Small data > Big data

Posted on September 3, 2012 by Rod Feuer

As ‘big data’ gets more and more attention with its fancy algorithms and predictive analytics tools, I can’t help but think that most problems companies face are really ‘small data’ problems.  These problems arise when management does not actively measure and manage the fundamental value drivers of their business.  At a high-level, these value drivers are the components of the customer lifetime value (LTV) formula, such as revenue, cost to serve, retention rate and customer acquisition costs.  But to really have an impact on these value drivers, managers must focus on their actionable sub-components — the drivers of the drivers, so to speak — or what I like to call ‘small data’.

Actionable Small Data: An Example

One company that I once worked with managed a chain of ~30 physical therapy clinics. To better understand the drivers of revenue, I disaggregated each clinic’s revenue into the following ‘small data’ sub-components:

# of patients * # of visits per patient * frequency of visits * # of procedures per visit * $ reimbursement per set of procedures

Similarly, I disaggregated their cost structure:

# of physical therapists (PT) * hourly labor cost per PT * labor utilization (# of labor hours worked / # of labor hours spent with patients) + # of square feet * ($ rent per sq ft + other overhead per sq ft)

‘Small data’ metrics aren’t just related to the income statement — the balance sheet also matters.  So, I broke down the cash collection process into its different components, which included:

Average time taken to process insurance claims and percentage of claims denied for addressable reasons (e.g., legitimate claim that was improperly coded)

The power of measuring these pieces of ‘small data’ was huge.  When we benchmarked average revenue reimbursement data by clinic, we identified insurance contracts that needed to be renegotiated for more favorable terms and determined which clinics could improve their billing procedures for higher reimbursement rates.  We also identified best practices for collecting insurance information upfront which dramatically reduced the cash collection cycle (often greater than 120 days).  Most importantly, we were able to start using ‘small data’ to better manage the clinics by giving them actionable metrics to which their employees were held accountable.

Why ‘Small Data’ Problems Exist

The good news is that ‘small data’ problems do not require sophisticated algorithms or an army of data scientists.  What they do require is: 1) identification, 2) systematic measurement, and 3) maniacal focus with supporting incentives. Sounds pretty basic… so why don’t most businesses manage ‘small data’ effectively?

1) Identification: The absence of the first requirement is a failure of modern business education. Writing down the profit equation — and it’s ‘small data’ sub-components — for your business model should be the first thing taught in a graduate or undergraduate business school curriculum (sadly, it’s not).  The Lean Start-Up movement is beginning to address this problem with its focus on actionable metrics… and at a fraction of the cost. The good news is that it’s a relatively straightforward exercise to write down the profit equation and its ‘small data’ sub-components for your business.

Recommendation: Start by giving yourself and your managers the homework assignment of writing down the ‘small data’ that drive value for their functions / business units.  Then work with then to identify which drivers have the most impact on the business and make sure they focus on them.

2) Systematic measurement: The blame for the absence of the second requirement should be placed squarely on three parties: 1) the Financial Accounting Standards Board (FASB) which promote GAAP accounting, and 2) the creators of general ledger software, which is used to present GAAP financials, and 3) investors who use GAAP financials to value companies. If general managers don’t (or shouldn’t) use GAAP financials to manage their business, then why is a whole ecosystem of regulators, software vendors and investors so focused on them? Putting my soap box aside, ‘small data’ usually exists in various tools and databases that can be aggregated to calculate your actionable metrics. It may take some time (and money) to get the data to talk to each other, but it’s probably there.  If not, you’d be surprised how well manual data collection in Excel can work just to get things started (though not for the long-term).

Hypothesis: There is a shit ton of money to be made for an entrepreneur to make financial software that tracks ‘small data’ for companies and translates them into GAAP financials (vs. the other way around). (Note: If you are a programmer who is interested in starting such a business, let me know!)

