Top 100 Content Marketing Question: How to set up your content marketing analytics?

How to set up content marketing analytics: 4 guidelines

1. Designate one manager of content marketing analytics. Grant your manager a broad license to collect a variety of metrics from around the company.
2. Make analytics relevant and meaningful for marketing and your executives.
3. Measure frequently, but not too frequently.
4. Hypothesize, experiment and test prolifically.

1. Designate one owner of content marketing analytics

In many companies, everyone in Marketing gets to grade their own report card. Website marketers measure the website, email marketers measure email, PR people measure PR results, events managers measure events, and so forth.

The problem is: too often, people who grade their own report cards highlight the positives and downplay the negatives. In addition, multiple functions may claim credit for the same successes.

This self-grading problem occurs even at the CMO level. CEOs complain that CMOs present various metrics to highlight recent successes, rather than presenting consistent metrics the way other departments do.

That’s why you need to designate one manager to be in charge of all your content marketing analytics. Because you need to present your content marketing analytics consistently over time.

You may find it easier to begin doing content marketing analytics in a greenfield market. Here’s why.

Make content marketing analytics boundaryless

Go for all-in analytics, collecting metrics from inside and outside the Marketing Department.

Avoid making piecemeal measurements. Similarly, avoid taking snapshots of one period of performance. Both approaches can mislead you or set you up for failure.

Why avoid piecemeal measures and snapshots? Because what really matters is how the whole content marketing system produces results to grow the business over time.

Grant the analytics manager broad authority to collect metrics from the Marketing Department and from related departments like PR, human resources, and customer service.

Why? Because all of these departments are interrelated functions that deliver part of the customer experience. For example, your company’s news coverage affects sales, employee recruiting, and customer satisfaction.

You may learn that multiple functions are taking credit for the same positive results. If 3 or 4 functions each claim to have generated the same $100,000 in additional profit, you face the thorny problem called “attribution error.”

Minimize attribution error

As marketing pioneer John Wanamaker said a century ago, “Half the money I spend on advertising is wasted. The trouble is I don’t know which half.”

Attribution error means, for example, knowing that $100,000 in additional profit was generated, without knowing exactly which marketing activities led to those sales.

Here’s how to think through the attribution error problem, which vexes many marketers:

• Some companies attribute results to the “first touch,” the first proven touch point when a customer interacted with the company (such as email or a web page).
• Other companies attribute results to the “last touch,” the last touch a customer experienced before a purchase (such as a white paper download).
• The problem: neither the first touch nor the last touch tells the whole story. More than a dozen touchpoints may have led to any particular sale.

Attribution by its nature is always partial at best, because there’s no way to measure certain invisible touchpoints such as word of mouth, dark social media, and directly forwarded emails. For example, as much as 84% of social media sharing happens in the dark on mobile devices, a RadiumOne study found.

That’s why you need to collect comprehensive content marketing analytics.

Direct your analytics manager to track metrics on fever charts that show the various metrics’ interrelationships over time. Once you see how performance varies week to week or month to month, you can learn a lot just by asking, “Why?”

And when you have 24 months of history captured, you become able to see how seasonality affects content marketing analytics and outcomes.

2. Make measurements relevant and meaningful for marketing and executives

Because Marketing and executives have different reasons for measuring performance, different measures are relevant and meaningful to each.

Executives want to know how Marketing drives growth: Did Marketing add sales and revenue? How many customers were gained? How many are in the sales pipeline? How many are qualified leads?

Calculate return on marketing investment (ROMI)

Executives may ask for a return on investment (ROI) calculation. To apply ROI to marketing sensibly, calculate a return on marketing investment (ROMI) like this:

(Profit from revenue growth – Marketing expense)/Marketing expense

For example, say that successful marketing increases sales by $10 million and produces an additional profit of $1 million. Was that investment a good one for the company?

It depends on how much Marketing is spent:

• If the marketing expense was $2 million, the company got a negative ROMI of 50%. That is, each $1 invested in marketing produced 50 cents in additional profit.
• If marketing expense was $1 million, the company broke even. The ROMI is zero. (Note: companies that launch a new market or product may consider such an investment worthwhile, nonetheless.)
• If the marketing expense was $500,000, the ROMI is 100%. Marketing produced $2 of incremental profit for each $1 invested in marketing.

