{"id":3488,"date":"2026-08-04T12:31:51","date_gmt":"2026-08-04T10:31:51","guid":{"rendered":"https:\/\/humes.pl\/how-to-study-purchasing-behavior-in-retail-and-e-commerce-when-customers-move-across-channels\/"},"modified":"2026-08-26T06:08:46","modified_gmt":"2026-08-26T04:08:46","slug":"how-to-study-purchasing-behavior-in-retail-and-e-commerce-when-customers-move-across-channels","status":"publish","type":"post","link":"https:\/\/humes.pl\/en\/how-to-study-purchasing-behavior-in-retail-and-e-commerce-when-customers-move-across-channels\/","title":{"rendered":"How to study purchasing behavior in retail and e-commerce when customers move across channels"},"content":{"rendered":"<p>Customers rarely buy today in a single, closed environment. They check a product in an app, compare prices on a marketplace, look at it in a physical store, and complete the purchase wherever it is most convenient at the moment. That is why <strong>omnichannel shopping behavior research<\/strong> requires combining declarative, behavioral, and transactional data; otherwise, it is easy to misread what really influences channel choice and the moment of purchase.<\/p>\n<h2>How should omnichannel shopping behavior research be conducted when the customer journey does not fit within a single channel?<\/h2>\n<p><strong>Omnichannel shopping behavior research<\/strong> starts with the correct definition of the unit of analysis. It is not only about a single transaction, but about a sequence of contacts with a brand, category, or product. In practice, this means studying not only the question &#8220;where was the purchase made?&#8221;, but also &#8220;where did the search begin?&#8221;, &#8220;where were options compared?&#8221;, &#8220;where did doubt arise?&#8221;, and &#8220;what determined the final purchase?&#8221;<\/p>\n<p>This is an important methodological difference. In a classic retail or e-commerce approach, one touchpoint is often analyzed, for example a store visit, a session on the website, or an abandoned cart in the app. Meanwhile, <strong>consumer behavior analysis<\/strong> in an omnichannel model requires looking at the process as a whole. A customer may start the journey in a search engine, move to social media, visit a brick-and-mortar store, and then return to an online channel. Each of these stages generates a different type of data and each has different interpretive limitations.<\/p>\n<p>In research practice, the key is to separate three levels:<\/p>\n<ul>\n<li>the level of channel contact &#8211; that is, where the customer encountered the offer or information,<\/li>\n<li>the level of decision &#8211; that is, the moment when they began narrowing down their choice,<\/li>\n<li>the level of purchase &#8211; that is, the place and form in which the transaction was completed.<\/li>\n<\/ul>\n<p>Without this distinction, it is easy to assign too much weight to the sales channel and too little to the influence channel. This is one of the main reasons why standard <a href=\"https:\/\/humes.pl\/en\/glossary\/shopper-research\/\">shopper research<\/a> should be designed more broadly in an omnichannel environment than just around behavior at the shelf or in the online cart.<\/p>\n<p>In this context, it is also worth understanding how journey research differs from a simple measurement of channel satisfaction. The former answers the question about the course and mechanics of decision-making, while the latter concerns the evaluation of the experience after contact. Both types of research can be useful, but they are not interchangeable. If the goal is to understand movement between offline and online, tools are needed that capture the sequence of events, the reasons for switching, and the role of stimuli encountered along the way.<\/p>\n<p>That is why <strong>omnichannel shopping behavior research<\/strong> is particularly useful when there is a discrepancy between what customers say and the sales data, when it is difficult to determine the role of the physical store in an online purchase, or when results from one channel do not explain the entire shopping process. In such situations, a single source of data is usually not enough.<\/p>\n<h2>What data should be combined and what methods should be used to study omnichannel shopping behavior?<\/h2>\n<p><strong>Omnichannel shopping behavior research<\/strong> is based on combining sources that show different layers of behavior. Some data tell us what the customer did, others what they remembered, and still others the context in which the decision was made. Methodologically, the safest approach is to combine at least two perspectives: behavioral and declarative.