Tracking the web based user journey is widely done and something that a number of tools, including Google Analytics, do well to a certain extent. However, for many businesses, this is only part of the story. What about the impact of online campaigns on in-store visits? Would a campaign with a below-target ROI still be below target if you could measure in-store conversions? Wouldn’t you run your digital marketing efforts differently if you had these answers?

Display campaigns are probably the most difficult of online channels to assess. The reason being that most of their impact reside in a user seeing and hopefully remembering an ad, rather than clicking on it. The industry average click through rate for display ads is 0.08%: of 10,000 banner views, only 8 will generate a click and a visit to the site (and some of these clicks are accidental but that’s another topic).

There are typically 2 ways to measure the efficiency of your display banners: you either trust the provider that you use (Google Display Network, Double Click, Criteo, Quantcast…) or Google Analytics. Both are poor sources of information and here is why with a real life example from one of our clients.

Most advertisers agree that retargeting, which allows you to get past visitors or cart abandoners to see relevant ads based on their previous visit to your site, help their business performance. There are a number of difficulties in assessing the true performance of retargeting providers, the main one being to understand if they are cannibalizing and taking credit for a sale that would have happened anyway or if they really lead a cart abandoner to convert. However, there is one aspect that is often overlooked when assessing retargeting performance: are they really focusing on retargeting or also mixing in prospecting?

Forecasting is key to reach your digital growth target. However, this exercise is difficult as you can't simply plan each channel in isolation of each other: an increase of 10% of SEM traffic for example is likely to have an impact of other channels through trickle-down effects that can be very complex to assess precisely. 

This is why we added a new turnkey feature in our new platform release: a new budget-planning tool based on machine-learning algorithms. 

In 2017, 60% of marketers today use artificial intelligence tools. In particular for product recommendations or to predict clients’ or prospects’ decisions.

Collectively, the tools that enable to anticipate customers’ behaviors on the basis of models that leverage data – also known as predictive marketing – are becoming key in the decision-making process of players in a variety of industries.

Machine learning techniques are gathering steam ; The field of advertising is heavily influenced by machine learning, as it is increasingly used to improve predictive marketing.

Measuring, understanding and optimizing investment performance is every marketer’s goal. To achieve this objective, web marketers have increasingly adopt the new concept of algorithmic attribution.

Wizaly explains this new concept and its potential.