Slay The Analytics Data Quality Dragon & Win Your HiPPO's Love!

Center MagnifiedTwo truths:

1] Turns out the readers of Occam's Razor are exceptionally gifted, they understand the challenges of web data

2] They are deeply motivated to do something about it, just not totally sure what.

This is a special unplanned post just for you, to help with issue #2.

My last post, Web Data Quality: A Six Step Process To Evolve Your Mental Model , unleashed an unusually exceptional set of comments from you all (sweet!). Today I want to share a cogent set of "next steps" ideas.

Practical strategies to deal with the problems you highlighted, nuances you can exploit, things you need to give up on, things you might consider doing more, bosses you need to ditch (!!).

Here's my core premise:

Uno.You understand that data collection is imperfect (even as we collect more and better data than any other channel on the planet bar none).

Dos.You accept (or soon plan to) the Six Step Mental Model on data quality .

Tres.Your gallant efforts to make progress with the data you have are stymied by the Overlords (or as we often lovingly refer to them as: the HiPPO's).

You there? Then let's go rock this thing.

My recommendations, mostly in the order of importance:

#1: Give up. Pick a different boss.

#2: Educate them about the "perfect" source they love.

#3: Distract your HiPPO's from data quality by giving them actionable insights.

#4: Dirty Little Secret One: "Head" data can be actionable in the first week / month.

#5: Dirty Little Secret Two: Data precision actually goes up lower in the "funnel".

#6: Realize the solution to your problem is not implement one more tool!

#7: Pattern your brain to notice when you've reached Diminishing Margins of Return.

#8: If you have a small site, you have bigger problems than data quality.

#9: Be Aware of two upsetting distractions: Illogical customer behavior. Inaccuracy benchmarks.

#10: Remember you can fail faster on the web.


The rubber meets the road now. . . .

#1: Give up. Pick a different boss.

Did you think I was kidding?

There is a entire generation of leaders in place today that don't get it. Many of them, sadly, will never get it. I don't blame them. They have seen the world in one way and they can't change now.

We simply have to wait that generation out. For now we have to wait for them to get promoted / take on other life challenges.

When I have found myself in situations where there is just no chance of movement in the HiPPO's mental model, I try to switch bosses.

Life is too short. There is too much money to be made. There are too many customers to be satisfied. Why waste your time?

If you can move on.

Find someone who is open to accepting the new data quality mental model. Someone who will take actionable recommendations and action them (even if they agree to just try one or two things first, perfectly ok).

One I have that small opening I work really really hard to make my new boss a hero. When I have done my job well the impact of that is huge. For me, for the boss and in turn on the company.

I realize that if you work at a small company this is a non-choice, you have one boss and She's all your company's got. In that case try to see if any of the things recommended below work.

Meanwhile remember to polish up your resume so you can find a better place of employment in case it simply does not work out. [The economic climate is bad right now, but it won't always be that way.]

#2: Educate them about the "perfect" source they love.

[Important: I am NOT saying that it is every a good idea to say: "look I am better because your favorite child is not perfect either!".]

More than once I have gotten a more open mind after I detailed to my executives the (irrational) faith they put in other sources of (what they don't know are) imperfect data.

Take TV as an example (a fav of med-large companies, sorry not small ones).

Nielsen uses a few thousand people (between 18k – 30k) to measure the viewing habits of 200 million plus Americans. I am sorry but in this world fragmented consumption (tons of choice) it does not matter how much sophisticated math you put on that data to account for anomalies, you are left with high grade non representative "data".

Consider this: Even Big 3 network CEO's have been forced to put back shows that they canceled because of "low" Nielsen ratings only to be astonished by massive fan rebellions or huge DVD sales.

Just imagine what happens to a 18 to 30k dataset's capacity to measure the non-major networks or the really long tail. CurrentTV anyone? : )

Yet those ratings and GRP's are taken as God's own word.

Even with 30% inaccuracy and the third-party sub optimal Omniture's cookie your web analytics data is better than that.

