[et_pb_section fb_built=”1″ admin_label=”Header” _builder_version=”4.24.0″ _module_preset=”default” background_enable_color=”off” background_color_gradient_direction=”90deg” background_image=”http:\/\/mindtron.io\/wp-content\/uploads\/2024\/02\/AI-background_03.png” custom_padding=”||0vw||false|false” locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.0″ _module_preset=”534cc526-6274-4e31-9e1a-7f18a5947bf1″ header_4_font=”Archivo|600||on|||||” header_4_text_align=”center” header_4_text_color=”#DB0EB7″ header_4_font_size=”13px” header_4_letter_spacing=”1px” header_4_line_height=”1.5em” custom_margin=”||0px||false|false” header_4_font_size_tablet=”” header_4_font_size_phone=”” header_4_font_size_last_edited=”on|desktop” locked=”off” global_colors_info=”{}”]<\/p>\n
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[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ specialty=”on” background_color_2=”#F1F5F9″ padding_top_2=”40px” padding_right_2=”30px” padding_bottom_2=”40px” padding_left_2=”30px” padding_top_bottom_link_2=”true” padding_left_right_link_2=”true” padding_2_tablet=”” padding_2_phone=”20px|20px|20px|20px|true|true” padding_2_last_edited=”on|phone” admin_label=”Contact” _builder_version=”4.24.0″ _module_preset=”default” locked=”off” global_colors_info=”{}”][et_pb_column type=”1_2″ specialty_columns=”2″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_row_inner _builder_version=”4.24.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_column_inner saved_specialty_column_type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.0″ _module_preset=”df96b77a-cd4f-44a8-a771-2d6347592b53″ header_2_font=”Barlow Condensed||||||||” header_2_text_color=”#000000″ header_2_font_size=”64px” header_2_line_height=”1.1em” custom_margin=”||16px||false|false” custom_padding=”||4px|||” header_2_font_size_tablet=”32px” header_2_font_size_phone=”20px” header_2_font_size_last_edited=”on|desktop” global_module=”1042″ saved_tabs=”all” locked=”off” global_colors_info=”{}”]<\/p>\n
[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.24.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
Client<\/strong>: Global Pharmaceutical Company<\/p>\n Challenge<\/strong>: A pharmaceutical company was struggling to analyze patient feedback from various clinical trials. They needed a way to quickly assess patient sentiment, identify trends in drug efficacy, and detect any emerging issues from unstructured survey data.<\/p>\n Solution<\/strong>: Mindtron implemented its AI-powered survey analytics tool to process and analyze thousands of qualitative responses. Using sentiment analysis, topic modeling, and anomaly detection, the tool identified key concerns related to drug side effects and patient adherence. [\/et_pb_text][\/et_pb_column][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.24.0″ _module_preset=”default” background_color=”#01012c” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.24.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.24.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Client<\/strong>: Leading Home Appliance Retailer in the Balkans<\/span><\/p>\n Challenge<\/strong>: The client wanted to understand customer satisfaction trends across different regions and product categories but lacked the tools to analyze large volumes of text responses in real-time.<\/span><\/p>\n Solution<\/strong>: Mindtron’s text analysis and comparative analytics tools were deployed to examine customer feedback from product surveys. The AI identified sentiment shifts and pinpointed areas where specific product categories underperformed. Comparative analysis enabled the retailer to track improvements across various time periods and regions.<\/span><\/p>\n Results<\/strong>:<\/span><\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.24.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.0″ _module_preset=”df96b77a-cd4f-44a8-a771-2d6347592b53″ header_2_font=”Barlow Condensed||||||||” header_2_text_color=”#000000″ header_2_font_size=”64px” header_2_line_height=”1.1em” custom_margin=”||16px||false|false” custom_padding=”0px|||||” header_2_font_size_tablet=”32px” header_2_font_size_phone=”20px” header_2_font_size_last_edited=”on|desktop” locked=”off” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ specialty=”on” background_color_2=”#F1F5F9″ padding_top_2=”40px” padding_right_2=”30px” padding_bottom_2=”40px” padding_left_2=”30px” padding_top_bottom_link_2=”true” padding_left_right_link_2=”true” padding_2_tablet=”” padding_2_phone=”20px|20px|20px|20px|true|true” padding_2_last_edited=”on|phone” admin_label=”Contact” _builder_version=”4.24.0″ _module_preset=”default” locked=”off” global_colors_info=”{}”][et_pb_column type=”1_2″ specialty_columns=”2″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_row_inner _builder_version=”4.24.0″ _module_preset=”default” width=”100%” global_colors_info=”{}”][et_pb_column_inner saved_specialty_column_type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.0″ _module_preset=”df96b77a-cd4f-44a8-a771-2d6347592b53″ header_2_font=”Barlow Condensed||||||||” header_2_text_color=”#000000″ header_2_font_size=”64px” header_2_line_height=”1.1em” custom_margin=”||16px||false|false” header_2_font_size_tablet=”32px” header_2_font_size_phone=”20px” header_2_font_size_last_edited=”on|desktop” locked=”off” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.24.0″ _module_preset=”default” custom_padding=”|||4px||” global_colors_info=”{}”]<\/p>\n Client<\/strong>: International SaaS ERP Provider<\/p>\n Challenge<\/strong>: The client wanted to better understand employee engagement and detect early signs of dissatisfaction that could lead to higher turnover. They needed a solution capable of analyzing employee survey responses at scale while providing clear, actionable insights.<\/p>\n Solution<\/strong>: Mindtron\u2019s employee profiling and causal analysis features were used to create detailed respondent profiles. By applying explainable AI (Shapley values) and clustering algorithms, Mindtron helped the company identify the primary factors affecting employee satisfaction and engagement.<\/p>\n Results<\/strong>:<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Footer” _builder_version=”4.24.0″ _module_preset=”default” background_color=”#01012C” custom_margin=”-8px|||||” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_4,1_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}” global_module=”1089″ theme_builder_area=”post_content”][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.16″ _module_preset=”default” header_4_font=”Archivo|600||on|||||” header_4_text_color=”rgba(255,255,255,0.58)” header_4_font_size=”13px” header_4_letter_spacing=”1px” header_4_line_height=”1.5em” custom_margin=”||0px||false|false” header_4_font_size_tablet=”” header_4_font_size_phone=”” header_4_font_size_last_edited=”on|desktop” locked=”off” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.17.4″ _module_preset=”default” text_font=”Archivo||||||||” text_text_color=”#FFFFFF” text_font_size=”16px” text_line_height=”1.8em” link_text_color=”#FFFFFF” custom_margin=”||-35px|||” custom_padding=”0px||0px|||” text_font_size_tablet=”15px” text_font_size_phone=”14px” text_font_size_last_edited=”on|phone” header_font_size_tablet=”55px” header_font_size_last_edited=”off|desktop” locked=”off” global_colors_info=”{}” link_text_color__hover_enabled=”on|desktop” link_text_color__hover=”#3C5BFF” theme_builder_area=”post_content”]<\/p>\n
Results<\/strong>:<\/p>\n\n
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Case Study 2: Retail Sector – Optimizing Customer Satisfaction with Sentiment Analysis<\/span><\/h2>\n
Case Study 3: SaaS ERP Company – Employee Engagement and Profiling<\/strong><\/h2>\n
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Contact<\/h4>\n