Discover What Your Data Could Do For You !

increase campaign roi

Traditional customer segmentation methods are based on demographics such as age, gender, postal code, household income. However, Big Data analytics helps you segment based on their behaviour, such as the sentiments they express to your call center agents, what they have recently searched on your site, and how they prefer to reach you (by phone, by email, or via social media). This intelligence lets you run micro campaigns that are tailored to each small group with similar preferences, regardless of their demographics. This reduces wastage and missed opportunities and increases uptake.

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send timely messages

Employing the right data analysis strategy allows you to know each customer by their profile. For instance, Tesco, the largest retailer in the U.K., uses big data technologies to process data acquired from purchases made using its Clubcard.

Based on their analysis, Tesco was able to better target mailings of vouchers and coupons to customers and, as a consequence of this, coupon redemption increased from 3% to 70%. Additionally, they were able to develop approximately a dozen core classifications, describing customer lifestyles. This enabled them to offer a range of personal lifestyle magazines with content and coupons that were matched to the life stages and orientations of customers who received them.

Similarly, Kroger, based in Cincinnati, also uses big data methods to predict which goods an individual customer needs to purchase, and then sends them customized digital coupons for several of the products. Redemption rates for these coupons have been high above the industry average, and have generated billions of dollars in additional revenue, as well as built loyalty.

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make offers relevant

By knowing the preferences of a customer, a more relevant message could be sent to them to pique their interest on an offer. For example, Netflix tracks granular user behavior such as where a viewer pauses in a show, and which parts of a show they back-track to view again. Based on a large amount of data, they understand the viewing patterns most favored by each viewer, such as which star is re-watched in the same show, and whether a viewer watches more male actor dominated shows or otherwise.

Using knowledge gleaned in this way, when Netflix launched House of Cards, they made 10 different promos, and matched a version to each viewer, based on how each individual had trended towards favouring certain screen and dramatic components.

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reduce attrition

Loyalty is built over time, through multiple interactions. Each interaction with the customer is an opportunity to set expectations and then surpass them. Today, interaction goes beyond purchase history and promo redeems. It includes many other attributes, such as a subscriber’s comment on your Facebook page, and what they spend time viewing on your website.

For example, when an item is repeatedly added and later removed from a virtual shopping cart by a number of your customers before they check out, this item could be used as the delighter of your next micro sale campaign targeting that small group. Understanding customer likes and dislikes allows you to offer the right perk at the right time. When a customer is consistently delighted, not only will they stick with you, they will also become advocates of your brand.

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lower financial risks

Financial risk reduction is the ultimate gain achieved after deploying the right strategy for data collection and analysis to help guide your business decisions. Whether you are looking to improve your campaign ROI, optimize your lead qualification criteria, reduce wastage in your production and shipping logistics, prevent subscriber attrition, or choose the highest yield locations to open new stores, choosing the right data science approach is important. A properly chosen analytics strategy will describe your situation from a clear perspective that would not be available through the traditional methods that are generally used.

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just-in-time logistics

Successful global brands such as UPS and Hitachi have long enjoyed the benefits of using data analytics to optimize their staffing, routing, scheduling, vehicle procurement, and storage space optimization. Analyzing large-volume data used to require massive investments in hardware, software, and expert monitoring. Today, with the cost of parallel computing and machine learning becoming affordable, and Big Data analytics platforms more user-friendly, companies that are less than global in size could also take advantage of analysis at this scale to help them compete.

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predict product uptake

If a restaurant could predict what dishes their customers will order, they could ensure that the ingredients in their freezer are as fresh as could be, and the kitchen staff could also optimize the pre-opening prep time. Similarly, for a grocery store to succeed, it is crucial for them to accurately predict the demand for each perishable item on the produce shelves. Big chains such as Walmart are veterans in big data analysis. AlgoTactica is able to bring this service to small and medium size businesses at an affordable cost.

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Who We Are

AlgoTactica is a data science solutions innovator and provider, specializing in the design of advanced diagnostic models and predictive analytics.

Our team embodies over 25 years of experience in data analysis and development of analytical software procedures, enabling AlgoTactica to expertly transform scattered, unstructured data into actionable business intelligence. By leveraging our extensive software resources and multi-platform compute cloud, we produce data-driven insights that maximize the impact of marketing campaigns and optimize business operational efficiencies.

