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BizReport : Advertising : December 28, 2011


How to use modeled data to engage online consumers

Using data to better target ads to consumers is not new, but the online environment offers marketers a better way to connect. Using data businesses can segment audience, make correlations between the actions of anonymous users and also target based on geography, demographic or other information.

by Kristina Knight

I chatted with Stuart Colman about trends in the data space; today, Stuart tells us why modeled data is important for marketers.

Kristina: Data has been a huge buzzword for probably three years now amongst online marketers; yet, many aren't using the best metrics to target their audiences. Why is that?

Stuart Colman, Managing Director Europe, AudienceScience: The reason for this is the traditional strength of online has now become its weakness. The web offers a huge wealth of immediate tracking measures be it click through rates, page views, unique visitors, conversion rates etc. - all meat and drink for the direct response advertisers that first recognised the power of online for driving results. However as more brand marketers look to embrace online, they need to answer crucial questions such as:

• what the impact of a campaign is on increasing brand awareness
• has the advertising had an influence on purchase decisions?

There is growing recognition that there is a need to move beyond the simple click and look at metrics around engagement, awareness and ones that can help identify the path to conversion. Reports from businesses like Bain and Starcom highlight why traditional metrics are no longer relevant but the challenge is for the industry to develop relevant and accepted standards.

Kristina: Modelled data is one of the 'new' ways to target, but is this a good way to target? --

Stuart: Modelled data has always been a recognised targeting approach - we've seen it used in offline direct marketing and we also see it in the online environment. In particular, it is good for developing reach, by helping expand the size of an audience by finding 'lookalikes' with similar characteristics.

Ultimately though, targeting is about relevance and precision.

Although there seems to be an increased leaning towards modelled data to create 'lookalike' audiences, the reality is there is no substitute for real data to define and audience accurately. That's because modelled data is based on 'best guesswork' whereas real data is based on fact.

When developing audience buying strategies, the 'who' - the person you want to reach - needs to be the critical factor. Of course an audience profile can be helpful and add insight - but they don't answer the fundamental question - does this individual demonstrate characteristics that imply they are in the purchasing funnel for a specific product or service?

Customer profiles or modelled data, by their very nature, are less accurate and operate on the assumption that lookalikes behave the same way as real buyers. This can limit potential because modelled data:

• excludes anyone that doesn't fit the definition but may be in the purchasing tunnel
• and doesn't take into account if someone is potentially interested in a product at that point in time

Real data can be used to understand and define precise audiences, based on a consumer's online actions and activity over a specified period of time. In addition real data takes into consideration recency and frequency to determine if / when the consumer is in the purchasing funnel. This allows advertisers to deliver content that is relevant and timely and most importantly affords them the opportunity to reach the consumer at the most critical time.

A weakness of modelled data is its inability to identify if the target is in the market to purchase a product or service, which can result in campaign wastage and increased spend for less return.

Ultimately, the accuracy of any model is actually driven by the proportion of actual data used to derive it. The smaller your sample size and the less real data used, the more guesswork is needed.

Tags: advertising metrics, Audience Science, consumer data collection, data collection trends, modelled data, online advertising, Stuart Colman










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