BigRio used X-ray digitization process to create DICOM images from archived films while meeting stringent NARA quality standards…

BigRio used X-ray digitization process to create DICOM images from archived films while meeting stringent NARA quality standards…

BigRio used X-ray digitization process to create DICOM images from archived films while meeting stringent NARA quality standards…

BigRio used X-ray digitization process to create DICOM images from archived films while meeting stringent NARA quality standards…

A rapidly growing niche analytics firm has developed cutting-edge models helping retailers within their industry make improved product recommendations to customers. These recommendations not only match appropriate products to… architected and implemented a distributed, secure collaboration and document sharing platform that allows parents to exchange…

eBay Enterprise Marketing Solutions (EEMS) contracted with to build fundamental components of a new Customer Engagement Engine (CEE) for their Commerce Marketing Platform. The CEE, a demand generation marketing solution, creates…

Deep Learning: Image and Video Recognition

Written by Bruce Ho’s Chief Big Data Scientist


This paper illustrates the advancements in implementing Deep Neural Networks for automatic feature extraction in image and video for applications including facial recognition, programmatic video highlights, and image segmentation and object classification. Given the limitations of human abilities in earlier extraction methods, these networks exponentially increase accuracy, output, and available feature selection options for further analysis. specializes in the following industry use cases:

  • Image Recognition

  • Video Highlights

  • Anomaly Detection


ABOUT BIGR.IO is a technology consulting firm empowering data to drive analytics for revenue growth and operational efficiencies. Our teams deliver software solutions, data science strategies, enterprise infrastructure, and management consulting to the world’s largest companies. We are an elite group with MIT roots, shining when tasked with complex missions: assembling mounds of data from a variety of sources, building high-volume, highly-available systems, and orchestrating analytics to transform technology into perceivable business value. With extensive domain knowledge, has teams of architects and engineers that deliver best-in-class solutions across a variety of verticals. This diverse industry exposure and our constant run-in with the cutting edge empowers us with invaluable tools, tricks, and techniques. We bring knowledge and horsepower that consistently delivers innovative, cost-conscious, and extensible results to complex software and data challenges. Learn more at



Over the past few years, Deep Neural Network (DNN) capabilities have surpassed human parity in recognizing and interpreting images. These DNNs use Convolutional Neural Networks (CNNs) to automatically extract features from an input image with the use of convolution filters. Backpropagation then facilitates the learning by these filters of their kernel functions, starting with random values and ending up with elemental features that best represent the class of images being trained (for instance, nose, eye, and jaw shapes for face images). Image recognition is also where the highly coveted idea of transfer learning got its early foothold. Pre-trained models based on certain categories of images can be repurposed for various classification applications using only a small dataset. Since data preparation and labeling is one of the most challenging steps when carrying out supervised learning, the impact this concept has on accelerating this process cannot be overstated. Published models and datasets by some of the biggest players in the field (Google, Microsoft, etc.) now serve as a strong starting point to build robust application-specific models for businesses with only modest means for development.



Similar to the adoption of best practices in big data and data science across several industry verticals, image video recognition solutions affect business outcomes across diverse government agencies and businesses. In this paper, we specifically examine use cases in the security and professional sports segments, but these solutions illustrate applications across all areas of video content creation, consumption, and monitoring.





