[et_pb_section fb_built=”1″ custom_padding_last_edited=”off|tablet” _builder_version=”4.17.4″ _module_preset=”default” min_height=”90vh” height=”90vh” custom_margin=”||||false|false” custom_margin_tablet=”||50px||false|false” custom_margin_last_edited=”off|tablet” custom_padding_tablet=”||||false|false” custom_padding_phone=”70px||0px||false|false” da_disable_devices=”off|off|off” global_colors_info=”{}” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row custom_padding_last_edited=”on|desktop” _builder_version=”4.17.4″ _module_preset=”default” positioning=”absolute” position_origin_a=”center_center” custom_margin=”||||false|false” custom_padding=”||||false|false” custom_padding_tablet=”100px||50px||false|false” custom_padding_phone=”50px||||false|false” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text admin_label=”eyebrow” _builder_version=”4.21.0″ _dynamic_attributes=”content” _module_preset=”default” text_line_height=”3.5em” custom_margin=”||30px||false|false” custom_margin_tablet=”||30px||false|false” custom_margin_phone=”||0px||false|false” custom_margin_last_edited=”on|phone” custom_padding=”||||false|false” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”5%” global_colors_info=”{}”]@ET-DC@eyJkeW5hbWljIjp0cnVlLCJjb250ZW50IjoicG9zdF90aXRsZSIsInNldHRpbmdzIjp7ImJlZm9yZSI6Ik1ldGhvZHMgJmFtcDsgVG9vbHM6ICIsImFmdGVyIjoiIn19@[\/et_pb_text][et_pb_text admin_label=”headline” _builder_version=”4.20.4″ _module_preset=”default” text_line_height=”3.5em” animation_style=”slide” animation_direction=”left” animation_delay=”500ms” animation_intensity_slide=”5%” header_font_size_tablet=”70px” global_colors_info=”{}”]<\/p>\n
[\/et_pb_text][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” max_width=”70%” max_width_tablet=”100%” max_width_phone=”100%” max_width_last_edited=”on|tablet” animation_style=”slide” animation_direction=”bottom” animation_delay=”1000ms” animation_intensity_slide=”5%” global_colors_info=”{}”]Relevant similarities instead of irrelevant differences.[\/et_pb_text][et_pb_button button_url=”#first” button_text=”Read more” button_alignment=”left” disabled_on=”off|off|off” _builder_version=”4.21.0″ _module_preset=”default” custom_button=”on” button_text_size=”20px” button_text_color=”#FFFFFF” button_bg_color=”#5D118F” button_letter_spacing=”0px” button_icon=”"||divi||400″ transform_translate_linked__hover=”off” background_layout=”dark” custom_margin=”60px||||false|false” custom_padding=”15px||15px|20px|true|false” animation_style=”slide” animation_direction=”bottom” animation_delay=”1500ms” animation_intensity_slide=”5%” global_colors_info=”{}” transform_styles__hover_enabled=”off|desktop” transform_scale__hover_enabled=”off|desktop” transform_translate__hover_enabled=”off|desktop” transform_rotate__hover_enabled=”off|desktop” transform_skew__hover_enabled=”off|desktop” transform_origin__hover_enabled=”off|desktop” transform_translate__hover=”0px|-5px”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”first” _builder_version=”4.20.4″ _module_preset=”default” background_enable_color=”off” background_image=”https:\/\/supersieben.com\/wp-content\/uploads\/2023\/06\/s7_tools_zielgruppenmaschine_bilder-1.jpg” background_enable_video_mp4=”off” da_disable_devices=”off|off|off” global_colors_info=”{}” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row column_structure=”1_3,2_3″ _builder_version=”4.20.4″ _module_preset=”default” background_enable_image=”off” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.17.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”4.17.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” text_font_size=”19px” background_layout=”dark” custom_margin=”||||false|false” custom_margin_tablet=”||||false|false” custom_margin_phone=”||10px||false|false” custom_margin_last_edited=”on|tablet” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”10%” global_colors_info=”{}”]A data-driven approach to realistic target group segmentation[\/et_pb_text][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” text_text_color=”#FFFFFF” text_line_height=”5em” header_2_text_color=”#FFFFFF” scroll_vertical_motion=”0|20|20|100|0.5|0|0″ scroll_horizontal_motion_enable=”on” scroll_horizontal_motion=”0|20|20|100|-0.5|0|0″ scroll_fade_enable=”on” scroll_fade=”0|20|20|100|0|100|100″ global_colors_info=”{}”]<\/p>\n
