5 Tips to Search Smarter
 
Many search engines have created search operators and tricks to help users conduct more detailed searches. Here are 5 tips with examples you may not have used or knew existed that can help you search smarter and better for what you are looking for in Google and other search engines!
 
1. site:https://www.contentcardinal.com
 
This operator looks for results associated only with a certain domain that you request. This is a particularly useful tool to see what pages are indexed for your site.
 
2. site:https://www.contentcardinal.com content
 
Adding a keyword to the first operator presented above allows you to search a site for that term, such as looking on contentcardinal.com for references to content. This is particularly useful if internal site search is not set up and you are looking for a page around a theme/subject.
 
Above we checked the root folder/homepage of the site, but you can add sub-folders or sub-domains to the site: operator to check out specific sections of the site (very useful for businesses that offer many different products).
 
3. related:wikipedia.com
 
This operator allows you to find sites that are like a targeted site. This could help you find competitors that have similar content as you. Not much may turn up if you site is small but that does not mean you do not have search competitors.
 
4. jaguar -car
 
The dash “-“ is used to exclude words/phrases/sites/info from your search results. This example is useful if you are trying to find information on jaguars and you do not mean the car brand.
 
5. "content cardinal" -site:https://www.contentcardinal.com
 
Quotes are used to show exact or literal results of a search term. This will make the results only show the same words in the same order as inside the quotes. This can be useful to see who is talking about your brand off your website.
 
Please note, some breaks such as backslash “/” or dash “-“ will be ignored. So, for the above example, you may see results like “content, cardinal” or “content-cardinal” that are not necessary relevant.  
 
These are some of the top 5 search operators many professionals use a daily basis while working in Search Engine Optimization. There is a more detailed lists online that can help you search better. Trying to be even more specific? Check out Google’s advanced search!
 

Chaos theory has been illustrated by one of its pioneers as “the fluttering of a butterfly’s wing in Rio de Janeiro, amplified by atmospheric currents, could cause a tornado in Texas two weeks later” – Dr. Edward Lorenz (Nader, 1996). This illustrates the unpredictability of the future, as a small disturbance to the environment such as a delicate butterfly flapping its wings could cause an effect so profound as a destructive tornado. Chaos Theory’s origins are often used specifically to describe atmospheric science, showing the difficulty in being able to make a long-term weather forecast. With January days in New York in the 60’s followed by a pending snow storm the next week, one can relate to the disorderly behavior of the environment.

Dr. Lorenz was my mentor’s (Dr. Alan Robock) mentor when I studied meteorology. But as I moved to digital marketing, I began to ruminate over the correlation between unpredictable atmospheric conditions and consumer habits, and how to better optimize for that relationship. Advertisers have already been leveraging the weather but still have a basic understanding of how it interacts with consumers and are unable to quantify the relationship. Curiosity made me start to wonder, do rainy days drive more people to shop online?

IBM currently uses cognitive computing to create actionable intelligence on weather forecasts. They collect data to help marketers better engage consumers at the right moments by understanding the emotions and behaviors that the weather creates. They achieved an “11.5% increase in market share for a pharma brand who discovered how, where and when to reach allergy sufferers” (IBM, 2017). IBM’s work on this subject has been possible because specific weather variables and their effect on human nature have been observed. A well-established connection is that as consumers are exposed to more sunlight, they are more willing to make purchases (Murray, DiMuro, Finn, & Leszczyc, 2010; Hirshleifer & Shumway, 2003; Busse, Pope, Pope, & Silva-Risso, 2014). More technical, the level of serotonin (which regulates happiness and mood) rises with prolonged and increased exposure to sunlight (Lambert, Reid, Kaye, Jennings, & Esler, 2002).

What about a lack of sunshine or the presence of inclement weather/rain? It is obvious how the physical deterrents of foul weather will keep consumers at home and affect a store’s foot traffic (Parsons, 2001) but do these shoppers then go home to shop online? To answer this question, one should look to the impact of rainy days on traffic to an e-commerce website. The relationship can be quantified by looking at the severity of precipitation on days it rains mapped with the number of visitors to the website. This information will be useful because with such a chaotic atmosphere, there may be opportunities to better utilize the weatherobservations (daily to weekly fluctuations) instead of optimizing only based on the traditional climatic information (longer, easily observed seasonal trends).

I found through my own research that as there is more precipitation, there is less traffic to a website. For example, one of the e-commerce companies I studied had over 15,000 website visitors in a day and ample data for specific geographic locations. Then downloading data that shows the number of visitors to the website daily from one area (I choose New York, NY), I could pair that data set with the total daily rainfall at that location. As precipitation increased, there was a statistically significant and decreasing linear trend in the amount of visitors to the website. The null hypothesis was that there is no effect. I received a low p-value (0.014) which suggests we can reject that null hypothesis and there is evidence of a relationship between precipitation and traffic to the website (negative since the slope is downward). More detail on my study (which looked at data for three different websites in three different verticals and found similar trends as well as unique behavior with respect to industry and extreme events like hurricanes) will hopefully be posted at another date when approved/completed.

