Take the entertainment and ticketing business.

Andrew Rentmeester, senior vice president of revenue planning and operations at The Madison Square Garden Company, sees it this way:

What the CMO uses now (and will always need) are simple scrapes of the ticket inventory system and what’s sold today. If you don’t have that, you really don’t know where you are in the business.

Rentmeester adds that, even though it’s just an inventory system for tickets, an old-school Excel sheet works. While it isn’t ideal, it’s needed, nonetheless.

Consider the marketing of tires. Tire manufacturers struggle to understand the market value of their brand and products.

Typically, they web-scrape prices listed by local retailers and make rough estimations of the value of their brand versus benchmarks.

Even the application of this crude method of price optimization improves margins in a very competitive market sector. 

Shawn O’Neal, vice president of global marketing data and analytics at Unilever, suggests that tool exploration begins before its analysis:

You have to know what you want before you build the database with data tools. You have to understand what attributes you’re going to scale in the hierarchy and segmentation before you ever build the database.

If you don’t have the database built for the data, you don’t capture it.

Big data tools on the CMO’s wish list

Rentmeester says he would like to start his morning in this way:

Marketing leadership and I need a dashboard concept that we look at and know what the overall state of our key marketing levers are so that we can use that to drive the business forward.

He likes the idea of a type of dashboard mechanism that allows for quick insight into key sales drivers, year-to-date numbers, and prior-year numbers (and one that also provides a way to view the revenue funnels in parallel).

For example, if you have a website and different digital marketing strategies for that website, you want to know which method is working best, how they are stacking up against each other and, most importantly, how they are relating back to sales. 

However, in a world where large, monolithic-type reporting engines still exist, Rentmeester finds that, by the time a report is generated, it’s already out of date because the questions have changed.

If he wants to see one specific metric — such as the average ticket price in Section 340 for Knicks games — how does he get that metric quickly?

He argues that the reporting structure isn’t usually oriented to answer that question at that point in time, which could prove to be a challenge for a CMO.

He’s looking for a tool that allows him to get granular and to get as much data as he needs in order to make use of it as quickly as possible.

Tools or Techniques?

Other executives claim there is more value to knowing a few standard analytical techniques above any one tool that should be leveraged.

Sandeep Sacheti, executive VP at Wolters Kluwer, suggests the following “big five”.

1. A/B Testing

A/B testing, as the name implies, involves a comparison or test. It is the simplest testing method possible, measuring the effectiveness of one path versus another.

Some ideas for areas to A/B test include webpage design or timing of messages in an email campaign. One can test creative and response rates to specific offers as well.

Although most marketers are well-informed of this basic technique, in the rush to get the job done and get to market, fewer employ it than one would expect.

2. Net Promoter Score (NPS)

NPS is an inherently simple concept to measure customer loyalty: It’s a tally of whether customers would recommend your business to others.

While it might seem crazy that entire consultancies have been testing and reporting something as simple as the NPS concept, that number leads to the need for real strategic changes if not at its ideal level.

Again, start simple: Are you asking your customers for it? And have you tracked how your score moves over time?

3. Customer Lifetime Value (CLTV)

Here’s another simple metric, but this one accomplishes a complex transformation — getting an organization to shift its priorities from quarterly profits to the health of customer relationships.

CLTV is most applicable to businesses that can successfully achieve an annuity from their clients (think financial services, for example).

Beyond that, it answers this question: “What is this customer worth to me?” to which there are three more consequential questions:

  • Should I encourage this customer’s business, or let it go? 
  • If I want to encourage his or her business, how likely is he or she to continue?
  • And what do I need to invest to keep that annuity going?

4. Recency, Frequency, Monetary (RFM) Analysis

This is the most basic method of measuring CLTV.

Scoring of all three — combined with a weighting of each to reflect the specific importance of each to your business — is the essence of this simple tabular calculation.

5. Customer Wallet Estimation

Maintaining a base level of analytics ensures you know what your customer spends with you in a given period.

However, in a competitive marketplace, do you know how much money that customer is spending in that same time period across your industry?

This measure involves more advanced statistical analysis and some outside market audit data, including a small sample of customer spend with competitors.

A reliable marketwide number can be derived from this small sample by employing rules of statistics.

Provided in context, knowledge of this number is good for relative comparison combined with other data.

For example, are some of your marketing dollars achieving as much customer money as your competitor’s marketing dollars are? 

Making the most of the CMO’s big data toolkit

What needs to happen to make these tools most effective for CMOs today and in the future?

O’Neal insists that setting up big data infrastructures with big groups of people and big budgets is no longer the way to go.

Analytics should be built to empower people to do work in a demand-driven way — and not in the way IT systems were built in the 1990s.

He goes on to agree with Rentmeester above:

We build minimum requirements that are highly alterable, not capacity models that hope demand will grow and become what you envision. Because what you envision today is changing so rapidly that, tomorrow, it’s out of date.

Rentmeester, however, believes that more people are the answer to actually marshal the data and make it quickly usable.

A large staff, with enough data sense and business acumen to drive business by the numbers, can achieve the right balance of analytical agility, innovation, and most importantly, actionable results.