TWI831287B - A target customer consumption preference behavior observation system and method - Google Patents
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Abstract
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本發明關於一種目標客群消費偏好行為觀察系統及方法，特別是一種利用虛實融合行為偏好常模並根據目標客群特徵及指定之人物誌型態產生對應指定之人物誌型態之目標客群消費偏好行為結果之目標客群消費偏好行為觀察系統及方法。 The present invention relates to a system and method for observing the consumption preference behavior of a target customer group, particularly a system and method that utilizes virtual and real fusion behavior preference norms and generates a target customer group corresponding to a designated persona type based on the characteristics of the target customer group and the designated persona type. System and method for observing consumption preference behavior of target customer groups as a result of consumption preference behavior.
根據經濟部批發、零售及餐飲業營業額統計，受疫情影響，2020年3月綜合商品零售業營業額年減34.3%、百貨公司年減21.6%。2020年第1季零售業網路銷售額年增19.1%，營業額來到811億元。如此消費板塊遞移現象，讓企業需重新思考行銷資源分配。過去實體門市與電商網購因科技進步逐漸打通隔閡，商品銷售朝向O2O(虛實整合，Online To Offline)發展，所謂O2O就是把線上(網路)的消費者，引導到線下(實體商店)，讓網路數據流與實際人流得以轉換，進而提升門市業績。過去O2O的確帶來人流，但也存在挑戰。例如無法全面掌握消費者偏好，導致許多品牌投入大量行銷資源，最後卻發現「轉換率」甚低。有別於O2O，OMO(虛實融合，Online Merge Offline)則重視精準行銷模式，以人為核心基礎， 需掌握不同目標族群的消費行為，才能精準觸及每一位顧客，使路人變成會員，將會員變成粉絲，以消費者偏好為核心，強調提高會員黏著度，整體推升虛實通路營業額。 According to statistics on the turnover of the wholesale, retail and catering industries of the Ministry of Economic Affairs, due to the impact of the epidemic, the turnover of the comprehensive merchandise retail industry fell by 34.3% year-on-year in March 2020, and that of department stores fell by 21.6% year-on-year. In the first quarter of 2020, online retail sales increased by 19.1% year-on-year, with turnover reaching 81.1 billion yuan. Such a shift in consumer sectors requires companies to rethink the allocation of marketing resources. In the past, technological progress gradually opened up the barriers between physical stores and e-commerce online shopping, and product sales developed towards O2O (integration of virtual and real, Online To Offline). The so-called O2O is to guide online (Internet) consumers to offline (physical stores). Convert network data flow and actual people flow, thereby improving store performance. In the past, O2O did bring traffic, but there were also challenges. For example, the inability to fully grasp consumer preferences has led many brands to invest a lot of marketing resources, only to find that the "conversion rate" is very low. Different from O2O, OMO (Online Merge Offline) attaches great importance to the precise marketing model and is based on people as the core. It is necessary to understand the consumption behavior of different target groups in order to accurately reach every customer, turn passers-by into members, and turn members into fans. With consumer preferences as the core, the emphasis is on improving member stickiness, and overall boosting the turnover of virtual and real channels.
不論是O2O或OMO，概念看似簡單，但對企業而言實際執行起來相當困難。主要是因為虛實通路的消費型態有落差，因此要從實體跨向電商，或是從電商跨足門市，企業都需要對消費者偏好有足夠的理解。因此有必要提供一種能讓企業瞭解其目標客群的OMO偏好，依據OMO消費行為進行分類標籤，藉此，可針對不同類型的會員量身訂製行銷活動，以實施精準的行銷策略溝通之系統，實為一值得研究的課題。 Whether it is O2O or OMO, the concept seems simple, but the actual implementation is quite difficult for enterprises. Mainly because there is a gap in consumption patterns between virtual and real channels. Therefore, companies need to have a sufficient understanding of consumer preferences to move from physical entities to e-commerce, or from e-commerce to stores. Therefore, it is necessary to provide a system that allows companies to understand the OMO preferences of their target customer groups and classify and label them based on OMO consumption behavior. Through this, marketing activities can be customized for different types of members to implement precise marketing strategy communication. , is actually a topic worthy of study.
本發明之主要目的係在提供一種利用虛實融合行為偏好常模並根據目標客群特徵及指定之人物誌型態產生對應指定之人物誌型態之目標客群消費偏好行為結果之目標客群消費偏好行為觀察系統。 The main purpose of the present invention is to provide a target customer group consumption that utilizes virtual and real fusion behavior preference norms and generates consumption preference behavior results of the target customer group corresponding to the designated persona type based on the characteristics of the target customer group and the designated persona type. Preference behavior observation system.
本發明之另一主要目的係在提供一種利用虛實融合行為偏好常模並根據目標客群特徵及指定之人物誌型態產生對應指定之人物誌型態之目標客群消費偏好行為結果之目標客群消費偏好行為觀察方法。 Another main purpose of the present invention is to provide a target customer that utilizes virtual and real fusion behavior preference norms and generates consumption preference behavior results of the target customer group corresponding to the designated persona type based on the characteristics of the target customer group and the designated persona type. Observation method of group consumption preference behavior.