Image recognition can go beyond classification tasks for an entire image. In dense prediction, we are asking the neural network to detect the semantic context of any given pixel in a document or image. CNNs work by first finding image features that resemble certain filter functions, then floating such features to a top-level representation as a translation-invariant descriptor (e.g., detection of a nose, regardless of its position within the image). By combining both coarse- and fine-grained features at different scales, we obtain both the semantic context and location information of any one pixel. This opens the door for pixel-level semantic segmentation (aka dense prediction). Recent work on Fully Convolutional Networks (FCNs) leverages this capability to extract semantic context of a digitized document. One could, for example, detect whether a particular pixel is a title, section header, figure caption, an image, or part of a long paragraph using FCNs. A mobile user could then easily re-layout or restyle an electronic document using the extracted semantic context. FCNs have also been successfully applied to segment parts of an image, as well as full documents, with remarkable accuracy. How does this system pick potential customers from an image of a crowd, a soccer team, or a room full of event attendees? Given a close-up face shot, is this person happy to be here, in the target age group, or giving a positive response to the last sales message? Being able to answer these audience measurement questions for marketing is one of the hot areas in need of a deep learning solution. Many classic approaches to facial feature extraction and classification, Support Vector Machines, for example, have been devoted to this long-standing problem. Deep learning research in facial identification is relatively new but already outperforming older techniques by a wide margin. This development, and many other impressive improvements achieved by deep learning, are generally attributed to the automatic feature extraction function of neural networks and the incremental accuracy boost that deep learning techniques achieve when given a huge training dataset. In many applications, a high-quality, close-up facial shot is not always available. Picking faces out of an ordinary action photo may be the first step before applying any facial feature analysis. For this, the region-based CNNs (R-CNNs) excel in both speed and accuracy. The R-CNN approach proposes a number of bounding boxes in the original photo using what is called Selective Search. In this method, initial object boundaries are set using a graphical pixel similarity approach. Neighboring boxes with high pixel similarity metrics are then merged to further reduce the object count. Finally, each boxed object can be classified based on a pre-trained image recognition model.


In other efforts, researchers have extended facial analysis to emotion detection. Classically, this simply involved image labeling where the subject exhibits a range of facial expressions and a group of volunteers would mark each as happy, sad, angry, etc. — typically up to eight emotions. More recent work also incorporates dynamic facial movements, for example, capturing the complete sequence of facial movements for a smile or frown. A more generalizable model can be developed using linear scoring along the valence- arousal graph. A prediction of valence and arousal scores on future subjects can then be interpreted using a wider range of emotion states instead of the initial selection of about eight.


valance arousal plot

Reference: G Paltoglout, M Thelwall, Seeing Stars of Valence and Arousal in Blog Posts. Issue No. 01 Jan-Mar 2013 Vol. 4, IEEE Transactions on Affective Computing.

Points on the valence arousal plot can be translated to commonly understood emotions.



There are numerous highlights in every major sporting event. Manual real-time extraction of these highlights by fully attentive labelers is error-prone, requires significant manpower, is very expensive, and doesn’t scale well. Furthermore, while the most recent games may benefit from manual labeling, there are years of archived footage that remain unprocessed. Most off-stats highlights are overlooked by human observers who are instructed to look for only specific events, for example, looking for a ball boy slipping while chasing a tennis ball or a Major League splitter in a Little League game.

Today, we can automate programmatic video highlights using video recognition techniques. In addition to applying CNNs to static image features, Recurrent Neural Networks (RNNs) are able to classify video segments using optical flow between image frames. This technique is easily trained not only to extract official stat events, but also to extract any interesting player motion not explicitly logged and indexed — for example, an alley-oop in basketball. Due to the automated nature of these extraction tasks, studios can come up with new ideas at any time to build upon an existing menu of highlights.

Going beyond sporting events, any kind of motion picture, video ad, or short-form video opens itself up for potential indexing and repurposing. For example, a DC Comics fan may want the ability to easily find all instances of girl superhero encounters within the DC universe. This task requires automatic video highlight extraction, which is the key to reviving and monetizing unlimited archive contents that would otherwise remain buried and forgotten.


Image: Durant eyeing Rihanna after hitting a 3-pointer (she was cheering for LeBron).



Independent Component Analysis (ICA) is one such approach with many proposed variants. An ICA-based deep sparse feature extraction strategy combined with a non-parametric Bayesian approach can automatically determine the most optimal dimension for the latent feature vector, removing the heavy labor in parameter tuning that a full deep learning approach would entail. The reported accuracy improvement exceeds 10% over previous results. Variants of Restricted Boltzmann Machines (RBMs) are another major direction of research for deep-sparse representation. While much progress has been made on the theoretical front, the experimental results thus far lag behind the best ICA models. Reference: Y. Cong, J. Yuan, and J. Liu, “Sparse reconstruction cost for abnormal event detection,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 3449–3456

The graph on the right is a sparse vector representation of the image on the left. The vector dimensions, called training bases, are laid out along the x-axis, with the bars representing the coefficients for the bases needed to represent the image. A normal sample (top) can be represented as a sparse linear combination of the training bases, while an anomalous sample (bottom) requires a large number of base elements.



