Web Analytics Consulting: A Simple Framework For Smarter Decisions

sharpfocusAs I've gotten older I've come to appreciate the value of frameworks a lot more.

When we are young, the answers to everything are simpler because, of course, we know everything.

What metrics should I use? Use BR & CV. What digital marketing works? Definitely Y, do that. How can I improve my business? Simple, do A then B and you're done. So on and so forth.

One upside (or is it a downside?) of age is the wisdom of realizing how much you don't know. Suddenly you don't have concrete answers because you realize: 1. You usually lack all the information you need and 2. Even the most mundane and obvious situations are incredibly complex and unique.

So you start answering questions like "What is two plus two?" with "Tell me a little bit more about what you are adding" or "It really depends on the process you use to add them" or … you get my point.

This is the main reason I love frameworks. They don't contain answers; rather, they help place a situation or a process or steps and encourage you to think a certain way. They force you to step back and think. They make you go talk to other people. They force you to say “hmmm …” And if you can make a person think, if you can encourage them to cover all the bases, if you can get them to ask themselves some tough questions, then you have given them the greatest gift of all. Not the pat answers, but rather the way to figure out the best answers for themselves all by themselves.

So, whenever possible, don't ask for perfect answers, ask how to think. You'll thank me.

Two of the frameworks I've built and shared on this blog are the Digital Marketing & Measurement Model (how to pick the best KPIs for your business that guarantee success, using a powerful five-step process) and the Clear Line of Sight Model (to ensure every bit of Marketing and Analytics you are doing is tied to the Net Income of the company).

The DMMM and CLoS are strategic frameworks (you should embrace them right away!), and in this post I want to share a really, really simple framework for structuring web analytics consulting contracts.

The Web Analytics Consultant Quandary

BB sent this query:

If I take on a consulting project then what could be expectations out of me?

From what I understand, I would be creating a Web Analytic Report and giving my recommendations. That would be one deliverable from my end.

What could be the other deliverable for a web analytic project? What could be their expectation beyond submitting the report?

Would I be required to set up various A/B and multivariate tests for that company?

And what if they are at initial stage and have just set up Google Analytics with no goals, events or internal search tracking. Would I be required to implement goals, events or set up internal search tracking as well as exit survey?

What is the timeline of a web analytic project in the above case where there is no tracking and as a consultant I set up tracking for them. When should I start creating reports?

When does this project end? I mean where do I put a stop.

When I get this type of open-ended query my instinct is to figure out how to create a framework that would encourage structured thinking, force for assumptions and flaws and opportunities to rise to the fore.

And it does not have to be complicated, even for something as open and expansive as the query above.

For any web analytics consulting contract, the beginning, middle and end really depend on the contract you've signed, and – you'll be surprised – not the actual amount of work that needs to be done. The contract, and the hourly rate it provides for, will motivate the consultant to do as much or as little as is required to meet the contractual terms.

So, what's the fix?

The Optimal Web Analytics Consulting Framework: DC – DR – DA

Before jumping into any engagement (and signing a contract) I recommend using this simple framework for web analytics consulting contracts: Data Capture. Data Reporting. Data Analysis.

Ask your client: "What is it that you would like to accomplish in these three simple buckets: DC, DR, DA?"

This will force them to think about what they really want to get done, and their reply will be a really huge gift to you because you'll know:

1. If what they want is a fit with the skills you/your company possess,

2. How long the contract will be, and

3. How much you should charge for the work required.

So, what type of work falls into each of these three buckets?

spider web canvas

Data Capture:

The work that falls into this bucket is to perform an audit and/or update current data capture mechanisms.

This could cover current or new javascript tag implementation (which has to be both correct and complete ). This could mean implementing new updated code (both to fix their current problems and to s.prop and eVar the code to collect new data). It could also mean getting into the tool's admin area, as in the case of Google Analytics, to configure internal site search data capture, setting up goals and goal values (if you don't have these last two things set up you are not doing web analytics, you are doing web letswasteeveryonestimedatapukingforthesakeofdatapukinglytics).

If you are a Web Analyst who is really an Implementation Specialist, this is work that you'll enjoy because it is right up your area of expertise. If you are Web Analyst who is really a data processor (bucket two, below) then you'll find this a little frustrating. If you are a true Web Analyst, you'll find this work to be utterly frustrating. It is important you know who you are, and what the contract/client requires.

Life is too short doing things you hate, so sweat details here. Always match skills with work required for the sake of world peace.

Data Capture consulting work is also quite thankless work because there is always someone who is willing to do this work for less (the web analytics consulting world is brimming with Web Analysts who are essentially Implementation Specialists, not that there's anything wrong with that).

Even for a very smart Implementation Specialist such as yourself, a unique individual with extremely valuable skills, these types of contracts are a lot less fun because all you are responsible is javascript tag hacking and begging the right people at the client to implement your hacking.

Just be aware of this. Talk to your client. Get specifics. Figure out if you want to do it (or someone at your consulting company).

There are lots and lots of pure Data Capture consulting contracts, and sometimes they'll also include our next bucket…

data reporting+

Data Reporting:

Essentially, this work is the client saying: "I want someone to send me my paid search performance every week" or "We have Google Analytics, we need a package of reports each week" or "Our Finance team needs their reports set up."

You'll get access to SiteCatalyst or CoreMetrics and you'll scrape the standard reports into PowerPoint and send it out each week. Or you'll set up some custom reports to give the client exactly what they want. You might have some back and forth with the clients that will help you pull the right metrics into the reports, but for the most part you'll be told what they need and you'll do that for them.

In some cases you'll use your license for Nextanalytics to completely bypass the web analytics tool, Google Analytics in this case, and create the reports and dashboards inside Excel using the tool's free API.

There is less thinking required in this work, you don't even have to be a real Analyst, you can just pass the Adobe certification, the GAIQ test or other tool front-end things and you might be able to do this work. It is also a little less thankless than data capture simply because meeting the clients needs and actually seeing their numbers come together is rewarding.

But there is a lot of competition for this type of work because it requires less experience and analytical sophistication to be successful, hence many Consultants enter the field with this work (then graduate to Capture and if they are really, really good move to Analysis).

Bonus Pro Tip: If you are going to take a lot of Data Reporting contracts, then you should create for yourself (and your company) a massive bank of the best of breed custom reports for various purposes (types of companies and types of reports requested). Then when you sign a Data Reporting contract you can pick the best custom reports from your bank, simply import them into your client's account, and boom (!) you're already in business. Don't forget to ask for a bonus for finishing early. :)

Bonus Custom Reports: You can download my favorite Paid Search Custom Reports and my Content Efficiency, Visitor Acquisition Efficiency and Search Micro-Ecosystem reports and get a head start with your own reports bank!

data analysis

Data Analysis:

This is the type of work that happens when the client gives you an open-ended assignment to really look at the data.

The client will not usually know what they want, they don't have specific guidance ("give me bounce rates!") and they really you to tell them:

1. What to measure,

2. What the data is saying, and

3. What they should do based on what the data is saying.

These are the most gratifying contracts, with a painful amount of work, because you have to really go in and create a Digital Marketing & Measurement Model (and how amazingly fun that is because you get to root causes, you get to work with an expansive set of company Sr. Leadership, you get to really, really nail down what's important for the client).

