The buyer journey used to be described as a funnel. But in the world of multi-channel, multi-device consumer engagement, retailers now must see it as an endless loop – with multiple entry points and possible paths to conversion. E-tailers must not only stay on this merry-go-round for the long ride, but also do it at scale – speaking to millions of consumers as individuals and using delivered targeted ad messages across channels.

Here are a few tips:

• Understand who your customers and prospects are by organizing offline and online data into actionable targetable segments.

• Understand what media influences conversions and the connections between media and data, identifying the best combinations to drive sales.

• Upsell to existing customers and acquire new customers efficiently, using all the first and third-party data at your disposal.

See how a retailer like you taps into granular analytics to keep his business growing.http://ow.ly/nI5sU 

To learn more on how MediaMath can help you gain new, high-value customers and build customer loyalty, visit us during eTail East, Booth #604 at the Philadelphia Marriott Downtown, PA.

Many people think retargeting is just about placing a pixel on an advertiser’s site to cookie users, and then buying ads against those users. If it were that simple, everyone would see the same results in all of their campaigns. The truth is that successful retargeting is more than a two-step process.

So, while nearly every advertiser sees a lift in conversions, two partners both doing retargeting may see very different results. The following factors all come into play when bidding on retargeting impressions, and each one could affect campaign performance.

The breadth of supply integrations. This includes media across display, mobile, video, social and across premium as well as remnant supply. The more impressions there are flowing into a system, the greater the chance of finding the retargeted user. Some platforms can support over 1 million queries per second, while others maybe 1/5th or 1/10th of that.

Match rates. Dropping a cookie is only half the story. You still need to sync that user with supply sources in order to bid on them in media. Match rates speak to the percentage of users that are successfully synced, and can vary from 70-80%, down to as low as 20-30% for some platforms (it will always be less than 100%, given that some users either don’t accept or delete their cookies).

Error/timeout rates. Just because an opportunity to bid on a retargeting user comes in, doesn’t mean the DSP will actually be able to bid. Platforms that fail to respond within the allotted time window of 50 to 100 milliseconds will timeout (e.g., because the system simply cannot handle the flow coming in), making their bid ineligible. Some platforms have timeout rates as much 20 times lower than their peers.

Optimization Finding a retargeted user is great, but knowing what to bid to achieve your ultimate goal is a different story. Not all retargeting users are of equal value, and even the same user could be of greater value when appearing on one type of site vs. another. Yet surprisingly, many platforms employ a simplistic fixed-bid approach (bidding the same amount every time, perhaps with some random variation), which is never the right answer. Why? Because any fixed bid will be either below a publisher floor (and never win that inventory) or above other floors (potentially overpaying for that inventory).

Moreover, most exchanges and supply side platforms (SSPs) employ dynamic price floors, altering the floor price depending on the bids submitted. For this reason, it’s critical to have a machine-learning algorithm that can bid dynamically on retargeting. This means bidding higher when you need to, and lower when you don’t.

Recency & frequency management. Building on the optimization point, it’s not enough to know who to bid on and what to bid. Advertisers must also know when to bid, as well as how often. Performance can vary significantly as a function of recency and frequency, and knowing how to incorporate those factors into a retargeting program may be the difference between decent performance and breakaway performance.

Dynamic creative. Retargeting users have recently interacted with the advertiser’s site, so leveraging dynamic creative to tailor the ad based on those interactions (e.g., showing specific products viewed, as opposed to generic ads) ensure greater relevance and dramatically lifts performance.

Fraud detection. The trade press has recently highlighted how many retargeting impressions available — more than many people realize — aren’t actually against humans, but rather against bots that have intentionally visited advertiser sites to trick unsuspecting buyers to bid for them. Sophisticated buying platforms should have algorithms that look at multiple factors to identify fraud and avoid bidding on suspect bot traffic. The net effect is a reduction in impressions and lower click-through rates (by eliminating fraudulent clicks associated with bots – actual human click-through rates are much lower), but a dramatic improvement in cost-per acquisition and order quality.

There’s been a lot of buzz lately around the topic of cross channel attribution or offline attribution, as marketers try to figure out how to better measure the performance of their media campaigns. However, many marketers only look at offline attribution in a very narrow sense, which is strictly defined as attributing conversion events happening outside of media to an online media event. As a result, they miss out on the greater campaign optimization opportunities brought along by their offline attribution processes.

Some forward thinking marketers have been using their offline attribution processes as opportunities to fine-tune their campaign attribution models, which lead to significant performance improvements. The reason behind this is the offline attribution processes allows advertisers to insert their own analytic and conversion filtering processes into the media buying cycles, resulting in flexible and effective attribution models.

To get more value out of offline attribution processes, advertisers need to do the following:

• Understand the main objective of their campaigns and identify the converters or the type of conversion events that would drive the most ROI.
• Filter conversion events accordingly. For example, a retailer can group their converters into multiple categories based on the amount they spend in store, and only bring those converters who spend more than $100 into the media buying platform as conversion events for attribution.

Intelligent media buying platforms use machine-learning algorithms to find more prospects with similar characteristics as these high value converters, target them online, and result in higher ROI for the advertisers.

The attribution model is extremely crucial to campaign optimization and performance. Please consider your offline attribution process as a golden opportunity to build a custom-tailored attribution model to extract the most ROI out of your media buys.