How Alembic takes on the challenges of measuring brand marketing
Brand marketing is essential for building long-term relationships with consumers and driving sustainable growth. However, measuring its effectiveness can be a daunting task. Unlike direct-response marketing, which delivers quick and measurable results, brand marketing impacts customer perceptions, emotions and loyalty over time.
Marketers face a host of challenges when trying to evaluate the success of brand-building efforts, from attribution complexities to integrating data across channels. Below, we’ll explore the five most common challenges in measuring brand marketing and explain how our platform helps overcome each one, enabling marketers to gain clearer insights and optimize their strategies.
Five reasons brand equity measurement is difficult:
1. Attribution complexity
Nearly all marketing teams today use an omnichannel digital strategy. To track campaigns, they rely on a multi-channel attribution and obsess over clicks and views, missing crucial touchpoints in your customer’s journey. Even worse, they often give too much credit to top-of-funnel activities while overlooking the vital work of nurturing leads and closing deals.
Multi-channel attribution assumes every touchpoint can be tracked, but this is often not possible due to privacy regulations, cross-device use or cookie expiration. It also doesn’t account for many touchpoints, such as interactions that happen on non-tracked channels or in real life, like a conversion at an event, over social media, or email. Finally, multi-channel attribution many times requires tracking individual users, which isn’t always possible or desirable.
2. Long-term brand equity analysis vs. immediate ROI
Brand marketing is a long game with a focus on long-term impact — but many leaders want to see immediate sales and ROI from marketing spend. Since brand marketing often takes time to show its full effect on consumer attitudes and behaviors, short-term measurement is less reliable. The effects of campaigns may take months or even years to present, and short-term measurements may not capture the true value of those brand-building efforts. This long-term horizon makes it hard to track and measure success using immediate KPIs, which requires marketers to consider more sophisticated, longitudinal approaches.
3. Siloed (or fragmented) data
Marketers rely on data from multiple sources, which may be siloed or incompatible. When campaigns span multiple channels (social, digital, TV, outdoor and more) and each has its own tracking system, it is hard to integrate data into a single, unified brand measurement dashboard.
Different platforms use different metrics, tools and ways of reporting, creating silos of data. As a result, marketers struggle to get a clear, cohesive picture of how brand marketing efforts are performing across all channels. Integrating this data requires advanced tools and processes, such as combining marketing attribution platforms with some form of media mix modeling (MMM), to analyze it comprehensively and understand how different touchpoints interact to drive overall brand performance.
4. Assigning value to intangibles
Marketers have long known that half of their budget is wasted. As John Wanamaker, a prominent 19th Century retailer, famously said, “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” This adage rings true even today.
Brand health metrics, unlike sales metrics, are more qualitative than quantitative. Brand marketing is an area where marketers need more tools to help them quantify and measure revenue impact, which will allow CMOs to more accurately allocate budget across channels and initiatives.
5. External factors
Seasonality, competitor activity, economic shifts and world events can influence a brand and its sales. These factors can also be difficult to isolate. A successful brand marketing campaign might coincide with a shift in consumer preferences or a competitor’s failure, making it hard to determine whether changes in brand metrics were driven by the marketing itself or by external forces. For example, a surge in brand awareness might happen during a broader cultural trend, skewing the measurement of a campaign’s impact. Separating these external factors from internal efforts requires advanced analytics and a deep understanding of the broader market context.
How Alembic outperforms other brand measurement tools
To overcome the challenges of brand measurement, companies may use a combination of tools, rely on a variety of KPIs and shift their perspective on what they consider brand marketing “success.” These obstacles highlight the need for advanced brand measurement framework and strategies.
Alembic overcomes common challenges of measuring brand efficacy by:
1. Providing attribution insights that do not rely on cookies and pixels
As a privacy-first company, Alembic specializes in cookieless attribution. Our marketing intelligence platform does not do user-level tracking, so there’s no need to explicitly tie data across devices. Instead, the platform uses an innovative aggregate, non-PII solution involving complex statistics, AI and signal processing to understand how users interact with a brand — both across channels and down the funnel. This makes Alembic immune to the impact of recent privacy regulations on cookies and tracking.