Recent advancements in image and video recognition pave the way for many business applications that would have been unimaginably hard or expensive to implement before. excels at the application of deep learning to images and electronic documents for use cases ranging from facial recognition, to programmatic video highlights, to image segmentation and object classification.

Advanced Recommendation Engines

Written by Bruce Ho’s Chief Data Scientist


When a shopper comes to an eCommerce site, how does she find the items she wants? Search is generally the default option when the site knows nothing about the shopper. Somewhere along the journey though, the shopper starts to feel that the site should know her preferences better than pulling up a few thousand randomly sorted options and dumping them on her. Certainly the corner store used to treat her with a more personal touch.

The key to making customers feel at home is getting to know their tastes, like the corner store used to and suggesting to them the newly arrived silver set that matches their placemats. This less charming but more consistent replacement for the store owner is called the recommendation engine. With Amazon’s rise to preeminence, recommendation engine technology has gained some aura of mystique, often called a killer app by industry watchers. The company’s continued rapid growth hasn’t dampened that enthusiasm. The Netflix $1M challenge only added more fanfare.

The Netflix Challenge

Movies are a good example for where the recommendation engine makes or breaks the business. Viewer tastes and movie features vary widely, and the title list for movies is effectively infinite from the viewer’s perspective. Search by title is equally fruitless; you cannot ask viewers to look for what they don’t know. Making the right recommendation is a critical factor in determining whether the user comes back again. At Netflix, 75% of the movies watched came from their recommendation engine.

Netflix shook the geek world in 2006 with their announcement of a $1M prize to find the best movie recommendation algorithm. The winning team emerged almost three years later and generously published their approach, which is an ensemble of many algorithms including exotic neural network designs. There are many lessons to be learned from Netflix’s investment, with one of the biggest being that Netflix never did deploy the winning algorithm into production. Reason number 1: the extra engineering complexity is not worth the benefit. This outcome very much highlights the importance of a system that’s flexible and scalable alongside the quality of the recommendation. More on this at the end of this paper.


Recommendation engine design is one area where common machine learning techniques such as linear regression or SVM (support vector machines) perform poorly due to extreme data sparsity. In 2015, Netflix data shows:

  • 74 million subscribers2
  • 13,300 titles3
  • Total cells (user-item pairs): 98 billion
  • Actual collected ratings: 5 billion ratings ~ 5%

As a result, designers resorted to two other approaches which deal with sparsity well-content similarity and collaborative filtering. Each, however, has its own challenges in real world applications.


Content similarity takes the intrinsic characteristics of users or items and estimates their affinity using similarity measures such as Euclidean distance, cosine similarity, or Pearson’s product moment coefficient. Someone who likes one comedy movie may like another. Mothers of teens may enjoy a film like “Mom’s Night Out”.

Content-based strategies require gathering and classifying product and user information that might not be available or easy to collect. The process of collecting features for all the products in the inventory is laborious and often manual. The algorithm is typically brand-specific, and therefore hard to commoditize.

On the user side, the user profile and preferences are often even harder to come by. Users generally won’t volunteer their demographics and interests unless they see the return value, which isn’t clear in the beginning. Although this strategy can work by relying on well-classified product content alone, at the very least the user must offer up some indication of their personal tastes in order to apply similarity matching.

Even when abundant features are collected, a content-based approach has the downside in that it cannot find hidden patterns. An article that contains “Operation Desert Storm” is probably of interest to readers of “Middle East foreign policy”, but the association would be overlooked unless identical keyword tokens appear in both.


Collaborative filtering simply takes user rating data for each product without doing any intrinsic classification. The basis for matching is that a user who shares interest in some of the same products as another user may also like her other favorite products.

For example, if bike riders often buy sunglasses, the association may indicate interest in sunglasses by another biker who hasn’t bought sunglasses, even though there is no direct match between bike and sunglasses features.

Figure 1. The input ratings matrix A is re-factored into a user vector and product vector, both expressed in terms of hidden factors with k dimensions.