You then get to create really cool custom reports and dedicated unique advanced segments (to deliver on the DMMM identified priorities). You can often force someone else to do the implementation right (let the cheaper Implementation Specialists take care of this important but repeatable work) – either a resource with your client, or someone inside your consulting company. You can focus deeply on data analysis and helping drive the recommended actions at your client.

This does mean that you must possess specialized skills for this type of a contract, you have to be a real Web Analyst and not a Web Analyst that is essentially a Implementation Specialist or Report Creator (both very important jobs but don't require analytical skills). You have to know statistics 201. You have to know analytical techniques. You don't compare percent differences (they hide more valuable insights); instead you have your own cluster of techniques like Weighted Sort . You know 19,000 ways to get optimal context for your KPIs and insert it into the dashboards. You have a superb amount of business experience in your industry/line of business, that understanding means you ask nuanced questions when it comes to people and data (killer!). So on and so forth.

This does mean that you'll be able to charge a lot for contracts that are heavy on, or all about, Data Analysis. During my experience I've seen people charge, depending on the client and the consultant skill, $500 a day and $5,000 a day.

Not even 3% of web analytics consulting companies have people with optimal skills to be called an Analyst, so you can see how easy it is to charge a lot for this resource.

Astonishingly, pure Data Analysis contracts are hard to come by because companies are still so obsessed with Data Reporting ("if we just data puke we'll automatically be data driven because everyone in our company is a data analyst"). And since most web analytics consulting companies are Implementation Specialists, there are also lots of Data Capture contracts. Both don't reflect optimally on our industry, but do explain why despite our ecosystem having more data than God should allow anyone to have, we are still mostly gut-driven.

But if you do get a contract with a large component of Data Analysis ("come in and really help us figure our DMMM and take it from there to delivering pure insights and actions we should take") then grab it (if you or your company has the skills). They are deeply satisfying. They are high paying. And you do get a chance to change the world.

So when does the work of a web analytics consultant start or end? How much can they charge for it? Are they required to fix the code or set up experiments? What about customized data dumps?

It all depends. Is it a Data Capture, Data Reporting or Data Analysis contract?

You would be right to state that there are probably no pure DC, DR or DA contracts. They are rare, mostly because when you start doing analysis you'll notice you can't get away from meeting some reporting needs at your client. When you do reporting and analysis you'll discover implementation problems and then someone (you?) have to go fix that.

There is most certainly a symbiotic relationship between the DC, DR and DA.

But it is not uncommon for a contract to be heavily weighted in only one of these three areas. If you use this web analytics consulting framework then you'll be able to identify that upfront and set optimal scope for your contract, charge an appropriate lump sum or hourly rate, and go about working like crazy to become super rich!

client consultant

A Client Perspective:

If your company is looking to hire a consultant then you should go through this exercise upfront as well. Before you call the blogger you're impressed with, before you sign on the dotted line from a consulting company that's "certified," before you extend a contract to the speaker at an industry conference.

What work do you actually have for the consultant/consulting company?

Is it majorly Data Capture? Data Reporting? Data Analysis?

What is your core weakness in terms of skills inside the company?

Why is it that your organization is HiPPO- or gut-driven, rather than you providing cogent insights to your HiPPOs so that they can mix data and their experience (or gut) to make optimal decisions?

It is never obvious.

But if you take our simple framework, ask the right questions and do some root cause analysis (or just soul searching or at least sleep on it for one night) then you'll be able to better understand what you need, you'll pay optimally for that need to be fulfilled (both contract amount and contract duration) and, I cannot tell you how brilliantly important this is, you'll find the optimal consultant who has the optimal skills you need.

It is not unusual for a million dollars to have been spent and the company to have progressed to zero percent data driven. That's because they thought they were getting a real analyst, they got a superb implementation specialist who's done data reporting but possesses zero actual analytical skills. This person, group of people if a consulting company, then spent a year (charging a million dollars) doing the world's most sophisticated implementation of Site Catalyst / WebTrends / Google Analytics. The company now has 900x more data than it needs, they have 25x more reports than they need. They just don't have any analysis.

That's a big company story.

But if you are a small business you don't have that kind of money. Hence it's even more critical that you go through, even a rough exercise, the DC, DR, DA framework. You likely need all three. Know that it is very, very hard to find the Purple Elephant that will be good at all three, so figure out where you have the greatest need. Hire her. When she's done with her core competence, go out and get the next person to take you to the next level. (And then the next.)

The Data Capture, Data Reporting and Data Analysis framework helps both clients and consultants have an immense amount of clarity on what the needs are (client), what skills are required to meet those needs (consultant) and how much time and money will be required (from the client to the consultant) to deliver glory.

I've created a helpful summary based on my humble experience along four key dimensions that I think you'll find to be of value (regardless of if you are the client or the consultant):

web analytics consulting framework dimensional summary

So use our delightful framework. Spread happiness in the world, happiness that only actions based on great data analysis can deliver.

Ok, as always it is your turn.

Do you have an alternative approach to sizing up the opportunity with a client? As a client, do you have a specific set of instructions you send out when looking for consultants? What kind of contracts are most common out there? Why can't we find more fantastic analysts in our ecosystem? What are your secrets to delivering joy to your clients? If you are a client, what secret ingredients did your last DC, DR or DA consultant possess?

Please share your insights, advice, kudos, and critique below via comments.

Thank you.

Comments

  1. 1

    That's an excellent post Avinash, it really organizes the consultancy practice into nice compartmentalized buckets.

    But one question: what about optimization?

    Do you consider it as something apart? Because sometimes the client is really not interested in what you are doing, but which value you are bringing.

    So I would consider "website improvement" as a fourth category, after data analysis.

    • 2

      Daniel: Great question.

      The work that is required for website optimization (A/B Testing, Controlled Experimentation) is described in the post under the Data Analysis bucket. Understanding real business needs using standardized frameworks, identifying actions to take and then working with the client to implement them.

      All of experimentation (sans the five minutes required to implement the tag) falls into the DA bucket.

      Perhaps, post your thoughtful comment, I should change DA to DAO (Data Analysis & Optimization). The O would cover insights from data then actions implementation (including experiments, but could also include other types).

      Thanks!

      Avinash.

      • 3
        Jay Adamsson says:

        Change DA to DAO only with clear definitions. As a mathematician with 20 years in business analytics before specializing in web analytics, I'm definitely in the DA group. However, I've found that I can't assume that my definition of optimization is understood universally. While I obviously take a rigorous, mathematical approach, most of the time it's understood much more loosely.

        Also, experimentation doesn't necessarily fall into the DA bucket all the time – it depends if you're the initiator or the implementer. If you initiate the experiment (along with a clear reason for why you're doing it and how it impacts the organization's goals and decisions), then you're DA. If you're experimenting on the site because you've been told that this is what you should do, then you're either DC or DR.

        • 4

          Jay: You are right about the need for a clear definition, and I'll certainly update DA to DAO at some point.

          On experimentation… I think life is so complicated for experimentation that if someone's approach is to just do DC or DR then that gets exposed very quickly and the person looks lame. That's simply because without analysis you simply can't understand the real results of the experiment, and worse you'll never be able to show repeatable performance from the lessons (of you just DR it).

          That ruthlessness ensures that people actually do DA, or they die (professionally).

          I wish clickstream analysis, and indeed the whole Web Analytics 2.0 spectrum, had that level of ruthlessness built in. Sadly it does not, it is simply too easy to fake DC and DR and survive for far too long!