為達成上述之目的，本發明之目標客群消費偏好行為觀察系統包括儲存裝置、觀察條件接收模組及觀察結果產生模組。儲存裝置儲存複數人物誌型態以及複數虛實融合行為偏好常模，其中各複數虛實融合行為偏好常模以複數人物誌型態為分類基礎。觀察條件接收模組產生觀察條件介面以接收一使用者由該複數人物誌型態指定其中之一之該人物誌型 態。觀察結果產生模組訊號連接儲存裝置及觀察條件接收模組，觀察結果產生模組根據指定之該人物誌型態由該複數虛實融合行為偏好常模產生對應指定之人物誌型態之目標客群消費偏好行為結果。 In order to achieve the above objectives, the consumption preference behavior observation system of the target customer group of the present invention includes a storage device, an observation condition receiving module and an observation result generating module. The storage device stores plural character types and plural virtual and real fusion behavioral preference norms, wherein each plural virtual and real fusion behavioral preference norm is based on the plural character types. The observation condition receiving module generates an observation condition interface to receive the persona type specified by a user from one of the plurality of persona types. state. The observation result generating module signal is connected to the storage device and the observation condition receiving module. The observation result generating module generates a target customer group corresponding to the specified persona type from the plurality of virtual and real fusion behavior preference norms based on the specified persona type. Consumption preference behavioral results.
本發明另提供一種目標客群消費偏好行為觀察方法，包括下列步驟：提供以複數人物誌型態為分類基礎之複數虛實融合行為偏好常模；接收由該複數人物誌型態指定其中之一之該人物誌型態；根據該指定之該人物誌型態由複數虛實融合行為偏好常模產生對應指定之人物誌型態之目標客群消費偏好行為結果。 The present invention also provides a method for observing the consumption preference behavior of a target customer group, which includes the following steps: providing a plurality of virtual and real fusion behavior preference norms based on a plurality of persona types; receiving one of the plurality of persona types designated by the plurality of persona types. The persona type; according to the specified persona type, a plurality of virtual and real fusion behavioral preference norms are used to generate consumption preference behavior results of the target customer group corresponding to the specified persona type.
本發明之零售消費行為觀察系統，以8類亞太人物誌模型觀察為基礎建構具有複數虛實融合行為偏好常模之消費偏好常模資料庫。蒐集跨世代消費者，在生活消費領域如食衣住行育樂面向，產業新零售虛實融合偏好行為等資訊，包含虛實融合導流方式、會員經營方式、通路經營方式及媒體偏好等內容。依據蒐集資料，建置餐飲與食品領域、健康與保健領域、個人與居家用品領域、教育與學習類領域、運動領域、娛樂領域、及/或旅遊領域之虛實融合行為常模資料庫資料，搭配人物誌資料庫，據以提供企業選擇適當消費者類型作為未來新零售通路目標對象行銷策略之擬定。 The retail consumption behavior observation system of the present invention constructs a consumption preference norm database with multiple virtual and real fusion behavioral preference norms based on the observation of eight types of Asia-Pacific personality models. Collect cross-generational consumers’ information on their preferences and behaviors in areas of life consumption such as food, clothing, housing, transportation, education, and new industrial retail, including virtual and real integration diversion methods, membership management methods, channel management methods, and media preferences. Based on the collected data, establish a database of virtual and real behavior norms in the fields of catering and food, health and wellness, personal and household products, education and learning, sports, entertainment, and/or tourism, and match The character profile database provides companies with the ability to select appropriate consumer types as the target audience for new retail channels in the future and formulate marketing strategies.
1:目標客群消費偏好行為觀察系統 1: Target customer group consumption preference behavior observation system
10:儲存裝置 10:Storage device
12:虛實融合行為偏好常模 12: Virtual and real integration behavior preference norm
123:虛實融合行為常模 123: Behavioral norm of integration of virtual and real
124:虛實融合資源分配常模 124: Virtual and real integration resource allocation norm
20:觀察條件接收模組 20: Observe conditional receiving module
21:觀察條件介面 21: Observation condition interface
22:目標客群特徵 22: Characteristics of target customer group
23:人物誌型態 23:Character type
30:觀察結果產生模組 30: Observation result generation module
31、31a、31b:目標客群消費偏好行為結果 31, 31a, 31b: Target customer group’s consumption preference behavior results
70:觀察規則 70: Observation Rules
312:虛實融合資源佈局建議 312: Suggestions on the layout of virtual and real integrated resources
313:虛實融合異業合作方式建議 313: Suggestions on cooperation methods between virtual and real integration in different industries
314:媒體偏好結果 314:Media preference results
315:通路偏好結果 315: Path preference results
316:虛實融合導流偏好結果 316: Virtual and real fusion diversion preference results
317:會員經營偏好結果 317: Member management preference results
90:客群屬性 90:Customer group attributes
8:使用者裝置 8: User device
80:消費領域 80:Consumption field
311:虛實融合策略佈局建議 311: Suggestions on virtual and real integration strategy layout
13:人物誌資料庫 13:Character database
圖1係本發明之目標客群消費偏好行為觀察系統之一實施例之硬體架構示意圖。 Figure 1 is a schematic diagram of the hardware architecture of one embodiment of the target customer group consumption preference behavior observation system of the present invention.
