Many advertisers using Google Adwords for their business are very much concerned about measuring their return on investment (ROI) particularly for lead generation based campaigns. The standard reports we get can give us metrics such as impressions, clicks, click through rate (CTR), conversions and cost per click (CPC) and there isn’t a tool that is available in Adwords to give you good predictions of what conversions or costs you can expect other than the estimate columns they recently added.

Questions that many advertisers would like answered

How much additional budget is needed to meet a specific sales target?
What is the impact of the new feature we added to our App in terms of App downloads figures?
How do we compare the effectiveness of a certain attribute across countries easily?
Can we predict the impact Paid Media has on Owned Media if campaigns were to be turned on/off during certain periods?

All of the above questions and more can be answered using Causal Impact tool from Studio R. In essence, estimating a causal effect accurately is extremely hard especially in cases where a randomized experiment is not available. Google developed a Bayesian structural time-series model that can be used for effective measure.

Using these models marketers, statisticians etc. can leverage the power of R to construct a synthetic control over what would have happened to a certain outcome metric in the absence or addition of a particular condition. This approach makes it possible for them to estimate the causal effect that can be attributed to the condition, as well as its potential evolution over time.

Article originally written by Dan Swanson from https://apps.apple.com/us/app/call-recorder-icall/id1447098963