bulk when they made the trip down to a store.

3. Path to personalization

By embedding test insights and learnings into marketing communications, brands carve a path to personalization, delivering tailored experiences to customers at scale. These insights from tests are critical to personalization. However, they carry no weight if not applied to and scaled across customer segments. Brands may invest in advanced marketing technology, hire the best marketing analysts, or have exceptional customer understanding, but personalization truly starts once they become comfortable scaling test insights and key findings across their larger customer databases.

For the airline company, we started small and selected lower-impact tactics such as email subject lines and social media creative assets. Then we ran simple A/B tests on them and measured their uplift on marketing metrics such as click-through rates and business performance metrics such as revenue. For example, we tested time of day and found that sending emails in the afternoon led to a higher email open rate than in the morning for the business flyer segment. This was likely because they spent their post-lunch lull periods on personal emails. We then scaled this learning across all business flyers in the customer database, given it was successful for our test group. While these small-scale tests were low effort, their impact when scaled was evident:

141 small-scale tests across eight weeks resulted in 30% higher email open rates, eight times higher click-through rates, and a 3% increase in members’ revenue over a three-month period.

We went a step further with the airline company, when we found that sending reminders on program benefits to members on the verge of disengaging helped bring them back. This boosted member revenue and retention rates, prompting us to look into the full customer life cycle to find similar key moments to drive behavioral change. We identified 47 key moments in a typical customer’s life cycle (e.g., initial sign-up, earned first points, did not earn in a week); prioritized the moments when revenue was likely to decline; and then codeveloped relevant messaging for members in these moments to encourage them to continue to purchase. This set the airline company on the path to transforming its customer life cycle management from generic, one-size-fits-all messaging to tailored communications with the potential to drive program engagement and revenue.

For the retail company, when initial tests across all customer segments showed that social media ads drove in-store sales, we scaled this learning across a subsequent campaign targeting existing customers only and found that the uplift in sales persisted, resulting in a 2.2% increase of in-store sales. This helped transform the insight into a golden principle to be applied across all customer campaigns, with social media earning its spot as a key media channel to drive offline sales revenue.

Conclusion

In times of economic uncertainty and a fast-changing technology landscape, we have seen nascent marketing teams daunted by the mammoth task of adapting their operations to stay relevant. But we have found the most successful organizations are those that operate with a strong and agile experimentation mindset and celebrate testing rigorously to move quickly from insights to impact.