Brands and their agencies are cookie-bombing consumers and wasting huge chunks of their advertising budgets by wrongly attributing sales to the first and last clicks in their online campaigns, a new white paper claims.

This warning is at the heart of a paper launched jointly today by I-COM, a global industry association focused on the effective use of data in marketing and advertising, ad management business Flashtalking and MAGI NYC, which examines the business case for multi-touch attribution, or MTA.

“The increasing reliance on programmatic buying has resulted in a race to the bottom of the funnel,” says report author Steve Latham, global head of analytics at Flashtalking. (For more, read WARC’s report: The overlooked value of multichannel attribution … and why last-click models should be killed off.)

“Last-touch attribution exacerbates the situation by rewarding low-funnel ‘cookie bombers’, and diverting budget from upper-funnel marketing opportunities. The cost of (the) status quo is no longer sustainable.”

In the white paper, The Business Case for Digital Attribution, Latham describes how traditional – and still widely used – attribution models tend to overstate the effectiveness of ads that consumers see just before they make a purchase. At the same time, they gloss over the value of ads that people see well ahead of actually spending their money, but which influence their decision.

“While the vast majority of advertisers realise the limitations of old attribution, many still view MTA as a ‘nice to have’, and view last-touch (or other static models) as sufficient,” Latham says. “Few brands seem to understand the propensity and cost of false signals from static attribution.”

These misleading signals lead to overspending on underperforming digital ads simply because they fall at the end of the consumer journey – and provide “perverse incentives” to bombard consumers with retargeted ads that actually annoy them rather than encourage purchase.

“Proper” MTA, the paper argues, can significantly improve the effectiveness of future spend, placement and creative, by monitoring tracks impressions, clicks, visits and conversions on multiple devices from paid, owned and earned media.

The use of machine learning and algorithms can determine the relative contribution of each interaction.

This, Latham says, provides a better understanding of the customer journey, allows for live optimisation of campaigns and expenditure, and gives a far more accurate insight into the performance of each media channel, strategy and creative.

In the end, it generates a higher return from digital media investment by allowing careful focusing on creative content, media placements and keywords.

Sourced from I-COM; additional content by WARC staff