圖2係觀察條件介面之一實施例之示意圖。 Figure 2 is a schematic diagram of an embodiment of the observation condition interface.
圖3A係目標客群消費偏好行為結果之一實施例之示意圖之一。 Figure 3A is a schematic diagram of one embodiment of the consumption preference behavior results of the target customer group.
圖3B係目標客群消費偏好行為結果之一實施例之示意圖之二。 Figure 3B is the second schematic diagram of one embodiment of the consumption preference behavior results of the target customer group.
圖3C係目標客群消費偏好行為結果之一實施例之示意圖之三。 Figure 3C is the third schematic diagram of one embodiment of the consumption preference behavior results of the target customer group.
圖4係本發明之目標客群消費偏好行為觀察方法之一實施例之步驟流程圖。 Figure 4 is a step flow chart of one embodiment of the method for observing consumption preference behavior of target customer groups according to the present invention.
為能更瞭解本發明之技術內容，特舉較佳具體實施例說明如下。以下請一併參考圖1、圖2、圖3A至圖3C關於本發明之目標客群消費偏好行為觀察系統之一實施例之硬體架構示意圖、觀察條件介面之一實施例之示意圖、及目標客群消費偏好行為結果之一實施例之示意圖。 In order to better understand the technical content of the present invention, preferred specific embodiments are described below. Please refer to Figures 1, 2, 3A to 3C below for a schematic diagram of the hardware architecture, a schematic diagram of an embodiment of the observation condition interface, and the target of an embodiment of the target customer group consumption preference behavior observation system of the present invention. A schematic diagram of an example of the results of customer consumption preference behavior.
如圖1所示，本發明之目標客群消費偏好行為觀察系統1譬如是一台或數台電腦伺服器並可與一使用者裝置8訊號連接。在本實施例中，目標客群消費偏好行為觀察系統1包括儲存裝置10、觀察條件接收模組20及觀察結果產生模組30，其中觀察結果產生模組30訊號連接儲存裝置10及該觀察條件接收模組20。儲存裝置10可以是固定式或可移動式的非暫態的電腦可讀取儲存媒體，包括但不限於隨機存取記憶體(random access memory，RAM)、唯讀記憶體(read-only memory，ROM)、快閃記憶體、光碟、或其他類似元件、或上述元件的組合，儲存裝置10儲存人物誌資料庫13及複數虛實融合行為偏好常模12，其中人物誌資料庫13包括複數人物誌型態，本實施例之複數人物誌型態包括精算管家型、享樂翻糖型、知性陀飛輪型、神秘暹羅貓型、刻苦力爭型、積極開創型、決策苦手型及 生活從眾型。複數虛實融合行為偏好常模12以前述8種人物誌型態為分類基礎，並可進一步包括如年齡區段、複數性別、複數消費領域及/或複數就業狀態等分類指標。具體來說，本發明將所蒐集的Y世代、X世代與嬰兒潮世代消費者在不同消費領域量化的調查資料，分析其平均數、標準差、百分等級等數據，建立標準分數常模、建構組間常模、組內常模，並依據複數人物誌型態以產生複數人物誌型態對應之複數虛實融合行為偏好常模12。在本實施例中，複數虛實融合行為偏好常模12利用12,000份樣本建立兩種不同的實務常模服務方案：複數虛實融合資源分配常模124及複數虛實融合行為常模123，本實施例之複數虛實融合行為偏好常模12一共有65,952個行為常模，可用來解釋Y世代、X世代與嬰兒潮世代消費者在不同消費偏好行為的程度，未來只需透過產品之目標客群特徵22及人物誌型態23，即可顯示目標客群特徵22及人物誌型態23所屬分類中的各類虛實融合消費行為相對可能偏好程度。根據本發明之一具體實施例，各該虛實融合行為偏好常模12包括複數觀察指標與對應各該觀察指標之行為偏好統計數據，其中該複數觀察指標包括複數消費領域、複數人物誌類型、複數性別資料(如：男、女)、複數年齡段資料(如：1955年前生、1965前後生)、或世代別資料(如：Y世代、X世代)、或/及複數就業資料(是否就業)。 As shown in Figure 1, the target customer group consumption preference behavior observation system 1 of the present invention is, for example, one or several computer servers and can be connected to a user device 8 via a signal. In this embodiment, the target customer group consumption preference behavior observation system 1 includes a storage device 10, an observation condition receiving module 20 and an observation result generating module 30, wherein the observation result generating module 30 is connected to the storage device 10 and the observation condition through signals. Receive module 20. The storage device 10 may be a fixed or removable non-transitory computer-readable storage medium, including but not limited to random access memory (RAM), read-only memory, ROM), flash memory, optical disc, or other similar components, or a combination of the above components, the storage device 10 stores a character database 13 and a plurality of virtual and real integrated behavioral preference norms 12, where the character database 13 includes a plurality of character profiles The plural personality types in this embodiment include the actuarial housekeeper type, the hedonic fondant type, the intellectual tourbillon type, the mysterious Siamese cat type, the hard-working type, the active pioneering type, the decision-making type and the Live a herd-like life. The plural virtual and real integration behavioral preference norm 12 is based on the above-mentioned eight personality types, and can further include classification indicators such as age range, plural gender, plural consumption fields and/or plural employment status. Specifically, the present invention collects quantitative survey data on consumers of Generation Y, Generation Construct the between-group norm and the within-group norm, and generate a plurality of virtual and real fusion behavioral preference norms corresponding to the plurality of character types based on the plurality of character types12. In this embodiment, the plural virtual-real fusion behavior preference norm 12 uses 12,000 samples to establish two different practical norm service solutions: the plural virtual-real fusion resource allocation norm 124 and the plural virtual-real fusion behavioral norm 123. In this embodiment, There are a total of 65,952 behavioral norms in the plural virtual and real integration behavioral preference norm12, which can be used to explain the degree of different consumption preference behaviors of Generation Y, Generation Persona type 23 can display