Learning From the Pang of Quantitative Defeat

…@NFLFantasy #PPR Matchup failure, that is.

Let me preface this by saying that immediately after drafting my team, a manic flurry of clicks & non-favorable sighs – I measured my lineup for week 0 & beyond in terms of likelihood to win the playoffs. And I came in last (discussed in a previous post).

My win/loss season ratio prediction ala @NFLFantasy  was terrible (regular season = 6wins:8losses. (Ouch)…Luckily, I troll the waiver wire and pluck unknowns before they become titans (Kareem Hunt, Alvin Kamara to name a few from this season), or slot in weekly bosses before the mainstream agree they should be added to some Deep Sleeper Waiver Report. It is what has propelled me since Year 1. It is what I am most proud about in terms of my algorithms fantasy predictions. And emotion aside, statistically, after 4 years of training, I can attest to it’s success in terms of accurate machine predictability season after season, week after week.

Because of this trolling, I am now projected to be #1 again in the League with either a 10:4 or 9:5 ratio.

Why that is important is I lost 4 games in a row and forgot this little fact above. I mean I was undefeated for the first 5 weeks. So, having 4 straight back to back losses hurt and I went underground to lick my wounds. Until I was reminded that I was always going to have a minimum of 4 losses .  And even if they were back-to-back, perhaps. they are now out of the way (unless the 9:5 ratio comes true). In either case, I am slotted to take back my #1 league bragging rites as we move into playoffs. 2017-11-13 (10).png

So, what I learned from my momentary lapse of positive model juju is that when I ignore the facts and outcomes of my model, no one wins. I embrace those losses because they were always part of my 2017 Fantasy Football predestination.

 

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Week 4 – @NFLFantasy PPR Play/Bench Using #MachineLearning

Week 4 – While it is 5am on Sunday and gameplay kicks off at 9:30am today (love those London games), I am technically getting this posted before the start. Ok, well, I missed Thursday – fair enough. But we are getting there:

League 1 (PPR):

Week4-@NFL-PPR-League1-@NFLFantasyAs I mentioned in my Week 3 post, there are some things that even the best algorithmic usage cannot predict (today); injuries are one of those things that today are sort of hard to determine unless player has a propensity or an existing / underlying issue that one is tracking – Very soon, I believe that the development of the RFID program that each player’s’ uniforms now carry will yield better and better data points that can, in fact, become the Minority Report of the NFL season and predict the next great injury, based on biometric + performance + known medical history with a fair degree of certainty. But we are not there yet (at least, what is being provided to those of us NOT connected to the team’s/NFL <– a girl can dream about this kind of data coming from this type of connection, can’t she?)

You’ll notice I have Jordan Reed in my line up – this is a hard one. He is expected to play but at what level? I picked up Charles Clay as my backup but unfortunately , Reed doesn’t play until Monday; Clay plays at 1pm today. So, I will refresh my model and then see if Reed drops any in the ranking. If nothing else, it will give me a better picture on points earned potential IF he were to play with his existing injury (albeit healed or otherwise).

I swapped out Doug Baldwin who is also questionable this week in terms of his health; and slotted Adam Thielen in. I also picked up Alvin Kamara off of the waiver wire. I have a feeling about him; as does my model. This week, he ranks right next to Terrance West given the matchup and alternates on my bench (oh woe is my RB situation) – Thank goodness for a stout WR lineup, even with injuries et al.

All in all, I am most worried about this matchup. This might be a week for a loss, even if numerically, it looks like a win on NFL.com. I guess I can thank Ty M. for making it a possibility. But O’dell B Jr. might have something else to say about that. Oh that and Melvin Gordon questionable status: if only it were so…Again, I love to dream…

League 2 (Standard): What made me thankful in League 1, has plagued me in League 2 (oh, Ty!) – In this league, I have already pulled out Jordan Reed because E. Engram ranked higher in my weekly ranking anyway. I am also taking a chance on Jarvis Landry this week (it is New Orleans after all. The point differential +/- standard deviation for Standard format sent Landry into the top 10 for the week – fingers crossed xx – #trustthemodel).

Oh yea, this is also the league where I have incurred 1 loss so far – 2 wins / 1 loss. 😦 And that loss was to none other than my man; we are competing in both League 1 and League 2; he beat me by 1.9 points Week 1 – somehow I don’t remember him feeling as bad for me with my loss that week as I did for his loss against me in Week 3…

In fact,  I think he did a victory dance perhaps akin to Tom Cruise in ‘Risky Business’ – but maybe that is too much information for a Sports ML blog posting :0):

Week4-Standard-@NFLFantasy-@NFL-@Laura_E_Edell

Week4-Standard-@NFLFantasy-@NFL-@Laura_E_Edell

League 3 (Standard): After Thursday night, I am doing ok – I predicted 12.5 +/- .75 st dev for Jordan Howard and he earned 12.30. So, pretty spot on. Aaron Rodgers came in at 23.06, which I predicted 23.60 (what, is that my dyslexia at play; no, there was also a        + / – 1.5 st dev at work, so again, spot on in terms of accuracy.  So, maybe, my 3 wins and 0 losses will become 4 wins after this week. But I am not counting any chickens ever before they are hatched. Just look at what happened to my opponent / my man in Week 3 (154 points for me; 45 points for him in PPR format- that is what we call, just brutal people). 