Or here's another one. Try to really understand the impact of a 180k panel data set from ComScore that monitors a couple hundred million Americans (in a even longer tail and more fragmented than TV world of the web). Contrast that with data that comes from HitWise (15 mil). Or is in the Google AdPlanner. Both substantially better (for similar data).

Yet the former is accepted as the truth. The latter are not. Because your HiPPO does not know any better.

1942 quit india postage stampStart a revolution:
1) Solve the major problem: Educate yourself. This is often the key flaw.

2) Present a dispassionate and non-personal education of each data source and its value.

3) Highlight how Web Data is less imperfect (if that is what you find) and how it provides more information (missing in other sources).

4) Ask for implementation of actionable insights (small at first) from web data.

[Big PS: Here's what I am not saying: I am not saying Nielsen (or ComScore) is not trying hard enough. I am not saying they are not applying the best mathematical algorithms Humanity has created. The problem is not either one of those issues. It is the core data they collect and how much of it. No amount of pretty Math can now accommodate for the new world order of content consumption on TV in their old word data set.]

#3: Distract your HiPPO's from data quality by giving them actionable insights.

Dazzle them with your intelligence!

Like you distract a baby by jingling your key chain.

This is what I am talking about:

Change the focus from silly unactionable aggregated numbers like Visits or Avg Page Views Per Visitor etc. Instead you can find key sources of traffic. You can run controlled experiments to measure offline impact. You can figure out how to get existing website customers to buy more or more frequently or abandon carts less.

web metrics analysis insights

Because your Senior Management does not know what the heck to do with total Visitors and the caveats associated with Unique Visitors they send you back to the data quality torture chamber. If you can distract them by giving them interesting insights they'll focus on the value.

[I have the privilege and the good luck to speak to lots of C-level folks at conferences or 1:1 meetings. I want you to know that I never lose an opportunity to educate them about the data quality issue and why they MUST look past it and focus on taking action. I am doing this every day. Every week. Every month. My tiny contribution to the Cause.]

#4: Dirty Little Secret One: "Head" data can be actionable in the first week/month.

I don't know why many people wait for 18 months to implement Omniture completely. Or WebTrends. Or NedStat. Ok ok ok, or even Google Analytics! : )

Yes the implementation has to be "complete" (translation: never going to happen). But there are things that are "big enough" (head) in the first week and getting complete data for them is irrelevant because it won't change your decision / insight.

Some of your data is good enough very quickly (dare I say even if not all your pages are tagged or you are still using third party cookies or have minor implementation issues).

data quality actionability long tail

You job during Week One is to look for the "head data", places with big numbers / happenings.

Say your imperfect data shows that 60% of your traffic comes from Google and the keywords "Avinash rocks", "Michelle is awesome" and "HiPPO's stink" account for 40% of that traffic.

You can start taking SEO / PPC action right away because marginal improvements in those big numbers won't really change what you do.

Or say you find, surprisingly, is sending you huge traffic to a part of your website that is related to porn (what!). You can start moving on that now.

Or the bounce rate on your home page is 65% (kill me now!).

Some things you don't want to know with full confidence before you start moving.

I recommend your web analytics approach have a more nuanced approach.

Tell your boss: "We have to start moving on these things because the numbers are large enough and they indicate we need to monetize opportunity x / we need to fix problem y. But as to how many people look at your bio on our website, I am afraid we might have to wait a little while on that "tail data" until after we complete our audit."

: )

#5: Dirty Little Secret Two: Data precision actually goes up lower in the "funnel".

What funnel you say?

Here's the one I am thinking about:

All site visitors ->
the # that see category (main cluster) pages ->
the # that see product pages ->
the # that add to cart ->
the # that start checkout ->
the # that abandon ->
the # that make it through ->
revenue, leads, average order size, etc.

As you go deeper into the "funnel" you are dealing with fewer and fewer people / visitors / sources / keywords / pages / vagaries of nature.

