User Journey

User Journey

Historically, the most sophisticated marketers have relied on top-down ad campaign planning. They develop econometric models by looking at the distribution of the whole advertising budget. They analyze changes in allocation and one-time promotions and see how those changes affect the key performance indicators (KPIs), which may be opening a new account, or making an in-store purchase, etc.

With the advent of big data, marketers worldwide are attempting to use data and analytics to solve problems previously out of their reach. Marketers now can apply big data and analytics to create competitive advantage within their markets, often focusing on building a thorough understanding of their customer base.

High-priority big data and analytics projects often target customer-centric outcomes such as improving customer loyalty or improving up-selling.

The promise of big data analytics is that marketers can analyze information about the digital activity of the purchaser and combine it with the knowledge of television, radio, billboard, and print campaigns to tailor marketing messages and, ultimately, improve return on investment.

But as marketers begin to execute on big data and analytics projects, many quickly run into a road-block.

Traditional data sources: addresses and contact details, identifiers, contracts and accounts, relationships and support history.

Marketers rely on digital scoring of actions, starting from the bottom up with the KPI, trying to work backwards to see the touch points along the consumers digital journey by telling exactly what a specific buyer sawing the ads, and how long the buyer watching a video or lingered on a page carrying the ad. It’s all found by backwards tracking from the point of sale of the product ultimately bought.

A key challenge for any marketer is deciding what mix of media will best promote a product or service. By media-mix modeling using big data and machine learning, there are a lot of micro-efficiencies we can tap into.

To understand the impact of temporal spacing between different touch points on a consumer’s journey of whether to purchase product, we create an individual level data set with exogenous variation in the spacing and intensity of ads. The data show that at a purchase occasion, the likelihood of a product’s purchase increases if ifs past ads are spread apart rather than bunched together, even if spreading apart of ads involves shifting some ads away from the purchase occasion.

In the behavior literature, repeated exposure to advertising for a product strengthens the memories associated with it, thereby increasing the likelihood of their recall at a purchase occasion, so short-term effects are important for the carry-over of ad effects to future occasions. To quantify the long-term effects of advertising on consumer’s decisions, on the other hand, researchers have used models of advising carry-over that accumulates due to advertising exposures. Estimating the impact of carry-over is the ability to measure the casual effect of advertising on consumer decision.

Challenges: individual level data, targeting of ads, selection problem.

Because online marketplaces aggregate products from a wide array of providers, selection is usually wider, availability is higher, and prices are more competitive than in vendor-specific online retail stores.

For marketers trying to maximize the return of investment, predictive analytics based on big data is an exciting new tool. The predictive analytics based on big data holds the promise of creating a detailed view of what works, providing guidance that has never been available before for the fine tuning of advertising campaigns.

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Data-driven marketers, continue to assign credit for campaign conversions in such a myopic way?

The problem is that last-touch and last-click attribution have been a pretty consistent standard. Collectively evolving to something better is one of the hardest changes to make in advertising. It is also one of the most important next milestones for the industry.

For media agencies, measuring performance at every level in digital media, especially with the ability to adjust in real time, means that view-through credit beyond last-touch is an integral part of the marketing plan and not an exception. It is becoming the new standard. View-through credit maps the user’s journey from first impression to conversion, whether they converted from a click-through or visited the advertiser’s site independently after being served enough ads to sway them into buying online. It’s what offline advertising always struggled to understand: Did my customer buy my product because of that billboard or my newspaper ads? Or did both contribute?

In theory, the algorithms should be able to allocate budget to advertising networks that police the inventory to avoid phony ads. They should generate more key-performance indicators. By the same token, ads that aren’t viewable won’t drive KPIs. It isn’t clear whether the promise is being fulfilled.

Predictive analytics may discover correlations among categories of potential buyers that whould be unlikely to occur to human marketers. Advertising is all about correlation.