The client conducted tracking surveys and analyzed data using proprietary algorithms (branded solutions) to calculate various indexes and parameters. The algorithm included numerous data transformations, new variable creation, recalculations, and standardization.

Reduce the time required for data processing and analysis. Automate processes to eliminate manual errors.

The client used SPSS for analysis and planned to continue using it in the future. All data transformations, processing steps, and calculations of indexes and parameters were implemented as an SPSS script. Some data transformations and recalculations required creating new variables, deleting variables, or saving data in external files for further processing. Procedures that could not be automated using SPSS syntax were implemented in Python.

The combined SPSS and Python solution automated data transformations and reduced data analysis time by more than 60%.

The client is a consulting company focused on intangible assets. Its core product consists of reports based on large-scale quantitative panel surveys. The reporting process traditionally involved preparing more than 100 slides and charts manually. This approach increased the risk of errors and inconsistencies.

Optimize reporting processes and reduce the impact of human error on the quality and accuracy of deliverables.

Reports were created using Power BI dashboards while preserving the original visualization formats and structure. Data was extracted from SPSS files, exported to Excel, and then used to build dashboards in Power BI. The dashboards were designed to match the client’s brand guidelines and replicate the appearance of the original PowerPoint slides. For tracking surveys, the client can now upload new data, which is automatically processed without the risk of manual errors.

Power BI dashboards reduced report development time by more than 70% and eliminated errors caused by manual processing during the reporting workflow.

The client is a consulting company that conducts numerous tracking surveys. Each survey includes multiple open-ended questions coded using the same code lists. In some cases, additional open-ended questions are introduced within a single wave. Manual coding of responses is time-consuming and increases the risk of errors, particularly for longer text responses.

Optimizing the time required for both recurrent and new open-ended questions, and reducing the 'human factor' in the quality and accuracy of coding. Enabling the automation of coding for 'simple' open-ended responses (such as brand attributes) and more complex open-ended responses that contain sentences or phrases.

The client used SPSS for data analysis, with all files managed in SPSS format. To automate coding of open-ended responses, an SPSS Python-based script was developed. The script processes open-ended answers, maps them to predefined code lists, and assigns the results to new variables (supporting multiple responses where applicable). Uncoded answers are exported to Excel for further review. If needed, the client can update the code list with new entries and rerun the script.

The script runs in SPSS, allowing the client to specify the variables to be coded and the path to the code list. Coding is then performed automatically and can be applied to both existing and new open-ended questions. The solution works efficiently for both simple and complex responses, as it relies on the code list and does not require modifications to the script itself.

The client has a special methodology that creates complex, multi-factor-based, highly customized segments for their various clients. This methodology heavily depends on the data gathered and must be adjusted each time.

The client required customized adjustments to segmentation coefficients based on the structure of the data, as well as the creation of new SPSS syntax for each segment.

Cluster and discriminant analysis were performed for each project wave to generate custom segment definitions aligned with the client’s methodology. The solution described the structure of key variables used in the dataset. Additionally, a set of Excel tables was created to validate and review the newly formed clusters.

A set of SPSS scripts was developed for each cluster type and situation, along with supporting Excel validation tables. This approach saved time and enabled the client to focus on interpreting the results.

The client produces numerous PowerPoint reports on a monthly, quarterly, semiannual, and yearly basis. Typically, they spend significant time on data processing and slide preparation, leaving limited time for the most critical tasks: drawing conclusions and making recommendations for their clients.

By outsourcing most of the time-consuming processes (such as Excel data output and PowerPoint slide preparation), the client aimed to focus on essential work with their clients, better understand their needs, and translate them into consulting insights and recommendations.

Our team handled two key parts of the process: analyzing raw data according to the client’s methodology and creating PowerPoint slides based on that data. The client provided the original templates, which adhered to their brand guidelines and overall delivery standards (including colors, types of analysis, comparisons, etc.).

This approach enabled the client to save significant time and receive both Excel outputs with data tables and PowerPoint presentations faster than their usual workflow allowed.

The client conducted internal research on corporate culture and employee evaluation (including both self-evaluation and peer evaluation). The company had a very complex, multi-level structure, and ultimately hundreds of employees had to be evaluated, due to this structure, employees at higher levels could have both their own teams and their own supervisors.

Each employee required a personalized report with their own survey results, comparison between self-assessment and team assessment, and dynamics between survey waves. These results needed to be highly customized for each individual, reflecting their team, KPIs, and their specific role within the company.

To address this, Excel Macros were designed to generate these personalized reports automatically. The macros created hundreds of new files, exporting results at various levels based on the employee’s position, team, and department, using multiple embedded Excel formulas. These reports were also automatically formatted to be client-friendly, including the appropriate colors, fonts, and other details.

Hundreds of personalized Excel reports were generated automatically, significantly reducing report production time.

The client manages several custom multi-country, multi-company, and multi-stakeholder survey trackers, all conducted through an online panel provider. These surveys typically run monthly and can undergo changes each time (in different markets/languages, with new questions or companies). Due to the complexity of the surveys, the client often cannot check all survey branches in time before the surveys go live.

