A growing share of the marketing budget is managed according to the principles of targeted-performance marketing: personalized messages, direct impact measurement at the level of individual users, near-time optimization, and partial automation. This is especially true for digital marketing activities that drive conversion and purchase.
But what about the media spend focused on the earlier stages of the consumer’s decision journey, such as brand awareness, including traditional media or “mid- and upper-funnel” portions of digital marketing? While these investments often account for more than 50 percent of the marketing budget, they are managed with far less rigor than online spending. This leads to an unclear understanding of performance and a tendency to rely on accepted wisdom. Or, worse, mid- and upper-funnel spend in digital are held to the same performance metrics as the lower-funnel performance, often leading to a false or misleading understanding of the impact of spending. As a result, marketers often rely on their media agency to tell them what to do.
Data-driven performance marketing, however, can now be applied much more effectively to branding and demand-generation activities. With a clearer understanding of consumer preferences and behavior at the early stages of their buying journey, companies report marketing efficiency gains of up to 30 percent and incremental top-line growth of up to 10 percent without increasing the marketing budget. On average, the impact is significantly higher than that of established marketing ROI (MROI) methods for branding activities.
This precision in branding is particularly crucial now as companies manage the effects of COVID-19 on the early stages of the consumer’s decision journey. While much is still unknown, we believe that data-driven performance marketing will give marketers an edge when it comes to reaching their target groups efficiently during and after the pandemic. In a consumer survey conducted in the US in April 2020, respondents said they intend to watch more live news (net intent +19 percent) and read more news online (+14 percent) to stay up to date. At the same time, almost 20 percent of consumers have switched their “go-to” brands due to COVID-19. As these habits evolve, granular data analysis and disciplined marketing-performance management will be essential for brands to stay in touch with their customers and drive MROI.
Existing MROI approaches have inherent limitations
Marketers spend $560 billion annually on global advertising.1 However, standard cross-channel MROI approaches, such as marketing-mix modeling (MMM) and multitouch attribution modeling (MTA), to track the effectiveness of that spend are often unsatisfactory due to the following:
- Existing models are overly focused on late-funnel stages.
- Because they depend on historical data, existing models are focused on the past.
- Existing data sets are not sufficiently granular (except for digital marketing).
- Existing approaches struggle to account for the impact of message and creation.
- There is a gap between MROI measurement and marketing execution.
- Most existing approaches do not support realtime optimization.
Would you like to learn more about our Marketing & Sales Practice?
CFOs are especially skeptical regarding the output of existing approaches. In a recent McKinsey survey, 45 percent of CFOs said the reason marketing proposals had been declined or not fully funded in the past was that they didn’t demonstrate a clear line to value, and 40 percent didn’t think marketing investments should be protected during a downturn.
To overcome the limitations of standalone models and win over the skeptics in the C-suite, marketers often combine multiple tools, such as MMM for budget sizing, MTA for digital marketing, A/B testing for in-channel optimization, and surveys for brand performance. While such efforts can help increase the validity of the overall outcome, the integration is usually a manual process, and continued efforts are necessary to maintain different approaches in parallel, manage the interfaces, ensure quality control, and resolve potentially conflicting recommendations.
Worse yet, many marketers don’t have full confidence in the integrated data and the insights derived from them. It remains difficult to derive clear, actionable recommendations for in-campaign steering across touchpoints and funnel stages. “Increasingly, it feels like we’re trying to solve tomorrow’s problems with yesterday’s tools,” a veteran marketer said recently in summing up the situation.
Moving to ‘performance branding’
The massive improvements in data-tracking technologies have made what we call “performance branding” a discipline that is as rigorous and successful as more mainstream performance-marketing practices.
As the ability to track shopper preferences improves, however, companies first need to establish and follow strict consumer-consent management approaches and tools to make sure the use of that data matches consumer expectations and is fully compliant with all relevant laws, rules, and regulations.2 With these rules, guidelines, and governance procedures in place, companies can use increasingly sophisticated data techniques to understand shoppers’ preferences across channels and devices.
They can now apply the principles of performance marketing to the early stages of the consumer decision journey and to traditional media. B2C marketers, in fact, have said that data-driven marketing focusing on the individual is their number one priority.3 According to a recent forecast, investments in such solutions will triple from $900 million in 2018 to $2.6 billion in 2022.4 Examples of concrete advances include the following:
- Media consumption. Increasingly, the impact of traditional advertising can be measured and optimized at the individual or household level. For example, many smart TVs have a unique IP address and are equipped with automatic content-recognition (ACR) technology that lets them create a record of which ads have been viewed.