the target customer group characteristics 22 and the relative possible preference degree of various virtual and real consumption behaviors in the category to which persona type 23 belongs. According to a specific embodiment of the present invention, each virtual and real fusion behavioral preference norm 12 includes a plurality of observation indicators and behavioral preference statistical data corresponding to each observation indicator, wherein the plurality of observation indicators include a plurality of consumption fields, a plurality of character types, a plurality of character types, and a plurality of behavioral preference statistics. Gender data (such as male and female), plural age group data (such as those born before 1955, those born before and after 1965), or generation data (such as Generation Y, Generation X), or/and plural employment data (whether they are employed or not) ).
虛實融合資源分配常模124係由單一消費領域四類虛實融合行為，該些四類虛實融合行為為媒體偏好、通路偏好、導流偏好及會員經營偏好，其中媒體偏好有10種行為、通路偏好有6種行為、導流偏好有6種行為、及會員經營偏好有6種行為，共計28種虛實融合偏好行為。再透過複數人物誌型態、性別資料、年齡資料、就業狀況資料等基本屬性，定義出 64種不同之消費者特徵，由7大消費領域(餐飲與食品領域、健康與保健領域、個人與居家用品領域、教育與學習類領域、運動領域、娛樂領域、旅遊領域)、28種虛實融合偏好行為、64種消費者特徵等，建構出12,544種虛實融合資源分配常模124。虛實融合資源分配常模124可讓使用者只需輸入目標客群之人物誌型態及性別資料、年齡資料、就業狀況資料等觀察資訊，運用虛實融合資源分配常模124，就能夠顯示目標客群與其他所有類型比較的相對地位。有助於產業業者進行廣告投放、營銷經營資源配置之參考。使用虛實融合行為常模123，則協助政府相關單位及企業，從行為面的角度，瞭解特定消費者虛實融合行為類型的涉入程度，以及消費者同一種偏好行為在不同產業領域之間是否有差異，以觀察台灣特定消費者目前虛實融合行為成熟度。使用者只要輸入目標客群目標客群之人物誌型態及性別資料、年齡資料、就業狀況資料等觀察資訊，即能夠瞭解該種類型的消費者在虛實融合偏好行為的滲透度與跨業產度，作為擬訂未來行銷發展策略的重點依據。虛實融合行為常模123係由將7種消費領域、4類OMO行為偏好(媒體、通路、導流、會員經營)，分別計算個別消費者得分情形；將消費者特徵依人物誌型態、性別、年齡、就業狀況，分別標記64種消費特徵；在將64種消費特徵、7種消費領域、4種OMO行為滲透度，組成巢狀樣態，分別計算OMO行為滲透度之平均數、標準差、前25%(第3四分位數)、中位數、後25%(第1四分位數)等數值，建構1,792組虛實融合行為常模123。 The virtual and real integration resource allocation norm 124 consists of four types of virtual and real integration behaviors in a single consumption field. These four types of virtual and real integration behaviors are media preference, channel preference, diversion preference and member management preference. Among them, there are 10 types of media preference behaviors, channel preference There are 6 kinds of behaviors, 6 kinds of behaviors for diversion preferences, and 6 kinds of behaviors for member management preferences, for a total of 28 kinds of virtual and real preference behaviors. Then through basic attributes such as plural character types, gender data, age data, and employment status data, we define 64 different consumer characteristics, consisting of 7 major consumption fields (catering and food field, health and wellness field, personal and home supplies field, education and learning field, sports field, entertainment field, tourism field), 28 types of virtual and real integration Preference behavior, 64 consumer characteristics, etc., 12,544 virtual and real integrated resource allocation norms were constructed124. The virtual and real fusion resource allocation norm 124 allows users to simply input the target customer group's demographic type and gender data, age data, employment status data and other observation information. Using the virtual and real fusion resource allocation norm 124, the target customers can be displayed. The relative status of the group compared to all other types. It is helpful for industry players to use as a reference for advertising and marketing and operation resource allocation. The use of virtual and real integration behavior norm 123 will assist relevant government units and enterprises to understand the degree of involvement of specific consumers in virtual and real integration behavior types from a behavioral perspective, and whether consumers have the same preference behavior in different industrial fields. Differences in order to observe the current maturity of virtual and real integration behavior of specific consumers in Taiwan. Users only need to input the target customer group's demographic profile, gender data, age data, employment status data and other observation information to understand the penetration and cross-industry industries of this type of consumers in the integration of virtual and real preferences. degree as a key basis for formulating future marketing development strategies. The virtual and real integration behavioral norm 123 is composed of 7 consumption areas and 4 types of OMO behavioral preferences (media, channel, diversion, member management), and separately calculates the scores of individual consumers; the consumer characteristics are based on demographic type, gender , age, and employment status, respectively mark 64 consumption characteristics; after 64 consumption characteristics, 7 consumption fields, and 4 OMO behavior penetrations are formed into a nested state, the average and standard deviation of the OMO behavior penetrations are calculated respectively. , top 25% (3rd quartile), median, bottom 25% (1st quartile) and other values to construct 1,792 sets of virtual and real fusion behavioral norms 123.