The implication of having done all normal things (start at the bottom of the funneltagged your site completely, are using first party cookies and the right ecommerce tag on your thankyou.html page) is that there will be few things that could mess up data at the end of your "funnel". The dataset is smaller, impacted with fewer vagaries of nature.

So when you start your web analytics journey start at the bottom of the funnel and not the top. You won't find yourself mired in quicksand on day one. And it is easier to reconcile data at the bottom of the funnel.

Compare your orders in IndexTools with your ERP system. Compare your leads in Google Analytics with Salesforce. They won't match, but it will be a million times easier to discover why (when compared to reconciling sources of data or average page views per visitor).

Here is the other psychological beauty: You know my utter devotion to measuring Outcomes. You start at the bottom of the funnel and you are starting with measuring Outcomes (inc rev, reduce cost, inc loyalty). Guess what? All HiPPO's LOVE Outcomes.

By the time you get to the top of the funnel 1. You'll actually be smarter and 2. Your management will be significantly more evolved in their thinking.

#6: Realize the solution to your problem is not implement one more tool!

Talk about compounding your problem.

I know bigamy, on surface, sounds really attractive. It is not. Monogamy rules.

I know. I know you prefer the former. : )

You believe data collected by WebTrends is of bad quality and so you implement Omniture (believe it or not I ran into two companies that have done exactly this!). Or you think Omniture is not working right so you implement Google Analytics as well.

You are just compounded your problem.

It is hard enough to follow the Six Step Decision Making Mental Model with one tool. It takes a lot of effort to understand one tool, get it right, move on to making decisions (remember your job is not to collect 100% accurate data, it is to find actionable insights!).

Two tools means reconciling a lot more, it means understanding sub nuances of two or three tools, it means chasing two vendors, it means more confusion, it means minor hell.

Remember there is nothing particularly magnificent about how Omniture collects data. Google Analytics does not have patent pending exclusive CIA techniques in its tags. WebTrends does not have any secret sauce.

Just use tags. Have 'em on all the pages. Use first party cookies. After this all tools are pretty close in data collection.

It is ok to date many, it is even ok to get engaged to a couple of 'em (hopefully at different times), marry one, then try to make that person perfect!!

I am going to get killed for that last one aren't I? :)

[PS: If you can please don't use multiple paid tools. A. You are wasting money. B. These tools come with so many svars and eprops and variables and massive customizations in implementation that reconciling data between them will make finding life on Mars look like a cake walk.]

#7: Pattern your brain to notice when you've reached Diminishing Marginal Returns.

I have come to love and adore this classic principle.

You should work to improve data quality (especially if you find problems :)). But realize that after a certain point it is simply not worth it.

Diminishing Marginal Returns

You can improve quality by another 3% but is the effort you put into that worth the ROI you'll get?

The fact that you'll feel good does not count.

Data quality seems to be such a holy crusade that it is hard to consciously walk away. The wise know when to walk away.

Remember your job is not to collect perfect data. Your job is to: Increase Revenue. Reduce Cost. Improve Customer Satisfaction/Loyalty.

To me the principle of Diminishing Marginal Returns is lovely because it both says you should work really really had to do the best you can but realize that beyond a certain point it is simply not work the effort.

Be rigorous about realizing you have reached that point. Then move on!

#8: If you have a small site, you have bigger problems than data quality.

You are a part time analyst, or a , hired to do Omniture analysis at a company and you find that even a 3 – 5% error turns out to be a big deal (because of small overall numbers).

Yes true. Realize that if you are a small company and a small number of people on your site then you have bigger problems than data quality.

For one perhaps focus on doing SEO to get more free traffic? Perhaps mine your existing customer data to find new ideas for product or customer sources? Maybe as an Analyst spend three weeks doing Marketing?

My point is: Is the best use of your time chasing the 5% error or getting an additional 150 people to your site (data be dammed!)?

Sometimes in life data does not become a problem until it becomes a priority.

