Search marketing and data analytics have always been connected at the hip. Even during the early days of search, part of the attractiveness of the channel was its transparency. It quashed the old adage that marketers don't know which 50% of their investments are worthwhile. Search represented this new advertising vehicle that demonstrated ROI really clearly -- and enabled smart analysts to drive incrementally higher returns if they acted properly in response to that data.
As search matured and more advertisers and organizations entered the fray, an environment of hyper-competitiveness for desirable clicks emerged. That competition spawned hundreds (perhaps thousands) of star-tup companies that sought to develop analytics technologies aimed at bringing greater visibility to search marketing performance and the mechanics behind managing programs properly.
Yet, despite the profound impact these technologies have had on the industry, many SEMs still rely on basic technologies like Microsoft Excel to help manage sophisticated programs. This is primarily the result of a fragmented marketplace for the search analytics buyer, one where needs often go unmet.
Driven in part by frustration, I decided to list four of my top (missing) features that would make for great additions within a definitive search analytics toolset. A couple of these features do exist within modern platforms, but don't get the universal appreciation they deserve. Other features are ones I would happily pay for access to, if they existed.
All you aspiring software entrepreneurs, take note:
Search query mining is one of the most under-appreciated elements to best practice paid search program management. Query mining is the process of identifying raw queries which were mapped to keywords within the search auction, and then extracting long tail derivatives and negative keywords to be explicitly introduced across the programs to enhance overall performance. This is an essential tactic for advertisers who rely on broad match keyword portfolios or are launching new programs.
Think of query mining as a way to help eliminate the unqualified noise, while enhancing the keyword portfolio with more precise phrase and exact match keyword targets.
The entire attribution category is hot right now, and for good reason. With billions being poured into digital advertising, it's becoming crucial to understand the entire range of influences and touches that ultimately result in a transaction. Legacy attribution models like last-click no longer cut it, yet we've been forced into accepting many such methods for attributing conversion by the analytics tools.
Rather than solve for the more complex multichannel attribution, search attribution is focused on the range of keyword queries that eventually motivated our audiences to take action. This is important for two reasons:
1) Properly crediting early touch keywords for playing an influential role during the fact-finding
2) Understanding consumer behavior as search queries are refined, even absent a click-through against the original query.
Keep in mind too that Google will serve carry-over ads from the original query to subsequent ones under the guise of "session-based broad match." Smarter search analytics would enable the advertiser to better understand where and why prospective customers refined their queries.
There are times when we know we need to pursue very broad keywords, despite having limited insight into whether those terms reach our intended audiences. A very timely example from one of our clients is for the keyword "cloud computing." We know that term belongs in our portfolio, we just don't know whether the clicks we are receiving against it are from those we were hoping to communicate with in the first place.
Understanding search-referred audience demographics mitigates that problem, and would allow for more meaningful messaging to be authored which would speak in a relevant way to discrete audiences. That discrete messaging may mean we have to set aside click-through rate and Quality Score as potential program KPIs, but it should yield higher engagement and conversion rates in the trade off.
Organic Search Rank at the Time of the Click
The solution to the ongoing debate around the utility of traditional keyword rank reports would be "rank at the time of the click." This metric would address the many factors known to influence natural search positioning across the results pages: personalized search, regional biases, new +1 results. SEOs could leverage that intelligence to make smarter re-optimization decisions, based on resultant on-site behavior patterns.
For example, if my site consistently lands within the top three positions yet engagement and conversion are both low, then perhaps I should pursue different keyword targets. Conversely, for keywords that drive high engagement and conversion, it may be possible to improve my position across the SERPs and receive higher volumes of traffic.
And this wish-list item isn't far-fetched either: Google is already passing this information in its referral string when the search engine user is logged into a Google account.
Though search analytics technologies have certainly advanced over the years, many more innovations are needed in order for SEMs to forever set aside the spreadsheet. These four are my biggies. I'd love to hear yours.