3) Maniacal focus: If a business doesn’t have the knowledge or measurement tools, then it’s unlikely that its managers will be able to incentivize employees using these ‘small data’ metrics.  In the absence of this information, managers will instead rely on heuristics and intuition, which often leads to mediocre outcomes.  As mentioned above, however, the process of identifying your actionable metrics is just a homework assignment away and many of them can be tracked with the most basic of tools.  Once small data is linked to performance assessment, it’s amazing how quickly behavior changes.  It’s not that your employees are bad at their jobs, it’s just that they’ve never known where to focus their time and energy.

Coming Full-Cirlce

The problems ‘big data’ are trying to solve are directly tied to ‘small data’ problems.  Algorithms are often used to improve conversion rates and reduce attrition, both of which are ‘small data’ metrics linked to the LTV formula. But businesses must first learn to crawl (and walk) before running.  So start by identifying, systematically measuring and maniacally focusing on small data — the fancy algorithms will still be there when you eventually need them.


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No positioning statement = No chance of winning

Posted on April 1, 2012 by Rod Feuer

How to clearly articulate your value prop

I dare you to throw the following pop quiz at your next management meeting: Can you clearly articulate your company’s positioning statement?

For (target customer) who wants / needs (statement of need or opportunity), our product is a (category of product) that provides (key benefit).  Unlike our (main competitor), our product does (statement of primary differentiator) because of (proof points the key benefit can be delivered).

If your executive team cannot fill in the blanks above — or if everyone responds with a different answer — there are three possible problem scenarios:

1) Your company has one, but you don’t know what it is
2) Your company has one but it’s muddled
3) Your company just doesn’t have one

If any of the above scenarios are true, then you should all go directly to jail (Monopoly-style… Do not pass Go. Do not collect $200).  Why?  Because a well-articulated positioning statement is a clear expression of a company’s raison d’etre: its value proposition.  If your company does not have a good understanding of what problem it’s solving for customers — and why it’s better than the competition — then it probably doesn’t have a great chance of succeeding long-term.

Scenario #1: You don’t know your company’s positioning statement

If #1 is true, then you suck at your job.  Ok, maybe that’s a bit harsh.  You probably suck at your job.  If you’re in sales, you probably have a hard time closing deals.  If you’re in marketing, you probably churn out meaningless drivel.  If you’re in senior management, then you should definitely pray that your CEO doesn’t like pop quizzes.  The good news: since your company has one, then all you need to do is learn and internalize it to the point where it rolls off your tongue without thinking.

I once worked for a CEO who started each company-wide meeting by making everyone recite our short-form positioning statement — and it was also prominently displayed on our website.  It should come as no surprise that I still remember it word-for-word.  However, we only got the first half of the statement right — the key benefit.  We never really nailed the second half of our positioning statement — why we were better than the competition — and I would argue it’s because we simply did not have a clear competitive advantage at the time.

This is power of the positioning statement: it exposes emperors who aren’t wearing clothes (and who don’t have a credible value prop).

Scenario #2 or #3: Your company has a muddled positioning statement — or doesn’t have one at all

If #2 or #3 is true, then your company probably doesn’t have a sustainable competitive advantage.  Why probably?  Because sometimes a company can be successful — in the short-term — even though it cannot clearly articulate its product’s benefits.  A good example of this is when a company’s competitive advantage is driven by its brand or reputation.  However, in the long-term, a real benefit must exist for customers that is easy to express and difficult to imitate.

Key elements of a good positioning statement

It’s not enough just to have a positioning statement — you need a good one.  Otherwise, the working assumption should be that your company has not yet figured out what it’s better at providing customers than the competition.  Here are some key attributes of a quality, defensible statement:

  • Unique: it truly differentiates your company from competitors
  • Narrow: the statement’s scope is appropriately limited
  • Believable: key benefits and differentiators will pass your customers’ sniff test
  • Durable: it will stand the test of time
  • Memorable: customers will remember it and be motivated by it
  • Consistent: it reflects your company’s overall brand identity

The process is just as important as the statement itself

Creating a positioning statement is not easy, because creating a sustainable competitive advantage is not easy.  The process of crafting one requires an intimate understanding of your customers and their unmet needs — known or unknown.  It requires a rigorous assessment of your company’s core competencies and the value they create for your customers.  And it requires an honest and respectful assessment of your competitors’ strengths and market positioning.  But by going through this difficult process, you and your organization will emerge more focused, and ultimately more successful as a result.


