When it comes to the local value that can be gained from online marketing, the key factor involves being able to glean valuable information from data gained. First, the business needs a good data source that regularly produces information about local area customer behavior. Second, the data needs to be meaningful, which only comes from understanding how to translate it both statistically as well as qualitatively. When the data itself can be filtered into meaningful information, then a small business can make changes that improve its local impression and the customer reactions that produce sales and revenue.
Where to Collect Data
Online marketing data comes from a number of sources, but visitors themselves create most of the useful information. There are two types to pay attention to:
The passive data is the most commonly captured because it can be done without a site visitor’s decision-making. Via cookies, an Internet browser tracking tool, general data on how many people visit a site per day or per week can be collected as well as from where geographically. Further, data can be collected on what other websites are forwarding those readers, giving a business a very good idea which websites or search engines are worth spending time on with investing more marketing.
Proactive data comes from the information that people actually provide a business’s website. This usually comes in the form of contact information, as well as payment criteria. However, set up right, a business can also collect customers’ input on performance of products, how well they do as intended, and what improvements could be made in the future. Comment and recommendation fields are great for this sort of capture but a company also needs to be ready to take some criticism once in a while when things don’t work out.
Basic Statistical Interpretation
Online data collection will come in a variety of shapes and forms. The most common involves activity within a time period. This kind of data can be measured over time in terms of trends, and can be qualified using basic statistical tools that start with averaging and become as complex as figuring out mathematical variations and how to remove outliers. The best way to measure this type of information is graphically. However, that only tells a business what visitors are doing in terms of website visits. It does not provide insight on what they are reading or the impressions a company’s website content or activity on social media is triggering. So while basic statistics are good for seeing if a general impact is occurring, and activity is increasing, the low level tools won’t say why local customers respond better to one type of marketing versus another.
A | B Split Testing
The term sounds highly technical and software-oriented, but A | B split testing is actually a very simple concept. Using two websites that are the same in all respects except for an intentional difference, the test shows how visitors will react to the isolated change. The approach, dubbed A | B split testing, basically uses a placebo approach that drug tests sometimes follow when studying the consumer effect of a new medicine.
In terms of a website, the split testing allows a website owner to see the direct relation of a new site change to how customers behave. This in turn allows the business to isolate the change and determine if it’s a good idea or not. Now a business can see why local customers react better or worse to a change. The data collected can then be used to filter out good, positive changes versus those that may drive readers and visitors away.
Online marketing data builds on itself, but to draw more accurate conclusions, users need to dig deeper than just basic visit statistics. Learning more about the reasons why customers behave a certain way when searching your website can make the difference between being just another source of information or actually converting the visitor to a customer.