“IA not AI” in Retail Commerce – Enhanced Tanpin Kanri

Highlights: Enhanced Tanpin Kanri is a specific example of Intelligence Augmentation. Store staff's local knowledge and engagement with their shoppers cannot be replaced; but Big Data & Analytics can provide a significant leg-up in their difficult job of hypothesis-generation by providing data-driven predictions that they can safely rely on and improve incrementally. In a highly competitive low-margin business such as fast moving consumer goods retail, pioneering use of IA in their operations will determine the winners.

Dr. PG Madhavan is the Founder and Chairman of Syzen Analytics, Inc. He developed his expertise in analytics as an EECS Professor, Computational Neuroscience researcher, Bell Labs MTS, Microsoft Architect and startup CEO. He has been involved in four startups with two as Founder. He has over 100 publications & platform presentations to Sales, Marketing, Product, Industry Standards and Research groups and 12 issued US patents. He conceived and leads the development of SYSTEMS Analytics and is continually engaged hands-on in the development of advanced Analytics algorithms.

More at www.linkedin.com/in/pgmad

Artificial Intelligence or “AI” is the technology of the future; it has been so for the past 50 years . . . and it continues to be today! Intelligence Augmentation or “IA” has been around for as long. IA as a paradigm for value creation by computers was demonstrated by Douglas Engelbart during his 1968 “Mother of all Demos” (and in his 1962 report, “Augmenting Human Intellect: A Conceptual Framework”). While Engelbart’s working demo had to do with the mouse, networking, hypertext, etc. (way before their day-to-day use), IA has increased in scope massively in the last 5 plus years. Now, Big Data & Analytics can truly augment human intelligence beyond anything Engelbart could have imagined.

IA is contrasted with Artificial Intelligence which in its early days was the realm of theorem proving, chess playing, expert systems and neural networks. AI has made large strides but its full scope is yet to be realized. IA on the other hand can be put to significant use and benefit today. Such is the story of Enhanced Tanpin Kanri in retail commerce.

As a keen observer of the retail ecosystem’s demand chain portion for the past 3 years or so, I am struck by the inefficiencies in large portions of Retail. Shoppers finding what they want on the shelf is considered a BIG problem in the industry (the so called “OOS problem” or Out-Of-Stock problem) – to the tune of $170 Billion per year! The following diagram illustrates the portion of Retail ecosystem on which we will focus in this blog.

The left half represents the current dominant model. “Push” model drives manufacturer’s FMCG (fast moving consumer good from “brands” such as Procter & Gamble, Kraft, Unilever and others) into the supply chain with store shelves as the final destination. As retailers move to the right half of the picture, they tend to be more agile and mature in their practices and are seeking a competitive edge through differentiation – they find that by focusing their store operations to satisfy the LOCAL customer via making available what she prefers on the store shelves, they can win. The poster-child of this revolution is 7-Eleven, the ubiquitous store at virtually every street corner around the globe!

7-Eleven’s use of the “Pull” model since early 2000’s has been very successful as captured in a case study at Harvard Business School in 2011 (HBS Case Study: 9-506-002 REV: FEBRUARY 23, 2011). Quoting from this study, “Toshifumi Suzuki, Chairman and CEO of Seven and I Holdings Co., was widely credited as the mastermind behind Seven-Eleven Japan’s rise” and goes on to say that ‘Suzuki’s emphasis on fresh merchandise, innovative inventory management techniques, and numerous technological improvements guided Seven-Eleven Japan’s rapid growth. At the core of these lay Tanpin Kanri, Suzuki’s signature management framework’.

Proof is in the pudding – “Tanpin Kanri has yielded merchandising decisions that has decreased inventory levels, while increasing margins and daily store sales” since the 2000’s, the HBS case study points out. So what exactly is Tanpin Kanri?

Tanpin Kanri or "management by single product," is an approach to merchandising pioneered by 7-Eleven in Japan that considers demand on a store-by-store and product-by-product basis. Essentially, it empowers store-level retail clerks to tweak suggested assortments and order quantities based on their own educated hypotheses . . . 