[\/et_pb_text][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” text_text_color=”#FFFFFF” link_text_color=”#FFFFFF” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”5%” global_colors_info=”{}” link_text_color__hover_enabled=”on|hover” link_text_color__hover=”#cccccc”]<\/p>\n
The classic way to segment target groups works like this: you look for one or more characteristics (age, consumption intensity, gender …) and sort people accordingly. In other words, you think up boxes. And then you divide people into those. [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.20.4″ _module_preset=”default” da_disable_devices=”off|off|off” global_colors_info=”{}” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row _builder_version=”4.20.4″ _module_preset=”default” width_tablet=”” width_phone=”” width_last_edited=”on|desktop” max_width=”800px” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” header_2_text_align=”center” text_orientation=”center” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.22.1″ _module_preset=”default” header_2_text_align=”center” text_orientation=”center” global_colors_info=”{}”]With the target group machine, we have succeeded in creating an algorithm that imitates the natural grouping behavior of people and therefore produces more realistic results. This is how it works<\/p>\n [\/et_pb_text][et_pb_text admin_label=”headline” _builder_version=”4.20.4″ _module_preset=”default” text_line_height=”3.5em” z_index=”10″ overflow-x=”visible” overflow-y=”visible” scroll_vertical_motion=”0|20|20|100|0.5|0|0″ scroll_horizontal_motion=”0|20|20|100|-0.5|0|0″ scroll_fade=”0|20|20|100|0|100|100″ global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_accordion db_closeable=”on” db_initial_state=”all_closed” open_toggle_background_color=”#FFFFFF” closed_toggle_text_color=”#000000″ closed_toggle_background_color=”#FFFFFF” toggle_icon=”L||divi||400″ icon_color=”#000000″ use_icon_font_size=”on” icon_font_size=”20px” disabled_on=”off|off|off” admin_label=”Akk Branchen” module_class=”close-icon” _builder_version=”4.20.4″ _module_preset=”default” toggle_font=”||||||||” toggle_font_size=”20px” closed_toggle_font=”||||||||” closed_toggle_font_size=”20px” background_enable_color=”off” width=”600px” width_tablet=”auto” width_phone=”auto” width_last_edited=”on|phone” custom_margin=”||||false|false” custom_margin_tablet=”||-30px||false|false” custom_margin_phone=”||-30px||false|false” custom_margin_last_edited=”off|desktop” custom_padding=”||||false|false” custom_css_toggle=”margin-bottom:0px!important;” custom_css_toggle_title=”font-family: lato sans, sans-serif !important;||transition: all .3s ease-in-out;||line-height:1.5;” global_colors_info=”{}” custom_css_toggle_title__hover_enabled=”on|hover” custom_css_toggle_title__hover=”margin-left: 10px;||transition: all .3s ease-in-out;” closed_toggle_text_color__hover_enabled=”off|hover” closed_toggle_text_color__hover=”#5D118F” open_toggle_text_color__hover_enabled=”off|desktop”][et_pb_accordion_item title=”Up to 400 properties” open=”on” _builder_version=”4.20.4″ _module_preset=”default” custom_padding=”|0px||0px|false|true” border_width_all=”0px” border_color_top=”#000000″ border_width_bottom=”1px” border_color_bottom=”#000000″ icon_color__hover=”#5D118F” icon_color__hover_enabled=”on|hover” global_colors_info=”{}” toggle_text_color=”#5D118F” closed_toggle_text_color__hover_enabled=”off|desktop” closed_toggle_text_color__hover=”#5D118F” open_icon_color__hover=”#5D118F” open_icon_color__hover_enabled=”on|hover”]<\/p>\n Which characteristics and attributes should be observed by the algorithm depends on your case: personality type, consumer behavior, favorite color, … everything is possible. The more the better.<\/p>\n [\/et_pb_accordion_item][et_pb_accordion_item title=”1.000 test subjects” _builder_version=”4.20.4″ _module_preset=”default” custom_padding=”|0px||0px|false|true” border_width_all=”0px” border_width_top=”1px” border_color_top=”#000000″ border_width_bottom=”1px” border_color_bottom=”#000000″ icon_color__hover=”#5D118F” icon_color__hover_enabled=”on|hover” global_colors_info=”{}” toggle_text_color=”#5D118F” closed_toggle_text_color__hover_enabled=”off|hover” closed_toggle_text_color__hover=”#5D118F” open_icon_color__hover=”#5D118F” open_icon_color__hover_enabled=”on|hover” open=”off”]We work with national and international online panels. Again, the more the merrier. This makes it easier to analyze subgroups. The algorithm uses Big Data methods to search for statistical twins (and nephews, cousins, and great aunts) among all the traits and their expressions. Groups are formed on the basis of these commonalities.