revenue-search

Generate Big Revenue in Paid Search With Big Data

It’s no secret that one of the fastest-growing ballooning marketing budgetary line item is paid search. Since its inception and – more recently – when Google introduced product listing ads in May 2012, paid-search costs have steadily increased. It’s shocking to hear that the top 10 AdWords spenders are shelling out more than $100,000 a day, with Geico reportedly leading the way at almost $400,000 per day!

While most in the digital commerce world are expecting PLAs to grow in usage compared to text ads, both are considerably important. But, what’s behind the curtain, after a consumer clicks on an ad – whether text or visual? Usually, you can expect poor experiences that have no ability to learn from the consumer’s behavior or intent.

Most retailers would want their customers to have an experience that is just as personalized, intuitive and comprehensive as a personal shopper armed with knowledge of individual behavioral patterns, history, preferences combined with a thorough understanding of every product available. After all, how better to provide exactly what someone wants in the quickest time, while offering opportunities for complementary purchases? But, if you are a retailer with 100,000 different SKUs for potentially millions of different existing and prospective customers, that collection of data points seems impossible to analyze and act upon in a timely fashion. Or is it?

Already, many of the world’s top search engines have done an incredible job with anticipating individual consumer’s queries and predicting the most likely match that fits their intent. Using big data, machine learning and advanced algorithms, it’s almost like Google is our confidant who’s worked with us basically emulating a real-life experience. Doesn’t it logically follow that this level of technology should be accessible to consumers on the other end of the click?

The answer to that problem is exactly what BloomReach Paid Search tries to answer. Every consumer interaction on a retailer’s site reveals intent. Think about when a consumer goes to a dressing room. They try on a garment, but it doesn’t quite work for any number of reasons. A salesperson can bring something in another size, color; can suggest a sale item or similar item that others like the consumer have bought. They may even remember the last time they were in the store. A retailer can have access to all of these intent cues, if interpreted correctly. Consider a customer who moves away from certain racks, toward others, or lets you know “definitely not this one” as bounce rate or time-on-page. Consumers that quickly bounce off of a landing page or convert more on a particular suggested product page are providing small digital cues where an intuitive system can learn.

Then add to that a complete understanding of attributes of every product across a site together with knowledge of consumer interactions and an understanding of constantly changing language models throughout the entire web. Continually collecting and synthesizing these billions of data points make up the core big-data power of BloomReach’s Web Relevance Engine.

The brands that provide premium and superlative experiences to understand their customers’ intent and provide the best content will hold the competitive advantage. As I mentioned in a December 2013 article for Search Engine Watch, technology is the great equalizer. Any brand who invests in big-data technology like BloomReach Paid Search can be just as successful as the behemoths like Amazon. Companies have focused gargantuan resources to widen the top of the marketing funnel – attracting as many as possible to a site, but have left the lower part of the funnel too narrow. In other words, they have dedicated money, research and data resources to predicting and optimizing for consumer searches, but have failed to use a data-driven approach to deliver onsite experiences for prospects that match their intent.

Like I said earlier, so many companies provide poor paid search experiences after investing lots of money. Search nearly any long-tail keyword, and you’ll see a bad landing page experience that requires clicking numerous times for the right produces, assuming anyone can take it. It’s like paying for signage outside a store to market new blue jeans and then having the customer walk in to find t-shirts on the shelves. The customer then is expected to search the bins until they find the advertised jeans. The probability of them returning and their perception of that company are severely downgraded.

Like all tenants of change, the first step is to admit you have a problem. Here are a few questions to understand that will help you gauge your paid search experience (also discussed further in the Search Engine Watch article):

1.       What keeps customers onsite if they don’t find what they want?

2.       Does a site suggest products intelligently for the business?

3.       How does a site adapt as its customers change the way they act?

Recreating the in-store experience at the size, scale and velocity of the constantly evolving online marketplace is not easy, but not impossible. It is a big data problem, one that only a big data solution like BloomReach Paid Search is working to address.