Revolutionizing decision making with analytics

Big data will change the way that business is done forever.  It will revolutionize marketing, product management, shipping, pricing, and even customer service.  The sheer amount of data coming in will provide companies with more predictability and customization power than ever before.

Where did big data start?  Big data started with the internet and the tracking of people on the internet through web analytics.  Web analysts were the trailblazers of big data and the first to start to explore how to use the power of data to predicting people’s behavior, desires and needs.

I’ve started to notice in my career a similar progression for any business decision as it slowly flows from a non-analytical business owner making ‘gut calls’ to completely data-driven decision making.  This transformation is widespread, occurring everywhere from marketing investment decisions to HR recruitment analytics to predicting IT system failures.   For the purposes of simplicity, I’m going to talk about marketing campaign decisions as an example — but the process seems to hold true no matter the context.

Think of the data available to marketing analytics back in the 1960s.  If you’ve watched Mad Men, you know how much gut instinct went into decision making.   In one episode, an analyst gets dismissed by Don Draper as knowing very little and being of almost no help.  Those gurus, who may not have even been able to articulate why they were making decisions, were the trusted leaders.  But, suddenly, relevant data becomes available and people begin to dream what this data could provide.  The first step is reporting on trends.  As the trend reports come out, the business starts to see connections between some trends and business performance.  This then leads to Key unnamed (2)Performance Indicator reports where a metric or set of metrics has gained business acceptance.  At this stage in the analytics evolution of a business, a report that referrals are down this quarter might cause concern that sales will miss their target.  Why are they concerned?  Because there is a belief that the metric is connected with performance.  This is an important and necessary step to move into the next phase.

Business Intelligence or Data Exploration is where you start to really dive into the data and combine it with the knowhow of those who have been doing the work for 20 years — the experts of the company who have an instinctive feel for why one campaign will work and another will fail.  This stage can also be critical to helping the analyst understand and dissect some of the decision making and for the current experts to start trusting the analyst team to understand of the intricacies of the business.  Sometimes this phase is skipped… but in my experience, this can be extremely detrimental to the progression of analytic decision making in a company, resulting in a more adversarial relationship, with analytics fighting traditional business decision makers and the experts they’ve relied on in the past.

The most exciting phase for most analysts is once the business is ready to not just use data to figure out what’s going on now, but to also use it to ask ‘what if?’: predictive modeling.  This is the phase where most statistics majors get excited – where advanced modeling can be created to predict which campaign will perform best, which IT systems are likely to fail, which patient is most likely to develop diabetes in the next year.  As these models continue to be developed and prove their worth, the next phase comes into play: the data is collected real-time and decisions can be made automatically in  response with analysts only monitoring the system.  New data continues to come in, and results in an iterative approach.  As new or more detailed data sources come in, we still go through the phases (Finding Trends, KPIs, Data Extraction, Modeling), but the integration of the new data goes faster.

At each point in these phases, the acceptance of the analytics from the business is an important part of progression to the next level.  This has become easier as other companies and industries have gained public renown for their use of analytics, and provided pressure to follow suit… but still there can be resistance.

unnamed (1)Ownership of the business decision making also follows a phased progression eventually ending with the analytics team taking over ownership of the business decision. Let’s take a brief walk down history lane. Pricing analytics actually first started in the airline industry.  The idea of pricing based on plane fill rate and timing came up and some executive was willing to give it a test — the first phase of the movement towards analytics under ownership of the decision.  Assuming the test is successful — as it most definitely was in the airline business — the business decision maker is willing to pass along more control and suggests developing across all markets or lines of businesses, but isn’t ready to hand over the reins, and wants to stay in the loop as a decision approver.  Eventually, even this approval effort becomes a formality as the analytics increases in sophistication and the third phase is reached where the analyst has the approval and is expected to review all decisions made by the model.  Eventually the speed and quantity of incoming data becomes great enough, and model sophistication becomes strong enough, that no human intervention is required in the business decision, only monitoring to improve the models.

unnamedAs an exception that proves the rule, here’s an example of where algorithms designed on models to set pricing resulted in an out of print book soaring to $2 M dollars before the analysts noted it and modified their pricing model.  Quite fun!  It shows the exciting and somewhat silly decisions that we have to look forward to as more of our day to day decisions become machine and model driven.

I think it also shows the importance of having analysis define reasonable upper and lower bounds for the decisions that come out of their model — it’s the lack of these that cause things like flash crashes in the stock market.

Eventually there will be complete integration of the business decision making and the analytics.  We are already starting to see this in several areas including pricing and digital marketing.  Can you imagine someone being in digital marketing that doesn’t understand page views and referrals and feels comfortable looking at numbers and predicting future impacts? I cannot.  This shows how integrated analytics has become with business decision making in the digital marketing space.  I believe in the next 5 years, this blurring of analytics and business decision making will continue to take over other areas as well.  It’s a great time to be in analytics!

