TagMeet: Speaker Q&A – Greenlight director of PPC Hannah Kimuyu on the role of search in user journeys

With her team, Greenlight director of PPC Hannah Kimuyu has been integrating TagMan path-to-conversion data to understand the true role of paid and organic search in user journeys. Here she outlines some of the amazing insight they are becoming to able to feed into client strategies and tactics.
TagMan: At TagMeet, you’ll be examining what we know – and what TagMan data tells us – about the true role of paid and organic search results in user’s online journeys. What are the main findings so far?

Hanna Kimuyu: Greenlight initially integrated TagMan data (client specific) to understand the relationship between paid and organic search, but have since expanded this into understanding the full user buying cycle. The data findings so far have confirmed our initial thoughts that the user doesn’t have a preference between paid or natural search when searching for products or services. In fact, the cross interaction between the two media, suggests that users are just focusing on their ‘search phrases’, and then picking the most relevant brand to buy with. Furthermore, as advertisers diversify their online strategies more, the conversation becomes bigger than just understanding the relationship between paid and organic search. Brands should be asking themselves ‘what is the relationship between all online channels’ ‘do I have the right media mix’ and ‘is my strategy cost effective’.

TM: How is Greenlight applying that insight to search strategies and tactics for its clients? Can you give any examples?

HK: Even though search marketing is over ten years old, most advertisers are still using the first or last-click model, meaning anything that happens in between is completely ignored. Having both full click and channel path data allows Greenlight to analysis the entire user search journey, from first click to conversion. Resulting in several things:

1. De-duplication of data, no more over paying affiliate commissions.
2. Introducing Attribution modelling, fairer way of awarding affiliate commissions and sales/revenue to contributing channels.
3. The importance of each channel, specifically the role each channel plays within the buying cycle.
4. Transparency of the full click/channel path, what are users doing, what search phrases are they using and what channels are they interacting with to find the most relevant product or service? Are there are patterns in how users search? Can we use this data to shape our online strategies?

Stage one is very much about discovery – understanding the data/making sense of it. It should be noted that this isn’t a quick process, and getting all the relevant people around the table to discuss and analysis the findings is critical. At this point it makes sense to apply a ‘version one attribution model’, which again will need to be discussed and evaluated before making any further changes. Although this may sound like a time and resource heavy exercise, it is beneficial. Our TagMan clients are seeing cost benefits in year one, and now have a transparent view of how their users interact between the channels before converting.

TM: Paid search listings are often accused of gaining disproportionate credit for sales thanks to the predominance of last-click models, and the channel’s intense measurability. What’s your view of that debate and is the TagMan data Greenlight sees providing any illumination?

HK: For a lot of advertisers paid search represents a larger proportion of their online advertising budget, suggesting almost a reliance on the channel. If this is the true, paid search represents a larger proportion of the sales because in most cases users will finish their search journey on a ‘branded click’ i.e. brand search term. Especially, if the advertiser is bidding on branded terms, then the last click model will of course favour paid search over the other channels. In this instance, this is very much down to the strategy in place and the fact the advertiser has chosen a last click model – hence why ‘paid search is often accused of gaining a disproportionate credit for sales’.

The TagMan data has been pivotal to highlighting the ‘real’ role brand and paid search (as a channel) plays in the entire user search journey. The trick is not to work to a last-click model continuously, but to start off with a last-click model as a benchmark before moving into attribution, so you can really appreciate what each channel brings to the overall picture. Having gone through the same exercise for a travel client, initially we discovered that, on the last-click model, paid search represented 29% of all sales (of which 22% were branded sales).

Having analyzed the full channel and click path (search only) data we then applied a fair attribution model, taking the decision to downgrade paid search branded sales. The outcome saw paid search still being the second biggest contributor, delivering 25% of all sales. Proving that paid search was working just as hard as the other channels, if not harder, to drive sales.