Week4-Standard-League3-@NFL-@NFLFantasy-@Laura_E_Edell

Week4-Standard-League3-@NFL-@NFLFantasy-@Laura_E_Edell

Week 3 – @NFLFantasy PPR Play/Bench Using #MachineLearning

Recap from Week 3 (sorry – I really am trying to post before Thursday night but it seems that between work right now and updating my model stats mid-week, I just run out of time).

Week 3 was wildly successful. NFL.com was closer this time in terms of predicting my win over my opponent but nowhere near to the results that I achieved. I will always stand by Russell Wilson – what kind of Seahawk would I be if I threw in the towel and in my 2nd league (Standard format), he did not fail! He was simply divine. But alas, he is not my primary league QB (Tom Brady is – a hard pill to swallow personally being a die hard Seahawks fan after what happened in a certain very important yesteryear game – but he has proven his PPR fantasy value in Week 3). Primary League Week 3 – Wins = 3 / Losses = 0 (remember, after draft day, I was projected to end the season with an 8-8 W/L ratio. So, this might be the week; maybe not).

But last week, I genuinely felt bad – Locheness Jabberwokies, my week 3 opponent, happens to also be my man. And, this annihilation just felt like a win that went one step over the line of fairness. I mean a win’s a win – but this kind of decimation belongs outside of one’s relationship. Trust me. But he was a good sport. Except, he will no longer listen to my neurotic banter about losing in any given week, even if all signs point to a loss. Somehow, when I trust my model, it all works out. Now, I can’t predict injuries mid game like what happened in Week 4 to Ty Montgomery (my League 3 Flex position player). Standard league wise, he brought home 2.3 points ~ projected to earn about 10.70 Standard points with a st. deviation of +/- 1.5. But this was my lineup for Week 3 across my 3 leagues:

League #1 (Primary PPR) – remember, I aim to not just win but also optimize my lineup. #nfl.com,#fantasyfootball,#PPR,#Week3,#2017

A bench full of points is a fail to me. But in this case, I benched Jordan Reed and picked up whomever was the next available TE off the waiver wire (granted he definitely contributed nothing). But out of my WR1 and WR2 + WR Flex, those I played were the best options (even though Mike Evans came in about 1.10 points less than Adam Thielen (bench), it was within the expected standard deviation, so either one would have been fine if played).

My RB situation has always been the bane of my league this year starting with my draft choices – Nothing to write home about except seeing the early value of Kareem Hunt (TG), even when NFL.com continued to project very little in his court.

Terrance West was supposed to be double digits but my model said to bench him vs. either Mike Gillislee or Kerwynn Williams. Both scored very little and essentially were within their own standard deviation negating their slight point difference.

All in all, players played worked out well and yes, though many stellar performances carried those that failed might be outliers in some regard (or at least they won’t bring home that many points week over week).  But the PPR space is my golden circle of happiness – after all, I built my original algorithm using PPR league play / bench + historical point spreads + my secret sauce nearly 5 years ago; and those years of learning have “taught” the model (and me) many nuances otherwise missed by others in the sports ML space (though I respect greatly what my fellow ML “sportstaticians” put forth, my approach is very different from what I glean from others’ work).

One day, I would love to have a league with only ML Sports folks; the great battle of the algorithmic approaches – if you are interested, let me know in the comments.

League 2 (Standard): Wins = 3/Losses = 0:

As you can see, I should have played DeSean Jackson over Adam Thielen or my Flex position Ty Montgomery. And geez, I totally spaced on pulling Jordan Reed like I did in League 1. This win was largely because of Russell Wilson, as mentioned before, Devonta Freeman and the Defense waiver wire pick up of the Bengals who Im glad I picked up in time for the game. oh yeah, I am not sure why Cairo Santos shows as BYE but earned me 6 points??? NFL.com has some weird stuff happening around 12:30 last Sunday ; games showed as in play (even though kick off wasn’t for another 30 minutes); and those that showed in play erroneously allowed players to be added from the wire still as though the games weren’t kicked off. Anyway, not as proud but still another win – Year 1 for Standard; perhaps after another 5 years training Standard like my PPR league, I will have more predictable outcomes , other than luck.

#NFL.com, #Week3, #Standard,@NFLFantasy, #machinelearning

#NFL.com, #Week3, #Standard,@NFLFantasy

Win @NFLFantasy PPR Leagues w/ ML

So, the past 3 years I have been using #machine-learning (ML) to help me in my family based PPR #fantasy-football league. When I joined the league, the commissioner and my partner’s father, said I would never win using statistics as the basis of my game play. Being cut from the “I’ll show you” aka “well, fine…I’ll prove it to you” cloth that some of us gals working in the tech industry dawn as we break down stereotypical walls and glass ceilings, is something I’ve always enjoyed about my career and love that there is an infographic to tell the tale courtesy of mscareergirl.com (only complaint is the source text in white is basically impossible to read but at least the iconography and salaries are legible):

glass-ceiling-2-620x400

infographic provided by Danyel Surrency Jones on mscareergirl.com

I’ve never whined that in my industry, I tend to work primarily with the male species or that they are “apparently” paid more *on average* because some survey says so.  My work ethic doesn’t ride on gender lines — This train departs from the “proven value based on achievements earned & result in commensurate remuneration” station (woah, that’s a mouthful).  I take challenges head-on not to prove to others, but to prove to myself, that I can do something I set my sights on, and do that something as well, if not better, than counterparts. Period. Regardless of gender. And that track has led to the figurative money ‘line’ (or perhaps it’s literal in the case of DFS or trains – who’s to say? But I digress…more on that later).

