Because Google has transformed its free Google Product Search into the paid Google Shopping service, retailers must re-evaluate their comparison shopping engine (CSE) strategies. Retailers must ask themselves: How should the budget be redistributed? Where should the money come from? Which CSEs have the most impact? What will this all cost per click? And, most important, what is the average revenue per click (RPC)?
Harness the Big Data
Those are Big Data questions that marketers must be able to answer. Like the Oakland Athletics coach who put together a winning team on one of the league's smallest budgets by harnessing granular data, marketers have to capture the right information to support more strategic, more profitable media buys on CSEs and elsewhere.
Call it the Moneyball approach to cross-channel marketing optimization and media attribution.
In that 2004 book, Author Michael Lewis tracked Major League Baseball Coach Billy Beane as Beane eliminated the biases and assumptions that were the norm in scouting players and building a team. Instead, he demanded hard data, and acted on it. The result was a lower budget and a whole lot of wins.
Marketers could learn a lot from the Moneyball effect. As CSEs evolve and marketers struggle to determine which touchpoints influence conversion, they must find ways to capture, track, analyze, and take action based on the hundreds of thousands of marketing actions their customers take. That online and offline data delivers an understanding of what really drives conversions. The insights from such in-depth data far surpass what we can gather from aggregate data.
Shoppers encounter advertisements across channels, devices, and time. The marketers who capitalize on those interactions, and take actions based on what they know works, reduce their overall spending and register greater profits.
Let go of misconceptions
Take the first step (it's free).
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