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Staff, structures and budgets: How to build your web analytics team

14th Feb 2013
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How much should you spend on staffing web analytics, and how can you build your team? Experts share their advice.

As with so many business projects, web analytics isn’t just about the technology – it’s also about the team. But through a combination of a shortage of talent, and a lack of investment, staff can often be forced to play second fiddle to the tools, even though it seems counter-intuitive.
A recent study by Econsultancy and Lynchpin, the Online Measurement and Strategy Report, demonstrated this, revealing that the proportion of companies that have no dedicated web analysts is actually on the rise – increasing from 25% in 2011 to 30% in 2012. Meanwhile, in the same period the proportion of companies that employ up to four analysts decreased from 65% to 58%.
“Web analytics technology is not a black box - it's not magical, it doesn’t fix problems on its own,” emphasises Joe Stanhope, principal analyst serving customer intelligence professionals at Forrester. “You need people to make sure that the tools are working properly and collect the right information and make sure the right people are your organisation can look at that information and interpret it and take action on it. And that's where a lot of organisations struggle. You've got web analytics and digital analytics in place - now what?”
So how much should your business be spending on staffing web analytics – what proportion of your budget should you be spending on people?
The Econsultancy report found that on average only 52% of web analytics expenditure is spent on internal staff, a figure which has not changed since 2011.This is despite 40% of companies in 2011 having planned to increase their budget on staff to analyse web data.
This is a considerably different ratio to the 10/90 rule that was first proposed by web analytics guru Avinash Kaushik in 2006 and that has been accepted as common wisdom for several years.
“The 10/90 rule says if you have $100 to invest in making good decisions on the internet, then invest $10 in tools and consultants to implement the tool, and invest $90 in the people who will analyse the data, because in the web that's the point of failure,” says Kaushik.
“We don't have enough money invested in smart people to analyse data and that's why I’m confident that companies that embrace the 10/90 rule will succeed, because it gives them a robust set of tools to use and the people to go back and do some amazing things with that data.”
Staffing analytics is a costly affair
But others have proposed alternative divisionof the budget. For instance, Eric T Peterson, author of Web Analytics Demystified, has argued that the 10/90 rule is unrealistic and so has favours a more balanced ratio, with what he calls the 50/50 rule.
In an interview, he explained: “I talked to a little more than two dozen companies that are getting it right when it comes to Web analytics. I asked them how much they spend on technology and how much they spend on people, whether that is staff, IT or outside vendors. Invariably it came out to the same amount. By the fifteenth time I heard this, I was convinced. This rule becomes useful to CMOs. How much do we spend on our vendor? How much are we paying staff and outside consultants? How much do we spend on technology? The further those two numbers are apart, the harder time a company has with digital analytics in my experience.”
If this is true, then Econsultancy’s findings would indicate that on average, brands are actually spending an appropriate proportion of their budget on hiring talent. But Stanhope is dismissive of such general rules and advocates that businesses establish the best balance for themselves.
“It really depends on the organisation. I don't think there's a hard and fast rule,” he says. “But I agree with the precept that probably over the long term, the majority of your expense will be in the staffing.”
Jim Sterne, founder of the Digital Analytics Association, also believes that the ratio will vary from company to company – but he adds that the specialised skills required of modern analysts means that they do not come cheap, thereby ensuring that staffing will inevitably always be a costly affair.
“It is completely a corporate culture question. It is entirely political question. There are rules of thumb that are entertaining - the Avinash Kaushik common thread is for every dollar you spend on tools you should spend nine dollars on people. That has never been more true as the tools become more plentiful, the tools are getting more powerful. But the ability to do analysis is this unique thing…” he says.
“The magical person called ‘the analyst’ understands all the data and how it is captured and how reliable it is. But they also understand what optimisation is about and what the business process looks like and what the business goals are. The analyst is that magic place in the middle where they understand the desired outcome, they comprehend the big picture and can look at Big Data and ask the right questions. It is the creative part. But they also have to be really good at communicating their insights out to the marketing people and the business strategy folks because if they have a great insight and they don’t know how to communicate it, it doesn’t matter.”