Money_Train

So, I joined his league on NFL.com, so aptly nicknamed SassyDataMinxes (not sure who the other minxes are in my 1 woman “crew” but I never said I was grammarian; mathematician, aka number ninja, well, yes;  but lingualist, maybe not.

Year 1, as to be expected *or with hindsight*,  was an abysmal failure. Keep in mind that I knew almost absolute nothing when it came to Pro Football or Fantasy sports.  I certainly did not know players or strategies or that fantasy football extended beyond Yahoo Pick ‘Em leagues, which again, in hindsight, would have been a great place to start my learning before jumping head 1st into the world of PPR/DFS.

At its core, it requires you pick the weekly winning team from 2 different competitors and assuming you have the most correct picks, you win that week. If there are no teams on BYE that week, you have 32 teams or 16 games to “predict outcomes” ; a binary 0 or 1 for lose or win in essence. Right? Never, she exclaims, because WAIT, THERE’S MORE: you have to pick a winner based on another factor: point spread. Therefore, if 0 means lost and 1 means won, you get a 1 per win EXCEPT if the spread of points is less than what the “book makers” out of Vegas determine to be the “winning spread” – You could technically pick the winning team and still get a goose egg for that matchup if the team did not meet their point spread (ooh, it burns when that happens). The team that wins happily prancing around the field singing “We are the Champions” while you are the loser for not betting against them because they were comfortable winning by a paltry lesser amount than necessary – Ooh, the blood boils relieving those early games – especially since my Grandmother who won that year picked her teams based on cities she liked or jersey colors that her ‘Color Me Happy’ wheel said were HER best COOL tones; the most unscientific approach worked for her so many times I now think she actually IS A bookie running an illegal operation out of her basement, which fronts as ‘her knitting circle” – Yah, as if any of us believe that one, Grandma :)! (She is a walking football prediction algorithm).

So, something as seemingly simple as Yahoo Pick ‘Em can actually be harder than it appears unless you are her. But, still…markedly easier than a PPR league; and light years easier than DFS/Auction style fantasy leagues when it comes to predicting gaming outcomes at the player, weekly matchup and league perspectives.

Hindsight is such a beautiful thing (*I think I have said that before*) because to espouse all of these nuggets of knowledge as though I am the Alliteration Arbiter of All –> The Socratic Seer of Scoring Strategies…And again, as always I digress (but, ain’t it fun!).

OK, let’s continue…So, we’ve established that fantasy football gaming outcomes requires a lot of *something* — And we’ve established that just cuz it seems simple, or did, when trying to predict outcomes along a massively mutable set of variables  *wait, why didn’t I just READ that sentence or THINK it when I started! If I could go back in time and ask my 3 year younger:

“Self, should I stop this nonsense now, alter the destination or persevere through what, at times, might seems like a terrible journey? *HUM, I think most pensively*.

And then answer myself, just like the good only-child I am:

“NEVER – Self, nothing worth getting is easy to get, but the hardest fought wins are the most worthwhile when all is said and done and remember,  don’t let the bedbugs bite; YOU’RE bigger than them/that.”

Or something along those lines, perhaps…

NEVER was my answer because in 2015 and 2016 (Years 2 and 3),  I was #1 in the league and won those coveted NFL.com trophies and a small pool of money. But what I won most of all was bragging rites.

Champion

Oh baby, you can’t buy those…

Not even on the Dark Web from some Onion-Routed Darker Market. Especially the right to remind a certain commissioner / neighsayer du jour / father-in-law-like that my hypothesis of using ML & statistics ALONE could beat his years of institutional football knowledge and know-how. I also won a 2nd NFL-managed league that I joined in Year 3 to evaluate my own results with a different player composition.

So Year 1 was a learning year, a failure to others in the league but super valuable to me. Year 2 was my 1st real attempt to use the model, though with much supervision and human “tweaking” ; Year 3, non-family league (league #2 for brevity sake; snarky voice in head “missed that 4 paragraphs ago” – Burn!), I had drafted an ideal team, rated A- .

Year 3 being a double test to ensure Year 1 wasn’t a double fluke.  Two leagues played:

League #1 with the family, was a team comprised of many non-ideal draft picks chosen during non-optimized rounds (QB in round 2, DEF in round 4 etc). But the key in both Year 2 and 3, was spotting the diamonds in the rookie rough – my model bubbled up unknown players or as they are known to enthusiasts: “deep sleepers” that went on to become rookie-of-the-year type players: in 2015, that was Devonta Freeman (ATL); in 2016, that was ‘Ty-superfreak’ Hill aka Tyreek Hill (Chiefs) and even better, Travis Kelce (who had been on my roster since 2015 but rose to the occasion in 2016, BEEEG-TIME) .  That has always been a strength of my approach to solving this outcomes conundrum.

So, that all being said, in this Year 4, I plan to blog PRE-GAME with my predictions for my team with commentary on some of the rankings of other players. Remember, it isnt just a player outcome, but it is player outcome in relation to your matchup that week within your league and in the context of who best to play vs. bench given those weekly changing facets. Some weeks, you might look like a boss according to NFL.com predictions; but in fact, should be playing someone else who might have a lower-than-you-are-comfortable-putting-into-your-lineup prediction. Those predictions folks HAVE ISSUES – But I believe in the power of model evaluation and learning, hence the name Machine Learning or better yet, Deep Learning approaches.

