Big Data Driven Real Time Valuation – The next wave in Performance Management Systems?

A recent study suggests that the value gap between the “stock price based market cap” and the “internal cash flow based market cap” of Fortune 500 companies, are constantly on the rise, especially in the last 50 years. The implication is that some companies are either grossly undervalued (up to 25% points in case of old economy style companies) or overvalued (up to 50-100% points, in case of dotcom/entrepreneurial style companies) - and rightfully so, this value gap, seems to be emerging as one of the top 5 issues, keeping the CEO, senior leaders and investors, on the alert.

Let’s face it - the true stock value of a company, in its essence is the combination of its “future cash flow value” and its linkage to “future expectation building abilities” discounted by today’s cost of capital. While there are quite a few valuation models (e.g. McKinsey's Zen of corporate Finance formula, DCF, NPV, IRR) available, to accurately quantify the cash flow value, there are not many proven models available, to accurately establish the causal chain relationship between the cash flow value and the “expectation building abilities” - and so, stock markets, often follow the tread mill effect of trading on mismatched expectations.

Part of the reason for that behavior is that, the so called “expectation building abilities” are often hidden within the “intangible” competitive advantage (CA) enabling perspectives of the company (i.e. learning/growth, customer, value chain and financial perspectives in BSC terminology) and so, they are not well understood by the street, within its right context. “Expectation building abilities”, in this context include, but not limited to are - the earning guidance, product portfolio pipeline for the next 5 years, share repurchase program schedules/dividend payout, management changes and other big ticket capital expenditures - provided by company executives on an ongoing basis, to better manage street’s expectations, and to boost their overall stock prices.

Trading Value vs. Creating Value

The implication is that street, either over reacts to those “expectation building abilities” in the form of higher stock prices, or in some cases, under react (or some might suggest punish) with lower prices – purely based on the way, it interprets the causal relationship between cash flow value and the “expectation building abilities”. As it turns out, this causality interpretation gap seems to be the single most important factor, that drives many company executives and investors, to get into this game of trading value, as opposed to creating them fresh, says Roger Martin.

What do we mean? From investor’s standpoint – while quantifying cash flow is important in making the near term investment decisions, interpreting the causal relationship accurately, is key for them to bet on a stock for the long haul. Similarly, from company’s leadership standpoint – while quantifying cash flow value is critical for making effective near term operating decisions (i.e. achieving Q-To-Q results), framing the “expectation building abilities” accurately, in the form of earning guidance statements, is very important for them to manage the expectations of their investor community, and to stay focused on their long term strategic choices.

How about Valuation in M&A deal scenarios?

As it turns out, this causality gap between “expectation building abilities” and cash flow value seems to be the biggest hurdle faced by leaders within M&A negotiation scenarios as well – as suggested by a recent study. For example in a M&A scenario, if we had to dissect the value components of a product life cycle of a target firm -

• Invisible value of discovering the unmet “Jobs to be done” value + Invisible idea/vision seed value +invisible incubator value + commercialization value(when there is no alternative product available) + commoditization value (when alternate products are available with heavy competition)
  

If we look at this equation, interestingly enough, they map 1:1 to the five value stations of the PTV framework picture above. Yet another interesting insight here is that- it reiterates our earlier point that stock prices are often decided based on the commercialization and commoditization vale components (i.e. financial value station), partly because, street does not have visibility to the unrealized invisible values of the early product life cycles, the primary driver behind those expectation building abilities.

Transparency without compromising the Integrity of Insider information
The follow-up question is how can we provide that end-to-end visibility, without compromising the integrity of the CA enabling insider information? While there is never a silver bullet answer for this type of questions, one of the best answers in our opinion, is to mine those “expectation building abilities, proactively from the tons of data that are often hidden within and outside the four walls of the companies, and integrate them within the performance management systems. Yes, you guessed it correct - that the emerging buzz word for that approach is “Big data”.

As it turns out, even company insides, often do not have visibility to all of their own company data, leave alone the external social media data, and so, the better way to frame the question is – how can this emerging big data concept, help us to establish the causal relationship between cash flow and “expectation building abilities” and provide that end-to-end visibility, without compromising the integrity of the CA providing insider information?

Big data – the panacea for establishing the causality between cash flow and “expectation building abilities”?
Let us face it - the key benefit of any analytics platform (leave alone the Big-data driven analytics platform), is to help make effective decisions with timely and accurate insights. With that definition, if we had to dissect the service components of a Big data analytics platform, it is all about delivering value in the form of “insights” using a set of “analytics services” (as the delivery vehicle), with a faster service delivery time (i.e. latency) than the traditional analytics platform. In other words, the three key service components of big data are:

• Matter or cost in dollars that are needed to create and deliver those insights


• Acceptable Time or latency involved in delivering those insights


• Space or size of the data of the data needed- which is often why the word big is put in front of data.

Big data classified using CLS index
Now that we have defined Big data with the building blocks of matter, time and space – it makes sense to augment our firm’s Experience Pool Portfolio (EPP) framework with some of the value-add features of Big data, to accurately establish the causal relationship between the cash flow value and the “expectation building abilities” that are being hidden within the large amounts of Big data. Speaking of intangibles being buried within big data, the next question is – what is preventing us from quantifying those intangible values within the big data – is it a cost (matter), latency (time) or storage issue (size)?

