Blogging, scraping, Google Analytics and traffic impact

Do you write a blog? How does it fit with your marketing and content strategy? How does your blog impact new traffic that visits your site? OK, enough questions. At Algospark, we were interested in a fast prototype to assess web traffic and how the blog is driving interest. We pulled together blog scraping, Google Analytics, predictive analytics and rapid dashboard prototyping to assess what is going on with the Algospark blog. As usual data, analytics and prediction are at the core of our interest. Having a better understanding of our content mix and traffic impact should help improve this blog. Read more about the concept here: https:\\algospark.com\#ideation

This is a simplistic first step, but gives great insight into the content mix and how it drives traffic. The application is predicting an 8% uplift in traffic over the next 4 weeks from this article. You can see how the impact evolves, our traffic dynamics and the updated forecast here.

Here’s to our evolving and improving blog posts!

Machine learning for new location selection

Location selection is key to offline business growth. A large amount of resource is usually involved with site screening, location visits, analysis, prediction and investment review. Algospark Location Analytics has built a framework to expedite the process, make the approach more consistent and reduce the amount of time spend screening and analyzing.

Site location involves numerous factors including: economic, demographic, size, customer experience, competitor and proximity outlet considerations. These factors often have complex interactions. And this is where a machine learning framework can help.

Getting the most from location analytics involves taking into account the insights from existing locations. This can be augmented by taking a cluster approach to locations. The value from machine learning comes from using a consistent approach that takes into account multiple location variables for inference and boils them down into a go / no- decision, supported by a projected sales forecast and profitability metric. Pulling all the factors together into a consistent framework avoids the painful work on multiple table and factor comparison.

The outputs from this quantitative site evaluation should then be used in conjunction with qualitative overlays such as site visits and site traffic analysis. Our approach to location analytics saves time and ensures consistent decision making to site selection. It also provides more accurate new site sales projections and trading patterns from the outset.

See how we make the process easier and reduce the risk of a failed new location on the links below.

https://algospark.com/#sales

https://wilkinsondi.shinyapps.io/locationanalytics/