How Big Should Your Digital Analytics Team Be?

If you’re an NBA fan, you might know about the Los Angeles Lakers’ recent troubles with injuries (Kobe Bryant, Pau Gasol, Steve Nash, etc.). On Feb. 5th, the team actually ran out of eligible players while playing against the Cleveland Cavaliers. With only 3:32 left in the game, the Lakers’ already depleted eight-man roster shrank to only four eligible players after two additional players were injured and two more fouled out of the game. In case you’re not familiar with NBA basketball, each team needs to have five players on the court and can have up to 12 active players on its roster. Luckily, due to an obscure NBA rule the Lakers were able to keep one of its fouled out players on the floor and close out the game by beating the Cavaliers 119 to 108.

Chris-Kaman-catches-a-quick-nap-on-the-empty-Laker-bench.-Screencap-via-@nbarocksstc

Normally, you’ll see six or seven players on the bench during a typical NBA game.

As humorous and unique as this situation was for the LA Lakers (and disappointing for Cavaliers fans), I wonder how many companies don’t have enough players on their digital analytics roster. On several occasions people have asked me how many individuals they should have on their analytics team. One of the biggest challenges that impedes many digital analytics programs from being successful is insufficient staffing. On a recent business trip to New York City, I met three companies that were struggling with inadequate analytics resources. One organization had no one owning its analytics program, another had one individual dedicated 15-20%, and the remaining billion-dollar company had only one dedicated analyst. Ouch.

Just like it’s hard to win basketball games with an incomplete roster, it’s also difficult to extract value from your data if you don’t have enough analysts, especially if you want to move beyond just basic measurement and reporting. While I wish I could give you a simple answer such as “you need at least X people”, it’s never that easy. The right number will be different for each organization, and the sweet spot can change over time as business needs evolve.

After interacting with lots of different analytics teams, I’ve identified several factors that contribute to how many people are needed on an analytics team. While I haven’t developed a magical formula or algorithm that will spit out the ideal number of analytics resources, I can share some team size considerations that I recently gave to a customer. They might help you to evaluate the resource needs of your specific digital analytics program.

First, I’ll lay out the baseline requirements and then review the different factors that will expand or contract the specific number of people you’ll need at your company.

Key Roles for a Digital Analytics Team

Before I get into how many people you’ll need on your analytics team, it’s important to understand the basic roles on a typical digital analytics team. Just like a basketball team has different positions such as power forward, point guard, or center, digital analytics teams also have different players:

  1. Program owner: Every company needs someone to own or manage the digital analytics function. Within smaller companies, a single individual may wear multiple hats and handle all aspects of the digital analytics program (requirements gathering, implementation, project management, reporting, analysis, etc.). They may even be tasked with other non-analytics responsibilities such as email marketing or paid search that pull them away from focusing on just digital analytics. Most mid-to-large companies have a manager or director who oversees the operations and performance of the digital analytics program. This role is essential to leading your data-driven efforts and gaining any sort of sustained momentum and success with analytics.
  2. Technical staff: At a minimum, you’ll need a web developer (JavaScript expertise) to deploy and update your analytics implementation. In many cases, this individual won’t live on your digital analytics team and will typically come from your IT group. For larger, more complex organizations, an embedded technical resource may lead major deployment projects as well as guide implementation standards and processes. While tag management solutions can lighten the burden placed on this type of resource, it won’t entirely remove the need for technical expertise.
  3. Analyst staff: Companies generally need at least one analyst to handle the reporting and analysis responsibilities. In some situations, the analytics manager might also be the analyst who distributes reports and insights throughout their organization. This analyst/owner combination will only work for small companies with limited analytics needs. Most organizations require a team of several analysts to glean insights from their digital data and use their recommendations to optimize the business. Analysts’ responsibilities also go beyond just slicing and dicing the data such as gathering business requirements, architecting new measurement strategies, tweaking existing tags, validating new deployments, training and supporting users, etc. One of the biggest mistakes that companies can make is to understaff in this critical function, which ultimately limits how much value they can extract from their web analytics tools.

Note: A fourth role may be needed at organizations that have a high volume of analytics projects. A project manager can prioritize, schedule, and manage different analytics projects. Project managers may also be essential in establishing and maintaining analytics-related processes.