          Avinash.

  2. 5
    Himanshu says:

    Hi Avinash!

    I was waiting for a post like this for a long time. Thanks

    Himanshu

  3. 6

    This is really a very important topic especially for us as data analysis or consultants. I agree that getting datas to the owners would really mean a lot to make smarter decision.

    Thank you for bringing this topic again.

  4. 7
    euan says:

    "1. You don't all the information you need…" Should this read " 1. You HAVE don't all the information you need"?

    Great post, shame the only thing I could add is a missed word correction!

    • 8

      Euan: I'm immensely appreciative of you finding the garbled sentence! I've fixed it.

      Thank you, and I'm so glad you found the post to be of value.

      Avinash.

  5. 9

    Avinash,

    Great topic. However . . . don't overestimate we here customers that we suppose that they know what they want. At least this is what I expierence not only for small but even middle sized companies. The problem is that they don't understand how insights in data, analytics and actions can improve their business. OK, they (better to say: we) implementend Google Analytics but that's all. Even when we propose to set goals they lose interest. And when we propose a test with e.g. two layouts (A/B analysis) and explain why that is important they are totally lost.

    Another aspect is the "accountability" of the costs. E.g. a customer is willing to invest $ 3.000 in webanalytics but they want to have an indication what it brings. I know, that's the wrong approach but very often the situation.

    You commented earlier that some (or many) customers don't understand the value of data and insight. But how can we give customers real insight in the value of data and webanalytics if we don't have a webanalytics consulting contract (indeed no data, no insight).

    The essence of your DMMM-framework is 'ask business questions'. But what if the website has low interest of the Hippo even when he says that the website is very important. How strange :) His gut feeling is import here. He knows what really works and doesn't need a confirmation what he already knows (no arguing helps here:) )

    Fortunately things are not always that bad. With some customers we have a real 'data analysis contract'. Yes, sometimes it costs a lot of $$$$ but because the customer comes back it is obviously of great value for him. However because of privacy reasons in most cases we cannot use those succes stories for prospects and new customers.

    So . . . how do you explain people the value of data and webanalytics?

    • 10

      Sander: Wonderful comment, thank you!

      It is a pretty fair assumption that companies, big or small, usually have no idea what they want if you think about metrics, analytics solutions, strategies. No matter where in the world you are, this is true.

      What companies do know, or can be helped to appreciate more, are what their problems are. Or better still, what amazing looks like.

      This is where frameworks like the Digital Marketing & Measurement Model (and it's ilk) come in. Apply a structured approach to what nirvana looks like. Then the consultant (you? :)) come in and help them understand what it is going to take (and charge appropriately for it).

      In my experience Clients don't engage deeply with Analytics Consultants at an optimal level because they/we fail to appreciate the value/impact. Hence my passing comment in the post about goals and goal values. If only they develop an appreciation for the impact of the digital (not analytics, digital) on the business, we can extract their deep commitment.

      You are right about the HiPPOs. Their heart and desire is in the right place when they make gut based decisions. The problem is that we've not adequately shown them 1. we know what we are doing 2. data can make decisions fast enough that have a impact (we are far too busy tagging the site for 18 months) and 3. we can impact the future and not just the present.

      How do you do it? In small chunks. For example I always start every contract not by making 50,000 tagging improvements but rather, for a ecommerce client, looking at the cart and checkout process and making improvements there using data. That translates into immediate money. Then I pick the next challenge. After two or three quick wins with what they already have I earn the right to talk about more complex data challenges, and a more expansive contract.

      If I reverse the above process you can see why I simply won't have the credibility to talk about anything other than minor changes or, God forbid, a cheap tagging project.

      Avinash.

      • 11
        Marc says:

        I like this approach.

        Much of it comes down to being able to relay your experience into terms they understand.

        An interesting metric we had to discuss the other day was that the client wanted to make sure they did not improve conversion rate by over a certain %.

        They had run their own numbers and knew their logistical capability would be put under too much pressure too soon if the conversion rate improvement was too great too quick.

        Fantastic post, every post I read spawns 5 more to read :D

  6. 12
    Ankit says:

    Avinash, I am already a subscriber via Google Reader. But this is in my Kindle now.

    I'm training few friends for Analytics (for Freelancing gigs) and you just added so much value. DC, DR and DA.

    How simple is this!!

    Thanks Again Avinash!
    Ankit

  7. 13
    Josh Braaten says:

    I love the framework you apply here, Avinash. The three buckets you suggest make a lot of sense, and it makes even more sense that bucket #3 is where the real money is.

    But you don't see many discussions like this going on across the web. I think it's because companies, as you say, are still focused on data puking and less on the analysis that could help them leverage all that data.

    You've been in this game for plenty long now. Do you think lack of web analysis is cyclical and it's just something people will get to as the industry continues to mature?

    Or is it like the last 20% that most will never achieve because it requires mastery of the fundamentals before the need is even realized? (I kind of look at this like CRO testing with search. Most agree that it's a great thing to do, but many have never performed any sort of testing because a) it can be hard and b) requires a lot of other things to be in place already).

    • 14

      Josh: It is a very complicated situation with many contributing variables (I wish it were simpler).

      Vendors encourage "constant implementation," just think of an average Site Catalyst or CoreMetrics implementation. Constant implementation is a nice revenue stream for vendors. Vendors also constantly "innovate," just think of the raw number of features released in Google Analytics in the last 12 months – insane things that not even 1% people on the planet need and not even 100 actual people in the world are using.

      This encourages perpetual Data Capture and data puking.

      There is very little analytical knowledge in the practitioners. No formal university training, no forced learning, and anyone can slap a GA tag and be a Analyst. But as they are skills hampered this just encourages Data Reporting, and nothing systematic about business analysis and data driven insights.

      This causes data regurgitation that does not move companies or the industry forward.

      Finally there are very low expectations from measurement at clients. The state of all other analytics is so poor that management teams are ecstatic to get page views (in a world driven by GRPs you can see the attraction). These low expectations translate into Data Capture being funded, Data Reporting being funded, but a distinct lack of funding for Data Analysis (because Management at mid and large companies don't ask for all the glory that is possible).

      This causes DC and DR, but very little to encourage DAO.

      Each of these three factors applies to a different degree, depending on the company. But you can see how that is irrelevant, the outcome is still the same.

      So we have to educate and raise management expectations (people who have the money), we have to educate and attract a new cadre of Analysis Ninjas who'll reject lameness when they see it, and we have to get Vendors to stop investing in creating glorified data pukers that are Google Analytics, Baidu Tongji, Omniture and IBM.

      Simple, right?

      I'm optimistic that the pressures that will make that happen are just around the horizon.

      Avinash.
      PS: And do please see Robert Miller's excellent comment below. A slightly different way of framing the problem, but wonderfully put.

      • 15

        Yes, but do you think it is the skill of the analyst that is at issue, or that the organization has no real capacity to convert data to information and then into action?

        If you are trying to improve a process, it can be helpful to think of it as a prediction/control problem – you make predictions about what will happen and if you take certain a actions (the prediction part, natch) and you have a controller that uses that information to take the actions in the world.

        Analytics tends to just focuses on the prediction side, but without a robust controller (the organization), you are always stuck doing the same thing, IMHO.