The input data of the rating matrix can be formidable in size – consider the data set example given above for Netflix. However, there is a machine learning technique known as Matrix Factorization that both reduces the computation complexity and discovers hidden rating similarity. Here, both user preferences and item features are expressed in terms of latent factors, vectors with only a fraction of the dimensions of the original ratings matrix. With the latent factors determined, the recommendation score for any unidentified user item pairs (missing from the original ratings matrix) can be derived using the dot product of the respective latent factor vectors. For example, the movie Troy would have a high values in latent factors “Action”, and “Brad Pitt” (these are not likely latent factors, only used here for illustration). Someone who enjoys “Action” and “Brad Pitt” would also have high values for these two factors. This results in a high dot product for this user – movie combination and, therefore, a high recommendation score.

This technique works well with rather sparse data. As shown with the Netflix dataset, even a 5% initial population yields surprisingly useful results. However, this method does have trouble with a brand new user who has no recorded ratings history. This is known as the cold start problem. Only generic recommendations can be made in that case, until ratings build up over time.

For this reason, content similarity is sometimes used to supplement collaborative filtering to overcome the cold start problem, at the expense of doubling the setup cost.

As well as the combination of content similarity and collaboration filtering works, there are notable areas where they fail to reach new heights. For example, sometimes features interact. Someone can rate both comedy and romance very low, yet enjoy the subcategory of romantic comedy. Considering the possible pairs of all the features, a direct inclusion of all combinations would quickly overwhelm the system, and likely for very a small percent in return.

Another area for improvement is freshness of ratings. How can the engine assign more weight to newer ratings and yet not completely discard older ones? A single ratings matrix has no provision for this enhancement, yet shopper taste does shift over time. It is naturally desirable for the recommendations to keep up.

In time, the bright minds in the field start to dream of a better way, which:

  • Accepts both user product rating and content features
  • Can be computed with only linear complexity
  • Takes account of features interactions in an efficient manner
  • Includes timeliness as one of the influencers

By implication, this method would behave well with sparse data, yet mitigate the cold start problem. If the algorithm is based on linear regression, it would be cream on top in terms of computational efficiency, as well as opening up wider inclusion of additional characteristics that might indicate a user’s preferences.


Factorization Machine was invented by German researcher Steffen Rendle in 2010. This method lays out both user and product as unique features, among any other general features (such as freshness of ratings), at the discretion of the data scientist. The key innovation is where Rendle introduces cross-feature terms (equivalent to quadratic polynomial minus self-interacting terms in standard linear regression), but then applies the latent factor technique to reduce computation complexity. The cross-feature terms, on the one hand, express the user item pairs inherent in collaborative filtering, and on the other, capture any potential cross feature interactions.

Figure 2. Factorization Machine accepts features which are a superset of content similarity and collaborative filtering. It includes both user item pairs and general content characteristics, while optimizing computation efficiency using the latent factor method of reduction.

Through clever formula reduction, Rendle brought the complexity back down to O (n).
No one ever got a job at Google or Amazon by proposing a O (n^2) solution! This reduction is critical in judging whether a solution is sound in practice.

Now that the problem is expressed as a system of linear equations, the direct implication is that parallelization techniques such as SGD (Stochastic Gradient Descent) can be applied to effect linear scalability. This leads to the system architectural considerations discussed in the next section.

Factorization machine is one of the many specialty approaches’s expertise can offer. It is an illustration of our philosophy to help our clients design the highest performing and most advanced solutions in any given situation, while balancing budget and other operational concerns.


What did Netflix learn for its $1M investment in the ultimate recommendation algorithm? According to Netflix: “Coming up with a software architecture that handles large volumes of existing data, is responsive to user interactions, and makes it easy to experiment with new recommendation approaches is not a trivial task.” Clearly, if production viability was included in the competition criteria, they may have made better use of the prize money. Case in point: 100 million ratings were published for the competition, but the actual production load was 5 billion.

The engineering disciplines necessary to bring advanced algorithms to deployment cannot be overlooked. The algorithm should be adaptable to a scalable implementation (such as Apache Spark), and not simply dropped into a faster box in its original “data science” form.

From a system architecture perspective, model training and on-demand predictions for online users have very different requirements in data volume, computational load, and response time, and should always be launched in separate subsystems.

Often, it is also useful to introduce a third caching layer, in which the system predicts the next set of predictions in response to user events. While users are digesting the first prediction or consuming the recommended item, the system pre-computes the likely follow-up and caches the results awaiting future user requests. The potentials for performance gain are numerous and only await diligent discoveries.