My advice: If you are a small site focus on recommendation #4 above.

#9: Be Aware of two upsetting distractions: Illogical customer behavior. Inaccuracy benchmarks.

This will drive your bonkers but a lot of data accuracy challenges stem from the clash of the logical tools with illogical customer behavior.

Web Analytics tools expect and work on the basis of a set of logical rules.

The internet is fundamentally illogical. Because we, the inter-dweebs exhibit illogical behavior.

Now like all mostly rational beings we only behave illogically x% of the time (quickly bouncing between sites, changing our minds constantly, never seeing obvious buttons, missing relevant results etc etc).

I have never seen a case where with enough work and experimentation I could not explain even the most illogical behavior. In most of those cases at the end all I had was a regret that I did not focus on doing better things!

Second, if the data is not perfect why aren't there benchmarks for how "bad" the data is?

In asking for benchmarks you are asking for what Donald Rumsfeld famously called the Unknown Unknown. The impossible.

The web is such a complex ever evolving beast that getting ranges for "inaccuracy" is just not possible right now. The huge difference between how sites are built, experiences are created, technologies at play, needs of each tool for each site does not make life easier.

You know a lot of known knowns in web analytics. Take action on that. Try to identify the known unknowns (do audits using tools like maxamine or observepoint or wasp), try to fix them. Then take action.

Benchmarks can become crutches / excuses. I am kinda sorta against that.

#10: Remember you can fail faster on the web.

The greatest gift the web gives you is the ability to fail faster. At low cost.

This translates into a insanely awesome ability to take higher risk. It also means you can move fast with less than 100% confidence and in the worst case that you are 1000% wrong that you can control the amount of damage.

This is not a privilege that exists in the offline world.

If I have only 80% confidence in the data I can send a small, 1,000, email blast and test the waters to see what will happen. I can send 3 different offers to different geo's to validate my hypothesis.

I can try 5 versions of the home page and see which world because I am not designing the "you can only try once" cover of the catalog or newspaper ad.

If you had 100% confidence in the data you would commit to spending $500k on affiliate marketing. But if you only have 98% confidence you can commit to a four week pilot program with a budget of $50k. Lower risk, still the possibility of high reward, and a near 100% possibility of making a more confident decision about the remaining $450k.

Don't wait. Just go.

Ok now its your turn.

What techniques you have used in improving data quality or simply getting around the nagging problem of data quality? What was your most successful "lets all get over this and move on" tactic? If you have come close to web metrics data perfection what did you do?

Which of the above ten strategies is your favorite? Which one do you think is simply baloney? It's ok. Be honest. I can take it. :)

Thanks much.

Couple other related posts you might find interesting:


  1. 1

    This is a really solid top 10 list, and I really don't have anything to add other than I hope there are some HiPPOs out there reading – take a good long look at #'s 1, 3, 6, and 8.

    Great post AK.

  2. 2
    Kyle Sheppard says

    Fantastic read, as always Avinash. I like #4 in your list, and I think it goes hand in hand with a core understanding of what you are trying to measure in the first place – what are your goals and objectives? Concentrate on those and take action on them. We get lost in the forest too often.

  3. 3

    Nice one Avinash, I especially like and am in agreement with point #1.

    There are a lot of people on the top of businesses that just can't / won't adapt to the changing realities of today.

    If you cannot adapt and create an efficient online web channel, your business will be toast… period.

    Even if you have the upper hand today, the web levels out the playing field. I wouldn't work with a company that wasn't forward/present thinking in its visions and strategies.

    Unfortunately, its very hard for people to move once they are settled, so this point is easier said than done.

    But why work with restrictions, constantly going up against a wall? Its just not worth it!


  4. 4

    Great post, the world would be an easier place if people understood that perfect web data was a myth!

  5. 5

    I don't imagine there are a lot of HiPPOs reading this blog, LOL! But we can hope.