        Matt

        • 16

          Matt: My comment to Josh outlined three areas that are the reason for the current malaise in the Digital Analytics space. 1. Vendors 2. Practitioners 3. Clients/Bosses.

          Analyst skills fall in category 2 above. The expectations of them (then directly translated as paying money for it) falls in category 3.

          We need to solve for both of them (along with category 1). All three need to evolve – perhaps in different degrees depending on the company.

          If I have you as a resource in my team, for example, I know I don't need to worry about category 2. I'll worry about 1 and 3. :)

          -Avinash.

  8. 18
    Robert Miller says:

    Thanks for the post Avinash, as always it was a good read and something that actually made me think.

    The part that really stood out was the question of why so few fantastic analysts that truly deserve the title. I'm a few years into my web analytics career and bettering my analysis skill set is something that is constantly top of mind. I think the main reason for such a low number (and I definitely realize I am not in the elite club just yet) is the amount of work out there for data analysis is so small.

    After thinking about this for a bit here are my top 3 thoughts on potential reasons for the low amount of work:

    1. It takes the right client #1. I think it takes a special client to be willing to not only trust the data they are presented with, but to pay for the analysis in the first place. With data analysis being a relatively new expense, compared to other marketing and business related expenses that clients are used to paying for, it takes a special client to put their neck and budget on the line for something they haven't experienced before.

    2. It takes the right client #2. Another big challenge I have seen with clients is the unwillingness to change, even when the data clearly shows they should. The response that is usually received is "X has gotten us to where we are now and has worked so far, so X should work in the future too." To counter the opposition to change, I have found that a really solid explanation of why things should be changed and the potential business impact can move some skeptics away from the fence. They might not always see things as clearly as we would like them to, so leading the way with easily understandable and insightful discussions should make everyone a lot more happy.

    3. "Analysts". This one is probably the largest issue because it can convert clients that are willing and ready to pay for awesome ninja analysis into bitter and scorned clients. I have had the pleasure to work with a few clients that paid the big bucks for data analysis in the past, but many of them felt cheated by the work they received. Looking through deliverables that were received it is justified that they feel cheated because they were paying the data analysis price tag and were getting the data reporting deliverables.

    This issue of many in the industry using "reporting" and "analysis" interchangeably in their work can really put a bad taste in the mouths of clients and make it that much harder for them to be willing to put their neck on the line for the work that we want to do.

    • 19

      Robert: This is a wonderful comment, I really love it.

      Thank you so much for taking the time to share your valuable experience with all of us! It is on the money, and I'm sure others will find it to be helpful as well.

      Avinash.

  9. 20

    Thanks for writing this up, Avinash!

    I think it does a phenomenal job of differentiating the different services within the analytics space. It provides clarification that newer/younger agencies can definitely use to further their development.

  10. 21

    Thanks for this thoughtful post. This is useful to help think about the various parts of the web analytics domain, and where one might fit in.

    Do you think that maybe you are overloading Data Capture a bit? I get where you are going on the actual implantation side of capture, but data capture is also a strategic consideration. What data do we capture – not just the mechanism to do so, and what do we think the marginal return might be off it, in order to justify capturing and storing it in the first place.

    Data Capture is intimately related to optimization, since you need to first go out an get the data, in the correct way, in order to then perform the analysis needed to guide decision making.

    Perhaps Decision Making should be the root, and everything else should branch down from there?

    Cheers,
    Matt

    • 22

      Matt: I'm biased when it comes to Data Capture. Almost all of it stems from our industry's irrational obsession with data capture. It comes at the cost of working with good enough data. It comes at the cost of realizing you need to earn the right for more time and money based on delivering insights. It comes at the cost of hiring the right talent (both at Consulting companies and Clients). It comes at the cost of… everything good.

      Hence my bias.

      I have to admit that I'm unconvinced that data capture is all that strategic in a pure web analytics context. The data we collect is too anonymous, too fragile, has too many gaps and simply can't stand the test of time in terms of usefulness. Say on a five year time horizon.

      But at the same time I'm with you when you say it is important.

      I just wish (see the table at the end of the post) the "Impact on the Client" column reflected "Amount of Work Available" reality.

      I just wish we would take the standard Adobe/GA/WebTrends tag, implement it in five days on the whole darn site. Start using data and making small changes/improvements showing data is important. Then implement the next tag update. Use that, show bigger impact, earn more credibility. Then go back and tag some more. Then show even bigger impact. Then go back….

      Today here is the reality: Tag for 18 months. Do some data puking in the interim. Realize every stakeholder has changed in 18 months, so has the business. Go back for 18 more months of DC.

      That is unsustainable. Hence my bias towards moving us away from DC and towards DR and hopefully DA (or DAO).

      -Avinash.

      • 23
        Beth Morgan says:

        Where I see data capture being very important is that it can often be at the heart of a dysfunctional reporting process.

        Step one of any engagement is to figure out what the client is getting now and how well that ties with what they need. In my experience web analytics often falls under the marketing department, and marketing is often explicitly put in last place in terms of taking engineering time. A good capture process involves:

        1. Talking to the product or engineering team to understand how the site is structured, how people flow, where the capture opportunities are, etc.

        2. Designing and implementing the capture

        3. EVALUATING the results, and usually going back to the product and engineering teams to make adjustments and ensure that you can rely on the data that is generated

        Where I have frequently seen failure is at step 3; auditing the data you're getting can take a fairly thorough understanding of the interplay of your database and your site that is often beyond the technical abilities of the marketing staff. That means Product/Engineering has to do it or at least help substantially, but they are already frustrated by time spent on what they consider to be low-priority marketing tasks. Proper auditing gets moved to the back burner, the numbers are never really "good", the reports are never trusted, real analysis never begins, and the person who started the engagement in the first place feels burned.

        And THAT ties into your posts about how to create a true data-driven culture; unless there's someone high up saying that setting up proper reporting is the highest priority, this un-virtuous cycle persists.

        Of course, as you say, having good data doesn't mean there will be someone on the other end making good insights about it. But as an analytical person I find that even getting capture set up right can be pretty difficult.

        • 24

          Beth: I agree with you 100% that getting clean data is important, and that is can be incredibly difficult and frustrating to get it.

          I also agree with the three steps you've kindly outlined, they are wonderful. I would make one small change: Let the first audit be defined not by what's possible to capture but what the client needs in terms of business analysis.

          What normally happens is that a hot shot web analyst goes in, does a audit and based on that proceeds to create the world's first perfect web analytics tagging implementation to capture everything molecule. Regardless of what the client needs, or what's possible to report today to start driving some action based on data – even if you can only do it for Search or your Email campaigns or your checkout process or whatever.

          That is all I would request in terms of an evolution (I addressed this in my reply to Matt). Focus on what the client needs now, see if you can meet some needs today, follow the process you describe in your comment. :)

          Avinash.

  11. 25

    Fantastic thoughts. I've been looking for easier, more accessible words for this distinction for some time.

    The framework I have been using is descriptive vs inferential statistics… data reporting vs data analysis in the nomenclature you suggest here.

    But the average layperson, or, for that matter, many quantitatively sophisticated marketers and "data analysts" don't quite get "descriptive" vs. "inferential…" Better to K.I.S.S!