<\/p>\n [\/et_pb_accordion_item][et_pb_accordion_item title=”Name commonalities” _builder_version=”4.20.4″ _module_preset=”default” custom_padding=”|0px||0px|false|true” border_width_all=”0px” border_color_top=”#000000″ border_width_bottom=”1px” border_color_bottom=”#000000″ icon_color__hover=”#5D118F” icon_color__hover_enabled=”on|hover” global_colors_info=”{}” toggle_text_color=”#5D118F” closed_toggle_text_color__hover_enabled=”off|desktop” closed_toggle_text_color__hover=”#5D118F” open_icon_color__hover=”#5D118F” open_icon_color__hover_enabled=”on|hover” open=”off”]<\/p>\n We determine what exactly connects these groups with each other with the support of artificial intelligence.<\/p>\n [\/et_pb_accordion_item][et_pb_accordion_item title=”Extrapolate” _builder_version=”4.20.4″ _module_preset=”default” custom_padding=”|0px||0px|false|true” border_width_all=”0px” border_color_top=”#000000″ border_width_bottom=”1px” border_color_bottom=”#000000″ icon_color__hover=”#5D118F” icon_color__hover_enabled=”on|hover” global_colors_info=”{}” toggle_text_color=”#5D118F” closed_toggle_text_color__hover_enabled=”off|desktop” closed_toggle_text_color__hover=”#5D118F” open_icon_color__hover=”#5D118F” open_icon_color__hover_enabled=”on|hover” open=”off”]<\/p>\n Since we work with large (and usually also representatively selected) samples, we can extrapolate the results to the population. <\/span><\/p>\n [\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.17.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_testimonial portrait_url=”https:\/\/supersieben.com\/wp-content\/uploads\/2022\/07\/Supersieben-Thomas-Walter.jpg” quote_icon=”off” quote_icon_color=”#5D118F” font_icon=”||fa||400″ _builder_version=”4.22.1″ _module_preset=”default” body_link_text_color=”#5D118F” animation_style=”fade” animation_delay=”500ms” box_shadow_style=”preset3″ global_colors_info=”{}” body_link_text_color__hover_enabled=”on|hover” body_link_text_color__hover=”#000000″ disabled_on=”on|on|on” disabled=”on”]<\/p>\n Talk to the expert:<\/strong><\/p>\n Thomas Walter<\/strong><\/p>\n tw@supersieben.de<\/a> LinkedIn<\/a> [\/et_pb_testimonial][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ disabled_on=”off|off|off” _builder_version=”4.20.4″ _module_preset=”default” background_image=”https:\/\/supersieben.com\/wp-content\/uploads\/2023\/06\/s7_tools_zielgruppenmaschine_bilder-4.jpg” background_position=”bottom_center” background_vertical_offset=”13%” min_height=”300px” da_disable_devices=”off|off|off” global_colors_info=”{}” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.20.4″ _module_preset=”default” da_disable_devices=”off|off|off” global_colors_info=”{}” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row _builder_version=”4.20.4″ _module_preset=”default” width_tablet=”” width_phone=”” width_last_edited=”on|desktop” max_width=”800px” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” header_2_text_align=”center” text_orientation=”center” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” header_2_text_align=”center” text_orientation=”center” global_colors_info=”{}”]Imagine you could describe someone with 10, 20, 100 or more characteristics. Wouldn’t that give you an extremely precise picture on the basis of which you could develop offers, measures and communication that fit the target group so well in a way that has not been possible before? That’s how it works: using algorithms to find target groups that you can really do something with. <\/p>\n","protected":false},"author":5,"featured_media":3655,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","inline_featured_image":false,"footnotes":""},"categories":[41,49],"tags":[],"class_list":["post-2356","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-methods-tools","category-target-group-engine"],"yoast_head":"\n
The advantage:<\/strong> At first glance, this always works.
