The 4 S’s: Identifying Datasets Marketers Can Use for Brand Measurement

4 s's

By Chris Kelly, CEO and Co-Founder, Upwave

It’s no secret that CMOs are constantly pressured to justify their brand investments.

They know that long-term brand building works, and that over-reliance on short-term goals destroys brand value, as seminal research by The IPA has shown. But telling a CFO to sit back and wait for long-term metrics to roll in will not help  CMOs keep their jobs.

Instead, CMOs are analyzing an array of datasets to answer questions about their brand — and they want those answers today. By reviewing data from the last few years about what CMOs want, we observed the emergence of four specific datasets: consumer sentiment, share of search, social media listening, and market share measured by sales.

In other words, the “4 S’s” of brand measurement: sentiment, search, social, and sales. Put together and used properly, the 4 S’s can unlock valuable, actionable insights for marketers.

Breaking Down the 4S’s

Sentiment data is used in a handful of ways — from measuring incremental media impact using experimental design to running traditional longitudinal brand trackers. Sentiment data can also be used for ad-hoc brand insights. But in terms of brand measurement reviewed by executives, the two most pervasive approaches are brand lift for measuring advertising effectiveness and brand tracking to observe ongoing brand KPIs.

In recent years, share of search measurement has become increasingly popular as an additional dataset. Share of search is used by brands and agencies, not only to have a pulse of ongoing brand health, but also because it carries a proven correlation between increased share of search and an increase in market share. Search data has the benefit of being observed, not declared or inferred, and available from an increasing number of sources (not just Google Trends).

Since the rise of social media platforms over a decade ago, marketers have used social media conversations to understand consumer perceptions of brands. This corpus is massive and exceedingly noisy, which is why tech platforms were developed to help marketers find signals from within that noise.

These platforms need to utilize natural language processing and weight for demographic biases of users, among other significant analytical challenges. But when used properly, social data can unearth brand insights and consumer trends not available elsewhere. Social also provides context unavailable in search data.

For example, a car manufacturer enduring an emissions scandal would observe a spike in share of search, but may not forecast a corresponding increase in sales. Social data would unearth the consumer outrage driving the increase in search query volume.

Sales data, perhaps, needs no introduction. Even marketers properly focused on long-term growth, and not just of quarterly sales, should tie their brand analytics to market share data underpinned by deterministic sales transactions. DTC marketers have the advantage of first-party sales data. Yet, even traditional marketers selling through retail channels saw an uptick in access to in-store sales data from loyalty card programs, receipt-scanning apps, and other innovations.

One caveat on sales data: Smart analytics teams know to look for the long-term (sometimes up to 24 months, depending on the category) impact of brand building on market share — no one sees a persuasive ad and immediately shuts off the TV to walk to the store to purchase.

Effective brand advertising generates demand and moves consumers down the funnel, so sales effects can be delayed. If marketers ignore this, they risk being the football team that only runs QB sneaks because analytics show that play has the highest chance of resulting in a touchdown. Marketers need to run campaigns that move them down the field.

How These Datasets Help Marketers

Marketers should view the 4 S’s as neither mutually exclusive nor directly competitive. Tying brand effects to market share can be illuminating. And linking share of search to share of the market can uncover the timing of when mid-funnel gains will result in lower-funnel gains.

Is there a panacea for measuring brand investments? No, and there probably never will be. But CMOs must leverage all available datasets and take advantage of emerging predictive analytics technology that can look at the immediate top-of-funnel impact and reliably forecast future lower-funnel wins.

By doing that, CMOs can get CFOs on the same page…at least until next quarter.

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