    I often find myself in the position of caring more about a client's website than they do. A few months ago I was grumbling about a client that's wasn't very concerned about cleaning up a real mess of a website. My wife laughed at me and said that consultants are paid to be ignored. From my limited dataset, I'd have to say the evidence for her argument seems pretty solid. So I'd suggest a kind of 1B, "Enjoy your boss, cash their paychecks, continue to give them actionable insights and don't take it personally when they override you."

  6. 6

    Well, this topic definitively will be one of my Occam's Razzor favourite posts from now on.

    Thanks for your time on writting and, mainly, for your great job on education/evangelization.

    Some points I would like to discuss:

    #1 Give up. Pick a different boss.
    It's big. But I feel lucky that I got a "new generation boss". Even not being focused on it, he is a enthusiast of web analytics.

    So let's say we talk the same language. But well, we still have much to work to educate our clients. Trust me, even with a "Flat World", the Web Analytics is still a child on Brazil. And yes, I know I need to work hard on education. Reading your posts is part of this process. Thanks, again.

    #6 Realize the solution to your problem is not implement one more tool!
    Oh well, that was my favourite. I've seen many web sites on Brazil using more then one tool. And I allways ask: "What for?" Every day (at least every week) I use to get (read or testing myself) new ideas on how to work with Google Analytics.

    And I do remember something you wrote here like it: "Your web analytics tool can do more then you think". Totally agree, experienced myself.

    #7 Pattern your brain to notice when you’ve reached Diminishing Margins of Return.

    Thats a point that many analysts should think about. And then think a little more. I hope everyone that works with Web Analytics wasted his/her time trying to get a better data quality, or I will think I was being (and well, maybe being still) very very stupid for doing it alone. hehe

    #8 If you have a small site, you have bigger problems than data quality.

    Dear Avinash, allow me to disagree with the way you addressed this point. Small sites are so delightful to work in, cause we usually work on a very specific niche and usually the outcomes looks good if we analyze the % margin.
    Ok, maybe on Brazil it is much easier cause many many sites still don't have any related work about SEO/SEM/Web Analytics. I know that you don't said it is not possible to bring insights for small sites, but I felt like you discouraged people about small sites and I rather to think by other angle: "They aren't big. Yet". :)

    Sorry for the long answer. And Thanks! :)

    Update: wait, wait! Couldn't let it pass.

    Something that Martin Kelley wrote is amazing: "I often find myself in the position of caring more about a client’s website than they do. A few months ago I was grumbling about a client that’s wasn’t very concerned about cleaning up a real mess of a website."

    Yeah! Sometimes I thought I was doing a crap job. I didnt! (well I don't think hehe). Today I can understand It really happens sometimes. So lets keep reading the Avinash's secrets and working on education! :)

  7. 7

    You'd be proud of my day job HiPPO's lately, Avinash.

    We're finally (eek!) starting email marketing and have developed next steps for SEO. I continue to feed them axioms plucked from Occam's Razor in the form of insights a la #3 above. Slowly but surely we're getting the buy-in we need to do something more.

    Thanks for another great post! Exactly what I needed today.

  8. 8

    Omar: I certainly agree with your thoughts.

    I know it is not easy to find another job, but atleast get the resume ready and look. Who know one of these days the right boss might fall into your lap. Wait. That sounded like sexual harassment! :)

    Diogenes: I was certainly not discouraging small sites to give up. I am encouraging people who work with analytics on a small site not to worry too much about investigating data quality to the nth degree.

    There is really good data in the "head", use it to identify opportunities. Go get more visitors! :)

    Josh: Your comment has made my day. I am glad that your hard word is starting to pay off with your hippos.


  9. 9
    Craig Hazledine says

    Point No. 2 : this article helps the "sell in" on accuracies to a point……

    An example if you need it : Nielson and comScore variance

  10. 10

    Avinash, great and insightful post.