You can tell that Tanpin Kanri lies well to the right in the Retail Ecosystem diagram above. To call out some features:

·       PULL model

·       For a buyers’ market

·       Focus on How to satisfy customer

·       Item planning and supply driven by retailer and customer

·       Symbolic of Consumer Initiative

I believe that it is simply a matter of time before Tanpin Kanri variants dominate the Retail demand chain model. As shoppers clamor even more for their preferences to be made available, Retailers will evolve incrementally.

So, where does the PULL Model provide most bang for the buck today?

Where ever Product Density (number of products to be stocked per unit area) is high, “customer pull” will help prioritize what products to stock. Today’s Tanpin Kanri at 7-Eleven Japan accomplishes “customer pull” incorporation via super-diligent store staff manually making the choices.

Here are the “hypothesis testing” steps that 7-Eleven store staff goes through in operationalizing Tanpin Kanri. Based on frequent interactions and personal relationships with the shoppers at a store, the staff generates hypotheses of the shoppers’ needs, wants and dislikes. Based on such information, store staff formulates hypotheses of what to carry (or not) on their store shelves (“merchandising”). Sales during the following days and weeks allow them to ascertain if their hypotheses should be rejected or not; this continuous iteration goes on over time to “track” shopper preferences. Clearly, Tanpin Kanri methodology has been highly successful for 7-Eleven according to HBS case study.

IA based on Big Data & Analytics can play a major role in Tanpin Kanri. In any scientific methodology, coming up with meaningful hypotheses is the HARD part! In Tanpin Kanri case, it is the personal relationships, diligence and intelligence of the store staff that help generate the hypotheses. This is the super-important human value-add that no AI can fully replace! However, we can AUGMENT the hypothesis-driven Tanpin Kanri with Data-Driven precursor that enhances staff intelligence by providing them with predictions that they can build on to formulate their hypotheses.

IA happens in the data-driven precursor step. 7-Eleven has vast amounts of transaction and customer data in their data warehouse. They can be “data-mined” to find shopper preferences at a particular store which can form the basis of what to carry and how much on the store shelf. The data-driven predictions then become the “foundation” on which the store staff adds their own “deltas” based on the shopper quirks that they have surmised through their all-important personal relationships.

Syzen Analytics, Inc. has accomplished IA integration using Machine Learning and a new development in Analytics called “SystemsAnalytics”. A dumb “prediction” for SKU shares is same sales as last year (see the multi-colored bar chart in the middle of the picture below) – in other words, historical sales is the “information-neutral” prediction. But surely, we can do better than that with Systems Analytics.

Syzen is able to provide SKU-by-SKU, store-by-store and week-by-week predictions using typical T-Log data that every Retailer has in its data archives. A typical prediction of Syzen’s ROG-0 SaaS product for a typical SKU at a particular store looks like this.

·       The purple uneven “picket fence” is the weekly predictions – lowest bar chart. This is obtained by combining different “masks”.

·       The papaya-colored mask is the new and most significant one. The values are predicted based on appropriate past intervals of T-Log data digested via Systems Analytics and updated adaptively.

·       The numbered masks in the middle accounts for things that the store manager knows that will happen next year such as a local festival or a rock concert in the nearby park.

·       The MANUAL part of Tanpin Kanri now only involves the store staff simply making daily small adjustments to the SKU “facings” based on local shopper “gossip” to the purple bar chart!

Convenience stores are drawn to the Enhanced Tanpin Kanri method because of the maturity of operations they already possess. With more agile supply chains and the desire to differentiate their stores in response to their local clientele, Syzen finds a lot of enthusiasm among “high-density” Retailers for our predictive solution that makes Tanpin Kanri more scalable due to lesser dependency on super-diligent store staff. Advances in Systems Analytics and other quantitative methods will refine products such as Syzen’s ROG-0 SaaS in the future to sharpen shopper-preference based product assortment predictions.