    Thanks for this. -J

  12. 26
    Barbara Frontera says:

    Unfortunately, my experience echoes that of Robert Miller. Many clients want to be "data driven" until the data tells them something they don't want to hear ("This has always worked just fine"). Or until it indicates a change they don't want to make ("We don't want to change that, we want to fix this"). Or until it gets hard ("Do you know how much work/money that would entail?").

    In addition, many clients are suffering from data or analysis exhaustion. Information is flying at them from numerous sources — and it often leaves them feeling anxious and uncertain.

    Thanks for this and all the other posts that help DCs, DRs, and DAs everywhere deliver better, more consistent results and ease client anxiety.

    Cheers,

  13. 27
    Pere Rovira says:

    Hi Avinash,

    I usually go "wow" after your posts, but somehow, this time you left me kind of cold. I will try to explain why, because this is a topic that is essential to me (I run a digital analytics consultancy). So please take it as a deep appreciation for your attempt at bringing attention to this very important issue.

    I think that by dividing our practice into 3 categories, you're actually creating the problem you want to point out. Analytics is not about capturing data, reporting data and doing analysis, as separate entities. To treat them as independent variables right from the start of a framework is quite a mistake, I believe.

    Sure, it's a nice way for the sales team to explain and sell what we do, but it's not very solid as a methdology. And methodology starts with the way you sell.

    A football team needs a striker, but also a goalkeeper and a midfielder. But most of all, it needs a coach. The best coach. You take the same players, and give them different coaches, and you get very different results. There's no coach in your model, and precisely, the most important role of a consultancy is to have amazing coaches. People that will tell you where to go, and how. This is not the role of the analyst, or the reporter, or the implementator.

    Consultancies can be of 3 kinds:

    - technology integrators
    - human resources
    - strategic

    It seems to me your model fits with a "human resouces" consultancy, where you provide hours of people with different skills. But I rather think that our sector needs better strategic consultancies, with a bit of the other two if possible as well.

    That is probably because I prefer to think of digital analytics consulting as the path from data to knowledge. It's a path, a journey, rather than ticking 3 different kinds of capture, reporting and analysis boxes.

    When you think of it as a journey, you start to think of services in a very different way. You start to think of how your services can help with the following:

    1) what is the culture of my client?

    2) why are decisions being made? (or not)

    3) who do I need to convince? what are the political forces that prevent / foster change?

    Also, creating categories underestimates the power of mixing categories. Is data visualisation capture, reporting or analysis? Can analysis be effective without the perfect "reporting" of results? How do you call it when you change the way marketing works with IT, to create an agile website driven by testing?

    Of course, I am sure you know all this. I am not discovering anything for you, and you even mention many of the things I say. My biggest problem is with the way people tend to interpret tables and categories such as the one you use to end your post. I always prefer Venn diagrams, shades of grey, so to speak :)

    If a client tries to encapsulate my work into DC, DR and DA, probably it's a sign they aren't really getting the full picture, the size of the problem. If a client tells me how to combine DC, DR and DA, together with some serious business analysis and change management, I will think that fun is waiting for me. Maybe it's just that I am not an analyst, who knows :)

    I'd really appreciate your thoughts!

    Cheers
    Pere

    • 28

      Pere: Change is good right? Can't have wow all the time! :)

      I concur with you that there is a symbiotic relationship between the three elements, you can just have one or the other. My hope was that as contracts get crafted that the distinct buckets would still be a simple way of 1. understanding what work is actually needed and 2. what skills need to get contracted. These two things go wrong almost all of the time.

      I did not take the consulting organization view in this model, it was very much a client perspective [with a very emphasis - hidden agenda :) - of trying to push for one of these three elements]. You are right that there many different models that a consulting practice can be organized into, and some might prefer strategic consulting to the often thankless and never ending technology integrations.

      The work you are describing you like falls into, perhaps the simplistically defined, DA (or DAO) category. My hope is that if a client comes to you with their needs defined into DC, DR, DA then:

      1. You'll very quickly understand if you want to do the contract or not. Be it because of the skills your company has or because you simply don't like the work. But you know it up front!

      2. You'll internalize how to approach creating a solution for the client (and then the contract).

      3. Educating the client, before you draw the contract, that their bucketing or emphasis of what they want to do in each bucket is wrong (or perfect). Through this education you can shift the client to a different emphasis (say less on DR and more on DA), and even before you sign the contract you have added value to them.

      This framework is not the be all and end all, my hope is simply that it puts the work under a very harsh light driving a better understanding, indeed prediction, of any future success.

      I'm very appreciative of your perspective. Please continue to challenge and please share what framework works better. We'll optimize together!

      Avinash.
      PS: I'll think really hard if I can make that table into a venn diagram. I love venn diagrams.

      • 29
        Pere Rovira says:

        Hi there!

        You flatter me :)

        I wish we could optimize together: I am sure my brainpowers would grow exponentially if only I could spend some weeks working by your side… who knows, maybe one day! :)

        Your framework is perfect for the purposes you mention. However, I accept the challenge and will try and work on an alternative version. Let me find some time; I should be able to find more time to write about the things that live only inside my head and my client's work. Unfortunately, I had more time to write when I had fewer things to say, I need to revert that! :)

        Thanks so much!

        Cheers
        Pere

  14. 30

    Hi Avinash

    Really fascinating post for someone like me who has been consulting in Web/Digital Analytics for the last 10 years. This is a rare opportunity to discuss that side of our field, side that was the main driver of progress I would offer.

    Couple of things: you're on the money about technology vendors hijacking the field. I guess the main reason is the conceptual simplicity of tools. By that I mean it is easier to learn technical skills than the arcane thought processes of analysis. Tools are also much simpler to buy ("At least we get something we can see") than hypothetical improvements of profit margins.

    This explains in part why the market is so much oriented toward DC and DR. Add to this that GA, while tremendously increasing awareness for Web Analytics by making tagging and reporting seamless (no budgets to approve), did not really increase the *quality* of the work. Since this market is still very much what is offered instead of what is demanded, demand is low for analysis because so few consultants really push it (or are able to do so).

    With SO many players entering the field in the last 3 years, I guess it was normal that the average skill levels would drop for a while. I see signs however of them increasing amazingly, though, and I am very confident that more and more people will deliver the value clients are more strongly demanding every day.

  15. 31
    Steven Grech says:

    Hi Avinash, great post – thanks for sharing more of your grey matter with us!

    Data Capture is the responsibility of the Analyst (who understands the client's business and their objectives and knows what data he/she needs to analyse) and the Technical Developer (who is a JS black belt and can create some funky integration with Content Management Systems that automate tagging of elements after the site goes live). We used to do lots of Data Reporting / Puking (DP) however we've moved away from this to Insight Reports – one page or 20 pages, depends on the amount of insight gathered for that week, month, quarter.

    As an analyst I think it's critical that you understand and get involved with every piece of the measurement jigsaw – you control the tagging with help from your technical buddies so you can measure website performance effectively and produce insightful reports that drive ££ and engagement…engaging with the design and development teams to implement your insight.

    Clients don't pay for the size of a report but for insight and continual strategic improvements driven from their data – data driven design rocks! Explaining data driven design to designers is another challenge – pretty yet functional! :o)

    • 32

      Steven: I concur with your overall approach, and the emphasis on Insights (as long and as short that might be) is wonderful.