My results indicate that days with larger precipitation events have less traffic to an e-commerce website - rainy days drive less people to shop online. The weather may prevent someone from traveling to the store, but they do not necessarily go home to then shop online. An explanation lies in the characteristics of rainy days. The correlation between sky cover that is overcast (the term to describe the lack of sunshine on a rainy day) during precipitation events may be the culprit. It has also been found that rainy weather may have a negative effect upon the mood of an individual (Persinger & Levesque, 1983; Conlin, O'Donoghue, & Vogelsang, 2007; Hsiang, Burke, & Miguel, 2013). But what is interesting and unique to my study was that the severity of rain seems to play a factor in the amount of traffic to an e-commerce website. As rain increases, less people are visiting an online store/website.

With the expectation of heavier precipitation events for the United States in the chaotic future (Pachauri & Reisinger, 2007), we can possibly expect more days with above average rain and below average traffic to an e-commerce website. If you run a paid marketing campaign, and we know that the mood to convert could be lower on rainy days, you may want to do some test and make some adjustments to your bid strategy. Perhaps we should revisit the term “save for a rainy day” and make it “save on a rainy day”.


References

• ​Abdi, H., Edelman, B., Valentin, D., & Dowling, W. J. (2009). Experimental Design and Analysis for Psychology. Oxford: Oxford University Press.
• Baxter, M. A., Graves, C. E., & Moore, J. T. (2005). A Climatology of Snow-to-Liquid Ratio for the Contiguous United States. Weather and Forecasting, 729-744.
• Busse, M. R., Pope, D. G., Pope, J. C., & Silva-Risso, J. (2014). The Psychological Effect of Weather on Car Purchases. The Quarterly Journal of Economics, 1-44.
• Conlin, M., O'Donoghue, T., & Vogelsang, T. J. (2007). The American Economic Review. Projection Bias in Catalog Orders, 1-33.
• Hirshleifer, D., & Shumway, T. (2003). Good Day Sunshine: Stock Returns and the Weather. The Journal of Finance, 58(3):1009-1032.
• Hsiang, S. M., Burke, M., & Miguel, E. (2013). Quantifying the Influence of Climate on Human Conflict. Science, 341(6151) 1235367-1 - 1235367-14.
• IBM. (2017, March 23). The Weather Company An IBM Business. Retrieved from advertizing.weather.com: http://advertising.weather.com/
• King, C., & Narayandas, D. (2000). Coca-cola's new vending machine (A): pricing to capture value, or not? Cambrdige: Harvard Business School Case # 9-500-068.
• Lambert, G. W., Reid, C., Kaye, D. M., Jennings, G. L., & Esler, M. D. (2002). Effects of sunlight and season on serotonin turnover in the brain. The Lancet 360, 1840-1842.
• Li, C., Luo, X., Zhang, C., & Wang, X. (2017). Sunny, Rainy, and Cloudy with a chance of Mobile Promotion Effectivness. INFORMS, 1-18.
• Menne, M. J., Durre, B., Korzeniewski, S., McNeal, K., Thomas, X., Yin, S., . . . Houston, T. G. (2012). Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.22. Station GHCND:USW00094728: NOAA National Climatic Data Center.
• Murray, K. B., DiMuro, F., Finn, A., & Leszczyc, P. P. (2010). The effect of weather on consumer spending. Journal of Retailing and Consumer Services, 512-520.
• Nader, L. (1996). Anthropological Inquiry Into Boundaries. Naked Science, 209.
• Pachauri, R. K., & Reisinger, A. (2007). IPCC, 2007: Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment.Geneva, Switzerland: Intergovernmental Panel on Climate Change.
• Parsons, A. G. (2001). The Association Between Daily Weather and Daily Shopping Patterns. Australasian Marketing Journal, 9(2): 78-84.
• Persinger, M. A., & Levesque, B. F. (1983). Geophysical variables and behavior: XII. The weather matrix accommodates large portions of varaince of measured daily mood. Perceptual and Motor Skills, 57(3):868-870.
• ​Roth, D. (2011, September 26). The Relationship Between Enterprise SEO & PPC: SMX East Recap. Retrieved from Search Engine Land: http://searchengineland.com/the-relationship-between-enterprise-seo-ppc-smx-east-recap-93430​

Wow. A blog! After running and managing so many blogs for clients in many different verticals, we are trying out a blog of our own. 

We will cover topics related to marketing, content creation, optimizing for changing search engines, a strong focus on Weebly as a CMS, and address any comments by you guys!

Looking forward to many great conversations!