    Your number 10 would be my number 1…and possibly my number 1 challenge when it comes to educating our clients as to what we build as an agency will not be 100% perfect from the outset…we try to get as close to 100% through proper planning and research…

    …but you will NEVER get it 100% right the first time round…this only helps me push analytics consulting with every project that's sold – helps increase transparency and everything is based on PERFORMANCE! You HAVE to love analytics:

    Information is power (if used correctly) :o)

  11. 11

    Great post as always. I think that you have a gift to translate what we encounter with web analytics in layman terms and makes it easy to understand. Not everyone can interpret web analytics data, questions always come up when looking at the data collected. It can be abstract for some. Communication is key, education and training make a difference. One thing is for sure, we never stop learning. Thanks for the insights

  12. 12

    Do you think data purity has a differing significance depending on whether you run a B2B or a B2C site?

    I do. B2C will often focus on trend analysis and then move to maximise 'customer lifetime value' through abandonment/cross-sell etc. This can be done with a broad brush. In this case optimising 98% of your thousands/millions of customers would be incredible – most sites feel lucky to optimise even 40-50%.

    B2B site owners on the other hand has a much tighter requirement – one single visitor could be worth millions £$£$ so accurate click stream data will be of MASSIVE importance!!!

    I just think that as the stakes are different for B2C and B2B then the approach to data purity should correspond.

    Scientists tag and track individual whales but not individual tuna – they have a different value :)

  13. 13
    Patrick says

    "Meanwhile remember to polish up your resume so you can find a better place of employment in case it simply does not work out. [The economic climate is bad right now, but it won't always be that way.]"

    I'm a little surprised at this one, Avinash! Remember my e-mail about the WA job market?;). Does the climate even make it hard for people in a sellers' market such as web analytics to find work at the moment? or do seasoned web analysts (at least in the US, where WA seems to be taken a bit more seriously than here in Europe (by companies)) still have a fairly easy time finding a job in case they were to "switch bosses"?

    I used to think that bad climate in the job market was a big exaggeration and just people panicking (considering the unemployment rate is at 7,1% here in Germany – which means 92,9% have a job – and of course the unemployment rate for people with a degree is even lower)…but then again even my friends with college degrees cant seem to find a job at the moment – probably because the unemployment rate among recent college graduates is way higher than among the general population (as theyre competing for only the available positions) – however, I still think its not so bad, because 19 in 20 people with a college degree will still get a job (unles the unemployment rate is going to 50% ;)), it only means they're going to have to wait longer to get one!

    Sorry for brabbling, once again ;-). I assume the WA job market is still doing way better than the general job market, right (as in easier to find a position)?

  14. 14
    Alice Cooper's Stalker says

    I love your mention of 'the data quality torture chamber' under your description for #3: Distract your HiPPO’s from data quality by giving them actionable insights. A very funny description for a very real place. I've spent too much time there!

    I think that my favorites from your list are these three.

    #1: Give up. Pick a different boss.
    #2: Educate them about the “perfect” source they love.
    #3: Distract your HiPPO’s from data quality by giving them actionable insights.

    For Item 1, I will add that you can always help your boss find a different job. Talk him/her up to your favorite head hunter and hope that they can work some magic.

    I like item 2, but some HIPPO's are defensive about their favorite data sources and will find a way to rationalize the differences. If a teacher has a teacher's pet…sometimes that teacher's pet can do no harm…in their teacher's eyes.

    I like item three…especially your analogy to shaking keys for a baby.

    Good Post, Avinash!

  15. 15
    Captain Obvious says

    Thanks Avinash, these are great building blocks.

    It seems to me, assuming you don't have to find a new boss, (I think most HIPPOs will listen if they feel that there's information that will help them make better business decisions) it's possible to prioritize these in various ways to fit most analysts' current situation and thus have a makeshift "process" with fall back positions.

    For instance, lead off with the following:

    2, 6 and 7 deal with the problem of educating a HIPPO. These are starting points to get the ball rolling. With proper respect, you can likely get a good dialogue going with your HIPPO.