Each update must be verified across all languages of the survey, including non-Latinscripts. Additionally, all logical conditions between questions must be tested. Any identified issues must be reported to the panel provider and verified again after being fixed.

A detailed SOP was developed to standardize, simplify, and organize the checking process across all survey waves and languages. Excel-based checklists were created to support systematic validation, ensuring that each wave was thoroughly tested and all potential issues were identified.

This structured Excel-based validation approach enabled the client to identify and correct the majority of issues before surveys went live, significantly reducing errors and saving time.

The client conducts multiple qualitative surveys across different stakeholder groups. These include open-ended responses from the general public and employees, as well as media analysis covering articles and social media mentions related to the client and its activities.

Open-ended responses and media data needed to be quantified and presented as numerical indices aligned with the client’s methodology and scale. The solution also had to reflect the relative impact of different topics. Additionally, media data required weighting, as some sources were more influential than others.

A particular procedure was designed to quantify the open-ended responses into categories: ‘strongly negative’, ‘slightly negative’, ‘neutral’, ‘slightly positive’, and ‘strongly positive’, with the application varying by topic. A specific SPSS recoding script was then used to present these topics on a client-friendly scale, weight the overall file according to media significance, and define the impacts of each topic on the overall image.

As a result, the client obtained a set of indices aligned with their methodology and KPIs. These indices provided additional insights for stakeholders by transforming previously separate qualitative datasets into measurable metrics.

The client has accumulated and stored various datasets over the years, covering multiple aspects of its multi-level, diversified business. These include CSR spending, salaries, media indices, investment levels, efficiency metrics across sub-entities, macroeconomic trends, and social climate indicators. In addition, the client continuously tracks reputation as a key performance indicator.

The client needed to identify the hierarchical impact of multiple economic performance factors on reputation as a final intangible KPI.

The solution was implemented in three steps. First, historical datasets from different departments were consolidated into a single database with all necessary transformations applied. Second, non-significant factors were identified and excluded from the model. Finally, a regression model was developed to determine the final set of key drivers.

The client obtained a clear hierarchy of the most influential economic and financial factors affecting reputation. In addition, separate regression models were developed both for the overall company and for its key business units. This provided deeper insights and supported more informed management decisions.

The client owns several large enterprises in different small towns across the country, historically having been economic strongholds for this profession and these towns. Over time, with the economy opening up, there is now a significant challenge in attracting young talent to this type of enterprise.

The client aimed to identify the key factors preventing young people from choosing this profession and working in these locations, as well as to define effective approaches to promote career interest and guide professional orientation.

The combined study approached this question from different angles: a) Special focus groups and in-depth interviews with pupils and their parents from these small towns, which unveiled the main concerns and reasons influencing professional orientation; b) Quantitative research among high school pupils.

As a result, the client received a comprehensive set of recommendations for promoting their enterprises and professions in these towns. This included methods to enhance professional orientation towards their field, addressing main concerns, and strategies to maintain a positive employer image in these towns.

The client has a long history of using Brand Strength indices as the main KPI for evaluating work performance. However, due to recent events, the company began to question the role of reputation in the overall company landscape, a parameter that also existed in the monthly longitudinal study.

The client needed to establish a statistically valid relationship between Brand Strength and Reputation, as well as identify the key drivers of influence between these metrics.

Since both parameters had been tracked monthly over a long period, the analysis was conducted in three parts. Time series analysis was used to examine trends, followed by correlation analysis between Brand Strength and Reputation. Additionally, reputation components were analyzed in relation to monthly longitudinal data to visualize their impact. The results were incorporated into reporting dashboards, enabling the client to clearly observe the relationship and any time-lag effects.

The client obtained statistical validation of the relationship between Brand Strength and Reputation. The analysis showed that the effect may not be immediate and can emerge over time. Additionally, the client identified which reputation components have the strongest impact and distinguished between immediate and delayed effects.

The client has a long history of using NPS as a key KPI for performance evaluation. Additionally, the company segments the general public into multiple internal clusters and tracks various reputation metrics.

The client needed to establish a statistically valid relationship between NPS and Reputation and to analyze this relationship across different clusters and subgroups to identify where the strongest effects occur.

A regression model was developed at the overall company level and across individual clusters. NPS was analyzed in relation to different reputation metrics and subgroups. The results were visualized in presentation slides for the client.

The client obtained statistical validation of the relationship between NPS and Reputation. Additionally, the analysis identified the subgroups most affected, allowing the client to focus on areas requiring attention. As a result, a comprehensive consulting strategy was developed, highlighting key strengths and weaknesses for management.

The client operates an online platform that continuously tracks reputation and other intangible asset metrics. The platform is pre-configured but offers limited flexibility, typically tracking only predefined parameters and not easily accommodating additional insights when needed.