      My one caution, for others, would be to not expect the Analyst (I'm assuming they are a real Analyst as described in the post) to be a JavaScript/tagging implementation God. This is a huge mistake companies make. They hire for Analysts who have the skills of an Implementation Specialists and then 1. They won't find them and then 2. They'll end up hiring a Implementation Specialist to be an Analyst.

      The real Web Analyst can be responsible for data capture, but only in the sense that she/he understands what data is needed and then can communicate that in a non technical manner to a Implementation Specialist (who is a implementation God and will make that happen).

      So for real Web Analysts overweight analytical skills, underweight on javascript godliness. :)

      Avinash.

  16. 33
    Charlie Wang says:

    Avinash, thanks for a very clear framework!

    As an IT professional that expanded into digital marketing, I'm proficient in the DC and DR parts, and I'm trying to expand into DA. But in order to do DA very well, do you think it's absolutely necessary to know statistics? Math and stat was never my forte and frankly it scares me quiet a bit.

    It seems to be there are still analysis that can be done without the complexities of modeling and stat, for example measuring channel effective seems quiet straight forward to me (good performance = increase, bad performance = decrease investment or A/B test).

    • 34

      I hope I am not speaking out of turn or hijacking Avinash's comments,, but AB testing is a form of hypothesis testing, which is a stats test. It would be extremely helpful to understand stats, in order to know

      1) how to collect the data and what issues can arise if it isn't collect correctly (and I don't mean JavaScript, but at a more general level of experiential design) and

      2) how to interpret the results of the test. You have no idea how many people confuse the meaning of p-values (because it actually is an awkward, non intuitive statement, that draws on an understanding statistical distributions).

      But don't dispare Charle, if you can do IT you can learn stats! Here is a trick that I have used on more than one occasion, contact your local university's stats/math department and ask if any phd grad student are willing to tutor you for a few bucks one a week or so.

      Good luck and appologies to Avinash.

      • 35

        And sorry to everyone else for all of those typos :)

      • 36
        Charlie Wang says:

        Thanks Matt! My thoughts exactly on the tutoring, I actually tried with a friend of mine that studied stat. Didn't work out too well though, it was too academic not applied enough… plus all those big scary math words scare me :P Will probably reach out to some friends that work in the insurance sector to see exactly how they do some of this stuff.

        • 37
          Robert Miller says:

          Hey Charlie,

          Here's a link to a new start up or venture something or other (not really sure what it is "officially") that has online courses from some pretty awesome universities. Here is a link to one of their statistics courses, https://www.coursera.org/course/stats1. It's put on by someone at Princeton, so I'm assuming it should be pretty good.

          Also, the courses are free, which is always a plus.

    • 38

      Charlie: I say this reluctantly but I believe that it would be difficult for a person to enjoy data analysis if they do not enjoy the finer joys of math and statistics. There are a lot more factors that make for an amazing analyst than proficiency in math, but it is a necessary condition.

      Please not my focus on the person and not the company. You can get a job, but I want you to have a million tons of fun at it because life is too short. :)

      You mention you enjoy the DC and DR aspect of our space. Know that both roles come with super high salaries (especially if you are a good cross tool cross source Implementation Specialist). So that is not an issue.

      All the best!

      -Avinash.
      PS: A couple months back I'd written a post about job families in the web analytics tools, along with a skills and salaries matrix. Please check it out, you might find it to be of value:

      ~ Web Analytics Career Guide: From Zero To Hero In Five Steps!

  17. 39

    Hi Avinash,

    After reading your post, I wanted immediately to write my comment. But then, based on the experience of the comments at older blog posts I decided to wait a while. Other people usually comment everything what was on my mind.

    This time it's slightly different. Pere Rovira, Daniel Waisberg, Sander Lenselink and Rosendo Cuyasen partially mentioned the problem as I see it.

    Your framework is helpful, there is no question about it. Also the fact there are actually very few people using the possibilities out there (…things that not even 1% people on the planet need and not even 100 actual people in the world are using…).

    BUT….

    My framework is slightly different. DC, DR and DA is a short and incomplete story from my stand point. My framework is DITO:

    Data => Interpretation & translation => Optimizing (This circle is going on forever)

    Just to explain in short, Data is what you call DC and DR. Based on Data comes the DA (+interpretation). And then comes the biggest problem from my experience. And we data analysts tend to underestimate this part huge!

    As a data analyst you have to be able to translate your data analysis to your client. I never met a HIPO, CEO or any other senior level executive who was stupid not to see or to adopt my recommendations. But only if I was able to translate my findings tailored for his business. I always failed when I didn't understood my client nor his business. You mentioned it:

    "The Data Capture, Data Reporting and Data Analysis framework helps both clients and consultants have an immense amount of clarity on what the needs are (client), what skills are required to meet those needs (consultant) and how much time and money will be required (from the client to the consultant) to deliver glory"

    I saw very smart and excellent statisticians failing, not because of the lack of knowledge, but because the lack of P2P skills and because the lack of 'wearing your clients clothes/shoes'.

    And if everything is going on well, the optimization part is actually 'the path of glory! (You and your client agree what should be improved/adjusted/tested). If You have done well your part, based on the results there was never a problem to ask for a bonus if the outcomes over-deliver the goals/objectives. $$.$$$ is achievable.

    I would recommend to all analysts out there to take Avinash's Market Motive course. For me, after watching the datapukinglytics explanation on path analysis, I got an epiphany. I learned very simple how to change my clients focus on path analysis. Before it was always hard for me to explain to them the meaningless efforts of analyzing paths.

    So in my DITO framework I would recommend analysts to improve their P2P skills. Your client is not a data/IT/statistics wizard. He's just running his business. It's the analyst who has to help the client understand your findings.

    Not by imposing but embracing.

  18. 40
    Hudson Arnold says:

    Just a shout out to Avinash, thank you from my core for continuing to provide deeply tangible value so regularly.

    The framework you present here is an excellent communications tool with which we (strategic/analytical/data-centric/best-practice/fail-forward/scientifically minded folk) can position the work that we all want to do; analysis that informs and shapes the decisions both big and small that move our businesses forward.

    People (especially marketers, right? :] ) LOVE segments, they love discrete categories of information, and they love A/B/C decision making. By communicating on their level (which is not WRONG by any stretch), we help everyone achieve their own success.

    As a hint to the frustrated out there, using this kind of 'bucketed' approach to communications is an effective way to communicate complicated problems to people who can't or won't understand the many subtleties in our work.

    In general, I think this brilliantly helps with might be the single biggest problem facing analysts; not a lack or quality of data, but the communication barrier with our internal (our own companies) and external (your company's clients) clients!

    Like Avinash's famed 10/90 rule of thumb (tools/people), I recommend allocating more of your time than you might guess toward honing your communications – whether its spoken, presentation, or conceptual (which I think the framework presented here is.).

    Much Love.

  19. 41
    sibel akcekaya says:

    Hi Avinash,

    Great post but I think DC's are highly undervalued here. I am saying this as a we analyst who started with DR and DA and saw the importance of becoming a DC.

    First if data is wrong, DA will have wrong decisions. This can even hurt business.
    Second if DC is not analytical enough, they will never have very smart tags to capture the critical data.

    Now I have mixed skills and I do DC,DR and DA. I love them all and they all help me to become a great web analyst.

    During DC process I understand the product very well, I can see opportunities or threats with this new product. I set up some of my hypothesis at this stage. So I always implement very smart tags to test everything on my mind.