<\/p>\n<p>The following types of data are most often combined:<\/p>\n<ul>\n<li>transactional data &#8211; show where and when the purchase took place,<\/li>\n<li>digital data &#8211; website and app journeys, clicks, drop-offs, search terms,<\/li>\n<li>declarative data &#8211; post-purchase surveys, panel studies, shopping diaries,<\/li>\n<li>qualitative data &#8211; in-depth interviews, ethnography, observation of the shopping process,<\/li>\n<li>contextual data &#8211; promotional exposure, product availability, type of shopping mission, usage situation.<\/li>\n<\/ul>\n<p>The choice of methods depends on the question the project is meant to answer. If the goal is to reconstruct the customer&#8217;s journey across channels, diary studies and event-triggered surveys work well. If the goal is to understand why a customer moved from online to a physical store or vice versa, qualitative interviews embedded in a specific shopping episode are needed. If the problem concerns the scale of the phenomenon, it is worth basing the project on <a href=\"https:\/\/humes.pl\/en\/glossary\/quantitative-research\/\">quantitative research<\/a> supplemented with behavioral data.<\/p>\n<p>In Hume&#8217;s Institute projects, the biggest interpretation error appears when the channel used to complete the purchase is treated as the most important channel of influence. A customer may buy online only because they previously viewed the product offline, consulted the choice with a salesperson, or checked the build quality on site. From a methodological perspective, this means that <strong>consumer behavior analysis<\/strong> based solely on e-commerce data will not explain the entire decision.<\/p>\n<p>A practical research framework for retail and e-commerce usually includes several steps. Each one addresses a different risk of measurement error.<\/p>\n<ol>\n<li><strong>Defining the category and the unit of purchase<\/strong> &#8211; a routine purchase is studied differently from an involved purchase, and differently again from a recurring service.<\/li>\n<li><strong>Mapping touchpoints<\/strong> &#8211; it is necessary to determine which channels actually participate in the decision, rather than only formally existing in the brand ecosystem.<\/li>\n<li><strong>Choosing data sources<\/strong> &#8211; it is necessary to check which data show behavior and which are only after-the-fact declarations.<\/li>\n<li><strong>Designing the research instrument<\/strong> &#8211; the questionnaire or interview guide should reconstruct the sequence of events, rather than ask only for a general opinion.<\/li>\n<li><strong>Combining and triangulating the results<\/strong> &#8211; findings from different sources need to be compared in a way that reveals discrepancies instead of smoothing them out.<\/li>\n<\/ol>\n<p>In retail research, short intercept interviews after a store visit also work well if they are combined with questions about earlier online activity. In projects in the area of <strong>e-commerce market research<\/strong>, surveys displayed after a purchase or cart abandonment can be useful, but only if the questions refer to a specific episode rather than general habits. The shorter the time gap between behavior and measurement, the lower the risk of memory distortion.<\/p>\n<p>If the goal is to capture seasonal changes, the impact of promotions, or shifts between channels over time, cyclical <a href=\"https:\/\/humes.pl\/en\/case-studies\/?filter_zakres=zwyczaje-zakupowe-i-zachowania-konsumenckie\">shopping trend analysis<\/a> delivers better results than a single measurement. Such a project makes it possible to see not only the structure of the journey, but also how the role of individual channels changes from one period to the next.<\/p>\n<h2>What mistakes should be avoided when interpreting omnichannel shopping behavior research?<\/h2>\n<p><strong>Omnichannel shopping behavior research<\/strong> is vulnerable to several common pitfalls. Most of them result not from the method itself, but from simplifications in the research design or in data interpretation. This is especially important in an environment where customers move between channels quickly and not always consciously.<\/p>\n<p>The first mistake is equating the journey with a funnel. A funnel assumes a linear order, while real customer behavior is often non-linear, with returns, breaks, and parallel comparison of offers. If the research instrument forces a simple sequence, part of the behavior will be lost.