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Customers: They’re not all created equal(ly profitable)

Posted on February 26, 2012 by Rod Feuer

Acquiring more customers is great for business — most people would find it hard to argue with this. But we should be more precise: acquiring more customers is great for increasing revenue. For any new venture, this is undoubtedly an important step toward validating your product-market fit and covering some of your expenses. But to remain a viable business, you will eventually need to understand which types of customers contribute long-term enterprise value.

Lifetime Value of a Customer (LTV)

A useful framework for determining the the value of your customers is LTV (see below):

When looked at though this lens, the profitability of each customer not only becomes measurable — it also becomes more actionable.  Let’s start with the latter.  Once the LTV has been calculated for different customer segments (see prior post on segmentation), a business can begin asking the following questions to increase enterprise value:

  1. Which customers contribute the highest gross profit?  What factors are driving this profit (e.g., frequency of purchase, value of purchase, or product margin)?  How can we find more of these high-value customers?
  2. Which customers cost the most to serve?  For lower value customers — those who contribute a low gross profit and have a high cost to serve — how can the offer mix be adjusted to make a higher profit (e.g., pricing, customer service tiers)?
  3. What tactics can be used to increase customer retention — particularly for the highest value customers (e.g., loyalty incentives)?  What is causing people to churn?
  4. How can acquisition costs be reduced (e.g., more effective marketing mix, more targeted sales prospecting)?

Now… the harder part of this process is actually calculating LTV.  There are two prerequisites: 1) accurate data and 2) a business analyst who can clean the data and crunch the numbers.  Getting accurate data is often the most difficult part, since it’s usually located in different databases that don’t talk to each other.  If you’re a business that is just getting off the ground, then you should invest early in creating data elements that clearly map to the above equation.  It will help you understand your business in the short-term and save you from undertaking a massive database project in the long-term!


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What’s the best cost reduction strategy? Start with opportunity cost

Posted on August 5, 2011 by Rod Feuer

Recently, the news has been filled with stories of corporate spin-offs, break-ups and divestitures: ConocoPhillips, Fortune Brands and now Kraft, just to name a few. The rationale for these split-ups is typically framed as “unlocking shareholder value” — however, this is just a polite way of saying that these businesses have little, if any, strategic overlap and that they are a total pain in the ass to manage under one corporate structure. Put simply:

Opportunity cost reduction is often the biggest source of value creation.

So why should any of this matter for YOUR business, which is likely not a multi-national behemoth? Because opportunity cost exists everywhere in your business, and it is eating away at your profits even though you can’t see it directly on your P&L. So, if you can’t see them, how do you know if they exist? Here are my “Top 10″ indicators that opportunity costs are weighing you down:

1. You can’t articulate your business’s value proposition in one clear sentence.

2. The customers, costs, competitors and capabilities required for different products / services don’t have much overlap.

3. You have to continually remind people — often senior management — what your business does and how it works. (Note: that probably means you’re in a non-core business.)

4. Management can’t agree on how to allocate capital — because there isn’t clear alignment on which strategy is best.

5. There are few opportunities for leveraging common marketing campaigns — because the target customers, messaging and channels are different.

6. You ask 10 different people what your company does and what you do best and you get 10 different answers.

7. One of your teams / businesses always feels like the “red-headed stepchild”.

8. Individuals are in roles that they’re not suited for (e.g., the controller is also handling HR, the ops person is handling pricing, etc.)

