Any company that uses marketing automation should be considering how the data being collected can not only inform what to market now, but also where to take the business in the future.
Last month, I looked at how Netflix provides us with a tangible example of how automation is used to recommend what its customers want at the very moment they are ready to watch a movie. But there's another important takeaway from The Atlantic article that I referenced, and which I did not have the opportunity to explore in my previous column: the idea that Netflix's data automation is not just driving customer recommendations, but actually helping the company make decisions about what to do next. This ultimately affects everything from licensing agreements to original productions - shows like House of Cards and Orange Is the New Black exist because careful analysis of Netflix data predicted their success.
It's not just Netflix that can use customer data to make sweeping decisions about where to take its business next; any company that uses marketing automation should be considering how the data being collected can not only inform what to market now, but also where to take the business in the future.
So much data collected on business-to-consumer websites, especially in the e-commerce sector, is collected and analyzed in order to create a tailored experience for the individual user at the time of his visit to the site - everything from customized recommendations to region-specific options to the general look and feel of the site. This happens based on data collected about the individual consumer, analyzed and processed by the site's automation engine and updated in real time to reflect behavior, location, and needs.
But this sort of consumer-specific predictive decisioning is an extremely limited view of its potential. Using the same tools currently in place for individual customer analysis, it's possible to flip the model. Instead of drawing conclusions about what a customer might want, the knowledge of what several customers do want can be used to decide what to market - or even create - next.
Missing the Boat, or Sailing the Wrong Direction
Without automation, there is no way for companies to measure in real time the effectiveness of a campaign as relates to consumer behavior, let alone any way to get ahead of an opportunity. All insights would come after the fact. For instance, it would be only after a daylong sale on laptop cases that a retailer would be able to measure the success of the campaign. If the campaign appeared to be a success, perhaps the retailer would invest more in promoting a similar sale in the future, or even developing its own laptop bag to compete with the other products that were sold.
There are two problems with executing a major marketing campaign or making major company decisions without having a data automation solution in place: either your campaign completely misses the boat - promoting a product or an idea without any concrete analysis indicating this should be done - or sailing in the wrong direction, relying on intuition instead of a clear map to steer your campaign in the right direction. You might eventually find another boat to get on, or point yourself where you want to be by sheer luck, but luck is unreliable in the face of data.
But instead of these retroactive approaches, the retailer should have been analyzing performance and sales in the moment, looking at which products were showing a propensity to perform well in a particular marketing channel. Automation would have allowed the retailer to capture the opportunity, getting more aggressive on certain laptop bags, and potentially finding related products that were also moving well that day. Those retailers who don't employ these strategies will continue to miss the boat.
I'll abandon my sailing metaphor here to give you the facts: automation is the only way you can keep ahead of the curve in a scalable way. It is impossible to manually capture data about product performance, and research and analyze product intelligence, competitive positioning, and search demand in anything resembling real-time. But it's this combination of data and trends, collected in real time or nearly and analyzed immediately upon receipt, that allows us to see how a product's performance is actually changing and get ahead of opportunities to perform in a marketing channel. Automation doesn't ask us to look at something after it happens, but rather provides insights into how performance of a product, or behavior of a customer, is changing. So if consumers are buying a little more of a certain product than they were yesterday, that product is contributing a little more to overall revenue and enhancing the likelihood that product is being searched for.
It's automation that acts on the data, in real time; if an opportunity is developing, automation can instantly capitalize on it. Marketing teams cannot manually capture all that data in real time, analyze it, and act upon it, to say: "Let's get ahead of this; let's take advantage of the opportunity brewing at this very second." Even if the teams can process all of the data they receive, by the time they finish, it will be too late to take any actions. Not everyone can develop the next House of Cards - not everyone needs to. But if there's something you can develop or market or provide to your customers because automation told you it would work, you're beginning to embrace automation's full potential: not just want to recommend now, but what to do next.
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Andre Golsorkhi is CEO of Sidecar, a leader in automated e-commerce marketing, powering paid search, comparison shopping engine advertising, on-site product recommendations, and email re-targeting for retailers ranging from $5M to $3B in annual online revenue, including Top 25 retailers in the US.
Prior to Sidecar, Andre was the CEO and Founder of Snipi, Inc. a patented system for people to save their interests while browsing anywhere online. Snipi was featured on the front page of the Wall Street Journal and established partnerships with the world's leading retail and entertainment brands.
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