Keynote presentation at SMX

Hi everyone – SMX recorded and published my keynote presentation while I was at SMX.  Please check it out.  THANKS!

SAS conference interview with Jack Chen

Below is the interview that Jack and I gave at SAS.  Thanks for watching.

Guest-Blogging at the 2013 INFORMS Conference on Business Analytics & Operations Research

I’ve been invited to guest-blog during the 2013 INFORMS Conference on Business Analytics & Operations Research in San Antonio this Monday and Tuesday!

During the conference, I (as well as a number of other wonderful people) will be blogging at .  Come and check us out — and if you’re reading this and are going to the conference, drop me a line and let me know!

Conference Presentations

I’ve presented at several conferences recently and wanted to post my slides here in case anyone at the presentations wanted to see them.

Social Media ROI Measurement: The Good, the Bad & the Ugly – eMetrics Confererence 2013, Toronto

The Elusive Brand – How to Measure Brand and the Communications Focused on It – Predictive Analytics World Conference 2013, Toronto

Strategic Case Study: Investment Optimization for Executives using Big Data – Data Day Texas 2013, Austin

If you attended one of the presentations, I’d love to hear what you thought — just comment here or email me!

Does the world revolve around you? Modeling bias of only the data you can control

Psychologists say when things happen to us, good or bad, we often believe it is due to our actions.  We look to our choices and decisions and wonder how we can improve for the future.  We know for a fact that any result has a multitude of factors – of which one’s own actions are a small piece

This self-blame bias seems to exist in the modeling world as well.  In fact, we are taught to use this approach in most business schools that say marketing alone drives sales.   I am often asked by my clients “How can my sub-organization increase revenue?”.  It is all too frequent that they choose approach such as the one pictured where they do a simple model using only economic factors, pricing and marketing spend to predict revenue.  But yet – if you ask that same person what impacts revenue sales or brand value – most people will list a plethora of levers.  Take revenue for example – it’s influenced by pricing, sales headcount, marketing investment, customer service reputation, product offerings etc….  It’s a complete driver set.

Looking at the attached chart, logical people given the information above will conclude that the marketing campaign was a failure because it would assume that marketing dollars alone drive revenue and would not consider the other factors.   Marketing afterall did increase and revenue remained flat despite that increase.  Without a multivariate model, it is impossible to articulate what the impact of Sales reductions and sales efficiency reductions had on revenue –  and whether those losses were in fact mitigated by the marcom spend increases – as only one example…

Considering only the business levers that you control or that are easy to measure can lead you to the wrong conclusions.  When I first joined the pricing team, we considered only our price, our competitors’ prices, and of course unit sales on the product in determining if we should move a price.  The model would recommend price moves and we would approve them.  In one instance, it recommended a price decline based on the fact that unit sales had declined significantly.   The next week, the model again recommended a price reduction as unit sales had declined even more so.   This perked my interest and as I pulled up the product online, I discovered the product was out of stock on backorder.  People are less likely to purchase products on back order so unit sales often decline significantly regardless of the price compared to the market.   Being hyper-focused on pricing as the only driver for model sales had resulted in me pulling on the pricing lever when instead I should have focused on working with our procurement organization to get the product back in stock.  At the very least, stock levels needed to be considered in our pricing recommendation model.

In my work, I’ve built a model that considers not only marketing measurements, but other business levers and how those work together to tell the full story of revenue and/or brand value.  A modeler could just focus on metrics that are easy to gather – like econometrics, but when difficult ones such as product innovation and competitive performance are just as important to include.

Modeling holistically can help you isolate influences from the difficult to capture metric influences such as social media expenditures and brand campaigns.   It also allows you to understand the interrelationship of the differing variables, their relationship with revenue and allows you to isolate the impacts of one business lever (marketing spend) in relation to all the other levers that impact revenue.  Using more advanced neural network modeling can even provide opportunities to include a greater number of business levers and better explain the complexity of what drives revenue.

My team has built a model using advanced neural networks to explain the drivers of revenue and how the input variables interrelate with one output shown below.


When a model is built with more business levers included, instead of a direct regression model, you’ll find that some marketing variables – what I call direct contact variables like direct mail, coupons, email -will have less of an impact than in the direct regression model.    Other variables such as overall brand campaigns, corporate social responsibility investments, and social media metrics value will often show an increase in impact with the more advanced multivariate approach.   Companies that use a direct regression model are often undervaluing their brand campaigns and social media investments in favor of direct mail, email etc – the levers that are easily tied to a sale.

This even allows you to even consider tradeoff decisions in terms of which business levers you should invest in as part of a Financial Strategy conversation.

As modeling infrastructure expands to allow for more variables, models will become more robust and more explanative of all the impacts that drive revenue – resulting in better decisions for businesses!