Side note: reminds me of that “SAP powered” player comparison tool:

fantasyimageswhich was DOWN / not accessible most of the aforementioned season when it was being hammered on by fans in need of a fantasy fix (reminds me of an IBM Watson joke but I was keep that one to myself ) – whomever is at fault – you should make sure your cloud provider “models” out an appropriate growth-based capacity & utilization plan IF you are going to feature it on your fantasy football site, NFL.com.

Next posting will be all about how I failed during Year 4’s draft (2017) and what I am planning to do to make up for it using the nuggets of knowledge that is an offshoot of retraining the MODEL(s) during the week – Plus, I will blog my play/bench predictions which will hopefully secure a week 1 win (hopefully because I still need to retrain this week but not until Wednesday :)).

In a separate post, we’ll talk through the train…train…train phases, which datasets are most important to differentiate statistically important features from the sea of unworthy options sitting out waiting for you to pluck them into your world. But dont fall prey to those sinister foe…They might just be the “predictable” pattern of noise  that clouds one’s senses. And of course, scripting and more scripting; so many lines of code were written and rewritten covering the gamut of scripting languages from the OSS data science branch (no neg from my perspective on SaS or SPSS other than they cost $$$ and I was trained on R in college *for free* like most of my peers) – well, free is a relative term, and you take the good with the bad when you pull up your OSS work-boots –> R has its drawbacks when it comes to the viability of processing larger than life datasets without herculean sampling efforts just to be able to successfully execute a .R web scraping script without hitting the proverbial out of memory errors, or actually train the requisite models that are needed to solve said self-imposed ML fantasy football challenges such as this. Reader thinks to oneself, “she sure loves those tongue twisting alliterations.”

And gals, I love helping out a fellow chica (you too boys/men, but you already know that, eh) — Nobody puts baby in the corner, and I never turn my back on a mind in need or a good neg/dare.

Well, Year 4 — Happy Fantasy Football Everyone — May the wind take you through the playoffs and your scores take you all the way to the FF Superbowl 🙂

Eye Tracking & Applied ML: Soapbox Validations

Anyone who has read my blog (shameless self-plug: http://www.lauraedell.com) over the past 12 years will know, I am very passionate about drinking my own analytical cool-aid. Whether during my stints as a Programmer, BI Developer, BI Manager, Practice Lead / Consultant or Senior Data Scientist, I believe wholeheartedly in measuring my own success with advanced analytics.  Even my fantasy football success (more on that in a later post) can be attributed to Advanced Machine Learning…But you wouldn’t believe how often this type of measurement gets ignored.
eyetracking
Introducing you, dear reader, to my friend “Eye-Tracker” (ET). Daunting little set of machines in that image, right?! But ET is a bonafide bada$$ in the world of measurement systems; oh yeah, and ET isn’t a new tech trend – in fact, mainstream  ET systems are a staple of any PR, marketing or web designers’ tool  arsenal  as a stick to measure program efficacy between user intended behavior & actual outcomes/actions.

In my early 20’s, I had my own ET experience & have been a passionate advocate since, having witnessed what happens when you compound user inexperience with poorly designed search / e-commerce operator sites.  I was lucky enough to work for the now uber online travel company who shall go nameless (okay, here is a hint: remember a little ditty that ended with some hillbilly singing “dot commmm” & you will know to whom I refer). This company believed so wholeheartedly in the user experience that they allowed me, young ingénue of the workplace, to spend thousands on eye tracking studies against a series of balanced scorecards that I was developing for the senior leadership team. This is important because you can ASK someone whether a designed visualization is WHAT THEY WERE THINKING or WANTING, even if built iteratively with the requestor. Why, you ponder to yourself, would this be necessary when I can just ask/survey my customers about their online experiences with my company and saved beaucorp $$.

Well, here’s why: 9x out of 10, survey participants, in not wanting to offend, will nod ‘yes’  instead of being honest, employing conflict avoidance at its best. Note, this applies to most, but I can think of a few in my new role who are probably reading this and shaking their head in disagreement at this very moment.

Eye tracking studies are used to measure efficacy by tracking what content areas engage users’ brains vs. areas that fall flat, are lackluster, overdesigned &/or contribute to eye/brain fatigue. It measures this by “tracking” where & for how long your eyes dwell on a quadrant (aka visual / website content / widget on a dashboard) and by recording the path & movement of the eyes between different quadrants’ on a page. It’s amazing to watch these advanced, algorithmic-tuned systems, pick up even the smallest flick of one’s eyes, whether darting to or away from the “above-fold” content, in ‘near’ real-time. The intended audience being measured generates the validation statistics necessary to evaluate how well your model fit the data. In the real-world, receiving attaboys or “ya done a good job” high fives should be doled out only after validating efficacy: eg. if customers dwell time increases, you can determine randomness vs. intended actual; otherwise, go back to the proverbial drawing board until earn that ‘Atta boy’ outright.

What I also learned which seems a no-brainer now; people read from Left Top to Right Bottom (LURB). So, when I see anything that doesn’t at LEAST follow those two simple principles, I just shake my head and tisk tisk tisk, wondering if human evolution is shifting with our digital transformation journey or are we destined to be bucketed with the “that’s interesting to view once” crowd instead of raising to the levels of usefulness it was designed for.

