“Drive relevance at every consumer touchpoint!” It’s the rallying cry in the war for customer share of wallet.
When customers are met with content that causes them to hit the back button, the real hit comes in at the bottom line. Today, too many companies are floundering in their attempt to infuse relevance and extend reach in their marketing activities: They’re spending even more money on a wider array of channels and pumping up funding in digital assets.
Achieving relevance at scale—where the most pertinent content reaches the most promising customers at the moments of greatest influence across multiple channels and markets—does not necessarily require more campaigns, websites and costs to appeal to each segment. It means putting analytics to work, confirming each campaign experience and every site visit is precise and relevant to each target. That demands rethinking conventional wisdom and the traditional processes and structures that govern media planning and buying, measurement, data management and everything in between.
Media planning and buying
Traditional media planning and buying is done months in advance. Consider “specials” in markets like the United Kingdom, or “upfronts” in the United States. They offered a way of controlling and budgeting from a long-term perspective based on a broad understanding of demographic targets. Upfronts, specials or other forms of pre-buys made sense before digital. Now, more and more media spend is moving to digital. In digital environments, the power of analytics is multiplied—insights move past demographic facts to an understanding of customer intent. Search-based media buying, for example, can be executed on an hourly basis akin to a Wall Street trading desk, enabling advertisers to respond in near real-time, offering what the customer wants at the point of need.In today’s fast-paced, always-on marketplace, long-term ad purchases are fast becoming a blunt instrument; a hammer in a paradigm where the scalpel of analytics can carve out and respond with precision to the intent of the end buyer.
Look behind the processes of most large consumer companies, however, and pre-purchasing is still very much the norm. By continuing to adhere to these traditional practices, advertisers effectively miss out on the true potential of analytics: to precisely sense and respond flexibly and quickly to the most promising potential customers wherever they are in the buying cycle.
Measurement and targeting
Achieving relevance at scale requires turning conventional wisdom on its head when it comes to measurement and targeting. In the new digital landscape, the intangible is now tangible; hard and fast demographic clusters are now pixilated—broken down to a much higher level of granularity. Let’s start with the intangible—namely word of mouth, or WOM. WOM has always been a bona fide force driving consumer preference. But how can you capture water cooler chatter or over-the-fence gossip? Today you can in the form of an abundance of social media outlets from Facebook and Twitter, to yelp.com and mumsnet.com, to name a few.
Take Company A as an example. Company A offered a product similar to its competitor, Company B, and spent more money advertising it. Yet Company A was losing market share to its rival. After measuring the impact of WOM, the answer was clear: Company B had much more positive ratings, comments, reviews and feedback in social outlets. Company A, able to quantify the impact of WOM, established customer experience as a strategic priority.
In terms of granularity, analytics are challenging some of the hard-and-fast rules of advertising. Consider set-top box status, which captures the viewer segments ascribed to certain programs that Nielsen uses to sell ads for a certain marketing region. The data models from sources like set-top boxes are often too broad (again hammer over scalpel), lumping a wide-range of geographies together for example, making it difficult to understand detailed characteristics of the true end viewer. By leveraging analytical techniques, marketers can gain a more accurate picture of which customer targets are viewing what and target down to specific neighborhoods.
And at an even more granular level, through broadband, it is now possible to deliver ads based on individual IP addresses. Increasingly retailers can customize messages by household or by neighborhood, making for a radical departure from “spray and pray” traditional mass marketing. While many regional companies are acting on the ability to finely target customers, most mass media companies are not. But imagine the efficacy of a campaign by a luxury car manufacturer, for instance, that could place messages to buyers with a minimum yearly income and to a specific street address.
There is evidence that in digital what content a consumer is exposed to in the current session has the strongest effects on conversion. Being able to leverage real-time behavioral data to gain insights about consumer intent alongside other data sources will make the relevance tuning even more precise. Consider a company that not only knows what searches a potential buyer has performed before entering their site, but also whether this visit is their first or fifth. This specific behavioral data provides insight into what follow-up content is likely the most relevant to drive the buyer to the next stage of their decision cycle.