Enhanced Tanpin Kanri is a specific example of Intelligence Augmentation. Store staff's knowledge of local happenings and engagement with their store shoppers cannot be replaced; but Big Data & Analytics can provide a significant leg-up in their difficult job of hypothesis-generation by providing data-driven predictions that they can safely rely on and improve incrementally. In a highly competitive low-margin business such as fast moving consumer goods retail, pioneering use of IA in their operations will determine the winners.

More on ROG-0 SaaS: http://advancingretail.org/product/syzen-rog-0-saas/

Syzen website: www.syzenanalytics.com

“Tanpin Kanri” is the next “Kaizen”!

“Tanpin Kanri” is the next “Kaizen”!

Does this spell the death of Retail Category Management as we know it?!

Highlights: Tanpin Kanri will be the next big business process/ philosophy export out of Japan after Toyota’s “kaizen”. Why? Tanpin Kanri has yielded merchandising decisions that has decreased inventory levels, while increasing margins and daily store sales”, says an HBS case study. Syzen is finding a lot of enthusiasm among “high product density” Retailers for our predictive analytics SaaS solution that makes Tanpin Kanri scalable.

Dr. PG Madhavan is the Founder and Chairman of Syzen Analytics, Inc. He developed his expertise in analytics as an EECS Professor, Computational Neuroscience researcher, Bell Labs MTS, Microsoft Architect and startup CEO. He has been involved in four startups with two as Founder. He has over 100 publications & platform presentations to Sales, Marketing, Product, Industry Standards and Research groups and 12 issued US patents. He conceived and leads the development of SYSTEMS Analytics and is continually engaged hands-on in the development of advanced Analytics algorithms.

More at www.linkedin.com/in/pgmad

As a keen observer of the retail ecosystem’s demand chain portion for the past 3 years or so, I am struck by the inefficiencies in large portions of Retail. Shoppers finding what they want on the shelf is considered a BIG problem in the industry (the so called “OOS problem” or Out-Of-Stock problem) – to the tune of $170 Billion per year! There are many culprits – let us consider the systemic ones.

The following diagram illustrates the portion of Retail ecosystem on which we will focus in this blog.

The left half represents the current dominant model. “Push” model drives manufacturer’s FMCG (fast moving consumer good from “brands” such as Procter & Gamble, Kraft, Unilever and others) into the supply chain with store shelves as the final destination. As retailers move to the right half of the picture, they tend to be more agile and mature in their practices and are seeking a competitive edge through differentiation – they find that by focusing their store operations to satisfy the LOCAL customer via making available what she prefers on the store shelves, they can win. The poster-child of this revolution is 7-Eleven, the ubiquitous store at virtually every street corner around the globe!

7-Eleven’s use of the “Pull” model since early 2000’s has been very successful as captured in a case study at Harvard Business School in 2011 (HBS Case Study: 9-506-002 REV: FEBRUARY 23, 2011). Quoting from this study, “Toshifumi Suzuki, Chairman and CEO of Seven and I Holdings Co., was widely credited as the mastermind behind Seven-Eleven Japan’s rise” and goes on to say that ‘Suzuki’s emphasis on fresh merchandise, innovative inventory management techniques, and numerous technological improvements guided Seven-Eleven Japan’s rapid growth. At the core of these lay Tanpin Kanri, Suzuki’s signature management framework’.

Proof is in the pudding – “Tanpin Kanri has yielded merchandising decisions that has decreased inventory levels, while increasing margins and daily store sales” since the 2000’s, the HBS case study points out. So what exactly is Tanpin Kanri?

Tanpin Kanri or "management by single product," is an approach to merchandising pioneered by 7-Eleven in Japan that considers demand on a store-by-store and product-by-product basis. Essentially, it empowers store-level retail clerks to tweak suggested assortments and order quantities based on their own educated hypotheses . . . 