    When data comes in, I start with data and create some random reports in Excel to prepare me for the third stage, DA. This is also very important stage for me as it inspires me to do more creative analysis. It is like a painter who is painting randomly on the canvas before shaping their real work.

    Then I am perfectly ready to focus on analyzing the data. I know everything about product, I know data is solid and I also know data trends. At this stage I will see results of my initial hypothesis and maybe find new ones..If there is a improvement to be found, I will sure find it.

    So each step feeds each other and I would not give up any of the steps as a passionate web analyst.

    thanks:)

    • 42

      Sibel: Allow me to share the nuance I'm trying to stress in this post when it comes to DC….

      The value of collecting data as cleanly and as completely as possible is of tremendous value. We should aim for nothing less.

      An obsession with data collection at the cost of DR and DA (especially DA) is ill-advised (and yet prominently practiced currently).

      Conceiving of and executing multi-year DC projects at the cost of starting analysis with good enough is ill-advised (and yet prominently practiced currently).

      I do not advice Analysts to wait for perfect data, I do not advice them to wait for the perfect implementation, I do not even advice them to wait for a complete implementation. Once the standard tag is cleanly implemented on all the pages (or inside the mobile application) start analysis, start the shift to making decisions (even if small), start having a impact, and start earning credibility. On that foundation keep expanding data collection by making deliberate choices about what's needed next and keep moving the ball forward.

      To reiterate: We have a shared passion for the GIGO principle! I'm also completely with you on inserting creativity into our analysis. :)

      Thanks so much for sharing your kind thoughts.

      -Avinash.

      • 43
        sibel akcekaya says:

        Thanks Avinash for taking time to answer my comment
        True… At the end we collect data to find good stuff to improve our performance :) Without that perfect data has no value at all.

        thanks
        sibel

  20. 44
    tyna_c says:

    Avinash,

    Great post! I like when things are boiled down to easy-to-remember buckets: DC/DR/DA.

    In my organization, it seems we do mostly DR, are not as pro-active as we should be with improving DC, and pay lip service to DA. While it has something to do with fewer people doing the same or bigger workload, I think part of it is that people aren't trained to do the DA part.

    Can you point me to companies (whether vendors or client companies) who do DA well? A lot of times, what is presented as DA are things that make us comment "that's interesting" but don't influence direction or decisions. Do you have references that you can recommend to beef up our team's insight work?

    Thanks!

    • 45

      Tyna: First I want to stress that the situation you are describing is not unusual, especially in large companies.

      A lot of time data analysis, no matter how cute it is, does not move the business because it is unclear how any of that analysis ties to what the business considers to be important/a priority. Hence my stress, in the post and in life, on building the entire focus of DC/DR/DA on a well defined Digital Marketing & Measurement Model. That ensures when analysis/insights are presented the Management team pays attention because it is delivering against their priorities.

      You asked for some references, here are a couple…

      This post outlines my perspective on what analysis looks like, and how to get there:

      ~ The Difference Between Web Reporting And Web Analysis

      Please see the comments, our readers contributed so many wonderful specific ideas.

      Often you won't get all the cooperation you need to create a good DMMM, especially in a large company with many divisions in multiple countries. In those cases we taken it upon ourselves to set the business agenda. Here's how…

      ~ The Biggest Mistake Web Analysts Make… And How To Avoid It!

      Finally, insights focus is simply impossible if we set ourselves low standards with unimaginative metrics. We pick hard metrics to analyze, life is tougher but the impact of our insights is significantly higher…

      ~ Your Web Metrics: Super Lame or Super Awesome?

      Hope this helps a smidgen, all the best!

      Avinash.

      • 46
        tyna_c says:

        Avinash,

        Thank you for your reply. I was afraid that you were going to say what you did–that you will know web analysis when you see it. :)

        We have used a lot of metrics that are in your "super lame" list but are moving towards some on the "super awesome" so we are headed in the right direction.

        Lots of organizational change from where I sit, and part of the opportunity is redefining my role towards more DA. The trick is I can only focus on DA if I find the right support model for DC and DR. One bonus: I might be able to change my official job title to Analysis Ninja. We'll see.

        Tyna

  21. 47

    Hello Avinash et al,

    Reading through the post and following commentary, a couple of valuable and equally important points got muddled along the way. These bear calling out plainly:

    1. Application of the consulting framework is iterative.

    2. Application of the DMMM must start simply and mature into more complex data and analysis.

    The consulting framework is iterative: as Avinash notes in comments, start small (limited-scope DR or better still DA), earn credibility, then suggest the next iteration (including DC, DR and DA), which earns more credibility, then suggest the next iteration (DC, DR and DA), and so on. In our experience this approach works very well. Prospects are often put off by sticker shock, timeline and scope of large-scale projects. Proposing a smaller more focused contract, which delivers real value (from the DA bucket), and iterating is usually received more positively.

    It's equally critical to apply a measurement model (we follow our own KPI Karta methodology) in order to know what success and looks like. KPIs must be clearly defined with the support of senior executives – this focuses your measurement and analysis efforts on things which directly impact revenue earned or costs saved.

    Often business objectives will boil down to individual measures which require either sophisticated system integrations, A/B/n testing, 1:1 marketing, etc. Avinash notes that you can't start with these, you must start small with a clean implementation of the standard tag and mature slowly into the more complex measures and analyses.

    This is really an application of a maturity model. In our practice (again we use our own model) we've found this a great way of guiding and framing a roadmap forward and shaping how we deliver incremental real value to our clients.

    In other words, each iteration of the consulting framework pushes our clients along the measurement model, starting with the simple basics and moving towards complex measures, integrations and analyses.

  22. 48

    Your "Clear Line of Sight" model reminds me, and should remind business owners, to think about businesses and our clients like a small retail business thinks in terms of advertising.

    The best of them track the impact (new customers, income, effect on sales of targeted items, etc.) of every dollar spent on advertising and marketing while continually adjusting their approach based on observable results.

    Even if you aren't selling pizza, it isn't a bad thing to learn a marketing lesson from pizzeria owners who know that adding a $2 coupon in the PennySaver increases their sales of soft drinks by 18%.

  23. 49
    Egan says:

    Avinash,

    I seriously thinking to change my career from SEO to Data Analyser now… That's huge opportunity!!!

  24. 50
    Guenter says:

    I'd like to add some more perspective from the client side.

    The more data a consultant can deliver, the better the client feels. This is because the managers on the client side need to report "their results" to senior management, owners etc. The best thing to happen is measurable and actionable scores.

    If the consultant has only "empty" slides with beautiful visuals and great mission statements, well, then it is a tough sell to your upper management.

    • 51

      Guenter: I would hazard a guess that no one will disagree with you on "empty slides with beautiful visuals and great mission statements." If that is what you get from a consultant, fire them instantly. :)

      I would just change a small thing in the first of your comment. I humbly submit that we should never judge success of a consultant by the amount of data they can deliver. In Web Analytics this is the easiest thing to do, we can puke out tons of data very quickly. We should judge success by their ability to deliver insights supported by *analysis* of data. Other than that you are 100% correct.

      Thank you.

      Avinash.

  25. 52
    Ben says:

    "Not even 3% of web analytics consulting companies have people with optimal skills to be called an Analyst, so you can see how easy it is to charge a lot for this resource."