根據本發明之一具體實施例，虛實融合行為常模123更包括虛實融合行為滲透度指標與虛實融合行為跨業廣度指標，其中虛實融合行為滲透度指標為檢視4類虛實融合偏好行為中，消費者涉入/喜好程度，以 觀察該特徵之消費者虛實融合行為深度，其中虛實融合偏好行為為媒體偏好、通路偏好、虛實融合(OMO)導流偏好、及會員經營偏好。虛實融合行為滲透度指標的計算方式為分別計算4類虛實融合偏好行為消費者涉入/喜好的行為數。在本實施例中，媒體偏好共有10種行為，具體包括社群/粉絲團/部落格、入口網站/關字搜尋、電子郵件/電子報/DM、YouTube、Clubhouse/podcast、論壇、電視、平面媒體、各品牌/店家APP、及內容網站。通路偏好共有6種行為，具體包括品牌門市/餐廳、連鎖通路、品牌官網、品牌/餐廳APP、購物網站/APP、及電話購買(電銷)。導流偏好共有6種行為，具體包括線上下單到店取貨、使用門市優惠券、參與門市活動、註冊會員、下載APP、及門市購買線上積點。會員經營偏好共有6種行為，具體包括會員紅利積點/里程回饋、會員限量/限時搶購、會員獨家折扣、會員專屬現金回饋、會員獨家贈品、及專屬你個人的推薦服務。前述各類虛實融合偏好行為，計算勾選項目數即為該行為滲透度分數，勾選項目越多，代表該領域消費時，該項虛實融合行為滲透度越高。 According to a specific embodiment of the present invention, the virtual-real fusion behavior norm 123 further includes a virtual-real fusion behavior penetration index and a virtual-real fusion behavior cross-industry breadth index. The virtual-real fusion behavior penetration index is the consumption index among the four types of virtual-real fusion preference behaviors. the level of involvement/interest of the Observe the depth of consumers' virtual-real integration behavior with this characteristic. The virtual-real integration preference behavior includes media preference, channel preference, virtual-real integration (OMO) diversion preference, and member management preference. The calculation method for the penetration index of virtual and real fusion behavior is to calculate the number of behaviors that consumers are involved in/like in the four types of virtual and real fusion preference behaviors. In this example, there are 10 types of media preferences, including community/fan club/blog, portal/keyword search, email/email/DM, YouTube, Clubhouse/podcast, forum, TV, print Media, various brand/store apps, and content websites. There are 6 types of behaviors in channel preference, including brand stores/restaurants, chain channels, brand official websites, brand/restaurant APPs, shopping websites/APPs, and telephone purchases (telemarketing). There are 6 types of behaviors for diversion preferences, including placing an order online and picking up the goods in a store, using store coupons, participating in store activities, registering as a member, downloading an APP, and purchasing online points in a store. There are 6 types of behaviors for member management preferences, including member bonus points/mileage feedback, member limited/limited time sales, member exclusive discounts, member exclusive cash rewards, member exclusive gifts, and your own personal recommendation service. For the aforementioned various virtual and real fusion preference behaviors, the penetration score of the behavior is calculated by calculating the number of checked items. The more checked items, the higher the penetration of the virtual and real fusion behavior when consuming in this field.