You can tell that Tanpin Kanri lies well to the right in the Retail Ecosystem diagram above. To call out some features:

·       PULL model

·       For a buyers’ market

·       Focus on How to satisfy customer

·       Item planning and supply driven by retailer and customer

·       Symbolic of Consumer Initiative

Looking at this list, you may be flabbergasted if I told you that this is NOT how all of Retail works! You may be wondering with all the emphasis on satisfying the customer and ours being a “buyers’ market” in virtually every case, why would any model other than varying shades of Tanpin Kanri exist in the decade of 2010?! That is exactly why I claim that there is an “inevitability” about Tanpin Kanri. And I conclude that Tanpin Kanri will be the next big business process/ philosophy export out of Japan after Toyota’s “kaizen”.

Push model exists virtually everywhere in US grocery retail, super and hyper markets, smaller store chains. The method of “Category Management” referring to a joint-decision process by CPG (consumer product goods) manufacturers and retailers determine what will be on the store shelves for the next 6 or 12 months. Often, all the stores in a chain (some as large as 3000 stores) will have the same “planogram” (a visual representation of product SKUs on the shelves) irrespective of the local clientele! Obviously simple to implement, why has Category Management not evolved to reflect customer preferences explicitly.

A FederalTrade Commission study may hint at the underlying issue; to quote, “The retailer and supplier also typically discuss funds – slotting, promotional, co-op advertising, or other introductory allowances or discounts – some of which would lower the retailer’s per unit purchase cost for an initial period of time”. As is obvious, this exchange of funds can short-change the customer since they are not directly part of the equation! CPG’s are hyper-wealthy compared to razor-thin margin Retailers – that the CPGs can improve the Retailer’s bottom-line directly with these funds will have a distorting effect on commerce due to the joint-decision process of Category Management.

I believe that it is simply a matter of time before Tanpin Kanri variants dominate the Retail demand chain model. If another FTC study finds the exchange of funds between CPGs and Retailers as collaboration of an "unsavory" nature, the transition to Tanpin Kanri will be rapid. If not, as shoppers clamor even more for their preferences to be made available, Retailers will evolve incrementally kicking and screaming – ultimately, we the shoppers pay them more money than CPGs!

So, where does Tanpin Kanri provide most bang for the buck today?

Where ever Product Density (number of products to be stocked per unit area) is high, “customer pull” will help prioritize what products to stock. Today’s Tanpin Kanri at 7-Eleven Japan accomplishes “customer pull” incorporation via super-diligent store staff manually making the choices. Surely, in these days of Big Data and Analytics, there must be a way to provide a helping hand to the store staff to predict “customer pull” or as we call it “Shopper Preferences” . . .

Syzen Analytics, Inc. has accomplished exactly that using Machine Learning and a new development in Analytics called “SystemsAnalytics”. Syzen is able to provide SKU-by-SKU, store-by-store and week-by-week predictions using typical T-Log data that every Retailer has in its data archives. A typical prediction of Syzen’s ROG-0 SaaS product for a typical SKU at a particular store looks like this.

·       The purple uneven “picket fence” is the weekly predictions. This is obtained by combining different “masks”.

·       The papaya-colored mask is the new and most significant one. The values are predicted based on appropriate past intervals of T-Log data digested via Systems Analytics and updated adaptively.

·       The numbered masks in the middle accounts for things that the store manager knows that will happen next year such as a local festival or a rock concert in the nearby park.

·       The MANUAL part of Tanpin Kanri now only involves the store staff simply making daily small adjustments to the SKU “facings” based on local shopper “gossip”!

As we discussed, many retailers are NOT ready to embrace periodic refinement to reflect shopper-preferences, especially if it involves foregoing lucrative fees from CPGs. However, the “old timers” can take advantage of the outputs above for old-fashioned assortment optimization – average the height of the purple figure and use the average as the SKU volume that they order every week; our result is store specific but they can average each SKU over all stores in the chain and generate one Planogram. Of course, they will be throwing out a lot of value (and revenue) by not being responsive to customer preferences on a store-by-store and SKU-by-SKU basis!