Come on now, how hard is it to remember to stick the most important info in that top left quadrant and the least important in the bottom right, especially when creating visualizations for use in the corporate workplace by senior execs. They have even less time & attention these days to focus on even the most relevant KPIs, those they need to monitor to run their business & will get asked to update the CEO on each QTR, with all those fun distractions that come with the latest vernacular du-jour taking up all their brain space: “give me MACHINE LEARNING or give me death; the upstart that replaced mobile/cloud/big data/business intelligence (you fill in the blank).
But for so long, it was me against the hard reality that no one knew what I was blabbing on about, nor would they give me carte blanche to re-run those studies ever again , And lo and behold, my Laura-ism soapbox has now been vetted, in fact, quantified by a prestigious University professor from Carnegie, all possible because a little know hero named Edmond Huey, now near and dear to my heart, grandfather of the heatmap, followed up his color-friendly block chart by building the first device capable of tracking eye movements while people were reading. This breakthrough initiated a revolution for scientists but it was intrusive and readers had to wear special lenses with a tiny opening and a pointer attached to it like the 1st image pictured above.
Fast forward 100 years…combine all ingredients into the cauldron of innovation & technological advancement, sprinkled with my favorite algorithmic pals: CNN & LSTM & voila! You have just baked yourself a popular visualization known as a heat/tree map (with identifiable info redacted) :
This common visual is  akin to eye tracking analytics which you will see exemplified in the last example below. Cool history lesson, right?

Even cooler is this example from a travel website ‘Travel Tripper’ which published Google eye-tracking results specific to the hotel industry. Instead of a treemap that you might be used to (akin to a Tableau or other BI tool visualization OOTB), you get the same coordinates laid out over search results in this example; imagine having your website underneath and instead of guessing what content should be above or below the fold, in the top left or right of the page, you can use these tried and true eye tracking methods to quantify exactly what content items customers or users are attracted to 1st and where their eyes “dwell” the longest on the page (red hot).

So, for those non-believers, I say, become a web analytic trendsetter, driving the future of machine design forward (ala “Web Analytics 3.0”).

Be a future-thinker, forward mover, innovator of your data science sphere of influence, always curious yet informed to make intelligent choices.

KPIs in Retail & Store Analytics

I like this post. While I added some KPIs to their list, I think it is a good list to get retailers on the right path…

KPIs in Retail and Store Analytics (continuation of a post made by Abhinav on kpisrus.wordpress.com:
A) If it is a classic brick and mortar retailer:

Retail / Merchandising KPIs:

-Average Time on Shelf

-Item Staleness

-Shrinkage % (includes things like spoilage, shoplifting/theft and damaged merchandise)

Marketing KPIs:

-Coupon Breakage and Efficacy (which coupons drive desired purchase behavior vs. detract)

-Net Promoter Score (“How likely are you to recommend xx company to a friend or family member” – this is typically measured during customer satisfaction surveys and depending on your organization, it may fall under Customer Ops or Marketing departments in terms of responsibility).

-Number of trips (in person) vs. e-commerce site visits per month (tells you if your website is more effective than your physical store at generating shopping interest)

B) If it is an e-retailer :

Marketing KPIs:

-Shopping Cart Abandonment %

-Page with the Highest Abandonment

-Dwell time per page (indicates interest)

-Clickstream path for purchasers (like Jamie mentioned do they arrive via email, promotion, flash sales source like Groupon), and if so, what are the clickstream paths that they take. This should look like an upside down funnel, where you have the visitors / unique users at the top who enter your site, and then the various paths (pages) they view in route to a purchase).

-Clickstream path for visitors (take Expedia for example…Many people use them as a travel search engine but then jump off the site to buy directly from the travel vendor – understanding this behavior can help you monetize the value of the content you provide as an alternate source of revenue).

-Visit to Buy %

-If direct email marketing is part of your strategy, analyzing click rate is a close second to measuring conversion rate. 2 different KPIs, one the king , the other the queen and both necessary to understand how effective your email campaign was and whether it warranted the associated campaign cost.

Site Operations KPIs / Marketing KPIs:

-Error % Overall

-Error % by Page (this is highly correlated to the Pages that have the Highest Abandonment, which means you can fix something like the reason for the error, and have a direct path to measure the success of the change).

Financial KPIs:

-Average order size per transaction

-Average sales per transaction

-Average number of items per transaction

-Average profit per transaction

-Return on capital invested

-Margin %

-Markup %

I hope this helps. Let me know if you have any questions.

You can reach me at mailto://lauraedell@me.com or you can visit my blog where I have many posts listing out various KPIs by industry and how to best aggregate them for reporting and executive presentation purposes ( http://www.lauraedell.com ).

It was very likely that I would write on KPIs in Retail or Store Analytics since my last post on Marketing and Customer Analytics. The main motive behind retailers looking into BI is ‘customer’ and how they can quickly react to changes in customer demand, rather predict customer demand, remove wasteful spending by target marketing, exceeding customer expectation and hence improve customer retention.

I did a quick research on what companies have been using as a measure of performance in retail industry and compiled a list of KPIs that I would recommend for consideration.

Customer Analytics

Customer being the key for this industry it is important to segment customers especially for strategic campaigns and to develop relationships for maximum customer retention. Understanding customer requirements and dealing with ever-changing market conditions is the key for a retail industry to survive the competition.