The downside:<\/strong> How do you know if the people you put in a box really belong together? For example, do “heavy users, male, 45+” really form a cohesive group?<\/p>\nRelevant similarities instead of irrelevant differences.<\/h2>\n
\nThe Target Group Machine does not look for what separates people from each other, but for what connects them to each other. The algorithm works with up to 400 properties, but instead of forming boxes from them a priori, it determines the combinations that arise all by themselves. Just as people with matching characteristics organize themselves into groups in real life.[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”first” _builder_version=”4.20.4″ _module_preset=”default” background_enable_color=”off” background_image=”https:\/\/supersieben.com\/wp-content\/uploads\/2023\/06\/s7_tools_zielgruppenmaschine_bilder-3.jpg” background_position=”top_center” background_enable_video_mp4=”off” min_height=”700px” da_disable_devices=”off|off|off” global_colors_info=”{}” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][\/et_pb_section][et_pb_section fb_built=”1″ custom_padding_last_edited=”on|phone” disabled_on=”off|off|off” admin_label=”Referenzen” module_id=”references” _builder_version=”4.17.4″ _module_preset=”default” custom_padding=”100px||100px||true|false” custom_padding_tablet=”50px||50px||true|false” custom_padding_phone=”10px||10px||true|false” da_disable_devices=”off|off|off” global_colors_info=”{}” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row column_structure=”2_3,1_3″ _builder_version=”4.20.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”2_3″ _builder_version=”4.17.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.20.4″ _module_preset=”default” text_text_color=”#000000″ text_font_size=”19px” background_layout=”dark” custom_margin=”||||false|false” custom_margin_tablet=”||||false|false” custom_margin_phone=”||10px||false|false” custom_margin_last_edited=”on|tablet” animation_style=”slide” animation_direction=”bottom” animation_intensity_slide=”10%” global_colors_info=”{}”]<\/p>\nThinking
without boxes.<\/h2>\n
\nn = 1,000 is the minimum.[\/et_pb_accordion_item][et_pb_accordion_item title=”Algorithm Magic” _builder_version=”4.20.4″ _module_preset=”default” custom_padding=”|0px||0px|false|true” border_width_all=”0px” border_color_top=”#000000″ border_width_bottom=”1px” border_color_bottom=”#000000″ icon_color__hover=”#5D118F” icon_color__hover_enabled=”on|hover” global_colors_info=”{}” toggle_text_color=”#5D118F” closed_toggle_text_color__hover_enabled=”off|desktop” closed_toggle_text_color__hover=”#5D118F” open_icon_color__hover=”#5D118F” open_icon_color__hover_enabled=”on|hover” open=”off”]<\/p>\n\n
+49 211 936706-77<\/a><\/p>\n
Xing<\/a><\/p>\n<\/blockquote>\nThe result:
\nReal-life, real-world target groups<\/h2>\n
\nAll this is provided by the target group machine.[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"