- “Identity graphs.” An identity graph is a collection of user-level data combined with identifiers such as digital cookies, physical addresses, email accounts, and mobile phone numbers. This type of database enables advertisers to better understand consumer preferences and habits along their entire decision journey and across channels and devices.
- Individual-level transaction data. In their efforts to understand and improve the impact of joint campaigns and promotions and also to monetize their data assets, retailers increasingly provide access to individual-level transaction data to manufacturers and advertisers. By combining their own data about advertising exposure with these records, advertisers can enrich their data sets to derive more reliable cause-and-effect relations.
- Personalization of consumer surveys and panel data. In the past, surveys generated insights about the average customer. Thanks to agile insights applications, questionnaires can now be customized, and individual responses can be matched with an existing individual-level database.
- Technological advances. The increasing capacity for data storage, faster IT infrastructure, and partial automations is allowing market researchers and data scientists to handle much larger sample sizes. Sophisticated algorithms can help companies understand individual customer behavior in real time.
As a result of these developments, a larger part of the marketing budget can now be managed like a true investment rather than as a sunk cost. Benefits include the ability to compare spend impact across media, analyze brand impact across a consumer’s entire decision journey (including “upstream” exposures that may have driven awareness but didn’t immediately drive a purchase), allocate in-campaign resources more effectively, and tailor content. We have also found that having this kind of unified view of the customer and clear view of the return on marketing spend provides an important bridge between the CMO and CFO. This data-driven perspective on marketing performance allows both to develop more thoughtful and aligned strategies for marketing spend.
A telco player, for example, used a data-driven performance-branding approach to determine which brand interactions contributed to an individual user’s choices, from TV-ad exposure and website searches to call-center contacts, and from acquisition to contract renewal. By applying this approach to its entire customer base, the company was able to increase brand consideration by 20 percent and marketing-driven gross adds by 15 percent.
In another case, a consumer-goods company used MROI measurement to determine the optimal creative rotation across channels. Based on the output of the new MROI model, the creative mix across selected channels was optimized, resulting in a 14 percent increase in advertising impact on sales.
One other often underappreciated benefit of performance branding and demand generation is that they help companies overcome the silo mentality of the past. Until recently, different parts of the marketing organization used different data and tools to make their cases. While digital marketers relied on attribution models to demonstrate the record returns generated by investments in search-engine marketing, brand managers cited marketing-mix models to make the case for a bigger TV budget. Performance branding, in contrast, serves as a single source of truth, and it helps companies create synergies between different elements of the marketing mix, such as online and offline, or between investments in brand perception and sales stimulation. “The new model has put an end to the bickering. For the first time, people are actually looking at the same data basis to figure out what is best for our brand, and we all get to go home earlier. That alone was worth the effort,” says the CMO for a large retail bank.
Core elements for getting performance branding right
Getting the full benefits of performance branding requires a fundamental shift in marketing practices toward greater precision and agility. Key ingredients of this shift include the following:
- Good data and a single source of truth that everyone agrees on. Strong data starts with building out a customer-data platform (CDP) that incorporates data from internal sources, external partners, and third parties in a compliant fashion and at the level of individual customers and provides links to execution platforms. The CDP needs to be developed and managed by a trusted team that can oversee the integrity of the data as a single source of truth for the business. As important as developing the insight is making it available through dashboards that CFOs and P&L leaders can easily access, a crucial capability to help stakeholders develop confidence in marketing’s ability to drive growth.
- Agile operations to move quickly and learn. These allow teams to both make quick tactical decisions and incorporate learnings to continuously improve offers. Marketers need to pursue a rigorous test-and-learn regime to ensure that new insights based on changes in technologies and consumer behavior are fed into performance-branding programs.
- Close collaboration with well-vetted agencies. Marketing teams should work closely with their agencies on key elements of performance branding, from detailed media planning to the ability to execute more changes at a much higher frequency.
When first introduced, performance branding is often met with skepticism and resistance, by both marketers and agencies. But when the value of this new tech-enabled capability is clearly demonstrated, we find that companies quickly move to adopt it.