虛實融合行為跨業廣度指標為檢視前述28種虛實融合偏好行為中，消費者在7種消費領域涉入/喜好的情況。用以觀察目標客群消費者虛實融合行為廣度。企業能夠使用虛實融合行為跨業廣度判斷特定消費者的偏好行為強度，並檢視該偏好行為是只侷限在少數領域，或是在各種領域都會使用或喜好。虛實融合行為跨業廣度指標計算方式為分別計算28種虛實融合行為(媒體10種、通路6種、導流6種、會員經營6種)，消費者在7大消費領域(餐飲與食品領域、健康與保健領域、個人與居家用品領域、教育與學習類領域、運動領域、娛樂領域、旅遊領域)涉入/喜好的行為數。 以虛實融合偏好下單一行為，在不同消費領域出現之數量，即為該行為廣度分數，單一行為出現在不同領域數越多，代表該項虛實融合行為廣度越高。 The cross-industry breadth index of virtual and real integration behaviors examines consumers’ involvement/preferences in 7 consumption areas among the 28 aforementioned virtual and real integration preference behaviors. It is used to observe the breadth of virtual and real behaviors of consumers in the target customer group. Enterprises can use virtual and real integration behaviors to judge the strength of specific consumers’ preference behaviors across industries and examine whether the preference behavior is limited to a few areas, or is used or preferred in various areas. The calculation method for the cross-industry breadth index of virtual and real integration behaviors is to calculate 28 types of virtual and real integration behaviors (10 types of media, 6 types of channels, 6 types of diversion, and 6 types of membership management). Consumers in 7 major consumption fields (catering and food fields, The number of behaviors involved/favored in the fields of health and wellness, personal and household products, education and learning, sports, entertainment, and travel. The number of a single behavior appearing in different consumption fields based on virtual and real fusion preferences is the behavioral breadth score. The more a single behavior appears in different fields, the higher the breadth of the virtual and real fusion behavior.
如圖1與圖2所示，觀察條件接收模組20於使用者裝置8之顯示螢幕產生觀察條件介面21，以供使用者於對應的欄位中填入觀察條件。在本實施例中，觀察條件介面21可供使用者填入的觀察條件包括消費領域80、人物誌型態23及目標客群特徵22、22a、22b，其中消費領域80包括餐飲與食品領域、健康與保健領域、個人與居家用品領域、教育與學習類領域、運動領域、娛樂領域、及/或旅遊領域；人物誌型態23為8種人物誌型態；目標客群特徵22為性別、目標客群特徵22a為世代別、目標客群特徵22b為就業狀態。使用者可分別於每個欄位中指定欲觀察之目標客群的條件。 As shown in FIGS. 1 and 2 , the observation condition receiving module 20 generates an observation condition interface 21 on the display screen of the user device 8 for the user to fill in the observation conditions in the corresponding fields. In this embodiment, the observation conditions that the user can fill in in the observation condition interface 21 include the consumption field 80, the demographic profile 23, and the target customer group characteristics 22, 22a, and 22b. The consumption field 80 includes the catering and food field, Health and wellness field, personal and home supplies field, education and learning field, sports field, entertainment field, and/or tourism field; Persona type 23 is 8 persona types; Target customer group characteristics 22 are gender, The target customer group characteristic 22a is generation, and the target customer group characteristic 22b is employment status. Users can specify the conditions of the target customer groups they want to observe in each field.
以圖2的例子來說，使用者指定之觀察條件為：消費領域80為餐飲與食品領域；人物誌型態23為享樂翻糖型之人物誌型態；目標客群特徵22為女性、目標客群特徵22a為Y世代、目標客群特徵22b為有就業。觀察條件接收模組20接收前述之目標客群之人物誌型態23及目標客群特徵22、22a、22b後，觀察結果產生模組30根據所接收之觀察條件利用複數虛實融合行為偏好常模12中之與觀察條件接收模組20所接收之觀察條件相對應之複數觀察指標與對應各該觀察指標之行為偏好統計數據產生如圖3A至圖3C所示之一目標客群消費偏好行為結果31、31a、31b。指定之該人物誌型態23、目標客群特徵22為女性、目標客群特徵22a為Y世代、目標客群特徵22b為有就業產生如圖3A至圖3C所示對應指定之該人物誌型態 23、女性、Y世代、有就業之一目標客群消費偏好行為結果31、31a、31b。本實施例之目標客群消費偏好行為結果31、31a、31b包括一虛實融合策略佈局建議311如圖3A所示、一虛實融合資源佈局建議312如圖3B所示、一虛實融合異業合作方式建議313如圖3C所示。且如圖3A至圖3C所示，虛實融合策略佈局建議311、虛實融合資源佈局建議312、虛實融合異業合作方式建議313皆包括一媒體偏好結果314、314a、314b、一通路偏好結果315、315a、315b、一虛實融合導流偏好結果316、316a、316b、及/或一會員經營偏好結果317、317a、317b。 Taking the example in Figure 2 as an example, the observation conditions specified by the user are: consumption field 80 is the catering and food field; character type 23 is the hedonic fondant type character type; target customer group characteristics 22 are women, target The customer group characteristics 22a are Generation Y, and the target customer group characteristics 22b are employed. After the observation condition receiving module 20 receives the aforementioned target customer group's demographic profile 23 and target customer group characteristics 22, 22a, and 22b, the observation result generating module 30 utilizes complex virtual and real fusion behavioral preference norms according to the received observation conditions. The plural observation indicators in 12 corresponding to the observation conditions received by the observation condition receiving module 20 and the behavioral preference statistical data corresponding to each observation indicator produce a target customer group consumption preference behavior result as shown in Figures 3A to 3C. 31, 31a, 31b. The designated persona type 23, the target customer group characteristic 22 is female, the target customer group characteristic 22a is Generation Y, and the target customer group characteristic 22b is employed, as shown in Figures 3A to 3C corresponding to the specified persona type. state 23. The consumption preference behavior results of the target customer groups of women, Generation Y, and those who are employed are 31, 31a, and 31b. The consumption preference behavior results 31, 31a, and 31b of the target customer group in this embodiment include a virtual-real integration strategy layout suggestion 311 as shown in Figure 3A, a virtual-real integration resource layout suggestion 312 as shown in Figure 3B, and a virtual-real integration cross-industry cooperation method. Recommendation 313 is shown in Figure 3C. As shown in Figures 3A to 3C, the virtual-real integration strategy layout suggestion 311, the virtual-real integration resource layout suggestion 312, and the virtual-real integration cross-industry cooperation method suggestion 313 all include a media preference result 314, 314a, 314b, a channel preference result 315, 315a, 315b, a virtual and real fusion diversion preference result 316, 316a, 316b, and/or a member operation preference result 317, 317a, 317b.