The answer in our opinion is not using the right set of data sources for the right type of analysis, and so, as a first step, we suggest to classify “Big data” with a new index called CLS index (Cost, Latency and Size), as shown in the chart below.

Depending upon which quadrant the data falls, we see five buckets of big data categories evolving.

• Transaction data producing the value for financial station (structured data with a low CLS mix index)


• P&S Purchase context data producing the value within the value chain station (structured data with a medium CLS mix index)


• P&S Purchase influencer data producing value within Learning/Growth station (structured data similar to IRI/Nielsen data with above average CLS mix index)


• Customer needs & want based data or” jobs to be done” data, producing value within Customer station. (combination of structured and unstructured data with high CLS mix index similar to social media/survey data in the form of opinions, likes etc)


• Purpose or Motivational driver’s data producing value within the central purpose station. (some combination of above four categories, helping us to answer the key purpose drivers question).
  

Big Data comes to life within PTV radial framework
Interestingly enough – this five types of big data map 1:1 to the five value stations of PTV - suggesting the need for four additional virtual exchanges on top of the wall street exchange ( i.e. primarily financial perspective driven)– and link them all together with causality - as outlined in the picture above and explained in detail in our firm’s Purpose driven strategic planning framework (http://www.managementexchange.com/story/strategic-planning-purpose-driven-way-using-nature%E2%80%99s-seedal-chain-principle)

• Job to be done or customer equity capital exchange, based on the formula -> Consumer/customer Value=Jobs-to-be done/Price



• Human capital equity exchange, based on human capital learning/growth, per one of my recent hack at MIX site (http://www.managementexchange.com/hack/reforming-performance-management-systems-%E2%80%93-virtual-purpose-equity-vizpity%C2%A9-exchange-way).
  


• Value chain equity exchange,based on the formula ->ROIC= Margin x Velocity



• Financial equity exchange, which is today’s’ street version based on the formula ->Share holder value= Profit x (1-g/ROIC)/ (WACC-g) - as outlined in our firm’s valuation driven PTV radial framework below.



• Purpose equity capital exchange ,based on our hypothesized formula -> Purpose value= Profit x (1-g/ROIC)/ (WACP*-g) where WACP=Weighted Average Cost of Purpose Capital
  

Providing this type of valuation visibility in these five dimensions, not only will help us to accurately establish the causal relationships, but also, help us to make the right call in M/A deal situations, as we had explained in one of our CPM articles (http://theacademyofbusinessstrategy-businessanalytics.com/2010/05/15/03/)

Conclusion with Implications

While the solution we have proposed is a 18 month solution, let me conclude with some immediate pragmatic next steps, that can help companies to lay the foundation for implementing this Big data driven performance management systems. First and foremost, companies must identify their causal/correlation chain relationships between “cash flow value" and the corresponding “expectation building abilities” impacting their overall stock prices. Some of the practical steps include, but not limited to are -

1. Identify the heavy hitters (top 50-100 investors) who have the “needle moving power” to influence value (and thus stock prices) within this causal/correlation chain relationship using a CPM framework like our PTV framework, as shown in the picture above.



2. Proactively manage the expectations of family owned and hedge/pension fund investors and document what makes them to tick.



3. Establish the causal chain of how they have reacted to the earning guidance and macroeconomic news/factors in the past, and then create a behavioral pattern map for their behaviors.



4. Depending upon those patterns, manage their expectations by releasing right type of insider information to media (and the analyst/investor community) in the right time, with a news staging mindset.



5. When company’s stock price changes, ask why the market moved and zero-in who bought, who sold, and why - with an empathetic mindset i.e. look at those investors with a lens of “alter-ego managers” or “indirect corporate owners”. In other words, learn to empathize with investors with various “what if scenario” options, within the context of the five perspectives of PTV framework.



6. Overhaul investor relations department and make them to actively manage the five big data types, by making them as joint data stewards, especially when it comes to regulatory data and annual reports.



7. Administer the big data registry with a causal map of shareholders and investor road shows, visits by analysts, and conferences; and major presentations to shareholders.



8. Integrate investor relationship management, part and parcel of strategic planning. Together make them responsible for managing the key-account processes to identify movers and understand their behavior. This way one can test all major plans, hypothesis and announcements, for their effect on the price of the company’s shares with various “what if scenario” options, and then suggest modifications to those earning guidance content, to better align with the views of key shareholders, thus becoming the key advisory arm of the CEO and senior leaders/board of directors.



9. Make investor relation leaders (in partnership with Corporate Strategy and Finance) as the people who co-own this big data driven valuation system and make them to proactively deliver the bad news when necessary. They will also have to be experts with “thinking on the feet” type communication skills, capable of handling tough interviews with investors who at times might be pressing them for information that cannot be divulged under SEC regulations or for maintaining CA integrity.




10. In closing, this type of big data driven integrated approach (i.e. structured and unstructured data driven approach) to strategic planning, Innovation, performance management, investor relations/valuation/stock price management and risk management, clearly require top notch talent, including the time and attention of senior management - and failing short on any of those commitments, would definitely leave the CEO, senior leaders and board of directors, in the constant game of never ending stock price guessing. It is a no brainer that no CEO and/or senior leaders/ board of directors, would ever want to be in that type of a guessing game, and so, the million dollar next step question is “What is in your docket ?” – and let that be our last word!