What sets us apart from most software-as-a-service and self-serve platforms is that AlgoTactica offers the balance between the art and science of big data analytics. From the outset, our team will excel in properly understanding your business context, helping you identify and prioritize your analytical objectives. Only then will we recommend the best-suited scope and methodologies to achieve them.

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What We Do

AlgoTactica helps businesses minimize financial risk and optimize customer lifetime value by using data analytics to design intelligent forecasting solutions that inspire insightful marketing strategies.

Identify Business Needs
Select, Merge, Clean Up Data
Action Plan

We develop specialized analytical models and evidential assessments which deliver strategic planning options by leveraging data signals that anticipate future marketplace dynamics.

Our designs evolve through a data-driven discovery process which leads to predictive solutions that continually learn and adapt as new information is acquired. To achieve this, we start by asking the right questions to understand the unique needs and objectives of each business, before proceeding to select the data science methodology most appropriate to the identified set of requirements.

Data engineering is also fundamental to our process because model efficacy is dependent on data quality. We are specialists in the design of matrix-analytic strategies that strip away noise and reveal hidden data patterns offering the best predictive power.

As data volumes become larger, analytic operations are performed using parallel processing on multiple platforms. AlgoTactica also has solid expertise in designing and building distributed data processing solutions to optimize your data storage and expedite the training of analytical models.


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Engagement Process

Our data science solutions are designed to conform with marketing science and financial risk reduction objectives. While each project is uniquely tailored to individualized requirements, our business engagement and product development work flows are typically sequenced as follows:

engagement process
id business needs, determine scope

We engage key decision makers at the earliest stage in order to progressively sharpen project focus and identify crucial business questions that need to be answered. A critical assessment of the accessibility and quality of the data needed for analysis is also performed at this stage.

consolidate, clean up data

We assay the data in order to identify flaws, such as missing and incomplete values, and then engage corrective actions where necessary, to ensure that the data sets are consistent and reliable. Additional profiling is conducted in order to understand statistical characteristics, to formulate investigative hypotheses, and to identify influential variables having high predictive or discriminatory power.

build model

We always engage a prototyping process that evaluates several different modeling techniques, in order to determine the best possible design. Our parallel computing cloud permits efficient simultaneous evaluation of these potential candidates, thus minimizing the duration and number of training cycles. The final model design is determined by comparing performance amongst the individual prototypes, in order to identify the strategy having maximum predictive accuracy.


After a reliable analytics strategy has been designed, we package and deliver the completed solution. This will include algorithms that can predict future outcomes, and that can also be retrained as new data is acquired. Business recommendations derived from the analytic results are also presented, along with supplementary data charts and tables.


Analytic processing reveals insights that lead to effective business decisions. This ensures better-targeted and more effective marketing campaigns, early discernment of subscribers’ intent to churn, as well as prediction of customers’ likelihood to engage a purchase. Financial risks are reduced by analytics-enabled just-in-time inventory and prediction of regional/categorical uptake of merchandise/services.


How do I gauge the quality of my data?

Big Data algorithms cannot be any more accurate than the data used to train them. If the data is sub-par in any way, then decisions that are made based on the analysis will be inherently flawed.

Assess the quality of your data based on these criteria:

  • Validity: make sure that the data set has all the relevant input variables required for the analytical model to produce best results.
  • Completeness: determine the extent to which there are missing and/or incorrect entries, and estimate level of effort needed for corrections.
  • Consistency: consider that business rules might have changed during the collection period, thus rendering earlier data inconsistent with later data.
  • Accuracy: ensure that the data is from a sample that is large enough to realistically represent the subject being modeled by the analytics.
  • Timeliness: confirm that any data from the distant past is not too outdated to be relevant, if it is to be used to make predictions about the future.

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How can smaller businesses benefit from Big Data?

For small business applications, it is possible to design an open source data analytics platform tailored to the specific needs of the small business, without incurring the huge software licensing costs normally associated with proprietary platforms offered by commercial vendors. The only direct costs involved would be associated with the purchase of computer hardware and the contracting of a data science consultancy following a firm fixed price quotation.

