One of the greatest advantages that digital marketing provides is the marketing data that demonstrates ROI (or lack of ROI) for marketing campaigns. Twenty years ago this was revolutionary!
All of a sudden we were able to provide clients with data showing how many people visited their website, where they came from, what content they consumed when they were on the site along with a whole range of additional data.
Ten years ago, Big Data was all the rage. Big Data is high-volume, high-velocity information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.
Big Data was going to transform the way that companies looked at their marketing. It was going to bring more insights into the marketing decision making process and catapult the ROI for marketing campaigns and transform decision making through out the organization.
Today, according to a report by Deloitte, only a few companies have utilized big data to drive their marketing, let along their companies or organizations.
What happened? How has it happened that the biggest promise over the past 10 to 20 years still hasn’t had a profound impact on businesses and organizations?
I believe that there are a couple of reasons that we’re still struggling with using the data that is so pervasive in today’s business environment and its importance being so clearly documented. The first is culture and secondly I believe is data complexity.
Who owns the data within your company? What is the marketing culture within your company? Is it one of continual improvement driven by data? Or, is it a culture of complacency or status quo?
Over the last few years there has been a lot of talk in companies of all sizes and in various industries about being “data-driven”. Using data to make both marketing and business decisions. Yet, in a recent survey of business executives Deloitte fount that:
- Most executives do not believe their companies are insight-driven.
- Most executives are not comfortable accessing or using data.
- They eliminated 35% of the survey respondents because they had zero interaction with analytics in their companies.
To determine where your company rests on the continuum between no data interaction to full on data driven, there is a data maturity model that you can use.
Analytics Aware: At this stage of data maturity in a company is at the very beginning of using data. Companies at this level are aware of data and analytics but they have no real process or strategy to use data in their marketing or business decisions.
Localized Analytics: At this stage of data mature in a company is beginning to use data and developing its strategy for how to analyze the data for decision making. In this stage much of the data collection is compiled an a very ad hoc fashion.
Analytical Ambitions: This is the stage when companies are implementing their plan, increasing their ad hoc processes and using the data for more than just marketing.
Analytical Company: This is when companies are “fully engaged” in data collection and analysis. Data, at this stage is coming from a wide range of sources
Data: Its own worst enemy
As the years have progressed from the late 90’s when digital marketing data first became available, we have seen a dramatic increase in the sear volume of data that has become available.
There are sources that report at least 2.5 quintillion bytes of data are created every single day! With this much data created every day it seems like it would be a extremely simple to get your marketing message to the right person, at the right time, and in the right place.
One of the side effects to this data is a growing need for marketers and business executives to attempt to keep up with the staggering growth and unprecedented velocity of data, both in terms of the numbers but also in terms of change in how to best use all of this data.
Here’s a handy fun fact. Google analytics has built into its reporting engine 78 standard reports that fall into five different categories. In addition to these standard reports there are countless ways that you can generate custom reports to slice your data up in infinite ways.
Ask yourself, how many of these reports are you using on a regular basis? How many have you ever used?
Vanity vs Real Data
Every marketer has been asked how many people are coming to a website every day. Or, what are people doing on the company site. But, do these really answer the real questions people are seeking behind these over simplistic requests?
Is it really enough to provide your manager with traffic counts, bounce rates, and page views every month? It shouldn’t be. These metrics are not an indicator of your digital marketing’s health!
All too often vanity metrics are being used as indicators of how well marketing campaigns are doing. They are being used to make business decisions and budget allocations when they shouldn’t be.
Vanity metrics can be very misleading. Vanity metrics are metrics that typically look good on the surface but don’t necessarily translate into any type of meaningful business result.
Here’s an example. Let’s take the metric that we talked about earlier, website traffic. On the surface this would seem like a very important metric for a business. After all, we need to get people to come to our website. But a traffic metric alone isn’t going to align with business goals.
A traffic metric is simply a number. It can’t demonstrate whether those people coming to your site are they type of people that you do business with. In fact, this metric in and of itself can’t even tell you for sure if these are people.
Here are some of the vanity metrics that are used most often:
- Raw pageviews
- Number of downloads
- Social media followers
- New users to a website
Output to Outcome
By now you’re probably wondering how to start using data more effectively in your marketing analysis and it really comes down to how you look at what you’re measuring.
There are two basic ways of evaluating marketing, either by measuring output or by measuring outcomes.
This is what the vast majority of companies measure. Output is typically the first metrics that show up on the scene of a new marketing strategy. For email as an example, output metrics are the number of emails that were opened in a campaign. For social media an output metric would be the number of friends or followers that you have. Or, as we discussed earlier an output metric for a website would be the number of visitors that you get to your website.
The problem with output metrics is that they don’t tie back, directly, to business goals.
As an example, if your company has a goal to increase revenue by 15% in the next two fiscal quarters and all you’re measuring is visitors to your website and followers on your social media account, you won’t be able to tie your marketing efforts to the company goal of 15% increase in revenue.
Outcome metrics, as you can imagine, are metrics that you can tie directly from your marketing efforts to your company’s business goals.
Let’s use the 15% revenue growth example, again. Your company has a 15% revenue goal. You know that your sales conversion rate is 30% of all of the quotes that you send out. You also have a request a quote form on your website.
In this scenario, rather than simply measuring how many people visit your website, you would now measure how many people come to your website, go to your request a quote form, fill that form out and then tie that person internally with your sales department to know whether or not they purchased what was quoted.
Then, to take it one step further you should track that person backwards in their journey to try and determine where they came to your website, from. Did they click on one of your PPC campaigns? Did they come from a social media ad? Have they engaged with your site in the past to possible learn about your company?
Now, you have a direct relationship between marketing efforts and revenue. A much better method for making marketing and business decisions. As you become more data proficient how and where you spend your marketing resources will have greater impact.