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Paid search is a $20 billion dollar industry that is responsible for over one-third of all search engine traffic to e-commerce websites. Despite these impressive numbers, the vast majority of CMOs view paid search as a basic transactional relationship with no real strategic value beyond the revenue from the click. Best case scenario: search engine marketers bid on keywords, a consumer enters a search query and clicks on an advertisement, Google or Bing charges the retailer for that click, and the consumer proceeds to the website and buys a product. Case closed.
This transactional model has served as the blueprint for the paid search industry since its inception, but forward-looking CMOs are now starting to reimagine how to maximize the value of their paid search spend. These are the same CMOs that were early adopters of big data technology. They realize that data is power, and that their paid search campaigns are really gold mines packed with data about consumer intent, behaviors, and preferences.
Just a few short years ago, the full power of this data was out of reach for most marketing professionals. Even if they could collect it, the ability to analyze and extract insights did not exist. But with the prevalence of big data and advanced analytics tools today, select CMOs are diving in headfirst. Paid search can and should be used as a real-time marketing feedback tool that can generate valuable business insights.
Search data is packed with insights
Depending on the size of a paid search campaign, the amount of consumer data produced can be staggering. In fact, there are few areas on the web as ripe with freely volunteered insights into consumer purchase intent, jargon and engagement queues. Imagine if a retailer could issue a survey to all prospective customers asking which products they would like to buy, how they would describe those products, how much they are willing to spend, and which product image they find most compelling. This information would be invaluable, right?
By examining search queries, retailers can extract all this data. When consumers enter a query, they are not just telling retailers what they want to buy. Queries also tells retailers how consumers describe products – which product attributes are most important to a consumer. Conversely, based on terms not contained in a query, which product attributes are least important. When a consumer does or does not click on an ad, they are indicating which visuals, promotions and prices they find compelling. Every click is a consumer direct response, and it can and should be used by marketers to continually improve their product titles, descriptions, images, promotions, prices and ads.
When this data is accumulated and averaged out among hundreds of thousands of interactions, it paints a vivid picture of customer engagement. The data can be used to inform strategies for aligning products with consumer desires. This extends beyond adjusting keyword bids. CMOs can use the data to increase the duration or frequency of promotions on the products people are searching for the most. They can share the data with the merchandising team so they can start carrying popular items that are not currently in the catalog. They can change the language used to promote items in a manner that better conforms to the syntax or jargon that correlates with highest conversions. The data is rich, and these insights are waiting to be tapped.
This isn’t just good for the web
These insights should not be confined to e-commerce. In fact, many learnings can provide marketers with valuable information for brick-and-mortar stores. For example, the CMO can pass the information to merchandisers who can use data on highly-searched items to determine product placement. If an item is driving significant clicks online, it makes sense to have that item prominently displayed in store. The outcome of integrating insights gleaned from paid search with your brick-and-mortar strategy will be a more cohesive, omni-channel experience for your customers.
As in all things, there are inherent challenges associated with using paid search data. As any search marketer would be quick to point out, the vast majority of products and keywords in paid search campaigns do not have enough data to be statistically significant. In order to make good inferences, CMOs need a highly structured way of borrowing data across similar products and queries. Luckily, graph technologies – think Google Knowledge Graph and Microsoft Satori – are helping retailers overcome this challenge by connecting products, consumer queries, and search campaign assets to maximize data synergies.
According to a Forrester report from July of this year, retailers are spending an average of 55 percent of their interactive marketing spend on paid search. There are no signs of this investment slowing down, and as a result, the majority of retailers already have or soon will have the necessary search data to glean valuable business intelligence. All this points to the need for CMOs to fully realize the value of their paid search campaigns. The tools are there, the data is readily available and select CMOs are already reaping the benefits.
In other words, the search gold mine is officially open for business.
Murthy Nukala is the CEO of Adchemy.