如圖3A所示，在本實施例中，目標客群消費偏好行為結果31顯示針對享樂翻糖型之人物誌型態；目標客群特徵22為女性、目標客群特徵22a為Y世代、目標客群特徵22b為有就業產生之虛實融合策略佈局建議311，也是虛實融合行為常模123之虛實融合行為滲透度指標產生的結果，此結果可觀察特定消費性格客群(人物誌型態)，在4類消費偏好行為何種消費行為較具佈局優勢，可由偏好行為觀察，當出現該類行為平均次數越高時，代表業者進行虛實融合佈局時，可投注之方向。可參考4類虛實融合消費行為，越靠上方者，資源投注比例建議越高，建議關注項目以「平均~最高」數為主。如圖3A所示，在本實施例中，這類消費者在餐飲領域，媒體偏好中，享樂翻糖型之人物誌型態女性、Y世代(26-40歲)有就業的消費者在媒體偏好的10種消息來源管道中，使用將近3種左右的消息來源管道；通路偏好中消費者使用不到2種管道進行消費行為；OMO導流偏好中消費者偏好的深度平均為2.2；會員經營偏好的平均為2.5。 As shown in Figure 3A, in this embodiment, the consumption preference behavior result 31 of the target customer group shows the demographic profile of the hedonic fondant type; the target customer group characteristics 22 are women, the target customer group characteristics 22a are Generation Y, and the target customer group characteristics 22a are women. Customer group characteristics 22b are the virtual and real integration strategic layout suggestions 311 generated by employment, and are also the result of the virtual and real integration behavior penetration index of the virtual and real integration behavior norm 123. This result can observe the specific consumer personality customer group (character profile), Among the four types of consumer preference behaviors, which consumer behavior has more layout advantages can be observed from the preference behavior. When the average number of occurrences of this type of behavior is higher, it represents the direction in which the industry can bet when integrating virtual and real layouts. You can refer to the 4 types of virtual and real consumption behaviors. The higher up, the higher the resource investment ratio is recommended. It is recommended that the items of concern should be based on the "average ~ highest" number. As shown in Figure 3A, in this embodiment, this type of consumers are in the field of catering and media preferences. They are fondant type, female, and Generation Y (26-40 years old) employed consumers in the media. Among the 10 preferred news source channels, nearly 3 are used; in channel preference, consumers use less than 2 channels for consumption behavior; in OMO diversion preference, the average depth of consumer preference is 2.2; member management The average preference is 2.5.
如圖3B所示，在本實施例中，目標客群消費偏好行為結果31a顯示針對享樂翻糖型之人物誌型態；目標客群特徵22為女性、目標客群特徵22a為Y世代、目標客群特徵22b為有就業產生之虛實融合資源佈局建議312，由虛實融合資源分配常模124產生，虛實融合資源佈局建議312可觀察特定消費性格客群，在4類消費偏好行為何種行為較具優勢，當出現該類行為頻率占比越高時，代表業者進行OMO資源佈局時，可投注之方向。可參考4類OMO消費行為，4大偏好中比例越高的項目，建議可以投注較多資源。建議依虛實融合資源佈局建議312選擇，例如虛實融合資源佈局建議312媒體偏好平均2個，則可選擇媒體偏好中最高的2種模式投入資源。 As shown in Figure 3B, in this embodiment, the consumption preference behavior result 31a of the target customer group shows the demographic profile of the hedonic fondant type; the target customer group characteristics 22 are women, the target customer group characteristics 22a are Generation Y, and the target customer group characteristics 22 are women. Customer group characteristics 22b are the virtual and real integrated resource layout suggestions 312 generated by employment, which are generated by the virtual and real integrated resource allocation norm 124. The virtual and real integrated resource layout suggestions 312 can observe specific consumer personality customer groups, which behavior is more favorable among the four types of consumer preference behaviors. It has advantages. When the frequency of this type of behavior is higher, it represents the direction in which the industry can bet when arranging OMO resources. You can refer to the 4 types of OMO consumption behaviors. The higher the proportion of items among the 4 preferences, the more resources you can invest in them. It is recommended to choose according to the virtual and real integration resource layout recommendations 312. For example, if the virtual and real integration resource layout recommendations 312 have an average of 2 media preferences, you can choose the two modes with the highest media preferences to invest resources.
