By Jeffrey Cheal, Episerver
There are two kinds of
marketers today: those who use a firehose to drink up data analytics and those
who haven’t even turned on the faucet. There’s a happy medium that exists when
purposeful metrics flow into every part of a retail business. Whether you’re
just dipping your toe into the analytics pond or you are drowning in data, here
are three reasons to invest in the right kind of metrics to move your
merchandise out and your marketing forward.
1. Analytics For Answers
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Although there are some common site metrics many retail organizations collect —
average order value, conversion rate — there is no single set of metrics that
works for all organizations. The importance of these data points and others center
around web site goals, a general understanding of your business and, most
importantly, what you want to figure out. If there is a question to be asked,
analytics can help.
Let’s take this question as an example: Is your product something that users extensively
research before purchasing? If so, site analytics need to be related to sales
and timing data, as users may purchase with less frequency but show higher-than-average
time on site. If correlations can be made between sales and time spent, then marketers
will need to develop more compelling reasons for people to stay on the site. If
there is no correlation, then they can focus on a quicker path to purchase,
such as traditional product pages versus products intertwined in content.
2. Analytics For Action
While analysis is great, action is better. Commonly, there are two action paths
marketers and merchandisers can take from their investment into data:
#1: Reproduce what works
Let’s say you find that users tend to purchase more on the weekends than on
weekdays. Once confirmed with higher weekend conversion rates, marketers can offer
special weekend promotions, adjust marketing cadence so customers receive
offers on Fridays in preparation for weekend shopping or retarget weekday
shoppers with personalized ads to get them back on Saturday or Sunday when they
are more likely to buy.
#2: Identify what does not work
Let’s say you use demographic data to find that users in smaller population
areas view your site less. This allows you to buy targeted media in these areas
or use alternative messaging in your site personalization when they visit. As
you dive into the data, you can determine more and more about why your message
does not resonate to users in these areas, and adjust your marketing
appropriately.
A good organization has a balance of this — while most brands will try to find
high AOV customers and clone them, they also are identifying what reasons low
AOV customers fail to convert at the same rate by looking at different
attributes. I recently worked with a banking client that saw the value in
collecting hobby and general interest data from users who came to their site
and did not sign up. As a local bank, they wanted to stay more in touch with
the community. To help with acquisition, they were able to adjust their on-site
lifestyle messaging and local philanthropic projects, to speak to audiences who
did not initially engage in meaningful ways with their brand.
3. Analytics For Anecdotes
Many brands find that digital commerce is a black box — users purchase but often
they don’t have the insight to paint a good picture of the difference between
their in-store customer and their online customer. Visitor journey data can
help paint that picture — and not just on-site journeys, but complete lifecycle
data including email interaction, social media interaction, viewing or
interacting with paid media, etc. While you will find anomalies in the data,
over time you will start to see weighted trends. Here are a few examples:
- Find the number of touch points before a user becomes a member of your
loyalty program, so as to quantify when to start promoting a sign-up page
instead of suggesting more content. - Map the number of visits to the site for a customer before they reorder a
product. This could help with more relevant email calls to action. - Examine shopping cart size. Users might buy less frequently, but when they do,
they may have higher shopping cart sizes. Your tactic should then be to drive
more upselling and cross-selling suggestions to maximize transaction sizes.
Despite our wishes, data only
confirms and quantifies. Keep asking ‘why’ and your team can get to the root
problem to help your analytics investment pay off.
Jeff Cheal is the director of product
strategy for Personalization, Campaign & Analytics at Episerver,
where he brings an extensive background in advertising sales, software and
marketing strategy. Cheal is based out of New York, serving the North American
market as an ambassador for the Episerver product suite, staying connected with
both the partner network and customer base.
