AIDA. That is the acronym written on the whiteboard in almost every marketing department. Awareness, interest, desire, action.
Promote your products to customers and prospects in that order. Start by making the customer aware of your product through mainstream print, broadcast, and billboard ads. Then steadily work customers through the funnel: Build their interest in the product. Make them desire it so much they simply can’t resist it any more. And then spur them into action, moving them to that decisive purchase phase.
It has worked for generations, increasing business growth and driving marketing return on investment (MROI). After all, customers rarely buy anything—whether it’s a car or a cupcake—without knowing something about the product, the brand, and the benefits it all brings.
Not anymore. Consumer behavior has changed beyond all recognition in the past five years. Those traditional ways of thinking about the consumer, such as the marketing funnel, simply don’t apply any longer. Don’t wipe AIDA off that whiteboard yet; it still has relevance. However, the consumer decision journey is now subject to many different moments of influence—which means that dynamic, complex journey is more difficult than ever to analyze.
Why is it so hard to analyze? Principally because of the increasingly fractured nature of marketing channels, including the Web, social, and mobile. Moreover, this fragmentation means there is more marketing data than ever to analyze. This conundrum is coined “big data”: a massive volume of both structured and unstructured data that is so large, it is difficult to process using traditional software techniques.
All of this is encapsulated in a compelling new McKinsey article, “Using Marketing Analytics To Drive Superior Growth.” McKinsey argues that companies now have so much analytical firepower at their disposal that they often become paralyzed, defaulting to just one planning and performance management approach.
McKinsey cites the example of a home appliance company that used to spend a large portion of its marketing budget in print, TV, and display ads. But data-driven analysis of the company's target buyer journey suggested spending should shift away from general advertising to distributor website content. The result? A 21% upswing in e-commerce sales.
So in a world dominated by social and big data, how can marketing professionals and their agencies analyze the needs, preferences, and behavior of their consumers and prospects? And maximize MROI? Here are three golden rules from the report to give clients better recommendations on where to put their precious ad dollars and reach the right people.
1. Work out which analytical approach works best for you: It’s a first step, but a profound one. You need to balance the arguments for and against each social/big data analytical tool. Marketing-mix modeling (MMM), for example, links marketing investments to other big data-related sales drivers, such as seasonality and competitor activities. It reveals intelligence, such as changes in individuals over time and differences among social media activities. However, MMM does require several years’ worth of sales and marketing spend data to be effective.
Meanwhile, heuristics (such as reach, cost, quality) break down each touch point into its component parts—the number of target consumers reached and the cost per unique touch, for example. Attribution modeling should also be considered an analytics tool. This comprises algorithms that govern how credit for converting social or other traffic to sales is assigned to online touch points, such as an e-mail campaign, online ad, or a social feed. Those credits help marketers evaluate the relative success of different social and online investments in driving sales.
2. Integrate your MROI tools : An integrated suite of MROI tools irons out the wrinkles in any single social/big data analytics solution. Let’s imagine a company spends 80% of its marketing budget on TV, digital, print, and radio. Since the resulting audience measurement data can be tracked longitudinally, it makes sense to use MMM. However, digital spending can be refined further using attribution modeling to pinpoint the activities within broad categories—such as social campaigns or search—that are likely to generate the most conversion. The company could then use heuristics analysis to monitor the remaining 20% of its spending.
3. Make data scientists your BFF : Stay close to the social intelligence. Don’t simply turn on outsourcers for analysis. Work closely with your data scientists, marketing researchers, and digital analysts to question assumptions, formulate hypotheses, and fine-tune the calculations. Turn to “translators,” too—individuals who both understand the social analytics and speak the language of business.
Now, more than ever, marketers need to programmatically understand what is being said inside social data streams. Follow these simple guidelines and you’ll begin to see a rising curve on your MROI.