Recommendation technology has come a long way since the dawn of eCommerce. Advances in matching algorithms have been nothing short of stunning. Factorization Machine not only eliminates the cold start barrier, it incorporates feature interactions and rating freshness; important functions that were missing from previous generation technology. At the same time, it fully embraces parallel processing and therefore is well suited to Big Data platforms. is uniquely qualified to not only fine tune the recommendation algorithm for your business model, but to deliver a complete solution which meets the scalability and operational requirements for a high-volume, fast-response production system.

An Adaptive and Fine-Grained Experience with Big Data & Data Science

Written by Bruce Ho’s Chief Data Scientist


Your customers tell you a lot about themselves, through their digital interactions and social media assertions. With all the internal and external data you have, you can get to know each one of them as well as the corner store owner. Uncover their preferences, forestall their defection and predict their demands by deciphering their digital behavior with advanced analytics and Big Data technology. In this paper, we take you through the expertise delivers to complete your 360 degree customer view.

The capabilities explored are:

  • Fine-grained Personalization
  • Social Media Sentiment
  • Next Best Action
  • Precision Recommendation
  • Detect Buyer Readiness
  • Market Segmentation
  • Real-time Customer Monitoring

ABOUT BIGR.IO is a technology consulting firm empowering data to drive analytics for revenue growth and operational efficiencies. Our teams deliver software solutions, data science strategies, enterprise infrastructure, and management consulting to the world’s largest companies. We are an elite group with MIT roots, shining when tasked with complex missions: assembling mounds of data from a variety of sources, building high-volume, highly-available systems, and orchestrating analytics to transform technology into perceivable business value.

With extensive domain knowledge, has teams of architects and engineers that deliver best-in-class solutions across a variety of verticals. This diverse industry exposure and our constant run-in with the cutting edge, empowers us with invaluable tools, tricks, and techniques. We bring knowledge and horsepower that consistently delivers innovative, cost-conscious, and extensible results to complex software and data challenges. Learn more at


Are your targeted prospects, even after making significant marketing investments, still just hanging around the shopping cart and not pulling the trigger? What is the last piece of “assurance” that will get them over the fence? What if you can read into their minds and find out what’s holding them up? What is that missing carrot that will boost your revenue?

Experienced marketers will tell you that the customer journey starts long before reaching the shopping cart. You can begin to win the prospects’ favor from their first curiosity about your product, throughout the nurturing process, until they reach the final stages of comparison shopping and conversion. To effectively channel these customers down the sales funnel, you want to remain in tune with their state of readiness, and be equipped to offer the right incentives at the right time.

But, “What is the right incentive?”, and “How do you determine the right timing?”, you ask. Isn’t every customer different? You must not only read minds, but millions of minds, on a moment’s notice. How can anyone but the IBMs of the world hope to attain this kind of market intelligence?

Fortunately, today’s playing field has been leveled; all businesses can and must embrace this capability now. With the emergence of Big Data technologies and recent advancements in analytics, offering a fine-grained customer experience, across all channels, is no longer just in the domain of giant multi-nationals. The cloud infrastructure removes the economic factor in utilizing unlimited compute and storage capacity. And specialty consulting firms like deliver the quantative expertise which makes analytics initiatives low risk have-to-have propositions.

In this white paper, we discuss the importance of customer analytics in terms of business impacts, and present how delivers customer acquisition through personalization.


Modern commerce is predominantly conducted via digital channels. This allows suppliers to collect unprecedented amounts of data on their customers. Advanced analytics techniques turn this data into insights that help businesses serve individual customers better and win more sales.

Customer 360-Degree View

A 360-degree view yields a complete profile of the customer by incorporating all available information, whether from internal repositories, email, voice records, or social networks. Such a detailed understanding translates into a superior customer experience, improved campaign effectiveness, boosted sales, and better retention.

Customer 360 View

Customer Journey – track each customer’s digital trail and decipher mood relative to the experience. These interactions contain the clues on how best to engage prospects and when.