TM: How, specifically, are organic listings revealing themselves as drivers of customers through the purchase funnel?

HK: First and foremost we’re confidently tracking all organic listings. I have to point this out specifically because in most cases the traffic and sales resulting from organic listings are normally assumed from the ‘whatever is left’ bucket. To explain, most advertisers will tag their paid for online activity, that being paid search, display etc, therefore leaving a bucket of remaining traffic and sales. With TagMan, Greenlight has been able to identify Direct to Site (DTS) traffic, i.e. domain, bookmarking and organic traffic specifically. Allowing us to confidently understand what value organic traffic and optimisation brings to an overall search strategy.

There’s no one approach or outcome though, the value is completely is down to the intended strategy. Using the travel client mentioned above, their intention was to use organic search to pick up the long tail, e.g. ‘hotels in New York with a swimming pool, close to Central Park’. This search term might sound unreal, but when a user is close to converting or has a specific product in mind, the long tail proves quite useful.

Traditionally (and it’s still the case) paid search picks up on most of this traffic. Long tail search, although low in volume, is cheap and effective, normally resulting in a strong conversion rate. However, if you have the flexibility to create specific landing pages for these type of terms (to which the travel client mentioned did) then it also makes sense to drive your organic listing there as well. Especially if targeting some of the more competitive terms does not cost in. With this strategy in place the travel brand was able to see the value in building such landing pages, and the role organic search played to its overall strategy.

TM: What new insight have you been able to derive about the way in which different keyword groups drive people from research to conversion?

1. The less surprising lesson but most valuable is the fact that a buying cycle does in fact exist. Users will combine generics, e.g. hotels in London, with branded, e.g. Guoman hotels in London; with more product/service led searches e.g. conference hotels in London. There’s no bias towards paid or organic (natural) search. If the listing or advertisement is appealing and relevant, a user will click – it’s simple! It may sound strange to highlight this point, but advertisers still don’t appreciate the value in combining a range of research, consideration and branded terms when bidding via paid search or optimisation for organic search. Users have become more sophisticated when searching for a product or service, therefore if a brand wants to be considered, then they have to be present throughout the users search journey.

2. The biggest surprise has seen the average buying cycles almost doubling for both retail and travel. Typically an advertiser will set a cookie length of 30 days maximum to capture a user’s interaction before conversion. From recent analysis into both the retail and travel sectors, it has become apparent that user’s are taking longer to convert. Part of this can be explained in the click/channel path. Firstly, the number of channel interactions have grown; as well as the number of searches conducted. Secondly, the user is looking for a ‘bargain’ – even if they decide they want to buy from a particular brand, they will still search for discounts. Lastly, users are savvier at searching, although generics still rule in terms of the number of searches made per month (volume) – users are combining a range of keywords before converting.

TM: Can we offer any more insight into the debate around the way in which paid and natural search strategy can be better integrated to increase conversions and reduce cannibalization?

HK: Yes – position analysis as part of the attribution. One of the biggest debates in search is – what is the benefit of having both paid and organic search listings for the same search term? Although we’ve been able to prove cannibalisation when advertising on the same keyword for both paid and organic listings via an attribution model; this data doesn’t take into consideration the position of each listing. There are plenty of research studies that prove there is an uplift in click through rate if both paid and organic listings sit alongside each other. That said, what is the perfect position combination?

TM: As online retailers gain clearer sight of their customers’ entire paths to conversion, how do you see their approach to paid and natural search evolving?

HK: Revenue-driven strategies; as a result of having a clearer understanding of how and what to integrate, how do we develop a strategy that can almost predict a revenue outcome? Allowing advertisers to decide where and when to spend. Furthermore, search as a channel isn’t just about paid or organic search. Social media advertising channels such as Facebook’s Placement Programme, or Google’s Display Network (AdSense and Double Click collaboration) are also components of search. How is this changing the data and our view on how search is contributing to the wider picture?

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