When tactics are executed across multiple platforms, marketers need the data to be normalized in such a way that the streams can be combined to find meaningful insights. This process makes it possible to combine all the data into a single dashboard, but that’s not the end goal: Alembic is pioneering a marketing intelligence-based approach where AI is used to generate insights and recommendations that reduce and eventually eliminate the need for a dashboard.
2. Surfacing the ROI of long-term tactics in real time
Alembic can capture attribution at many time scales. For longer-term impacts, Alembic will only surface those insights when the model can tie back to the relevant top-funnel activity, no matter how far in the past it was. For example, it might show a user that a conversion today relied upon a TV campaign from 3 months ago. Thus, the results are provided in real time, but only once they’ve matured enough for the model to be confident in them.
Our platform only makes attributions when they are relevant — so links in the chain will be drawn even if the root cause was many months in the past. Real-time is most useful for reducing media waste. Users can see quickly whether something is working and, if not, stop and redirect resources.
3. Reducing human error in analyzing marketing data
At Alembic, we think carefully about how to normalize data so it can be used to make apples-to-apples comparisons, instead of apples-to-oranges. Another differentiator is our focus on time series, which enables causal analyses. When necessary, the platform will also clean and reconstruct data to reduce human error, and extract time series wherever that dimension may be lacking.
The causal approach of Alembic’s marketing intelligence platform is inherently non-linear in the sense that it follows touchpoints in whatever direction the data suggests, to find accurate paths that impact revenue. There is less of a reliance on connected data sources, as many other sources are ingested: including third-party media, FRED data, foot traffic and more.
4. Making qualitative data easier to analyze
The user interface of the Alembic platform uses AI via a large language model (LLM) to translate the analysis of data and surface key insights in what we call an executive intelligence briefing. The deliverable is a bulleted brief, similar to an executive memo, which provides highlights that identify the most important opportunities, feedback and ideas to improve campaign performance. Alembic also offers an add-on product that gives market access to high-quality, qualitative brand survey data, which is incorporated into the system and used alongside quantitative data.
5. Using algorithms that consider extraneous factors
Alembic collects data from a wide range of sources (both online and offline) and aggregates it to provide a macro-level view of marketing performance. Its algorithms take extraneous factors, such as loyalty and seasonality, into consideration as insights that are surfaced to incorporate a wide spectrum of data inputs.
Based on historical analysis, Alembic can forecast the outcome of replicating a tactic or campaign. The platform will also soon allow users to tweak that forecast by adding or removing touchpoints to see the impact that would have. Users can also project deal value for long sales cycles based on historical conversion rates and average deal cycles — so they know how much leads are worth and when they will mature to realized revenue.
Test drive Alembic to measure marketing’s full impact
Brand marketing measurement doesn’t have to be a guessing game. The Alembic marketing intelligence platform offers solutions that help executives overcome the most common challenges in tracking and optimizing brand performance. In sum, Alembic:
- Tackles attribution complexity with a proprietary solution that holistically considers how users interact with a brand, without relying on user-level cookies and/or pixels.
- Surfaces insights that directly impact ROI in real-time for long-term, brand-building efforts.
- Normalizes, cleans and reconstructs data to reduce the human error of analyzing siloed or fragmented data sources.
- Analyzes and assigns value to qualitative brand health metrics.
- Considers the impact of anomalies and external factors to forecast campaign outcomes with greater accuracy.
Interested in learning how to measure brand awareness and engagement? The Alembic platform was designed to empower marketers with the insights needed to make more informed, data-driven decisions. Start using our brand marketing measurement tools today to realize the full potential of your marketing efforts. Book a demo with Alembic to see our brand lift measurement in action.
