As an online reputation management company we care about ROI because it reveals the true value of our service and product.
We have clearly seen that working with us results in a valuable increase in web traffic for our clients. However, as a Data Science team we want to go deeper in order to identify and quantify what really drives better online traffic.
Towards this goal, I analyzed web traffic data (in particular Google My Business Insights data) from the GMB pages of our customers and specifically focused on the volumes of actions (calls, website clicks and driving direction requests) and views (appearances of the listing on Google Search and Google Maps) that these listings had over time. Throughout my exploration, I tried to tie this data with data we have internally such as rating and review histories for these locations and Google Search results for these locations, which gives us information about the locations and their competitors.
The difficulty of this analysis lies in the fact that this data has a lot a variance due to various factors, only some of which are tied directly to Online Reputation and Reputation Management. For instance, traffic fluctuations might be due to seasonality, changes in search engine ranking algorithms, improved customer reaction toward a GMB that has more/better reviews, a new advertisement campaign or a sale the company could have launched, or just underlying variance due to the unpredictability of hundreds of millions of consumers.
Despite these challenges, we have been able to come up with some interesting insights when looking at views and actions across different locations in particular enterprises or industries. I’ll discuss two of these here.
Distributions of views, actions, and reviews
Unsurprisingly, a location that has a high number of views also has a high number of actions. Actions (click throughs) can only come from people that have viewed your page.
What’s interesting is how different these volumes of GMB views can be. The distribution of the number of views by location for locations within an enterprise tends to follow a log-normal distribution. (It is interesting to note that we would have exactly the same type of distribution histogram by considering actions instead of views). This skew can make analysis or modeling very challenging.
Furthermore, it turns out that the distribution of the number of reviews by location has a similar very-right-skewed shape and is highly correlated to the view volume. Not surprisingly, the bigger the traffic size of the location is, the more reviews it usually has.
As mentioned earlier, one of the key challenge of related analysis is being able to work with data that is in different orders of magnitude. Besides the very nice smoothing effect that a logarithm transformation of these values (views, actions, number of reviews) provides, it makes sense in the way that fluctuations of these values over time are mostly relative, and that gives a nice interpretation of the log values. Indeed, a locations that has 100 reviews might get 10 more in the coming month about as easily as a location that has 10 reviews could get 1 more, and it is the same for web views.
However, this transformation has some caveats. One of them is that locations with smaller values tend to fluctuate by a relatively higher percentage than bigger and more stable locations. It is the simple manifestation of a regression to the mean phenomenon (higher values tend to decrease, smaller values tend to increase) but is challenging to take into account in models. Another one, is that values in general, and aggregated values lose their primary quantitative sense. Indeed having models minimizing a metric (MSE for a regression for instance) over log values and then switching back to real values will not be successful if you ultimately want to optimize MSE over the real values. Defining what metric to use is something that is not evident but fundamental, and is of course, part of the art of Data Science.
More on the relationship between reviews, views, and actions
Admittedly it is not terribly newsworthy that locations with more views have more reviews on average and vice versa. We obviously want to dig deeper and study questions such as: “Does having or generating more online feedback (i.e. reviews) for a location generate more online traffic or actions.”
We have been looking at this relationship from a number of directions, and one of the most interesting insights has come from looking at the conversion rate (the ratio of actions to views) for a location as a function of how many reviews that location has.
As we discussed, there is an obvious correlation between views and actions, but this conversion rate is not strictly constant. In fact it is generally distributed in a range between 0 and 0.3 and provides us interesting information.
Furthermore, we have found a strong relationship between this rate and the volume of reviews per location. In the graph below, we plotted this conversion rate for different groups based upon their relative number of reviews to their top competitors. We define this relative number of reviews as the ratio of our location’s number of reviews to the average number of reviews of the top three locations that show up on a Google categorical search for this location (e.g. “tire store near me”. We defined this variable, because we found that even more than the absolute number of reviews, having more reviews than your competitors plays a critical role in influencing customer behavior.
As I mentioned above, we are performing other analyses to understand how not just having more reviews, but generating more reviews, generates more online views and conversions, and we will continue to surface over the coming months.