    Where in the world did you get this stat from? It would be kind of ironic if you made it up ;-)

    • 53

      Ben: We, one of the companies I'm associated with, did a survey to assess analytics consulting companies who have staff with education in marketing analytics techniques, statistics, design of experiments, etc. There were also questions about work experience prior to joining and current efforts. And a bunch of other non analytical experience questions (because the primary purpose was not to get a feel for depth of analytical skills).

      The number comes from that survey (from a large US sample). A small clump of the questions were bundled to measure how many people had strictly data analysis skills.

      Because the survey was for an internal purpose only the results were not public. I hope this context helps.

      Avinash.

  26. 54

    Great post Avinash.

    For me best projects are those that involve the full analytic cycle; determining goals, building measurement plan, designing a tracking solution against the plan and implementing it, collecting data, analyzing data and making recommendations.

    These projects are few and far between since they span months in a world where clients want answers the next day.

  27. 55
    Asmi says:

    Great post Sir!

    Reading it brought much excitement along with a few questions.

    I have worked as a web analyst with an emphasis on Data analysis and Optimization in the past. I recently entered consulting business with a client who needs us to work on the implementation side more like a Business analyst role (30%) + Strategize A/B (30%) multivariate testing and Provide detailed analysis + Data analysis (40%) .

    This role demands DC+DA and DA.

    While this does not fall in to one specific bucket or another, would you still suggest building an expertise in just one category over time or having 360 degree knowledge of each of these categories?

    Any advice/suggestion/recommendation will be of tremendous help to me.

    Thanks a ton!
    Asmi

    • 56

      Asmi: It is definitely possible that you can build expertise in all three areas, DC, DR, DA. But it is exceedingly rare. Exceedingly. Simply because the combination of skills required is insanely hard for one person to earn (no matter how highly they think of themselves :).

      Most people will be really good at DC, DR or DA. One thing. If you can afford a team of three, then go with three. Individually they will also be cheaper.

      If you can't afford a team, look for people who are really good at one thing, but also good enough at the adjacent one. So they are really good at DC but can also do DR. Or they are exceptionally good at DR but can also do some DA. Or they are exceptional at DA but push comes to shove they can do DR. These people are more expensive. But you don't need three people then.

      It is very hard to get people who are really really good at DA and can also do DC. It is also very hard to get people who are really really good at DC and can also do DA (this is rare, not impossible but rare).

      I hope this helps.

      Avinash.

  28. 57
    Daphne says:

    I never thought of frameworks for web consulting before, not until I read this post.

    I'm into web designing and SEO now, and maybe, if time permits me, I'll consider this along with the too.

    Thank you for this post! :)

Trackbacks

  1. [...] Web Analytics Consulting: A Simple Framework For Smarter Decisions 1 Upvotes Discuss Flag Submitted 1 min ago Himanshu Analytics kaushik.net Comments [...]

  2. [...] Web Analytics Consulting: A Simple Framework For Smarter Decisions, http://www.kaushik.net [...]

  3. [...]
    Avinash Kaushik posts “Web Analytics Consulting: A Simple Framework For Smarter Decisions” at Occam’s Razor.
    [...]

  4. [...]
    How to do Web Analytics? A simple but insightful framework by Avinash Kaushik, covering the steps of Data Capture, Data Reporting, and Data Analysis. http://www.kaushik.net/avinash/web-analytics-consulting-framework-smarter-decisions/
    [...]

  5. [...]
    In Web Analytics Consulting: A Simple Framework For Smarter Decisions, Analytics guru Avinash Kaushik offers some tips to those of us interested in improving consulting engagements management. Note that the article is geared toward Analytics consultants, but the framework he presents is useful to any web marketing consultant.
    [...]

  6. [...]
    De momento es una buena base de partida para todo aquel que quiera empezar a entender este mundillo, donde creo que todavía hay bastante confusión en cuanto a terminología, y donde las empresas gastan mucho dinero en la solución tecnológica pero sin embargo no invierten lo suficiente en los analistas.
    Termino con un artículo reciente del gurú del web analytics (que venía del BI), Mr. Kaushik, que elabora sobre las diferencias entre implementación, reporting y análisis:
    http://www.kaushik.net/avinash/web-analytics-consulting-framework-smarter-decisions/
    [...]

  7. [...]
    Web Analytics Consulting: A Simple Framework For Smarter Decisions
    Avinash does an incredible job detailing the different components of consulting as a web analyst.  I've always clumped the three aspects together but his framework has helped identify my strengths/weaknesses and what clients I should take on in the future.  That being said, I believe every SEO should be competent in data analysis (DA).
    [...]

  8. [...]
    In response to a question from a reader, Avinash Kaushik wrote an article recently on web analytics consulting and how to create a web analytics project framework. Although the article was primarily written for consultants, it could apply equally well to anyone responsible for a web analytics project.
    [...]

  9. [...]
    One my great teachers on web analytics wrote this blog on consulting frameworks. Its pretty dense and I usually shy away from showing complicated things to clients (preferring to make complicated issues simply understood) however I feel that our clients should read this post (and others if you dare ) to understand the thought process behind excellent web analysis. A good framework is defined by Avinash Kaushik here:
    [...]

  10. [...] Web Analytics Consulting: A Simple Framework for Smarter Decisions (Occam's Razor) [...]

  11. [...]
    Web Analytics Consulting: A Simple Framework for Smarter Decisions (Occam's Razor)
    [...]

  12. [...]
    Managing analytics for a higher ed institution can get pretty complicated. It takes a range of skills sets, of varying levels of sophistication to do it well. In a recent blog post, Avinesh Kaushik, Google’s Digital Marketing Evangelist, breaks down web analytics into its three component parts and then discusses some of the individual skills sets and capabilities that individuals require to manage these areas well.
    [...]

  13. [...]
    Web Analytics Consulting: A Simple Framework For Smarter Decisions
    [...]

  14. [...]
    分析のフレームワークについて、
    またコンサルタントとしての心得に関して、kaushikの記事があったので紹介。
    Web Analytics Consulting: A Simple Framework For Smarter Decisions
    彼はいつも、ハッとするというか、
    いつも感じていても言葉に出せないことを記事に書いている気がするが、
    少しだけ僕と違っている感覚だった。
    [...]

  15. [...]
    Det var også Avinash sitt budskap, og vi vil kjenne igjen hans «mantra» i interfacet i neste versjon av Google Analytics. Selv om neste versjon av Google Analytics enda ikke er lansert «for mannen i gata», finnes tankesettet bak det som kommer allerede: Et rammeverk for digital analyse og måling.
    [...]

  16. [...]
    As I started doing some research, I came across another one of my favorite bloggers, Avinash Kaushik. My e-Marketing professor, Paul Cubbon, first introduced me to his work in class. In this particular blog that I found, Avinash takes the reader through a Web Analytics Consulting process. He stresses the importance of building frameworks to encourage thinking and brainstorming. By building frameworks and models, one is THEN able to build digital marketing campaigns. But before all of that, Avinash recommended using a simple framework to begin and recognize what the client really wants to get done.
    [...]

  17. […]
    In response to a question from a reader, Avinash Kaushik wrote an article recently on web analytics consulting and how to create a web analytics project framework. Although the article was primarily written for consultants, it could apply equally well to anyone responsible for a web analytics project.
    […]

Add your Perspective

*