Examples of customer journey include:

  • Opening a new account at a retail bank
  • Shopping for new music online
  • Monthly billing for an online service

Customer Acquisition – customer profiles combined with nurturing collateral are the carrots that win the hearts of new clients. The 360-degree view is the basis for tactical engagements through all touchpoints, from first exposure to final conversion and continued engagement.

Customer Churn – Conversely, every existing customer retained is worth a new customer earned. Existing clients who exhibit signs of dissatisfaction or inclinations towards switching can be detected through real-time analytics and remedial efforts can be made in time to retain loyalty.



  • Contact list through referrals, social media, blogs, linkedin, events, website
  • Enrich each prospect by cross referencing to CRM account and other available information
  • Segmentation for email / Ad campaign

Engaged – first response of any sort

  • Ad targeting
  • Email reminders
  • Webinar / live event invitations


  • Promotional offers
  • Assigned to sales rep
  • Call center active list
  • High search keyword, CPM targeting


  • Account management / customer care
  • Purchase history
  • Satisfaction survey


  • Recommendation upsell / cross sell
  • Product release announcements


  • Loyalty program
  • Monitoring signs of dissatisfaction
  • Frequent re-engagement
  • Encourage viral advocacy

Fine-Grained Personalization

The ultimate customer care is when the buyer is automatically given or presented with the most probable match to their needs or wants. Personal preferences are ingrained in the breadcrumbs left along the entire digital journey. can help you collect, analyze and serve them up for each and every instance of customer interaction. Implementation of personal profiles as active records means dealing with the challenges of rapid access and sheer volume; these are the very missions of Big Data engineering. By embracing the new generation of computing infrastructure, the forward-looking enterprise can incorporate this high degree of personalization into daily operations.

Blend CRM Data with Social Media Sentiments

This is where the Navy meets the pirate; the orthodox meets the avant garde. Businesses shouldn’t pretend they understand all there is to know about their customers, regardless of the extent of their CRM database. Consumers are attracted to coolness, and what’s cool is in the Facebook likes, the midnight Tweets, and the Instagram posts.

Deciphering social media sentiment is a fascinating and daunting challenge. All the factors that motivated the Big Data movement come together under one roof. The data is unformatted, sketchy, nearly impossible to authenticate and validate, and often short-lived in relevance, yet likely to be highly honest. Once the author’s identity is mapped to the user ID in the CRM, the marketer gains insight about a person’s habits, range of motion, purchasing pattern, and strongly held opinions that otherwise would not be revealed in a targeted survey. As a whole, social media data also points out market trends and help companies adapt to changing customer taste. This effort is one of the most important steps in completing the 360-degree view of your customers.

Next Best Action

Customer insights become gold if the business can plot the Next Best Action (NBA); the appropriate course of action that matches the customer’s individual needs. Adaptive, Data-driven decisions are far superior to gut feelings and intuition when it comes to consistent results. Predictive analysis finds the best combination of timing and marketing levers that elicits the most optimal response from the buyer. Call center operators can work from a list of most likely customer concerns before picking up. Visiting reps can arrive at meetings prepared with the most appealing agenda. Customer support agents can proactively offer assistance or promotional material before frustration builds.

The model itself can be retrained at the appropriately set intervals to remain adaptive. Through Predictive Model Markup Language (PMML), personalized NBAs can integrate directly into operational systems such as marketing automation systems, call center software, consultation scheduling systems; anywhere automated or manual processes take place.


Today’s marketing activity has moved way beyond the back office CRM. Marketing automation systems, ad targeting, email delivery systems, A/B and multivariate testing tools, consumer surveys, crowdsourcing, and social media monitoring are all necessary parts of a complete arsenal to achieve marketing success. The escalation in tools and data also means the enterprise must utilize cutting-edge analytics to orient its campaigns and drive personalized marketing efforts in an ever-more competitive landscape.

Precision Recommendations with Factorization Machines

A recommendation engine is the most obvious of eCommerce strategies for any business trying to boost revenue from its existing client base. Collaborative Filtering is the most commonly-recognized technique for finding likely customer-product matches, given only sparse purchasing history data. A recent innovation, Factorization Machines, far exceeds collaborative filtering in recommendation precision to the point where a single customer-product pair can be identified to set the recommendation score. In addition, this new technique incorporates cross-feature influences which may be too important to be overlooked. For example, a viewer may actually dislike romance and comedy movies, but is fascinated by romantic-comedies.