  • Average order size per transaction
  • Average sales per transaction

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How Do You Use LinkedIn? (Social Media Infographics)

How often do you refresh your LinkedIn profile pic? Or worse, the content within your profile? Unless you are a sales exec trolling the social networking site or a job seeker, I would surmise not that often; in fact, rarely is most apropos of a description. Thoughts…? ( yes, she’s back ( again), but this time, for good dear readers…@Laura_E_Edell (#infographics) says thanks to designinfographics.com for her latest content postings!

And just because I call it out, doesn’t mean you will know the best approach to updating your LinkedIn profile. And guess what …there’s an infographic for that! (http://www.linkedin.com/in/lauraerinedell)

Check out my profile on LinkedIn by clicking infographic

Check out my profile on LinkedIn by clicking infographic

MicroStrategy World 2012 – Miami

Our internal SKO (sales kick off) meeting was the beginning of this years’ MSTR World conference ( held in Miami, FL at the Intercontinental Hotel located on Chopin Plaza). As with every year, the kickoff meeting is the preliminary gathering of the salesforce in an effort to “rah-rah” the troops who work the front lines around the world ( myself included).

What I find most intriguing is the fact that MicroStrategy is materializing for BI all of those pipe dreams we ALL have. You know the ones I mean : I didn’t buy socialintelligence.co for my health several years ago. It was because I saw the vision of a future where business intelligence and social networking were married. Or take cloud intelligence, aka BI in the cloud. Looking back in 2008, I remember my soapbox discussion of BI mashups, ala My Google, supported in a drag and drop off premises environment. And everyone hollered that I was too visionary, or too far ahead. That everyone wanted reporting, and if I was lucky, maybe even dashboards.

But the acceleration continued, whether adoption grew or not.

Then, i pushed the envelope again: I wanted to take my previous thought of the mashup a morph it into an app integrated with BI tools. Write back to transactional systems or web services was key.

What is a dashboard without the ability within the same interface to take action? Everyone talks about actionable metrics/KPIs. Well, I will tell you that to have a KPI BY DEFINITION OF WHAT A KPI IS, means it is actionable.

But making your end users go to a separate ERP or CRM, to make the changes necessary to affect a KPI, will drive your users away. What benefit can you offer them in that instance ? Going to a dashboard or an excel sheet is no different. It is 1 application to view and if they are lucky, to analyze their data. If they were using excel before , they will still be using excel, especially if your dashboard isn’t useful to day to day operations.

Why? They still have to go to a 2nd application to take action.

Instead, integrate them into one.

Your dashboard will become meaningful and useful to the larger audience of users.
Pipe dream right?

NO. I have proved this out many times now and it works.

Back in 2007-2008, it was merely a theory I pontificated with you, my dear readers.

Since then, I have proved it out several times over and proven the success that can be achieved by taking that next step with your BI platforms.

Folks, if you haven’t done it, do it. Don’t waste anymore time. It took me less then 3 days to write the web services code to consume the salesforce APIs including chatter, ( business “twitter” according to SFDC), into my BI dashboard ( mobile dashboard in fact).

And suddenly, a sales dashboard becomes relevant. No longer does the salesforce team have to view their opportunities and quota achievement in one place, only to leave or open a new browser, to access their salesforce.com portal in order to update where they are at mid quarter.

But wait, now they forgot which KPIs they need to add comments to because they were red on the dashboard which is now closed, and their sales GM is screaming at them on the phone. Oh wait…they are on the road while this is happening and their data plan for their iPad has expired and no wireless connection is found.

What do you do?

Integrating salesforce.com into their dashboard eliminates at least one step (opening a new browser) in the process. Offering mobile offline transactions is a new feature of MicroStrategy’s mobile application. This allows those sales folks to make the comments they need to make while offline, on the road , which will be queued until they are online again.

One stop, one dashboard to access and take action through, even when offline, using their mobile ( android, iPad/iPhone or blackberry ) device.

This is why I’m excited to see MicroStrategy pushing the envelope on mobile BI futures.

MicroStrategy Personal Cloud – a Great **FREE** Cloud-based, Mobile Visualization Tool

Have you ever needed to create a prototype of a larger Business Intelligence project focused on data visualizations? Chances are, you have, fellow BI practitioners. Here’s the scenario for you day-dreamers out there:

Think of the hours spent creating wire-frames, no matter what tool you used, even if said tool was your hand and a napkin (ala ‘back of the napkin’ drawing) or the all-time-favorite white board, which later becomes a permanent drawing with huge bolded letters to the effect of ‘DO NOT ERASE OR ITS OFF WITH YOUR HEAD’ annotations dancing merrily around your work. Even better: electronic whiteboards which yield you hard copies of your hard work (so aptly named), which at first, seems like the panacea of all things cool (though it has been around for eons) but still, upon using, deemed the raddest piece of hardware your company has, until, of course, you look down at the thermal paper printout which has already faded in the millisecond since you tore it from machine to hand, which after said event, leaves the print out useless to the naked eye, unless you have super spidey sense optic nerves, but now I digress even further and in the time it took you to try to read thermal printout, it has degraded further because anything over 77 degrees is suboptimal (last I checked we checked in at around 98.6 but who’s counting), thus last stand on thermal paper electronic whiteboards is that they are most awesome when NOT thermoregulate ;).

OK, and now We are back…rewind to sentence 1 –

Prototyping is to dashboard design or any data visualization design as pencils and grid paper are to me. Mano y mano – I mean, totally symbiotic, right?