A low-cost data analytics system can be built that encompasses the following data management areas of practice, and their associated free-of-charge open source technologies:

  • Data Stream Capture: social media channels, as well as other data streams that are generated in real-time, can be captured and managed using Apache Spark Streaming, as well as Apache Storm.
  • Data Storage: storage of Big Data across multiple computer systems can be achieved by utilizing the open-source Hadoop Distributed File System (HDFS), as well as open source distributed databases that include Apache Cassandra and Apache HBase, amongst others.
  • Data Analytics: analytical and predictive algorithms can be designed and trained using Apache Spark machine learning library, H2O machine learning library, Microsoft R Open, and Python.
  • Data Visualization and User Interaction: graphical user interfaces for data display and user input can be designed using several library packages in Microsoft R Open, as well as JavaFX. Moreover, displays written in Java/JavaFX can directly call Microsoft R Open and invoke statistical analysis procedures on demand via the rJava interface.

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What are Predictive Model Factories?

Businesses today are routinely acquiring vast amounts of data, often from a broad range of sources. This presents tremendous opportunities for gaining unprecedented insights into matters critical for growth and profitability. However, traditional analytics cannot always accommodate the extremely large scale and rapid prototyping needed to get the strongest competitive advantage in the shortest amount of time possible.

Predictive Model Factories upscale the capabilities of big data analytics by enabling an extremely large number of individual models to be simultaneously trained in an automatic manner, without operator intervention. This permits a much greater number of models to be produced without requiring extra resources, and offers several benefits:

  • Model retraining cycles can be iterated much more frequently, especially during overnight sessions when computer resources are otherwise lightly used.
  • Allows a business to develop a separate model for each customer, to predict future buying preferences or timing.
  • In a majority of cases, models for newly acquired customers can be initiated and trained automatically, without any manual intervention whatsoever.
  • This also ensures just-in-time data discovery in very large and continually refreshed data sets, allowing the analytic model to learn, adapt and maintain predictive accuracy as new data is added.

As an example, Cisco Systems has deployed propensity-to-buy (P2B) models on a modest compute cluster of 4 computers, with 24 cores overall and 128GB of combined memory. This basic arrangement can calibrate 60,000 P2B models in a matter of hours, representing an overall efficiency gain that is 15x faster than their traditional methods.

In business, relevance and agility to adapt are key success drivers. Parallel computing and on-demand processing power enable a business to easily integrate these insights into the business workflow, and thus compete successfully.

At what point do I engage external data science providers?

Organizations that have previously realized value from using spreadsheets, and other similar small-scale analytical methods, will ultimately develop a need to accomplish even more with the data sources available. As they aim for higher accuracy, faster processing, and specific insights, the following are indicators of the need to move towards using structured big data methodologies:

  • It takes too long to generate the desired ad hoc reports because volume and velocity (speed of arrival) of data overwhelms the current processing system and analytical methods.
  • Standard spreadsheet applications can no longer address the business questions with sufficient granularity, specificity, or timeliness.
  • There is a need to combine data from separate silos of different formats, different granularity, and different sources, to form multi-structured data sets.
  • When unstructured data is to be mined for sentiment content, such as text messages, comments from social media streams and contact log/notes/recordings from the call centre.

Overall, you make the shift when there is much more data than can be managed by your existing software tools. From realizing this need to establishing an in-house team, there is a transition period during which it is best to seek help externally from seasoned experts. Smaller businesses may find it more cost efficient to continue to use external providers.


What does a data science solutions provider do?

A data science solutions provider specializes in delivering highly effective business advice based on data-driven insights, derived from the use of predictive models and analytical software technologies.  The provider employs specialized technical skills and domain knowledge, to identify big data opportunities that are aligned with the business goals of the client. During this process, advanced data science techniques are used to discover patterns in marketplace dynamics and customer behavior that enable the client to anticipate future business opportunities. Using this prior knowledge, the client can then execute actions that will strategically position their business in order to exploit these opportunities for optimum profit and competitive advantage.

