Our 10th analytics class focused on how to best analyze and understand data.
The top-level message I came away with is a data metric shouldn't be evaluated in isolation, which is meaningless. For example, knowing only that users spent an average of five minutes on a page offers no insight to validate or adjust your strategy. It may mean only users from a paid referral on one day of the year spent this much time on the site and on other days or referrals from organic/social/direct bounced after 10 seconds. This would call for course corrections to improve results but without looking at the 'big picture,' these issues might go undetected.
Ideally in related segments, such as: referral sources (paid/social/organic/direct), days of the week (weekends and weekdays), times of the day or platforms used to access a site.
Sofia cautioned against comparing unrelated data metrics, such as tablet use with social referrals or creating compound or 'super' combinations like Alexa page rank with inbound links.
I found it interesting to note that higher numeric data (e.g. 10,000 contact page exits on every 100,00 visits), might deliver exactly the same statistical significance as smaller data (e.g. 1,000 contact page exits out of 10,000 visits) but is more effective when expressed as a clear outcome, such as 10%, versus a 'muddy' ratio like 10,000:100,000 or 1,000:10,000. However, I think higher numbers do strengthen a metric's value.
For the class exercise, we re-examined the metrics set for our senior project, identified segments to be assessed and ways they might be visually depicted.
Find out what's happening across the organization, such as other initiatives; business changes or help desk calls.
Consider external events that might impact data, such as holidays, market trends or even weather/power outages.
Possibly do surveys, click density analysis or other research….
….to get the full picture and extract maximum value from your data.
Illustration Source: Haml via Morgue File.
Our fourth Analytics class focused on the first of two parts on analytics for social media. This first part highlighted facebook, twitter and LinkedIn in terms of what users can do on these sites and "how" you can use these platforms to measure online, as well as offline, initiatives.
As with all analytics, you need to identify your SMART goals, KPIs, metrics and measurement methods in advance to ensure you capture all the relevant data and don't waste time or lose important stats.
For this week's in class exercise, we had to develop a social media strategy for promoting Parks Canada's "Unplugged" campaign. For this, we had to identify SMART Goals and tactics for achieving them, as well as KPIs and metrics to measure progress. I found this exercise a little confusing because we had just learned that traditionally KPIs are metrics that relate directly to your goals and generally impact the business. Specifically, KPIs traditionally apply to revenue-related metrics (e.g. cost per lead, return on invest) or direct conversions that impact the business (new memberships). However, I learned that the KPIs for a social media campaign are an exception to this. That is, they are social, as are the SMART Goals that support the campaign (e.g. generating X number of tweets with a specific hashtag). This is good to note, as 'when' I launch my senior project, I will likely do it with a social campaign to drive traffic to the site and ideally generate conversions (member sign-ups).
In the exercise, we had to create a strategy for Twitter, Facebook and LinkedIn. The first two were straightforward, as the campaign was consumer-facing, which both Facebook and Twitter can be. However, LinkedIn forced us to think 'outside the box' as it's more of a B2B channel. This was a challenge but good because sometimes clients decide they want to use a specific social media channel and you need to find a way to make it relevant to your marketing needs.