But, wireframing is torturous when you are in a consultative or pre-sales role, because you can’t present napkin designs to a client, or pictures of a whiteboard, unless you are showing them the process behind the design. (And by the way, this is an effective “presentation builder” when you are going for a dramatic effect –> ala “first there were cavemen, then the chisel and stone where all one had to create metrics –> then the whiteboard –> then the…wait!

This is where said BI practitioner needs to have something MORE for that dramatic pop, whiz-AM to give to their prospective clients/customers in their leave behind presentation.

And finally, the girl gets to her point (you are always so patient, my loving blog readers)…While I biased, if you forget whom I work for, and just take into account the tool, you will see the awesomeness that the new MicroStrategy Personal Cloud is for (drum roll please) PROTOTYPING a new dashboard — or just building, distributing, mobilizing etc your spreadsheet of data in a highly stylized, graphical means that tell a story far better than a spreadsheet can in most situations. (Yes, neighseyers, I know that for the 5% of circumstances which you can name, a spreadsheet is more àpropos, but HA HA, I say: this cloud personal product has the ability to include the data table along with the data visualizations!)

Best of all it is free.

I demoed this recently and was able to time it took to upload and spreadsheet, render 3 different data visualizations, generate the link to send to mobile devices (iPads and iPhones), network latency for said demo-ees to receive the email with the link and for them to launch the dashboard I created, and guess what the total time was?

Next best of all, it took only 23.7 minutes from concept to mobilization!

Mind you, I was also using data from the prospect that I had never seen or had any experience with.

OK, here is how it was done:

1) Create a FREE account or login to your existing MicroStrategy account (by existing, I mean, if you have ever signed up for the MicroStrategy forums or discussion boards, or you are an employee, then use the same login) at https://www.microstrategy.com/cloud/personal

Cloud Home

Landing Page After Logged in to Personal Cloud

2) Click the button to Create New Dashboard:

Create Dashboard Icon

  • Now, you either need to have a spreadsheet of data OR you can choose one of the sample spreadsheets that MicroStrategy provides (which is helpful if you want to see how others set up their data in Excel, or how others have used Cloud personal to create dashboards; even though it is sample data , it is actually REAL data that has been scrub-a-dub-dubbed for your pleasure!) If using a sample data set, I recommend the FAA data. It is real air traffic data, with carrier, airport code, days of the week, etc, which you can use to plan your travel by; I do…See screenshot below. There are some airports and some carriers who fly into said airports whom I WILL not fly given set days of the week in which I must travel. If there is a choice, I will choose to fly alternate carriers/routes. This FAA data set will enable you to analyze this information to make the most informed decision (outside of price) when planning your travel. Trust me…VERY HELPFUL! Plus, you can look at all the poor slobs without names sitting at the Alaska Air gate who DIDNT use this information to plan their travel, and as you casually saunter to your own gate on that Tuesday between 3 – 6 PM at SeaTac airport , you will remember that they look so sad because their Alaska Air flight has a 88% likelihood of being delayed or cancelled. (BTW, before you jump on me for my not so nice reference to said passengers), it is merely a quotation from my favorite movie ‘Breakfast at Tiffany’s’ …says Holly Golightly: “Poor cat…poor old slob without a name”.

On time Performance (Live FAA Data)

If using your own data, select the spreadsheet you want to upload

3) Preview your data; IMPORTANT STEP: make sure that you change any fields which to their correct type (either Attribute or Metric or Do Not Import).

Cloud Import - Preview Data

Keep in mind the 80/20 rule: 80% of the time, MicroStrategy will designate your data as either an Attribute or Metric correctly using a simple rule of thumb: Text or VarChar/NVarChar if using SQL Server, will always be designated as an Attribute (i.e. your descriptor/Dimension) and your numerals designated as your Metrics. BUT, if your spreadsheet uses ID fields, like Store ID, or Case ID, along with the descriptor like Store DESC or Case DESC, most likely MicroStrategy will assume the Store ID/Case ID are Metrics (since the fields are numeric in the source). This is an Easy Change! You just need to make sure ahead of time to make that change using the drop down indicator arrows in the column headings – To find them, hover over the column names with your mouse icon until you see the drop down indicator arrow. Click on the arrow to change an Attribute column to a Metric column and vice-versa (see screenshot):

Change Attribute to Metric

Once you finish with previewing your data, and everything looks good, click OK at the bottom Right of your screen.

In about 30-35 seconds, MicroStrategy will have imported your data into the Cloud for you to start building your awesome dashboards.

4) Choose a visualization from the menu that pops up on your screen upon successfully importing your spreadsheet:

Dashboard Visualization Selector
Change data visualization as little or as often as you choose

Here is the 2010 NFL data which I uploaded this morning. It is a heatmap showing the Home teams as well as any teams they played in the 2010 season. The size of the box is HOW big the win or loss was. The color indicates whether they won or lost (Green = Home team won // Red = Home team lost).

For all you, dear readers, I bid you a Happy New Year. May your ideas flow a plenty, and your data match your dreams (of what it should be) :). Go fearlessly into the new world order of business intelligence, and know that I , Laura E. your Dashboard Design Diva, called Social Intelligence the New Order, in 2005, again in 2006 and 2007. 🙂 Cheers, ya’ll.

http://tinyurl.com/ckfmya8

https://my.microstrategy.com/MicroStrategy/servlet/mstrWeb?pg=shareAgent&RRUid=1173963&documentID=4A6BD4C611E1322B538D00802F57673E&starget=1

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Business Intelligence Clouds – The Skies the Limit

I am back…(for now, or so it seems these days) – I promise to get back to one post a month if not more.