These three key roles form the basic building blocks of every digital analytics program. As long as you have at least one person fulfilling one or more of these roles, you have the beginnings of a digital analytics practice. However, getting off the ground with digital analytics and really capturing value from it are two separate things.

I’ve found that organizations that struggle with digital analytics have often underinvested in analytics staffing. While consultants can bridge short-term staffing gaps, companies eventually discover they need in-house talent to take their analytics programs to the next level. When a digital analytics team is understaffed, it is susceptible to significant setbacks created by employee turnover. Whereas an adequately-staffed team can generally absorb losing a key individual, an understaffed team can’t. I’ve seen web analytics programs take two or three steps back simply because one key employee left, and the team couldn’t easily recover from losing their expertise and knowledge. You don’t want to avoid this potential situation at your company.

foot_soldiers
Whereas you only need one program owner and some ad-hoc technical coverage, you will most likely need multiple analysts. Primarily, most of the staffing concerns will center on this resource type as they are the foot soldiers (and potential action heroes) of your digital analytics team. The following factors will shape how many analysts you will need for your particular business:

  • Number of business units or groups that require analytics support. How many business units require data or information on their digital properties? How many internal teams or groups need help with reporting and analysis? Multinational organizations need to factor in localization needs and language coverage as well (i.e., a San Francisco-based analyst isn’t going to be as beneficial to your Nordics region as someone based in Stockholm).
  • Number of internal customers per business unit. How many active customers are there in each group? How many potential customers could you have within each business unit? A single large internal team may need more than one analyst. In other cases, a single analyst may be able to support multiple teams if each group only has a few internal customers. I’d estimate that a single analyst can support between 10-20 internal customers who need more than just basic measurement and reporting support (i.e., in-depth analysis and actionable recommendations).
  • Relative data-neediness of customers. How frequently are customers asking questions about the data? How sophisticated or complex are their questions? Are they simply looking for recurring reports or actual analysis with findings and recommendations? On one hand, an analyst who is only building and sharing reports and dashboards could potentially “support” hundreds or even thousands of internal users. However, the same analyst could be completely consumed by analytics projects generated by a single data-driven executive. The data-neediness factor will be shaped by several considerations:
    • Corporate culture: Data-intensive or gut-driven?
    • Tool accessibility: Ability to self-service, level of tool training, etc.
    • Importance of your digital initiatives: If most of the revenue comes from other non-digital channels, digital data won’t be as critical or strategic to your business.
    • Complexity of your online business: Number of digital properties, homogeneity of those properties, mix of digital channels, frequency of major campaigns/events, release cycle cadence, sophistication of the analytics implementation, etc.
  • Number of analytics tools used. What analytics tools are being used by your company? You may have different analysts that specialize in using different types of tools such as data visualization tools, statistical packages, A/B & multivariate testing, tag management systems, etc. It’s unreasonable to expect an average analyst to be an expert on multiple systems.

While the aforementioned factors help determine how many analysts are required, there are still some other considerations that may weigh into your analytics staffing decisions:

  • Level of expertise. How experienced do you need your analysts to be? While seasoned resources can accomplish more than junior analysts, they are also more expensive to hire and retain. Using senior analysts to perform menial reporting work isn’t a good use of their skills and experience (not cost effective either). Ideally, teams should have a blend of senior and junior-level analysts. Senior analysts can be valuable mentors for junior analysts and help to accelerate their development.
  • Level of centralization. How standardized or coordinated does your analytics program need to be? How much global or enterprise-wide reporting and analysis is required? Should analysts be embedded in the business units they serve? It’s common for large organizations to use a hub-and-spoke model where a centralized analytics team (hub) works with embedded analysts in different business groups or regions (spokes). Depending on your team strategy, you may require less or more headcount.
  • Level of oversight. How wide or narrow is a typical manager’s span of control at your organization? What’s appropriate for your analytics team? You may need to factor in some sort of management layer to ensure a large analytics team can function effectively. If everything is funneling through one analytics leader then it’s going to be difficult to scale and be agile as a team. However, too many chiefs and not enough Indians can also be a problem.
  • Level of outsourcing. How much do you depend on consultants for day-to-day tactical or strategic work? As a former consultant, I recognize the important role that consultants can play in filling temporary staffing gaps, shoring up expertise, and providing an outside strategic perspective. Sometimes headcount funding can be hard to obtain; whereas, consulting fees can come from a different budget. In addition, it can be difficult for companies to find experienced talent in a timely manner so hiring consultants may be the only immediate option.
  • Level of automation. How many repetitive, menial analytics tasks (e.g., reporting) have been automated or streamlined with technology? Wherever you can use technology to offset the need for human beings to perform low-value, labor-intensive tasks will free up analyst resources to focus on more strategic, high-value responsibilities (e.g., deep-dive analysis). With analytics talent being difficult or expensive to acquire, technology can offer some supplemental support.