Convenience stores are drawn to this “predictive” Tanpin Kanri method because of the maturity of operations they already possess. With more agile supply chains and the desire to differentiate their store in response to their local clientele, Syzen is finding a lot of enthusiasm among “high-density” Retailers for our predictive solution that makes Tanpin Kanri more scalable due to lesser dependency on super-diligent store staff. Advances in Systems Analytics and other quantitative methods will refine products such as Syzen’s ROG-0 SaaS in the future to sharpen shopper-preference based product assortment predictions.

More on ROG-0 SaaS: http://advancingretail.org/product/syzen-rog-0-saas/

Syzen website: www.syzenanalytics.com

Predictive Analytics – Where from and where to?

Dr. PG Madhavan developed his expertise in analytics as an EECS Professor, Computational Neuroscience researcher, Bell Labs MTS, Microsoft Architect and startup CEO. Overall, he has extensive experience of 20+ years in leadership roles at major corporations such as Microsoft, Lucent, AT&T and Rockwell and startups, Zaplah Corp (Founder and CEO), Global Logic, Solavei and Aldata. He is continually engaged hands-on in the development of advanced Analytics algorithms and all aspects of innovation (12 issued US patents with deep interest in adaptive systems and social networks).

Big Data is big business but the “open secret” in this business is that what the paying client really wants is Predictive Analytics (“what should my business do next to improve?!”). To be explicit, Big Data is all about the technology for storage and manipulation of lots of data (terabytes to petabytes) as well as new types of data such as graph and unstructured data. This is an extremely important precursor to doing anything useful with data – 10 or 20 years ago, we had to settle for small amounts of representative data or simulated data. Outcomes were clever and new data analysis *techniques* rather than useful answers!

The next step is to make sense of the large amounts of data at your disposal (now that you have Hadoop and NoSQL and Graph database). This is where visualization and Descriptive Analytics come in. They provide historic “snap-shots” with graphs and charts – another important precursor to coming up with answers to the question, “What should my business do next to improve?”

In 2013, Big Data is maturing with still many open technology challenges; Descriptive Analytics is in its pre-teen years. Predictive Analytics is in its infancy, nurtured by its stronger siblings, Big Data and Descriptive Analytics!

Predictive Analytics (PA):

In a business context, Predictive Analytics (PA), attempts to answer the question, “What should my business do next to improve?”

Prediction is an old preoccupation of mankind! What comes next, next meal, next rain, next mate? Techniques have changed little from Stone Age till recently; see what has happened before, notice the cyclical nature and any new information; combine them to forecast what may come next. Take tomorrow’s temperature for example:

1.       Predict it as today’s temperature.

2.       Predict it as the average of Tuesday’s (today) temperature for the past month.

3.       Predict it as the average of Tuesday’s (today) temperature of the month of August for the past 50 years.

4.       Look at the pattern of today’s, yesterday’s and day before yesterday’s temperature; go back in the weather record and find “triples” that match (your criteria of match). Collect the next day’s temperature after matching patterns in the record and average them as your prediction of tomorrow’s temperature.

5.       And so on . . .

As you can imagine (and can easily demonstrate to yourself), predictions from 1 to 5 get better and better. If your business is in shipping fresh flowers, your business may be able to use just this simple Predictive Analytics method to lower your cost of shipping! Simple PA but answers your “What next?” question. By the way, this PA technique is not as naïve as it looks; complexity-theory-“quants” used to use its extended forms in financial engineering on Wall Street.

So one feature of PA is clear; there is a time element, historical data and future state. Historical data can be limited in time as in the first method of temperature prediction or as extensive as in the fourth method. Descriptive Analytics can be of use here for sure – looking at the trends in the temperature data plot, one can heuristically see where it is headed and act accordingly (to ship flowers or not tomorrow). However, PA incorporates time as an essential element in quantitative ways.

Quantitative Methods in PA:

My intent here is not to provide a catalog of statistical and probabilistic methods and when and where to use them. Hire a stats or math or physics or engineering Ph.D. and they will know them backwards and forwards. Applying it to Big Data in business however requires much more inventiveness and ingenuity – let me explain.