9. Your R&D team isn’t innovating as quickly as your competitors, because they’re not focused enough.

10. Activist investors purchase shares in your company :)

The question you should always be asking yourself is this: What is the first best use of my company’s resources? If you find that your company is engaging in work that does not align with this “first best use”, then there is probably an opportunity cost waiting to be reduced.


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The only five questions that matter in business

Posted on May 1, 2011 by Rod Feuer

Lots of people have “great” ideas for new businesses.  These ideas are often phrased like this:

Wouldn’t it be great / cool /awesome if you could do X?  Let’s go start a company that will do X, and we will make lots of money.

Uh, no.  No you won’t.  Except for Kevin Costner in Field of Dreams, the “if you build it, they will come” model rarely works (e.g., the Segway, Coke C2, Iridium satellite phone, etc).  So, if you find that you or one of your close friends is uttering the above, please step back and ask the following five key questions (also known as the “What you have to believe” questions from a prior post):

  1. What problem are you solving for customers?
  2. How large is the addressable market for this problem?
  3. Why is your solution better than the next best alternative?
  4. How much will people be willing to pay for your solution?
  5. Can your business model make a profit based on answers to the above?

That’s it.  You don’t need an MBA for this — trust me, I have one — you just need common sense, ‘cojones‘, and a good idea (which is usually the hardest part).

Whenever I discuss VC due diligences on biomedical start-ups with my good friend — Dr. Kevin McGarvey, Founder of BioStarter Consulting in Boulder, CO — I always start with these five questions.  If we can answer those questions with clarity in the first conversation, then it is an early indicator that the company is worth a deeper look.

Warning: your friends / colleagues may not like you very much for asking these questions. Asking these questions can sometimes make you sound like a “Debbie Downer”, because you could be perceived as pissing on their dreams.  You will probably hear things like “You just don’t get it” or “Whatever”.  So be sure to frame your questions carefully and ask with a respectful tone.  Sometimes the right idea is there, but there needs to be a few “pivots” (apologies for using the most overused word in 2011) before the right product-market fit is achieved.


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Your customer data is free… so start using it

Posted on April 26, 2011 by Rod Feuer

I recently viewed a great presentation on data-driven start-ups by David Cancel of Performable.  The point of the preso was that capturing and using three types of data — operational KPIs, conversion funnel and customer cohort data — is critical to the success of start-ups (or any business for that matter).  It was also hilarious, so take a moment to check it out.

David’s presentation got me thinking more about data, how it’s often not collected, and when it is collected how it’s not used as much as it should be. In fact, if your business has been successful thus far without using much data, two things are probably true:

  1. You might know WHO your customers are, but not WHY they’re your customers
  2. You’re eventually going to have a CHURN problem that you won’t know how to address

In the first case, you’re leaving money on the table; in the second, you’re leaving your business at risk.  So let’s take a closer look at some tools that can help you address #1 (customer segmentation) and #2 (churn models).

Customer Segmentation

It should be no surprise that all customers are not created equal — the “80/20″ rule typically applies.  However, two important questions are commonly overlooked by this basic analysis:

  1. WHY are some customers worth more than others?
  2. WHAT can customers tell us about our product that we don’t already know?

Customer segmentation is a great tool for answering #1, and it doesn’t necessarily require knowledge of fancy statistical models.  A more simple — but not overly simplistic approach — is to identify a few “highly leveraged” customer classifications (e.g., ways to slice and dice your customer dataset) based on product usage, for example.  You can “mine the data” to develop customer groupings that display similar usage patterns.  We recently did this for our own customer base and found four distinct customer buckets — each of which had much different usage profiles and much different customer lifetime values (think higher revenue, lower churn).  The data also showed us HOW each customer bucket uses our product, which helped us with new product development.  It was free customer research!!!