NPS (Net Promoter Score): You Manage What You Measure

We have a new revolution in brand measurement mentality – making this decade forever known as the Net Promoter Score (NPS) era.  Are there any marketing professionals that haven’t heard of NPS (invented by Fred Reichheld of Bain Consulting)?  If you haven’t, it’s time to get educated:

NPS is a fantastic measurement for any company to use!  It is truly a revolutionary concept, consolidating lengthy brand survey results into a single trackable KPI that is easy to understand and intuitive.  Companies who focus on NPS improve the customer experience in order to enhance customer retention and increase brand reputation.  Because of this, NPS is an attractive metric for executives and helps our business decision makers quickly incorporate business analytics into their decision making.

NPS is Everywhere

One friend of mine told me his company even displayed NPS scores for the employee benefits hotline, separate from the NPS score for HR as a whole.  I see NPS-driven signs at hotels, at restaurants — even on the phone when calling my health insurance, they tell me that “a score of 8 is not good enough and if I can’t give them an 9 or higher, please contact their management so they can attempt to change my mind.”  NPS is absolutely everywhere!

So, if NPS is so wonderful, what’s the problem?  NPS is everywhere on everything, and has become synonymous with brand equity.  When having a conversation with people about brand health or brand reputation, NPS and brand are used interchangeably.  For example, take this question I got last week:  “Can you tell me what I can do to improve my business’s NPS?  I really want to improve the brand reputation this year.”

NPS is a great indicator of brand, but it is not actually brand – it’s simply one way of attempting to measure brand.   It is extremely popular due to its intuitive, straight-forward nature and how easy it is to track – but it is only one indicator of brand reputation and not brand itself.

Other Measurements of Brand

Before NPS and after NPS, there are hundreds of other ways of measuring a company’s brand – including many other types of survey methods that are still used today.  (Particularly noteworthy is that NPS is solely based on existing customers, where other metrics may capture potential customers who still can have a word-of-mouth influence on your brand — despite having never been your customer. These potential customers would be reflected in other brand awareness studies, but not in NPS.)

We can’t leave out the long-famous pricing test, discrete choice conjoint analysis — taking your product and put a competitor’s label on it, asking customers what they would pay for it versus the correctly labeled version.

There are even financial estimates of brand value, such as Top 100 Brands which attempts to quantify a company’s brand equity from financial evaluations.

Lastly, just like survey results, brand equity measurements can be surmised from social media analytics.

Brand Measurements

My point is that valuing a brand is difficult and complicated, and multiple approaches exist.  The multitude of methods may provide differing results at the same time.  No one single method of measuring brand is right — all are attempting to give a company indications of an underlying brand value that is very difficult to measure — and NPS is just one of those indicators.  Understanding what is really happening is where skilled analysts come in, to help business decision makers get a clear picture.

For example, even companies with the exact same NPS score may have widely different brand values by other metrics — in 2008, Amazon and Google had the same NPS score (73), but in Interbrand’s Top 100 Brands for that year Google was ranked 10th and Amazon was way down the list at 58th.

Optimizing NPS is not optimizing Brand

Sales compensation is often an indication of revenue or profit margin numbers, yet sales compensation is not perfectly correlated with revenue or profit margin numbers — as most sales managers know all too well.  Changes in sales compensation plans are managed very closely because individuals can game the system,  using unintended means of maximizing their sales compensation number that don’t result in a matching increase in sales for the company.

This can also be applied to NPS — there are methods of optimizing NPS scores that may not increase the overall brand equity of a company.

When a window installer says, after finishing, “In a few days, you’ll be given a survey and if you can’t give me  9 or 10s on all the questions, would you allow me to change your mind now?” is he really going to make a difference to the company’s brand?  Do you think that hanging up a sign in your hotel or dry cleaners that says “Giving us a 8 on our survey is not good enough. Talk to our management if you can’t give me an 9 or better” is optimizing your brand?  Really?  How is this impacting your brand any more than hanging up a sign that says – “Not fully delighted with your experience? Talk to management so we can help”?  The first sign, which calls out survey numbers specifically, will indeed have a much greater impact on your NPS score — but it won’t impact your brand equity any more than the second sign.

In addition, NPS is more heavily weighted towards customer service metrics than other brand measurements. My research  showed that NPS is more heavily correlated to (and therefore likely influenced by) customer support than the other brand metrics measured.   If you choose to use NPS as your company’s only method of measuring brand equity, it could result in your emphasizing customer service over other factors.  If an upscale jewelry company only measures NPS , it may work to further improve its already fantastic customer service – but may overlook billing issues that are keeping some customers from returning.  Even more so, these billing issues could be relatively easy to mitigate, providing a larger ROI than improving the already great customer service.

Finally, looking only at NPS could also limit you to only considering your existing customer pool –which may be acceptable for some mature businesses that already own a large piece of the market,  but could be extremely limiting for others.

So, the plan to improve brand reputation may be different than the plan to improve NPS.  Don’t get me wrong – it is a good idea to attempt to improve NPS – just remember NPS isn’t the same thing as brand equity. NPS is just one of many ways to measure it.