PA has an essential time element to it. That makes prediction possible but life difficult! There is a notion called “non-stationarity”. While reading Taleb’s or Silver’s books, I have been puzzled by finding that this word is hardly mentioned (not once in Taleb’s books, if I am not mistaken). One reason may be that those books would have ended up being 10 pages long instead of the actual many 100’s of pages!

Non-stationarity is a rigorous concept but for our purposes think about it as “changing behavior”. I do not look, act and think the same as I did 30 years ago – there is an underlying similarity for sure but equally surely, the specifics have changed. Global warming may be steady linear change but it will systematically affect tree-ring width data over centuries. Some other changes are cyclical – at various times, they are statistically the same but at other times, they are different. Systems more non-stationary than this will be all over the place! Thus, historical and future behavior and resultant data have variability that constrains our ability to predict. Not all is lost – weather can be predicted pretty accurately for up to a week now but not into the next many months (this may be an unsolvable issue per complexity theory). Every system, including business “systems”, has its window of predictability; finding the predictability window and finding historical data that can help us predict within that window is an art.

I do not want this blog to be a litany of problems but there are two more that need our attention. Heteroscedasticity is the next fly in the ointment! This is also a formal rigorous statistical concept but we will talk about it as “variability upon variability”.  Business data that we want to study are definitely variable and if they vary in a “well-behaved” way, we can handle them well. But if the variability varies, which is often the case in naturally occurring data, we have constraints in what we can hope to accomplish with that data. Similar to non-stationary data, we have to chunk them, transform variables, etc. to make them behave.

The third issue is that of “noise”. Noise is not just the hiss you hear when you listen to an AM radio station. The best definition for “noise” is the data that you do not want. Desirable data is “signal” and anything undesirable is “noise”. In engineered systems such as a communication channel, these are clearly identifiable entities – “signal” is what you sent out at one end and anything else in additional to the signal that you pick up at the receiver is “noise”. In a business case, “unwanted” data or “noise” are not so clearly identifiable and separable. Think of “Relative Spend” data among various brands of beer in a chain of stores; or sentiment analysis results for those brands. Depending on the objective of the analysis, the signal we are looking for may be “purchase propensity” of each shopper (so that we can individually target them with discount offers). Relative Spends and Likes are not pure measures of “purchase propensity” – I may have bought Brand X beer because my friend asked me to pick some up for him which has nothing to do with my purchase propensity for that brand! Purchases like that will pollute my Relative Spend data. How do you separate this noise from the data. There may also be pure noise – incorrect entry of data, data corruption and such errors that affect all data uniformly.

Fortunately, there is a framework from engineering that provides a comprehensive approach to containing these issues while providing powerful analytics solutions.

Model-based Analytics (MBA):

Model-based and model-free methods have a long history in many areas of Engineering, Science and Mathematics. I take an Engineering approach below.

Data that you have can be taken “as is” or can be considered to be generated by an underlying model. “Model” is a very utilitarian concept; it is like a “map” of the world. You can have a street map or a satellite map – you use them for different purposes. If you need to see the terrain, you look for a topographic map. A map is never fully accurate in itself – it serves a purpose. As the old saw goes, if a world map has to be accurate, the map will have to be as large as the world – what is the point of a map then?

Let us call “model-free” methods as today’s “Data Analytics (DA)” to distinguish it from “Model-based Analytics (MBA)”. DA analyzes measured data to aid business decisions and predictions. MBA attempts to model the system that generated the measured data.

Model-based methods form the basis of innumerable quantitative techniques in Engineering. Theory and practice have shown that data analysis approaches (similar to periodogram spectrum estimation) are robust but not powerful, while model-based methods (similar to AR-spectrum estimation) are powerful but not robust (incorrect model order, for example, can lead to misleading results - needs expert practitioner).