Churn models

Even the most satisfied customers churn eventually.  And some customers churn quite quickly.  The trick is to know, in advance, which customer is going to be which.  As long as you have a large enough dataset, I highly recommend using a statistical approach such as logistic regression (or similar models) to identify which customer attributes (e.g., tenure, product usage, credit score, # of emails sent, etc.) are strong predictors of churn.  Once they’re identified, you can figure out how much churn is due to your product vs. non-core customer.  It will also enable you to set up alerts in your back-end and forecast your P&L more accurately.  I know it sounds complicated, but I promise that a few online searches and a basic statistical package later, you’ll have this all figured out.  If not, find a statistics intern at a local college / university :)

If you don’t have a large dataset or don’t want to deal with math, then customer surveys can also help you identify key predictors.  Your sales and marketing team (and investors) will be much happier, if you’re not just filing in the ditch that your unsatisfied customers are leaving behind.  Reducing churn by even just 1pp goes a long, long way — so it’s worth spending time to figure this out.

The beauty of using existing data for customer segmentation and churn is that this information is FREE and EASY TO GET, which are two great things when you’re strapped for cash.  Ignore it at your own risk!


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What are the right KPIs? (Hint: they’re not on your P&L)

Posted on February 19, 2011 by Rod Feuer

I used to work for a private equity firm that invested primarily in services-based businesses.  This PE firm differentiated itself in the marketplace through its ability to add value through strategic and operational improvements vs. pure financial engineering.

Despite the claim that their principals were experienced operating professionals, if you looked under the hood it was clear that they were still more financial-engineering driven than operationally driven.  Why do I say that?  Because most of their KPIs were financial: revenue, gross margin, EBITDA, etc.  Moreover, most of their monthly ops meetings were all about line items on the P&L and balance sheet.

So why are financial metrics not the best KPIs?  Because they can only provide a high-level perspective as to whether the collective set of a business’s strategic and operational activities are creating economic value. I have some ground on which to stand here, as I used to be an equity research analyst and have earned the right to use the CFA designation.  (In other words, I used to analyze financial statements for a living.)  It wasn’t until I worked as a management consultant — and now as a general manager — that I learned how to create value and monitor its creation through KPIs.  (Note: sadly, two years at a top business school did not do this.)

So what ARE the right KPIs?  Naturally, this is going to depend on the type of business you’re running, but the short answer is they are metrics that measure the primary activities underlying each element of the almighty P&L.  There are different levels of KPIs for each P&L line item.  Generally speaking, the more activities that drive a line item, the more KPI levels are required.

Let’s start with revenue.  The first-level KPIs start simply as:

# of customers x Avg. revenue per customer

No kidding, right?  The second level requires a deeper view of your customer base:

(# of customer-type A x Avg. revenue per customer-type A) + (# of customer-type B x Avg. revenue per customer-type B)

Now we’re getting somewhere.  In most businesses, 20% of the customers generate 80% of the revenue.  Hence the 80/20 rule.  But until you actually do the work to identify which customer-type is most valuable, then you won’t know where to focus your limited resources.  So what’s the next level?  The driver of # of customers: sales and marketing.  For businesses with an inside sales team it’s all about the number of new customers, which is determined by:

# of prospects contacted x # of ‘wins’ per prospect contacted

No kidding (again).  But have you asked yourself what the ‘right’ # of new customers is?  And, if this number is too low, is it because your sales team is not productive enough — or is it because your win rate is too low?

You can begin to figure out productivity by looking at # of calls per salesperson per week.  If you see a wide variation, you should probably take some time to understand why that is.  If people who make fewer calls have higher win rates, then that’s ok.  But if there are salespeople with low call volume and low win rates, then you’ve got your answer.

But what if your win rate is below target… and you’re confident that you’re targeting the right type?  Time to dig into a deeper lever of the ‘win rate’ KPI:

# of calls per week x # of conversations per call (% contact rate) x # of proposals per conversation (% proposal rate) x # of wins per proposal (% close rate)

Is the breakdown consistently due to a low proposal rate?  If so, there might be an issue with your overall value proposition or just the sales lyrics themselves.  Or is it because of a low close rate?  Maybe your pricing is not competitive or your actual product / service didn’t live up to expectations when presented in more detail.  This would obviously be a more fundamental problem — beyond salesforce effectiveness — and needs immediate attention.

