The founders of Complexica have worked together for almost 20 years building enterprise software applications that are “smart” and “adaptive”. Such applications bring together the disciplines of algorithmic science and software engineering, as the “science” enables the "smarts" within the software. By applying decades of research in automated data mining, analytics, and expert systems, Complexica has developed Larry, the Digital Analyst® – a cloud-based software application that incorporates the latest advances from the field of Artificial Intelligence called Cognitive Analytics.
In general terms, Cognitive Analytics refers to sophisticated algorithms that can manage and analyse big data sets to help people make better, faster decisions. Each day, enterprises collect or aggregate vast amounts of data from very diverse sources and want to analyse these data sets to gain specific insights – for example, to understand how different customer segments respond to different product and service offers, how customer preferences change over time, how customer loyalty is generated and maintained, how promotional campaigns impact volume, margin, and product cannibalisation, and so on. The accuracy of such insights can provide a competitive edge.
Based on Cognitive Analytics, the primary task of Larry, the Digital Analyst® is to manage the interaction with internal data warehouses and external data sets, and to automate the associated data loading, handling, and analytical processes that lead to optimal decisions. Larry consists of a collection of machine-learning algorithms (e.g. Bayes nets, artificial neural networks, rough sets, classifier systems, support-vector machines, decision trees, genetic algorithms) that are used for various data mining problems and advanced analytics. These include:
- Anomaly detection (outlier/change/deviation detection) is used to identify unusual data records that might be interesting or contain data errors that require further investigation. Anomaly detection is of key importance in the process of cleaning (and understanding) data.
- Association rule learning (dependency modelling) is used for finding relationships between variables. For example, a supermarket might gather data on customer purchasing habits, and using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
- Clustering is used for discovering groups and structures in the data that are in "similar", without using known structures in the data. Data is viewed as points in a multidimensional space and points that are “close” in this space are assigned to the same cluster. For example, an organisation may group their customers into micro-clusters for personalised promotional campaigns.
- Classification is used for classifying new data to existing categories. For example, an expert system might attempt to classify a credit card transaction into a "legitimate" or "fraudulent" category.
- Regression is used to find a mathematical function that models the data with the least error. In other words, regression is a statistical process for estimating the relationships among variables (i.e. the relationship between a dependent variable and one or more independent variables). The discovered relationships aide in understanding how the typical value of the dependent variable changes when any one of the independent variables is varied. A common application of regression is in the area of price elasticity.
- Summarisation is used to provide a more compact representation of the data set, including visualisation and report generation. For example, after clustering is carried out, the clusters themselves are summarised (e.g. by generating the centroid of the cluster and the average distance from the centroid of points in the cluster) and these cluster summaries become the summary of the entire data set.
- Simulation is used to evaluate a variety of scenarios, thus addressing many important what-if questions. Simulation allows for the analysis of a complex model and understanding of how individual elements interact and affect the simulated environment.
- Predictive modelling is used to identify the likelihood of future outcomes based on historical data. It allows going beyond knowing what has happened by providing the best assessment of what will happen in the future. It can be used, for example, to better understand changing market dynamics, or identifying the next best conversation for each customer.
- Optimisation is used for recommending the best possible decision (whether for investment, distribution, allocation, etc.) by identifying the key variables, business rules and problem-specific constraints, and taking into account many objectives (e.g. inventory levels, DIFOT), thus providing trade-off analysis.
- Promotional planning, pricing & ranging
- Sales territory mapping, resource distribution & customer segmentation
- Call planning & sales effectiveness
- Pricing & margin
- Cross-selling, up-selling & churn
- Personalisation & Next Best Conversation™
- CRM automation
Because most companies maintain large data warehouses on what their customers bought and when, it’s possible to apply Larry to these problem domains to automate the data analysis process and generate actionable recommendations and optimised decisions that create value.
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