MBA go beyond data slicing/ dicing and heuristics. In our previous “brands of beer in a store chain” example, model-based approach hypothesizes that there is a system, either explicit or implicit, behind the scenes generating customer purchase behaviors and purchase propensities. From measured data, MBA identifies the key attributes of the underlying hidden system (to understand commerce business quantitatively) and provides ways to regulate system outputs (to produce desirable business outcomes).

MBA does not solve but alleviates the three pain points in Predictive Analytics quantitative methods: (1) Non-stationarity, (2) Heteroscedasticity and (3) Noise.

MBA – Personal Commerce Example:

I do not have an MBA (the university kind) nor have I taken a Marketing course. So here is a layman’s take on Personal Commerce. My main objective is to show a practical example of model-based predictive analytics within MBA framework.

Personal Commerce has 2 objectives: (1) Customer acquisition and (2) Customer retention. There are 3 ways to accomplish these 2 tasks: (1) Marketing, (2) Merchandizing and (3) Affinity programs.

Let us take “Merchandizing” (the business of bringing the product to the shopper). An online example is “recommendation engine”. When you log into Amazon, their PA software will bring products from multiple product categories (books, electronics, etc.) to your tablet screen. An offline example is brick-and-mortar store that arranges beer brands on their physical shelf such that it will entice their shoppers to buy a particular brand (assume that the store has a promotion agreement with the brand and hence a business reason to do so). This type of merchandizing is called “assortment optimization”. Note that both Assortment Optimization and Recommendation Engine are general concepts that have many variations in their applications. The MBA approach below applies to the 2 Personal Commerce objectives and the 3 programs to accomplish them.

Assortment Optimization:

As practical example of MBA, let us explore Assortment Optimization. From the various data sources available to you, you construct a model of the shopper groups with their beer-affinity as the dependent variable. Then construct a model of a specific store with the shopper groups as the dependent variable. Once these 2 models are in hand, you combine them to obtain the optimal shelf assortment for beer at that particular store so that the store revenue can be maximized.

Clearly, I have not explained the details of the construction of these models and how they can be combined to give you the optimal product assortment. That is not the focus of this blog – it is to show that such an approach will allow you to contain the 3 banes of PA quantitative methods and hence get powerful results. In my actual use case, we achieve “sizes of the prize” (in industry parlance; the potential peak increase in revenue) greater than any current merchandizing Big Data methods!

(1)    Non-stationarity: As we discussed earlier, different systems vary over time in their own way. If you always used the past 52 weeks of data, it may be appropriate for some products but not others. For example, in certain cases of Fashion or FMCG, non-homogeniety can be minimized by selecting shorter durations but not so short that you do not have enough representative data!

(2)    Heteroscedasticity: There is a fundamental concept here again of choosing just enough data (even if you have tons in your Big Data store!) that address the business question you have but not too much. When you have selected just enough data, you may also escape severe heteroscedasticity. If not, variable transformations (such as log transformation) may have to be adopted.

(3)    Noise: As we noted, Noise is unwanted data. Consider the previous Merchandizing case but where you tried to analyze 2 product categories together, say, Beer and Soap. Since the fundamental purchase propensity driving-forces are most likely different for these two product categories, one will act as noise to the other – deal with them separately. In addition, doing some eigen-decomposition pre-processing may allow you to separate “signal from noise”.

Many of you will find this discussion inadequate – part of it is because they are trade secrets and part of it is because there are no magic formulas. Each business problem is different and calls for ingenuity and insight particular to that data set and business question.

I have only scratched the surface of Model-based Analytics here. The sub-disciples of Electrical Engineering such as Digital Signal Processing, Control Theory and Systems Theory are replete with frameworks and solutions developed in the last two decades or so for deploying model-based solutions and extending them to closed-loop systems. Thus, we go beyond predictions to actions with their results fed-back into the model to refine its predictions. Next five years will see a great increase in the efficacy of Predictive Analytics solutions with the incorporation of more model-based approaches.