Yes, I am known for my frequent use of puns, bordering on the line between cheesy and relevant. Forgive the title. It has been over 110 days since I last posted, which for me is a travesty. Despite my ever growing list of activities both professional and personally, I have always put my blog in the top priority quadrant.

Enough ranting…I diverged; and now I am back.

Ok, cloud computing (BI tools related) seems to be all the rage. Right up there with Mobile

BI, big data and social. I dare use my own term coined back in 2007 ‘Social Intelligence’ as now others have trade marked this phrase (but we, dear readers, know the truth –> we have been thinking about the marriage between social networks / social media data sets and business intelligence for years now)…Alas, I diverge again. Today, I have been thinking a lot about cloud computing and Business Intelligence.

Think about BI and portals, like Sharepoint (just to name 1)…It was all of the rage (or perhaps, still is)…”Integrate my BI reporting with my intranet / portal /Sharepoint web parts…OK, once that was completed successfully, did it buy much in terms of adoption or savings or any number of those ROI / savings catch – “Buy our product, and your employees will literally save so much time they will be basket weaving their reports into TRUE analysis'” What they didnt tell you, was that more bandwidth meant less need for those people, which in turn, meant people went into scarcity mode/tactics trying to make themselves seem or be relevant…And I dont fault them for this…Companies were not ready or did not want to think about what they were going to do with the newly freed up resources that they would have when the panacea of BI deployments actually came to fruition…And so, the wheel turned. What was next…? Reports became dashboards; dashboards became scorecards (became the complements for the former); Scorecards introduced proactive notification / alerting; alerting introduced threshold based notification across multiple devices/methods, one of which was mobile; mobile notification brought the need for mobile BI –> and frankly, and I will say it: Apple brought us the hardware to see the latter into fruition…Swipe, tap, double tap –> drill down was now fun. Mobile made portals seem like child’s play. But what about when you need to visualize something and ONLY have it on a spreadsheet?

(I love hearing this one; as if the multi-billion dollar company whose employee is claiming to only have the data on a spreadsheet didnt get it from somewhere else; I know, I know –> in the odd case, yes, this is true…so I will play along)…

The “only on a spreadsheet” crowd made mobile seem restrictive; enter RoamBI and the likes of others like MicroStrategy (yes, MicroStrategy now has a data import feature for spreadsheets with advanced visualizations for both web and mobile)…Enter Qlikview for the web crowd. The “I’m going to build-a dashboard in less than 30 minutes” salesforce “wait…that’s not all folks….come now (to the meeting room) with your spreadsheet, and watch our magicians create dashboards to take with you from the meeting”

But no one cared about maintenance, data integrity, cleanliness or accuracy…I know…they are meant to be nimble, and I see their value in some instances and some circumstances…Just like the multi-billion dollar company who only tracks data on spreqadsheets…I get it; there are some circumstances where they exist…But, it is not the norm.

So, here we are …mobile offerings here and there; build a dashboard on the fly; import spreadsheets during meetings; but, what happens when you go back to your desk and have to open up your portal (still) and now have a new dashboard that only you can see unless you forward it out manually?

Enter cloud computing for BI; but not at the macro scale; let’s talk , personal…Personal clouds; individual sandboxes of a predefined amount of space which IT has no sanction over other than to bless how much space is allocated…From there, what you do with it is up to you; Hackles going up I see…How about this…

Image representing Salesforce as depicted in C...
Image via CrunchBase

Salesforce.com –> The biggest CRM cloud today. And for the last many years, SFDC has

enbraced Cloud Computing. And big data for that matter; and databases (database.com in fact) in the cloud…Lions and tigers and bears, oh my!

So isnt it natural for BI to follow CRM into cloud computing ?? Ok, ok…for those of you whose hackles are still up, some rules (you IT folks will want to read further):

Rules of the game:

1) Set an amount of space (not to be exceeded; no matter what) – But be fair and realistic; a 100 MB is useless; in today’s world, a 4 GB zip drive was advertised for $4.99 during the back to school sales, so I think you can pony up enough to help make the cloud useful.

2) If you delete it, there is a recycling bin (like on your PC/Mac); if you permanently delete it, too bad/so sad…We need to draw the line somewhere. Poor Sharepoint admins around the world are having to drop into STSADM commands to restore Alvin Analyst’s Most Important Analysis that he not only moved into recycling bin but then permanently deleted.

3) Put some things of use in this personal cloud at work like BI tools; upload a spreadsheet and build a dashboard in minutes wiht visualizations like the graph matrix (a crowd pleasure) or a time series slider (another crowd favorite; people just love time based data 🙂 But I digress (again)…

4) Set up BI reporting on the logged events; understand how many users are using your cloud environment; how many are getting errors; what and why are they getting errors; this simple type of event based logging is very informative. (We BI professionals tend to overthink things, especially those who are also physicists).

5) Take a look at what people are using the cloud for; if you create and add meaningful tools like BI visualizations and data import and offer viewing via mobile devices like iPhone/iPad and Android or web, people will use it…

This isnt a corporate iTunes or MobileMe Cloud; this isnt Amazon’s elastic cloud (EC2). This is a cloud wiht the sole purpase of supporting BI; wait, not just supporting, but propelling users out of the doldrums of the current state of affairs and into the future.

It’s tangible and just cool enough to tell your colleagues and work friends “hey, I’ve got a BI cloud; do you?”