He adds: “We have lots of IT people. We have marketing people out the wazoo. The business people in charge of setting the direction of the company are thick on the ground. But we’re scrambling to find the analyst who can help us solve the problem.”
Staffing checklist: analysts, data scientists and technical talent
Indeed, a variety of skill sets are required, and Stanhope believes that as a result there is increasing specialisation and diversity, particularly amongst larger companies, leading to the emergence of new roles and structures in an effort to harness the potential of web analytics.
“Different companies have different approaches to it and it can vary on industry but certainly there's a traditional analyst-type role – a digital analyst that look at, interpret and develop insights to push into the business – and then many organisations are also exploring a second level of analysts,” Stanhope observes.
Often referred to as a ‘data scientist’, this role complements the traditional analyst, taking on more of an exploratory analytics approach to mine all of the data that's available and start coming up with non-obvious, more ad hoc/advanced analysis work, whereas the traditional analyst would cover more of the tactical and day-to-day analytics work that the business depends on.
Stanhope continues: “There’s so much data coming in so quickly and so much of it is new. We might understand things like web data fairly well in its own context but some of this social data and mobile and application data is new to us. We haven't necessarily mastered yet, it's still evolving very quickly. We need these kinds of data scientists who can take a look at this data that we don't understand and start applying structure to it and figuring out what data matters and what data doesn't. It’s a very different kind of skill.”
There is then also the need for technical skills to support this – which is where Stanhope believes things can get tricky. “Behind all of these tools and analysts and data, you need to have database architects and the developers and the application managers and administrators to keep these systems up and running so we can use that to perform an analysis,” he says. “There’s a variety of skills sets there that I think, and especially larger companies, are starting to diversify into.”
For his part, Kaushik believes there are four distinct ‘families’ of analytics roles that exist within businesses.
  • Technical individual contributors – roles including senior product managers, senior architects, senior tech lead, that typically sit in the IT function and report up to a director or a VP. Responsibilities include setting policy, rules and regulation, and acting as the point of contact with the business team.
  • Business individual contributor – roles including senior analyst, internal evangelist. Reporting to director or CMO, this person provides analysis and creates dashboards, as well as responsibility for rolling projects out across the organisation.
  • Technical team leader – roles including manager for analytics implementation senior manager, group manager for web operations reporting, and senior manager of website analytics. Reporting to senior manager of director, they are often in the business analytics team in the CIO/CTO function.
  •  Business team leader – roles including senior manager of web analytics, group manager for analytics and optimisation, director of web research and analytics. This role reports to senior directors, VP’s and/or the CMO.
Creating a central analytics group
But for those leading-edge businesses keen to evolve into analytics-driven organisation, Eric Peterson advises against spreading the web analytics talent across multiple departments. Instead, he advises that those firms that are serious about putting analytics at the centre of their operations should manage their web analytics talent by taking a centralised/decentralised approach.
This ‘hub and spoke’ model requires the creation of a centralised team (the hub), which takes responsibility for core analytics functions such as data collection, data vetting, data analysis and the education of the organisation about the system and data. This central analytics group feeds into marketing, operations, management, commerce and external agencies, and supports those analytics users (the spokes) within the organisation that have partial dependency on web-based data to do their jobs.
Peterson believes that an appropriate web analytics staffing model will be characterised the following attributes:
  • You have a senior person who ‘owns’ analytics
  • You have dedicated resources for web analytics
  • You know who your ‘analytics amateurs’ are (those that have partial dependency on web-based data)
  • Your analytics hub supports the whole company
  • Your analytics hub produces insights and recommendations, not just reports
And perhaps unsurprisingly, those businesses that were the early adopters of web analytics technology, have been quickest to acknowledge the importance of investing in web analytics talent, and supporting them with structures and models.
Stanhope says: “The companies with the most skin in the game have been the most aggressive with analytics - retailers, ecommerce companies, some of the online gaming companies like Zynga, Disney, large credit card companies... The companies with the largest online presence with the largest amount of money on the line in digital have been the most aggressive early adopters of analytics in general - and of not only getting the technologies in place but investing in these staffing needs to support it.”

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