Post Script: Other major “flies-in-the ointment” are non-linearity and non-normality; I am of the opinion that methods that are practical and efficient are still not available to battle these issues (I know that the 1000’s of authors of books and papers of these fields will disagree with me!). So, the approach I take is that non-linearity and non-normality issues are minor in most cases and MBA techniques will work adequately; when in a few cases I cannot make any headway, I reckon that these issues are so extreme that I have a currently-intractable problem!

Analytics: New Insights & Actions in Brief

Facebook connected us in a vast network – this is only a first step. The deep reason for the fascination with social networking can be understood from an example. Shoppers are enmeshed in an ever-changing network of social interactions and preferences; desirable behavior can be made to emerge by perturbing the interactions within the network.

Insight #1: Network embedded data.

Today in Analytics, data are treated as isolated bits of information. In reality, data exist in *embedded* forms in preference and influence networks of the shopper as well as distributed in time and space. There are few if any analytics techniques that explicitly exploit embedding – if new ones do, such techniques will be very powerful.

Insight #2: Closing the Analytics Loop.

Analytics extract meaningful patterns and information from raw data. But what do we do with those insights? The promise of Analytics will be fulfilled only when we close the loop via actions that lead to profitability in the broad sense. Emergent Marketing is an example of closing the loop of “analytics” drawn from retail data and shopper network activity back to the customer generating purchase activity and additional new analytics.

Insight #3: Emergent Marketing.

Emergent Marketing is a low-level multi-node intervention in a human social network that creates the emergence of a ground-swell of a desirable activity (“emergent” activity) without identifiable one-to-one causality. In a “crawl-walk-run” approach to developing full-fledged Emergent Marketing techniques, JIT Branding is a “slow crawl”.

Insight #4: Just-in-time Branding.

JIT Branding using Linear Influence Model (LIM) is a primitive Emergent Marketing tool. LIM models minimally incorporate the dynamics and the ability to perturb the network in a fine-grained manner. The design and number of “contagions” for the emergence of desirable “ground-swells” of activity are also open issues at this time.

·          The true value of LIM modeling for JIT Branding is that key shoppers’ influence functions are estimated from historical data. In general, these influence functions are not available and naïve guesses have to be employed (such as exponential decay).

Closed-loop Analytics:

Insights from analytics can be “closed” in multiple ways: via the shopper, via merchandising and via many stages of the supply chain. Our focus is on closing the loop via the shopper; they involve advertisements, discounts or loyalty programs plus new approaches.

1.       Proximity Marketing using Passive Organic Search™ - implemented (New).

2.       Viral Marketing using Shop Ally™ - ready for implementation (relatively NEW).

3.       Emergent Marketing using JIT Branding™ - technology ready (brand NEW).

More details at . . .

o    “Emergent Marketing: A New Force in Social Commerce”http://pgmadblog.blogspot.com/2012/10/emergent-marketing-new-force-in-social.html

o     “What does ‘Emergent Properties in Network Dynamics’ have to do with Shopping?” http://pgmadblog.blogspot.com/2012/10/what-does-emergent-properties-in.html

o     “Social-Mobile-Cloud Framework” http://pgmadblog.blogspot.com/2012/07/so-mo-clo-framework-for-cus-dr.html

o     "Business IPTV Service – A New Cloud Business Vertical" http://pgmadblog.blogspot.com/2009/09/advertisers-are-always-looking-for-ways.html

Dr. PG Madhavan was CTO Software Solutions at Symphony Teleca Corp. Previously, he was the CTO & VP Engineering for Solavei LLC and the Associate Vice President--Technical Advisory for Global Logic Inc.  PG has 20+ years of software products, platforms and framework experience in leadership roles at major corporations such as Microsoft, Lucent, AT&T and Rockwell and startups, Zaplah Corp (Founder and CEO), Solavei and Aldata. Application areas include retail, mobile, Cloud, eCommerce, banking, enterprise, consumer devices, M2M, digital ad media, medical devices and social networking in both B2B and B2C market segments.  He is an innovation leader driving invention disclosures and patents (12 issued US patents) with a Ph.D. in Electrical & Computer Engineering.  More about PG at www.linkedin.com/in/pgmad