Each organization’s analytics needs are unique, and each of these factors will be slightly or significantly different from company to company. In order to help you evaluate your own needs using these criteria, I’ll provide a couple of sample company scenarios to help illustrate how the different factors influence the digital analytics team size.

Team Size FactorCompany A (Financial Services)Company B (Retail)
Number of business units or groups that require analytics support5 (different product divisions and regions)1 (single ecommerce site)
Number of internal customers per business unit20 active users per group (100 total)5-10 active users
Relative data-neediness of customersHigh (data-driven culture, multiple web properties & campaigns)Low (design-focused culture, physical stores more important than website)
Number of analytics tools used4 (digital analytics, online surveys, data visualization, statistical package)2 (digital analytics, testing/optimization)
STAFFING ASSESSMENTMinimum: 1 program owner + 5 analysts

Rationale: With a user base of 100+ people company-wide, the program needs someone to own this important function. With each BU having 20+ active users, they would at least need one analyst per BU. In some cases, they might need more than one analyst due to data demands and tool expertise. Junior analysts might complement a senior analyst for each BU. With the BU’s being fairly autonomous with little cross-over, there’s no need for heavy centralization. However, if governance were an issue then you might add one or two individuals for just this purpose.
Minimum: 1 program owner/analyst + 1 testing expert

Rationale:
Right now the data demands are relatively low, but they do have both digital analytics and testing platforms. One person couldn’t effectively own both initiatives. While many organizations lump its testing team in with its analytics team, they represent complementary but different skill sets. This company may need to lean on consultants until the organization decides it wants to invest more in this area and grow this expertise in-house.

You might go through the different factors I’ve presented and realize you’re even more understaffed then you thought you were. Don’t despair! While it’s true that without a strong executive sponsor it will be hard to get the budget to hire several new analysts at one time, it doesn’t mean you can’t start small. If you can demonstrate the staffing gap and advocate for hiring at least more person, you can begin to climb out of the hole. That might mean hiring an intern, a consultant, or a college grad as a junior analyst to begin with.

If you document and evangelize the wins that are generated by the additional help, you might be able to hire a second team member and then a third. Analytics teams generally grow when they repeatedly demonstrate their success and positive ROI to key stakeholders. Eventually, you’ll reach a team size where adding one more resource wouldn’t add sufficient incremental value to warrant an additional hire (diminishing returns). However, I’d argue most digital analytics teams are nowhere near hitting this threshold and have plenty of capacity to grow.

An HBR study found that companies with above-average analytics talent had significantly higher marketing ROI than those with below-average analytics talent (+4.18% vs. +2.51%). If you think one action hero is dangerous, imagine a whole team of them. Companies with successful digital analytics programs have figured this out—hopefully your organization will too before your competitors do.

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4 Responses to How Big Should Your Digital Analytics Team Be?

  1. Jon Narong says:

    would be nice…

  2. a_crutch says:

    Awesome post! I’m currently an analytics manager at a company which provides financial services – felt like the post was written for me. Do you have any tips on project management for analytics projects?

  3. David Pittman says:

    Great post, Brent. I co-host the weekly #bigdatamgmt Twitterchat on Wednesdays, noon ET. I would like you to be a guest on an upcoming chat that’s about “Building A Big Data Team.” This post fits very well with that. Email me or tweet @TheSocialPitt or @IBMbigdata if you’re interested.

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