A comprehensive ROMI calculation includes direct expenses, plus a share of marketers’ compensation and benefits.

One problem with ROMI is that I can lead to a mechanical mindset: the higher the profit from sales growth, and the lower the marketing expense, the better. That mindset is dangerous because some CFOs conclude that marketing expenses should be zero, so they cut marketing and strangle their brands.

Inside Marketing, apply content marketing analytics to drive operations and improve performance over time.

Consider content marketing analytics such as these:

• Website and SEO metrics such as search rank, keywords, website traffic, time on a webpage, bounce rate, and conversion path. See “Content Marketing Metrics: 10 Easy Ways to Measure Effectiveness,” a blog?by Andy Crestodina.
• Email metrics?such as subscribers, deliveries, open rates, and click-through rates. See “Email Analytics, The 6 Email Marketing Metrics and KPIs You Should Be Tracking,” a?blog?by Lindsay Kolowich.
• PR metrics?such as share of desirable coverage, conversions, and cost-effectiveness. See “Katie Payne’s 5 Data Points You Need in Your PR Dashboard,” a?blog?by Steve Goldstein.
• Event metrics?such as new leads, social media reach, new customers, and customers reached with product demonstrations. See “The 4 Most Important Metrics for Measuring Your Trade Show Marketing ROI,” a?blog?by Rachel Sprung.
• Social media metrics?such as followers, likes, shares, and user-generated content. See “7 Social Media Metrics that Really Matter – and How to Track Them,” a?blog?by Eddie Shleyner.
• Brand value, as calculated by Brand Finance.

Build a dashboard to pull relevant metrics together

A dashboard helps because, when you look at all the measures together, you begin to pick up patterns. So, you can ask questions such as:

• Why do certain metrics trend up or down together, consistently?
• Do certain metrics move in tandem by coincidence, or does one drive the other?
• Which measures reflect consumer behaviors rather than outputs or attitudes? Behavioral measures often are the most telling.

Over time, your team learns to develop hypotheses about why content marketing analytics is moving up or down. Once you have come up with multiple hypotheses to test, you can run A/B tests and experiments to find answers to questions like these:

• Do better headlines drive more clicks? Which words add the most power?
• Can evergreen content be boosted with newsjacking?
• Is there a clear, simple, logical path that starts with content and leads to purchase?

Start with a broad set of metrics, since you’ll be able to narrow them down over time. For example, at Ameritech, we took a broad approach to PR analytics. At first, we measured dozens of separate metrics.

Over two years, we learned that only two metrics drove optimum news performance:

• Did the company initiate the story (rather than a reporter)?
• Did the company spokesperson stay on message?

The gist: when a company initiates most news stories and trains spokespersons to stay on message, its news coverage becomes dramatically more positive in tone. When a company allows the news media to initiate most stories, the tone of news coverage turns decidedly more negative.

Once analytics proved this hypothesis, we focused all our efforts on two crucial tasks — creating news hooks and preparing spokespersons.

Similarly, a disciplined approach of consistent measurement over time will reveal what works best to optimize your content marketing analytics.

3. Measure frequently, but not too frequently

Avoid the temptation to measure content marketing analytics too often, such as hourly or daily.

That’s like taking a series of snapshots. The problem: they’re devoid of context.

Why? Because there’s just too much noise infrequent content marketing metrics to be meaningful.

Depending on the size of your company, weekly, monthly, or quarterly measurements will serve you better to help separate the signal from the noise.

As you chart results, be sure to compare this month’s performance to the year-ago month. That ensures you have a time scale that can more accurately measure the longer-term impacts of content marketing.

4. Hypothesize, experiment, and test prolifically

Now that your content marketing analytics system is in place to measure performance comprehensively, you gain the freedom to:

• Hypothesize about why results turned up or down.
• Conduct experiments to prove or disprove that hypothesis.
• Carry out A/B testing to identify lessons learned, which you can apply to all your content marketing.

To maximize the success of your content marketing analytics, follow these guidelines:

1. Designate one owner of content marketing analytics. Grant them a broad license to collect a variety of measurements from around the company.
2. Make measurements relevant and meaningful for marketing and executives.
3. Measure frequently, but not too frequently.
4. Hypothesize, experiment, and test prolifically.