The expertise offered by a data science provider typically involves a combination of skill sets, including business and marketing science, software design, database development, statistical analysis, and machine learning. By collaborating as a multidisciplinary team, the resident specialists typically engage the following activities that are focused on delivering maximum value to clients:

  • Design and implement machine learning software algorithms that are programmed to mine the big data stores and produce a detailed analysis for vetting by business and marketing experts.
  • Apply marketing and business expertise to interpret discovered trends and develop a data-driven business strategy for clients, which will enable them to leverage maximum benefit from findings.
  • Implement data hygiene protocols by cleaning and validating the data to ensure that it is accurate, complete, and internally consistent with respect to original collection methodologies.
  • Engage data exploration and interpretation to identify analytics opportunities and insights that offer the greatest benefit for the client, and apply data-driven techniques in developing solutions.
  • When dealing with limited data, apply statistical methods to identify business questions that the available data can answer, thus maximizing the information potential for the data set.
  • Collect large sets of structured and unstructured data from various sources, identifying and reformatting those of best quality, and then determine the variables with best predictive power.
  • Design Java software architectures for managing distributed computing and database storage applications, and for networked communications involving data feeds from real-time sources.
  • Maintain comprehensive knowledge of a broad range of statistical and machine learning software tools, so that blended solutions can be designed using the best advantages of each tool.
  • Develop data visualization software strategies that intuitively communicate important findings to stakeholders from various non-technical backgrounds.
What is metadata and how is it related to big data?

Metadata refers to small granules of data used to capture basic knowledge that provides a descriptive overview about very large volumes of data. It is higher level information that is used to organize, locate, and otherwise manipulate extensive data sets when it is not practical to work directly with that data itself. Aspects that metadata can summarize about large data sets include structure, content, quality, context, ownership, origin, and condition, amongst others. Because the metadata is much smaller than the data it describes, it acts as a search index to facilitate quick identification and retrieval of archived data sets that are being sought for a given business objective.

In the modern era of big data, the role of metadata data has become much more critical than it has been in the past. It is now very important for businesses to manage the continuously-growing volumes of structured and unstructured data, in order that they can efficiently leverage it to maintain competitive advantage. For instance, semi-structured and unstructured data is often spread across many different storage devices and locations, can be stored in a diversity of formats, and is difficult to organize overall. Consequently, cost-effective usage and management can only be achieved if there exists a metadata oversight program, that minimizes the time and expenditures associated with discovery of the relevant in-house sets of big data.

As data volume and diversity continues to grow, for each new big data project that is launched there will be an ever-increasing efficiency imperative that the relevant data sets be identified through searching of a descriptive and accurate metadata layer. In fact, at the final reporting phase, metadata summaries can provide an audit trail to authenticate the quality of source data from which the analytic findings are drawn.

How does AlgoTactica differentiate itself from larger providers?

Many large data science providers are focused on offering prepackaged solutions which involve combining commercial-off-the-shelf (COTS) analytic software products offered by multiple third-party vendors. Depending on the stated needs of the client, this might involve delivery of an analytics platform based on well-known COTS software components, delivery of COTS distributed database systems to replace existing legacy relational databases, or some other similar combination. In all these cases, the larger provider is actually performing in the role of a systems integrator, as opposed to an OEM provider of custom-designed analytics solutions.

Although the large provider might well have data science knowledge in-house, when it comes to addressing a client’s need, that knowledge is likely to become focused on identifying prepackaged software that will ultimately prove to be only an approximate solution and not necessarily the best fit. Given the scarcity of available professionals with advanced data science skill sets, large providers that are focused on maximizing sales volume cannot acquire a talent pool sufficiently large as to facilitate detailed investigation of each client’s specific data science need. This ultimately leads to proposed solutions that are commoditized to appeal to a large range of clients, even though the solution might not be precisely conformant with the exact needs of any individual client.

At AlgoTactica, we focus on investigating each client’s data problem in detail, and then proposing the appropriate approach that will yield the optimal solution. After an exploratory data analysis (EDA) stage, we engage a scientific due diligence process during which a uniquely relevant set of algorithmic candidates are evaluated against the client’s data to identify the best one. Once identified, we can quickly build a one-of-a-kind customized product by compiling software from our in-house mathematical algorithm libraries. Therefore, the ultimate solution is designed specifically to the needs of the individual client and will not attempt to be an omnibus solution for a commoditized marketplace.

The principals at AlgoTactica have graduate degrees in specialized fields of marketing science and engineering mathematics.  Furthermore, we have decades of combined experience in market development, design of analytics software, and data science involving machine learning and statistical analysis.  We have built our professional careers by maintaining an awareness of the latest innovative advances in our field, and then leveraging those innovations for delivery of highly-customized solutions to well-known industrial brand names.

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Algorithmic Science for Tactical Business Insight

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55 Hickory St. E., Ste 212
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