<\/p>\n<p>The second mistake is overreliance on retrospective declarations. Customers often remember the final purchase well, but the earlier micro-decisions less clearly. That is why in <strong>shopper research<\/strong> and projects in the area of <strong>e-commerce market research<\/strong>, it is worth using questions anchored in a specific purchase rather than asking generally about &#8220;usual decision-making.&#8221;<\/p>\n<p>The third mistake is mixing levels of analysis. Data are interpreted differently at the user level, session level, cart level, store visit level, or household level. If indicators from different levels are combined in one model without clear rules, the results may look coherent but lead to incorrect methodological conclusions.<\/p>\n<p>The fourth mistake is ignoring category context. Omnichannel behavior does not look the same for impulse products, planned purchases, high-risk products, or regularly purchased items. <strong>Consumer behavior analysis<\/strong> alone, without consideration of the type of purchase decision, may overestimate the importance of one channel or fail to capture the role of physical contact with the product.<\/p>\n<p>The fifth mistake is treating digital data as a full record of reality. Logs, clicks, and website journeys show what happened in a given environment, but they do not show what happened outside it. If a customer checked a review on another site, took a photo of the product in the store, or discussed the purchase through a messenger app, the analytics record alone will not show it. That is why <strong>omnichannel shopping behavior research<\/strong> should be designed around triangulation of data sources, not a single measurement.<\/p>\n<h2>How can you check whether an omnichannel research project has been designed well?<\/h2>\n<p>Before launching a study, it is worth going through a short methodological checklist. It helps assess whether the project really studies movement between channels, or merely describes a fragment of the process.<\/p>\n<ul>\n<li>Does the study cover more than just the purchase completion channel?<\/li>\n<li>Do the questions refer to a specific shopping episode, rather than only general opinions?<\/li>\n<li>Does the project include behavioral and declarative data?<\/li>\n<li>Has the starting point of the shopping journey been defined?<\/li>\n<li>Is it clear at what level the data will be analyzed: user, transaction, session, or visit?<\/li>\n<li>Does the instrument make it possible to identify a channel switch and the reason for that switch?<\/li>\n<\/ul>\n<p>If some of these questions do not have answers, there is a risk that <strong>omnichannel shopping behavior research<\/strong> will show only a fragment of the process. Then even correctly collected data will not answer the right research question.<\/p>\n<h2>Frequently asked questions<\/h2>\n<h3>How do omnichannel shopping behavior research and a standard customer satisfaction study differ?<\/h3>\n<p>A satisfaction study focuses on evaluating the experience after contact with the brand or channel. <strong>Omnichannel shopping behavior research<\/strong> reconstructs the course of the decision, the sequence of touchpoints, and the reasons for moving between channels. These are different research questions and different data sources.<\/p>\n<h3>What methods work best when analyzing customer movement between offline and online?<\/h3>\n<p>The best results come from combining methods. In practice, it is worth pairing the transactional and digital data the client already holds with a research layer: a post-purchase survey, a shopping diary, or a qualitative interview about a specific journey. This setup supports <strong>consumer behavior analysis<\/strong> well without limiting it to a single perspective.<\/p>\n<h3>How should shopper research be matched to an omnichannel journey?<\/h3>\n<p><strong>Shopper research<\/strong> is not limited to the brick-and-mortar store &#8211; it covers every moment when the customer makes a purchase decision, including in the digital environment. The difference lies in the context: in retail, the display, the shelf, and direct contact with the product are often decisive, while in e-commerce it is the product page, reviews, category layout, and the offer comparison path. In an omnichannel model, the two contexts cannot be treated separately, because the customer moves between them within a single purchase process.