    When the HIPPO seems on the verge of understanding the value of what you are explaining, pour it on by following up with 3 and 4. This will likely get them on board with your efforts. Eventually, based on the basic education they've gotten, they will expand their understanding.

    When the HIPPO starts to become enlightened and sees 8 and 9, you counter with 5 and 10 to keep them on the education train.

    HIPPOs love finding holes in strategies, but what they love more are the people that fix the holes before the HIPPOs can fall through them. (Had to have a little fun at the HIPPOs expense ;)

    Picking the right time to reveal these gems and framing them in the proper perspective will help keep a good HIPPO moving toward becoming one that embraces the web revolution.

    We all have to help out, Avinash can't do it all by himself!

  16. 16

    Rob: I am afraid I don't believe that.

    While there is a tendency to believe that it might be that much more important all the reasons B2C data might be imperfect apply to B2B, and any recommendations for B2C apply to B2b (like don't obsess about getting perfection and focus on the head data right away).

    The only recommendation amongst the 10 that might be unique to B2B might be #7, it is possible that in B2B the diminishing margins of return might be a point "further up" than B2C, though the path to identify would be exactly the same. IMHO.

    With regards to your metaphor, the reason for tracking individual whales is not their size (with respect to the tuna) it is their vastly diminished number. I am sure if there where just as many tuna in the world as whales then each tuna would get tracked.

    I am sure I have stretched your metaphor in ways you did not intend!! :)

    Patrick: Overall it would be fair to say that the market is growing, slower in some cases and faster in others but it is growing.

    I would focus on ensuring that I have good skills that are in demand (in our specific case being a true Web Analytics 2.0 person and a Analysis Ninja). I have not known those kinds of people to ever have a hard time finding a job, recession or not.

    Captain: I love your Prioritized Execution Model.

    Start with: 2, 6, 7.
    Immediately follow up with: 3, 4.
    Before they realize: 8, 9.
    Seal the deal with: 5, 10.

    PS: Greatest quote ever: "HIPPOs love finding holes in strategies, but what they love more are the people that fix the holes before the HIPPOs can fall through them." :)


  17. 17

    Avinash, another fine post. I'm still waiting for one where I don't 'star' it for another read.

    I thought all the points were well made, especially #4 – the different points at which data quality becomes an issue. I suspect though for some HiPPOs, the data quality argument may be trotted out due in part to fear, and wanting to grasp the one thing that puts them back back in the driving seat, e.g. ".. this web analytics can't be that useful, the numbers never make sense".

    I think the earlier in the proess, we can explain to clients or senior managers that, although we are working with the numbers, the quality of the numbers, is less important than what insight we can gain, the better.

    Although, the topic of data quality is not one that I am in love with, it has to be one of the first topics in introducing clients / managers to web analytics.

    Keep up the good woork

  18. 18
    Ted Gray says

    Reading this material is like a language immersion for me, but as I am kicking off a highly specific, one product site I don't believe I can survive long term without a simply structured but effective analytics plan. I will have my ears on…

    PS: given the sheer volume of quality, prioritization is key….TG

  19. 19

    Hi Avinash,

    I love the idea of starting at the bottom of the conversion funnel and working out that way where the barriers are – hadn't approached it that way before.

    This may not be the right place to discuss this but in a HIPPO environment, I find that BRAND is the biggest obstacle (especially from sem perspective). When offer e.g. actionable items – the business managers love it! Yes, more customers, more sales, qualification through PPC/SEO keyword targeting – then you meet the BRAND HIPPO who cares only about the possibility of brand dilution and the equity of the brand as opposed to a thriving and surviving online venture.

    Any tips?

    Nikki Rae

  20. 20

    Nikkie: Test. That's my tip.

    We did a test recently where we pulsed the paid search spend at various levels, including zero, for a period of over six weeks and collected data on visits and conversions. For brand keywords where they already ranked #1.

    Turns out there was marginal value in doing that. Proved using data. It would have been much better to go after the long tail and bring new people to the franchisee.

