Predictive Services & Forecasting

Power of Predictive Analytics

Predicting future events and trends across industries is critical and is the very foundation of predictive analytics. Of the many lessons learned from the COVID-19 pandemic is how industries, governments, and institutions relied on predictive analytics to make data-informed decisions and strategic plans. While we hope such significant world-changing events can be held in check, planning and preparing for what might happen in the future require best practices driven by robust forecasting- and future-focused decision-making.

Kline is uniquely positioned to deliver forward-looking, data-driven insights and strategies for our core industries—chemicals, consumer products, and energy. We harness the power of Kline’s industry-leading data and experts with advanced skillsets and AI tools brought forth by our equally powerful digital and data analytics team. This combination of deep industry expertise and analytical excellence is the reason companies engage Kline to bring certainty to an uncertain future.

Data Analytics, Forecast Modeling Experts

Leading our digital strategy and predictive analytics, our Vice President of Innovation and team of data analysts, experts, and product managers are continuously advancing Kline’s next-gen capabilities for our syndicated products (e.g., industry reports) and consulting service offerings. This includes the development of a portfolio of Intelligence Centers (e.g., Professional Hair Care Intelligence Center) and statistical decision-making tools (e.g., price elasticity models) to ensure that marketing, strategy, and insights leaders can explore growth opportunities and use this knowledge to transform business economics. By collaborating with Kline, our clients learn continuously from a new type of information generated using methodologies and techniques underpinned by Big Data, data science, machine learning, and statistics expertise.

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Francis Taloen leads the digital and innovation team. He has an extensive background in insights, consulting, data, and product roles in the consumer products industry and held senior positions at MasterCard, GlobalData, and Evalueserve. Max Kaye heads up Kline’s predictive analytics practice. Previously, he was Head of Forecasting at GlobalData, where he led the forecasting operation for both internal databases and bespoke consulting. Our predictive analytics and forecasting team are at our U.S., U.K., and India offices to ensure 24/7 service and client access. Team members include data scientists, financial risk managers, forecasting industry specialists (e.g., automotive and mobility), industry research analysts, and product development managers. Promising team members from our Analyst Academy can intern and potentially join our Predictive Analytics and Digital Innovation team.

Forecasting & Predictive Analytics Capabilities

Kline Proprietary Data, Leading Third-Party Data

Kline leverages several market-leading third-party providers, as well as its own proprietary information, to ensure that the most accurate data possible are used in the predictive process. As part of the data input process, our data scientists use advanced machine learning algorithms to evaluate the historical relationship between parameters and market data. With global data sets covering products, pricing, competitors, marketing, manufacturing, and more, we are uniquely positioned to provide clients with actionable and forward-looking data solutions, including industry forecasting tools for all of our core sectors.

(Specialty Chemicals)

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Kline’s specialty chemical databases cover such sectors as biochemicals and renewables; coatings, adhesives, sealants, and elastomers; personal care ingredients; materials and polymers; and industrial and institutional cleaners. For example, for personal care ingredients, that means robust data on over 250 ingredients for over 70 countries dating back to 2015. For synthetic latex polymers, we capture global manufacturing capacities, raw material pricing, production margins, and much more.

(Lubricants; Mobility/EV)

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With products such as our LubesNet and EV Fluids databases, Kline is already providing clients with foundational data on consumer and industrial power evolution and transformation from petroleum products to electric and other carbon neutral solutions. Our Mobility Intelligence Center will serve as key input for mobility forecasting products and services.

(Beauty; Professional Beauty)

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Kline is the go-to source of market data for the global professional beauty industry. For salon hair care, our data archives cover over 70 countries, with select country markets having expansive data sets on salon services, pricing, and consumer sentiment. Additional databases include professional skin care and professional nail care. Forecasting tools for each of these industry sectors are advancing in our product pipeline.

Forecasting & Scenario Planning

Kline applies strict processes and advanced data science techniques not only in its forecasting tool but across the suite of its predictive analytics products, including multiple linear regression, Bayesian analysis, natural language processing, decision trees, and clustering. Kline utilizes an end-to-end analytics platform, powered by Python programming software, resulting in highly robust predictions.

  • Multiple linear regression: The relationship between each parameter and the market data is analyzed to build a series of multiple linear regression equations that represent the market trajectory. Both linear and nonlinear terms (for example, logarithmic and power terms) are considered here.
  • Bayesian analysis: The multiple linear regression equations are then recurred using a Bayesian iterator to develop a model that fits the data with the greatest degree of accuracy. This equation is used to determine future market forecasts.
  • Scenario calculations: The multiple linear regression equation is used to calculate the change in market data, based on changes made to any of the factors. This provides you with the capability to input contrasting future scenarios and decipher the effect of these events on your products and the overall market.
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Forecast veracity is scrutinized extensively. All predictive models are tested from both theoretical and practical viewpoints to ensure that the predictions are as robust as possible, with results feeding back into algorithms in a closed learning loop.

  • Theoretical error: Statistical techniques are applied to decipher the theoretical error terms of the predicted data, with continual refinement to reach high levels of accuracy.
  • Real-world comparison: Historical predictions are compared against observed data and short-term future predictions, as well as measured against industry expert expectations, to test their real-world success.
  • Quality control: Quality checks, including trend analysis and cross-country validation, are undertaken by a dedicated team. Feedback is provided to the domain experts for further enhancement.
  • Industry expertise: Predictions are further reviewed and refined by industry experts, considering hard-to-predict events, such as legislation changes or key company marketing campaigns.
  • Scenario planning: Revising future parameter values provides updated market forecasts based on correlated relationships.
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