The client needed to integrate open-source data into their platform to analyze not only intangible asset dynamics but also financial indicators such as share prices. This would enable continuous comparison and assessment of the relationship between these variables.

The solution was split into two parts: a) additional coding that allowed the API connection with the open-source data and adding it to the visual dashboards; b) exporting these two parameters and running the correlation between them, proving that the link exists.

The client significantly enhanced their visualization platform, gaining deeper insights from combined reputation and financial data. The identified relationship between share prices and reputation enabled more informed analysis and integration of these insights into reporting and strategic decision-making.

The client, a bank, engaged an international consulting firm to develop a reputation management strategy. After a year of dealing with a reputation crisis, the bank needed to restore and manage its public perception and strengthen its image as a responsible member of society.

The bank aims to develop a custom reputation model with attributes specifically relevant to its business and industry. These attributes will later be incorporated into evaluation research and used as part of the KPIs.

The solution was implemented in three stages. First, a series of in-depth interviews was conducted to identify key topics and parameters. Second, qualitative research was carried out to refine and validate these criteria. Finally, statistical analysis was performed to validate the results, including correlation and regression analysis. This confirmed the reliability of the model and its relevance to the bank’s business and strategic objectives.

As a result, the client received a validated custom methodology along with a set of tailored attributes. These now serve as the foundation for future research, insights generation, reporting, and ongoing reputation management.

The client has a long history of reputation management and has been working with various attributes for some time. They developed a specific model that required a revised methodology, including consistency, causal relationships, and the direction of relationships within the model.

The model required standard regression coefficients, as well as an evaluation of how its elements interact with each other, whether these elements produce the intended results and the direction of impacts between model components.

The solution was implemented using the Path Analysis module in SPSS: special combinations of the existing attributes were evaluated one by one. Some attributes were found to be redundant while others were assigned to incorrect categories or dimensions leading to inconsistencies in the required reporting. The direction of the impacts was also evaluated.

The client received a detailed chart illustrating all elements of the model represented by specific coefficients and arrows. Using this chart, the client was able to identify the most and least influential factors optimize their model and reshape it for future use and alignment with their KPIs.

The client, a consultancy company, was developing a communication and reputation strategy for a large telecommunication company, which is one of the leading providers of mobile communications and the Internet. For this purpose, a large quantitative survey was conducted. One of the approaches used for data analysis was audience segmentation.

The client aimed to apply more advanced segmentation criteria that were not based on demographic groups or product usage.

Two segmentation approaches were applied: one based on social class and another based on values. A set of indicators was developed for social class segmentation, including income, education, property, savings, and lifestyle. Respondents were grouped into low-, middle-, and upper-class segments. For value-based segmentation, statements from the Schwartz Value Survey methodology were used. Two types of analysis were conducted: a “prior” segmentation based on predefined value segments and a “post hoc” segmentation using factor and cluster analysis. The resulting segments were more generalized and consolidated compared to the initial classification.

The client received segmentation models with strong explanatory power, including both social class and “post hoc” value-based segments derived from the Schwartz Value Survey. These segmentations enabled the client to prioritize target groups and develop more effective communication strategies.

The client has worked with various NGOs and charitable organizations, collecting extensive data on support, awareness, brand strength, and willingness to donate. One of the charities requested an analysis of the key factors influencing donation behavior.

The objective was to identify a set of variables with a combined impact on support for a specific charity. This included factors related to awareness, recommendations, charity engagement, and personal connections to the cause.

The solution was implemented in several steps. Initial cross-correlation analysis was conducted to identify the most relevant variables. Regression models were then developed to evaluate their impact. Key demographic segments influencing the dependent variables were defined, followed by model optimization and the identification of key predictors. Finally, the results were compared across models to distinguish between common and unique drivers.

The client identified a set of key factors influencing donation behavior at both the overall level and across key demographic groups. While some factors were already known, the analysis provided deeper insights and actionable findings. The results included model coefficients that enabled the client to better understand drivers of donation and support targeted communication and recommendations.

The Client (bank) initiated a corporate culture diagnostic project to increase employee loyalty and reduce staff turnover. A quantitative employee survey was conducted to measure the bank's current situation and develop an action plan for increasing employee loyalty.

Understanding the heterogeneity of employees, significant differences in the employees' loyalty, and distinctive characteristics of different departments, the Client wanted to distinguish groups or segments of employees that are different, describe and characterize these groups, understand the degree of burnout of employees in each of them, and identify key instruments and messages for communication with each group.

Standard corporate culture diagnostic approaches (e.g., Cameron and Quinn, Denison, and Schein) did not fully address the client’s needs. Therefore, an extended set of characteristics was developed to enable post hoc identification of employee segments. Factor and cluster analysis were applied to identify customized employee groups. Each segment was described in detail, including key characteristics, behavioral patterns, motivations, barriers, and pain points.

The analysis provided detailed insights into employee segments, their size, and key characteristics. This enabled the bank to develop a roadmap and action plan tailored to each segment. As a result, internal communication became more relevant and effective, contributing to reduced staff turnover.