The New RTB formula: Fresh data + individual targeting = great results

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Our guest blogger this month is John Lamphiere, who is Director of Sales & Operations at Quantcast

Real-time Bidding (RTB), a key component of the programmatic advertising revolution, represents a massive platform change in the way we advertise online by bringing the relevance and efficiency of search to display. RTB spend will reach 429 million GBP this year (eMarketer) as budget moves away from more traditional channels.

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If advertisers consider RTB for display there are a couple of tips to help get it right:

Advertise to the individual not the ‘segment’
Every customer is slightly different from the next. With this in mind the traditional marketing method of grouping customers into ‘buckets’ does not always make sense. For example, consumers within the “mums” segment will be vastly different. A mother with a newborn will have hugely different needs to one who is just about to send their eldest off to university. Everyone has unique demographic traits – income levels, hobbies, family statuses, interests, etc.
Grouping customers into segments relies, partly, on human perception, but driving performance is not always intuitive in the same way.

RTB means targeting at the impression level as opposed to traditional media buys of large chunks of inventory. Therefore, there is a unique opportunity to target individuals – segments of one. Machine learning and the application of algorithms make this possible. In the past guesswork decided the majority of ad buys and creative execution, now, through machine learning, we can use computers to impact specific performance goals.

Fresh data is key for reaching and influencing consumers

While the number of data sources available for advertisers is never-ending advertisers must remember that not all data sources are created equal. The time between collecting the data and using it is an important factor. Commonly datasets used in RTB can be over a week old, this means they are stale and fail to identify the right customer at the right point in their purchase journey.

These data sources also lack the reach large enough to provide insights that are worthwhile. Datasets are often combined to overcome this. A lack of freshness and integrity are therefore common, leading to gaps and inconsistencies in the data and flawed targeting models.
A truly effective online display ad-targeting model requires fresh data on a massive scale.
Freshness is paramount in order to target your customers at the right point in their purchase journey. For example, if it takes the average customer four days to make a purchase and the data is a week old; then the customer has fulfilled their entire purchase cycle before you even know they exist. You lose the chance to effectively influence their purchase journey.

Fresh data for true relevancy

Advertising is increasingly data driven and as such is only as effective as the data put into it. The data and targeting methods advertisers use directly affect performance results. Stale, limited datasets mean missed opportunities for advertisers when using RTB. Conversely large, fresh datasets, built into targeting capabilities that act at an individual level, translate directly into new prospects, repeat site visitors and incremental revenue.

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Our guest blogger this month is John Lamphiere, who is Director of Sales & Operations at Quantcast

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