While the above KPI examples may look obvious, the reality is that most businesses do not systematically measure any of them well, if at all.  Their decisions are based mainly on intuition and anecdotal information.  While this may work from time-to-time, as the above examples show, there can be several reasons for a below target # of new customers.  Financial statements may show that revenue is not meeting targets, but unless you have the right KPIs in place, it will be difficult to know which problem needs solving.

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P&L Profit vs. Economic Profit: A critical distinction

Posted on February 6, 2011 by Rod Feuer

I’ve heard the following question time and again from companies that have multiple business units:

We’re making a profit, so why shouldn’t we keep investing in this business?

The answer is simple: because this business is not making an economic profit.

Now I know what you’re thinking: “economic profit” sounds like a purely academic concept this isn’t relevant in the real world — if your revenues are greater than your costs, then you’re making a profit.

This line of thinking is completely understandable. The IRS does not tax your business based on its economic profit; GAAP does not require your business to report economic profit on your financial statements; investors value your business based on multiples of EBITDA not economic profit.

So, why then, should ANYONE care about economic profit? Because if you’re not making an economic profit, you could be making even more P&L profit by using your time and money more efficiently.

Economic profit is commonly defined as:

Economic profit = P&L profit – Opportunity cost

But there is one problem with using economic profit to help manage your businesses: it can be difficult to calculate — particularly opportunity cost. In its simplest form, opportunity cost is defined as: the cost passing up the next best investment choice. So now what?

There is one relatively easy way to determine if a business is earning an economic profit — albeit indirectly: calculate P&L profit per employee (PPE) and compare it to similar businesses.

If there is a significant difference between two similar businesses — or two different businesses in your company — then you should consider whether the resources in the lower PPE business should be reallocated to achieve a higher return.

The importance of this analysis cannot be understated — in fact, it should lead you down the path to an even more important discussion: what is your “core” business, the one in which you have the most compelling customer value proposition and most defensible competitive advantage?

Higher profit per head can be a strong indication that this is the core business in which you should be investing more of your time and money. Even at the expense of another business that makes a “P&L” profit.

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Analytics: The Foundation of Value Creation

Posted on January 23, 2011 by Rod Feuer

It is telling that I begin my inaugural post by asserting that analytics is the foundation of value creation. So what exactly is analytics?  In my own words:

Analytics is asking the right hypothesis-driven questions, that when answered with the right data, generate value-added insights that lead to better strategy and execution, thereby increasing a firm’s economic profit.

Not surprisingly, the first right question is: what are the right questions? Here are a couple that generally yield productive insights:

1) What do you have to believe for ‘X’ to happen? This is the hallmark question for strategists, and it forces you to enumerate the assumptions that must be true so that your firm can actually make ‘X’ happen.  Here is an example list of high-level assumptions for a “what you have to believe”, where ‘X’ is doubling a Firm B’s profit in three years:

  • Industry A is $1,000M in size
  • Industry A will grow 10% per year for the next three years
  • Firm B will increase its market share from 10% to 15%
  • Firm B will maintain its current profit margin of 10%

If all of these assumptions are supported by primary and secondary data, then Firm B should indeed double its profit.  If not, then the profit target needs to be adjusted. The chart below provides a useful visualization:

2) What is the value of doing ‘Y’, and how easy will ‘Y’ be to implement? This question leads to the valuable exercise of prioritizing strategic initiatives before making an investment. For example, let’s say there are three initiatives a firm is considering (see chart below). Initiative C is clearly the least attractive, since it has the lowest value and would be difficult to implement. Although Initiative B is worth the most, it would be even more difficult to implement (for example, it might take twice as long to complete or tie up all of a key department’s resources). Therefore, Initiative A would most likely be the preferred investment.

2 x 2 Prioritization Chart


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