Detection of the Prospect Funnel State – Buyer Readiness

The sales funnel is an important marketing concept used to direct sales efforts and organize campaigns. While marketing automation platforms offer prospect scoring, the feature is always set manually. Advanced analytics techniques such as the Hidden Markov Model can automatically and optimally score each prospect based on digital behavior. Digitally-intelligent enterprises can leapfrog the competition with aggressive adoption of data driven innovations and harness these important hidden indicators.

Advanced classification for market segmentation

While fine-grained personalization is well within the capability of today’s app serving platforms, from a management perspective it is of practical necessity to conduct campaigns at a cluster level. Analytically sophisticated organizations can make guided decisions in choosing amongst a host of clustering techniques from simple (k-means) to advanced (latent classification) to handle data at the extreme scales of volume and complexity. The mathematically derived classifications reduce dependency on gut feelings in favor of consistent returns.

Streaming analytics for real-time customer monitoring

There are a number of use cases where a timely response from an organization is vital to success. A dissatisfied customer can exhibit familiar disapproving behavior patterns right before defecting to competitors. Other digital signatures are indicative of high receptivity to promotional efforts. The concept can be extended to fraudulent transactions. The urgency for response in these cases are self evident. Streaming data analytics brings real-time pattern detection within reach of every progressive organization.

Data Management Platform

Effective marketing campaigns are essential to business expansion and bringing in sales. can help you design and build your Data Management Platform (DMP) to help drive your marketing initiatives. A DMP incorporates data on retail transactions, catalogs, social media, and online advertising. Our DMPs supports hypothesis-driven, high-impact analyses, and helps your organization move from simply collecting data to surfacing actionable insights and delivering business results.


Let’s use a fictional company called Ace Shopper (“Ace”) and a fictional customer named Hank to illustrate the above concepts in action. Ace is a specialty consumer electronic super store which actively profiles its buyers with customer 360-degree view analytics. Its records have customer Hank on file as a busy executive with three teens, two of which are musically gifted. In the past 3 years, Hank’s family spent over $10K per year on musical instruments and accessories in the store.

Ace’s marketing department categorizes its customer population based on the most distinguishing features, which are found to be indicative of their spending patterns. Hank falls in the group for highly educated parent with college bound children. His purchase records show a preference for high end electronics and incidental interest in laptops.

When a new electronic keyboard came to the market, Ace’s marketing department ran their recommendation engine and found Hank to be a top 10% match. Ace proactively includes Hank in their email campaign on seasonal product releases. The email addresses Hank by name, and thanked him for his purchase items in the past one year.

Soon after the email campaign, the customer tracker application lists Hank as a customer whose interest level moved from “unaware” to “interested” due to his short visit to Ace’s eStore. The marketing platform does not fire additional emails yet, but starts retargeting Hank with music instrument ads.

In a few months when summer break nears, the tracking application picks up frequent visits by Hank on the eStore and each time the click trail dives deeper into product details. Click tracking software also picks up signs of comparison shopping. Hank’s readiness status is now upgraded to “active”. With this status, the marketing platform now authorizes promotions valued up to 5% of Hank’s annual purchase.

At the same time, one of Hank’s Facebook influencers posted a complaint about a failed circuit board on an electric guitar, which Hank actively responded to. Apparently, this issue worried Hank sufficiently that he opened a chat session with Ace’s sales agent. Before responding, the agent sees Hank’s readiness status, purchase history, issue list, and suggested promotional offers prepared by customer analytic engine. The agent greets Hank, assures him on the quality of the new product line, and offers a free two-year extended warranty, plus an invitation to attend an in-store live performance by a famed local artist. Within a week, Hank became a happy repeat customer.


Customer 360-degree view means a complete understanding of a client’s needs and a personalized customer care presence. This level of intelligence and responsiveness can only be achieved with a highly integrated system that readily can access all data sources and continually refreshes the status of the customer.

Big Data technology and advanced analytics brings fine-grained personalization within reach of all forward looking companies. It’s no longer a matter of economics but a matter of will on the part of business owners. with its team of high-caliber consultants can help architect a platform, comb though your data, and implement a custom analytic solution that matches your unique marketing practice.