    Now in your case it might prove to be different. But you can test and go back to the hippo and he can't argue with data (well he will argue only he will look silly after a while :)).

    And in case you need more practical tips on dealing with hippos, here a post you might find to be of value:

    Lack Management Support or Buy-in? Embarrass Them!


  21. 21
    Ryota Masaki says

    Good post, keeps me coming back to this blog. Reaffirms what I've been doing and picked up some great advice.

    #8 is so true. I've managed some smaller websites and it's all about traffic generation.

    Data quality and hippos… we've all ran into this before. I always think that it's still good to look at a fuzzy picture and try to understand it. I also keep steering the conversation to results and dollar amounts.

  22. 22
    Patrick says

    "It would be fair to say that Paid Search it has also gotten very complex"

    A few months ago I mentioned on an SEO forum (I started learning about SEO close to 3 years ago) how I thought what everybody told me in the beginning (3 years ago) didnt seem to be true at all anymore: People pretended one would have to read 10 blogs and participate in 10 forums a day just to keep up with the algorithms. However, very little had changed since then (only details)…I also said (and truly believe) that I thought PPC was changing much more quickly than SEO these days, and most people agreed!

    Then again there's some odd movement in the rankings, again all of a sudden ;)

    EDIT: Im sorry Avinash, no idea how that happened, I was goign to post this in the c omments of another blog post..


  1. […] Slay The Analytics Data Quality Dragon & Win Your HiPPO’s Love! […]

  2. […]
    Just imagine, a whole set of applications that finally free the video screen from its HTML-caged rendering/display and enable the presentation of desirable content through an intuitive, immersive video screen/application. In this engaging, app-mediated format, the proven monetization engine of brand advertising that powers television and other entertainment media will finally be possible in the online world. And the analytics that can be gleaned through the App Web promise to be better than the 18,000-30,000 viewers that Nielsen uses to predict/analyze the TV habits of 200 million American viewers.

  3. […]
    Uplift, bounce-ratevermindering en shopping-cart-abandonmentverlaging zijn termen die veel van ons bekend in de oren klinken, maar voor managers (vaak de mensen met de budgetten) vakjargon zijn. Ze moeten te diep nadenken over de exacte betekenis ervan. Dankzij de inzichten van andere bloggers, waaronder Avinash Kaushik en zijn HIPPO-theorie, mag het algemeen bekend zijn dat het communiceren in ‘euro’-taal vaak het meeste effect heeft op het winnen van steun van deze managers.

  4. […]
    Uplift, bounce-ratevermindering en shopping-cart-abandonmentverlaging zijn termen die veel van ons bekend in de oren klinken, maar voor managers (vaak de mensen met de budgetten) vakjargon zijn. Ze moeten te diep nadenken over de exacte betekenis ervan. Dankzij de inzichten van andere bloggers, waaronder Avinash Kaushik en zijn HIPPO-theorie, mag het algemeen bekend zijn dat het communiceren in ‘euro’-taal vaak het meeste effect heeft op het winnen van steun van deze managers.

  5. […]   (1)我觉得最好的方法,但也是最不被Hippo重视和注意的方法,即为尽量为所有的预置点都设互动链接并进行介绍(但并不一定要打开新的窗口,可以用展开式)。好处有二,其一是能让网站分析工具记录点击密度,其二是更详细的说明能够进一步帮助预置点增加影响力和说服力并促进转化。 […]

  6. […] The HiPPO in your organization decides that the number 24 is the highest number there is, no questions asked. 24 represents the way it way it was, the way it is and the way it will be. […]

  7. […]   (1)我觉得最好的方法,但也是最不被Hippo重视和注意的方法,即为尽量为所有的预置点都设互动链接并进行介绍(但并不一定要打开新的窗口,可以用展开式)。好处有二,其一是能让网站分析工具记录点击密度,其二是更详细的说明能够进一步帮助预置点增加影响力和说服力并促进转化。 […]

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