It is important
to understand why someone visits a web site and also why someone leaves a web
site but understanding where someone came is a key to measuring campaign
efforts.
Can you think
back to a time when you weren’t asked how you were referred or how did you find
us?
“Referrer is a
generic term that describes the source of traffic to a page or visit” (Web
Analytics Association, 2008). Referral
sources can be direct, external or search engines.
Direct referrals
include URLs within an organization’s web site or within an e-mail marketing
campaign, Facebook link via an organization’s post, a link within a Tweet, etc. Direct referrals also come from bookmark
usage and typing in the company name into the URL of a browser.
External
referrals are those that direct traffic from outside of the organization’ web
site. These “include blogs, industry
association sites, forums, etc.”
(Kaushik, 2009, p.78). Search engine referrals are both organic and paid traffic referred from search engines such as Bing, Yahoo and Google.
(Kaushik, 2009, p.78). Search engine referrals are both organic and paid traffic referred from search engines such as Bing, Yahoo and Google.
Depending on the
goal of a marketing campaign or campaigns that are being monitored a high
percentage of direct traffic could be a good thing. However, if large sums of money are being
invested in paid traffic then a large number in direct traffic may require a
second look into the campaign.
Understanding
where your customers come from is the first step to deeper analysis into the
effectiveness of efforts in each channel.
Coding each marketing campaign differently allows for additional
segmentation to determine effectiveness.
One company that illustrates the power of understanding campaign
segmentation is Williams-Sonoma.
Williams-Sonoma
found itself with a plethora of customer data.
They also had numerous channels in which to engage their customer:
e-mail, catalogs, paid banners, direct links from Facebook, Tweets, etc. The first challenge is to understand which
channel or channels best match their customers “while being careful not to
fatigue their customers” (Lampitt, 2012).
To add further complexity, Williams-Sonoma needed to understand if
certain channels were more effective during certain times of the year.
By carefully
coding their campaigns (direct and external) Williams-Sonoma was able to
determine customer patterns of shopping (time of year) and most effective
channel. In all fairness to the
Referrals metric, Williams-Sonoma hired outside firms and employed additional
analytics to determine trends over a period of time. This allowed them to determine what type of
campaign would be most successful based upon time of year and even which of
their customers to target with which products and if special incentives would
encourage conversion.
Every company
must start somewhere and that is with knowing their customer. Thus referrals won’t deliver a pizza with
everything on it but it does provide a foundation that any company large or
small should leverage to meet their goals.
For example, a
small local business that has recently opened, wants to find out how customers find
their company. They could ask when a
customer visits but that may not yield a great customer experience or
necessarily the truth. However, looking
at the web site one could identify if a local blogger is singing their praises
on a blog or if a tourist posted a great review and linked to them on a travel
social media portal or people are Tweeting about the great service or product
line. The owner of that business may
want to reach out to these individuals to cross-promote or to encourage
continued goodwill and traffic to grow the business.
Additionally,
the same local businessperson can assess if the precious marketing dollars are
being put to good use or are people finding the businesses by driving by while
in the neighborhood.
Sources:
Kaushik, Avinash
(2009-12-30). Web Analytics 2.0: The Art of Online Accountability and
Science of
Customer Centricity. Wiley. Kindle Edition.
Lampitt, D. (2012, November 8). Williams-Sonoma uses
big data to zero in on customers | Big
Data - InfoWorld.
Retrieved January 19, 2014, from
Web Analytics Association. (2008, September
22). Web Analytics Definitions
20080922 For
Public Comment.
Retrieved on January 19, 2014
from
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