<\/p>\n<p>If the goal is to design a study that truly shows customer behavior across retail and e-commerce, it is worth discussing the scope of the project with <a href=\"https:\/\/humes.pl\/en\/contact\/\">Hume&#8217;s Institute<\/a>. This is a good time to match methods and data sources to the actual shopping journey, rather than to a simplified channel model.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Customers rarely buy today in a single, closed environment. They check a product in an app, compare prices on a marketplace, look at it in a physical store, and complete the purchase wherever it is most convenient at the moment. That is why omnichannel shopping behavior research requires combining declarative, behavioral, and transactional data; otherwise, [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":3495,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[912],"tags":[],"slowa_kluczowe":[],"class_list":["post-3488","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-badania-i-analizy"],"acf":[],"_wp_attached_file":null,"_wp_attachment_metadata":null,"wpml_media_processed":null,"_wpml_media_usage_in_posts":null,"_wp_attachment_context":null,"_oembed_35c905c64c03156f243b94f18c4eb80f":null,"_wp_attachment_image_alt":null,"rank_math_description":"See how purchasing behavior in retail and e-commerce is tracked across channel contact, decision, and purchase stages using behavioral data.","rank_math_focus_keyword":"purchasing behavior in retail","rank_math_contentai_score":null,"_wpml_post_translation_editor_native":null,"_menu_item_type":null,"_menu_item_menu_item_parent":null,"_menu_item_object_id":null,"_menu_item_object":null,"_menu_item_target":null,"_menu_item_classes":null,"_menu_item_xfn":null,"_menu_item_url":null,"_wp_page_template":null,"rank_math_og_content_image":null,"_wp_trash_meta_status":null,"_wp_trash_meta_time":null,"_wp_desired_post_slug":null,"rank_math_primary_category":910,"_acf_changed":null,"wp_pattern_sync_status":null,"_form":null,"_mail":null,"_mail_2":null,"_messages":null,"_additional_settings":null,"_locale":null,"_hash":null,"_config_validation":null,"_wp_old_slug":null,"rank_math_internal_links_processed":"1","_top_nav_excluded":null,"_cms_nav_minihome":null,"_thumbnail_id":"3495","_last_translation_edit_mode":"native-editor","_wpml_word_count":"2022","_dp_original":null,"_edit_last":"9","_edit_lock":"1787661656:9","rank_math_seo_score":"57","_wpml_location_migration_done":null,"_wpml_media_duplicate":null,"_wpml_media_featured":null,"_wp_old_date":"2026-08-25","copied_media_ids":[],"referenced_media_ids":[],"rank_math_title":"How purchasing behavior in retail shifts | Hume's Institute","job_department":null,"_job_department":null,"job_location":null,"_job_location":null,"job_offer_external_link":null,"_job_offer_external_link":null,"footnotes":null,"inline_featured_image":null,"blog_podtytul":"","_blog_podtytul":"field_blog_podtytul","blog_czas_czytania":"","_blog_czas_czytania":"field_blog_czas_czytania","blog_dalsza_lektura":"","_blog_dalsza_lektura":"field_blog_dalsza_lektura","slownik_krotka_definicja":null,"_slownik_krotka_definicja":null,"slownik_cytat":null,"_slownik_cytat":null,"slownik_na_stronie_glownej":"1","_slownik_na_stronie_glownej":null,"slownik_slowa_kluczowe":null,"_slownik_slowa_kluczowe":null,"slownik_w_praktyce":null,"_slownik_w_praktyce":null,"slownik_powiazane":null,"_slownik_powiazane":null,"slownik_kluczowe_punkty":null,"_slownik_kluczowe_punkty":null,"lang":"en","translations":{"en":3488},"pll_sync_post":{},"_links":{"self":[{"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/posts\/3488","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/comments?post=3488"}],"version-history":[{"count":1,"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/posts\/3488\/revisions"}],"predecessor-version":[{"id":3489,"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/posts\/3488\/revisions\/3489"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/media\/3495"}],"wp:attachment":[{"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/media?parent=3488"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/categories?post=3488"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/tags?post=3488"},{"taxonomy":"slowa_kluczowe","embeddable":true,"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/slowa_kluczowe?post=3488"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}