Here's the context.
10 years ago:
Finding data to process and interpret is difficult and expensive.
Imagine you had:
1. Done marketing on billboards,
2. Hired a celebrity to be on the billboard, and
3. Conducted roadshows.
In measuring outcome of your marketing efforts, you would look at your sales numbers because that's a number most easily accessible. If there's a jump in sales, marketing works, vice versa.
However, what's missing is measuring other impacts of marketing that are not sales related e.g. branding, goodwill, and soft conversions.
Unlike yesteryears where marketing is usually measured by impact to sales, you and I would be flooded with data (in the form of numbers and metrics) from the different digital channels adopted. Namely:
1. Facebook Insights
2. YouTube Analytics
3. Twitter Analytics
4. Google Analytics
5. App Analytics
With ease of data access, marketers soon realise metrics shared are more often than not vanity metrics when viewed in silo.
1. Site reached 100,000 people
2. Post engagement was 20,000
3. Page have gotten 1,000 new followers
4. Brand received 200 complaints
The next obvious question when dealing with data is "What can I do with these information to improve my efforts?"
A metric questioned by marketers we have talked to is "Engagement Rate". The question is what is the portion that contributes to positive engagement, vice versa.
The answer: Insights.
the capacity to gain an accurate and deep intuitive understanding of a person or thing.
"this paper is alive with sympathetic insight into
a deep understanding of a person or thing.
plural noun: insights
"the signals would give marine biologists new insights into the behavior of whales"
synonyms:understanding of, appreciation of, revelation about;
"an insight into the government"
Insights is more than vomiting metrics onto excel sheets that is then put into charts on powerpoint.
Insights is cross-referencing metrics to find the "OWH", or "AHA" moments on how a marketer could improve their marketing efforts to recruit, retain, and engage their audience.
Let's take Facebook for example, by referencing when your audience are online with time you are publishing posts, you would be able to identify new posting time opportunities.
By referencing across data platforms such as Google Analytics and Facebook insights, Google Analytics enable you to have consumption data (how people are behaving with your product and site), while Facebook provides consumer data (lookalike audience, time they are on Facebook, etc). Referencing both GA and FI would enable marketer to find lookalike audience in Facebook (recruit), understand consumer sentiments from Facebook comments (retain), and have ads to coincide with product consumption peaks (engage).
Let's visualise the following:
1. Facebook page audience are online from 6 to 9pm.
2. Product purchases on website peaks at 7pm.
Consumers are online during website order peaks but no content/ads are dedicated to prompt for upsells.
Produce ads/content to be shown at order peaks on Facebook to reachout to the already loyal customers.
While insights driven data analytics has been the "talk phrase" by marketers, the effort required to cross reference metrics is a challenge. That is the main reason why we are still stick with vanity metrics reporting.
To put it into context:
1. Facebook have 60+ metrics
2. Google Analytics have more than 100 metrics
Manually pairing metrics then becomes time consuming. The future, we marketers believe is automation of metrics cross-referencing in providing marketers with insights based on context of their work.
Till that time comes, there are teams like us (catjira) working pairing metrics to drive insights for marketers.