如圖3B所示，在本實施例中，由虛實融合資源佈局建議312可知，此類消費者使用實體通路比例高，即透過品牌門市/餐廳(50.8%)與連鎖通路(32.3%)進行餐飲消費，但此類消費者有將三成左右的消費者會使用線上的消費通路，包含購物網站/App(36.4%)與品牌/餐廳App(33.2%)，整體而言實體通路略大於虛擬通路，除了電銷(14.7%)之外，其餘通路皆有將近三成以上的消費者會使用，多元性高。虛實融合導流偏好中，偏好廣度最高的為註冊會員與門市購買線上積點，說明這個類型的消費者在4個領域以上皆偏好這兩類的導流活動。若企業以此類消費者作為目標客戶，且要一次針對多種領域推出促銷活動，即請客戶註冊會員與門市購買線上積點兩種活動的效果最好 As shown in Figure 3B, in this embodiment, it can be seen from the virtual-real integration resource layout suggestion 312 that such consumers use a high proportion of physical channels, that is, through brand stores/restaurants (50.8%) and chain channels (32.3%) for dining. Consumption, but about 30% of such consumers will use online consumption channels, including shopping websites/Apps (36.4%) and brand/restaurant apps (33.2%). Overall, physical channels are slightly larger than virtual channels. Except for electronic sales (14.7%), nearly 30% of consumers use other channels, showing high diversity. Among the virtual and real integration diversion preferences, the ones with the highest breadth of preferences are registered members and online points purchased in stores, indicating that this type of consumers prefer these two types of diversion activities in more than four fields. If a company targets this type of consumers and launches promotions in multiple areas at once, the best results would be to ask customers to register as members and to purchase online points in stores.
如圖3C所示，在本實施例中，目標客群消費偏好行為結果31b顯示針對享樂翻糖型之人物誌型態；目標客群特徵22為女性、目標客群特徵22a為Y世代、目標客群特徵22b為有就業產生之虛實融合異業合作 方式建議313，也是虛實融合行為常模123之虛實融合行為跨業廣度指標產生的結果，藉此可觀察特定消費性格客群(特定人物誌型態)，在4類消費偏好行為中何種行為較具優勢，當出現該類行為頻率占比越高時，代表業者進行OMO資源佈局時，可投注之方向。可參考4類OMO消費行為，4大偏好中比例越高的項目，即為可進行異業結盟合作。數值代表跨領域合作的強度。舉例而言，在媒體偏好(OMO行為)中的「社群/粉絲團/部落格」行為，若某特徵消費者在7大消費領域中，共有6個領域都出現此行為。則該偏好行為廣度中，得分6分，以該特徵消費者為目標客群的企業可多投注心力進行異業結盟合作。 As shown in Figure 3C, in this embodiment, the consumption preference behavior result 31b of the target customer group shows the demographic profile of the hedonic fondant type; the target customer group characteristics 22 are women, the target customer group characteristics 22a are Generation Y, and the target customer group characteristics 22 are women. Customer group characteristics 22b are virtual and real integration of employment and cross-industry cooperation. Method suggestion 313 is also the result of the cross-industry breadth index of virtual and real fusion behavior norm 123, which can be used to observe the behaviors of specific consumer personality groups (specific character profiles) in the four types of consumer preference behaviors. It is more advantageous. When the frequency of this type of behavior is higher, it represents the direction in which the industry can bet when arranging OMO resources. You can refer to the 4 types of OMO consumption behaviors. The items with a higher proportion of the 4 major preferences are the ones that can form cross-industry alliances and cooperation. The numerical value represents the intensity of cross-domain cooperation. For example, in the "community/fan group/blog" behavior in media preferences (OMO behavior), if consumers with a certain characteristic appear in 6 of the 7 major consumption areas, this behavior occurs. In the breadth of this preferred behavior, the score is 6 points. Companies targeting consumers with this characteristic can put more effort into cross-industry alliances and cooperation.
需注意的是，上述各個模組除可配置為硬體裝置、軟體程式、韌體或其組合外，亦可藉電路迴路或其他適當型式配置；並且，各個模組除可以單獨之型式配置外，亦可以結合之型式配置。一個較佳實施例是各模組皆為軟體程式儲存於記憶體上，藉由目標客群消費偏好行為觀察系統1中的一處理器(圖未示)執行各模組以達成本發明之功能。此外，本實施方式僅例示本發明之較佳實施例，為避免贅述，並未詳加記載所有可能的變化組合。此外，在本發明之一具體實施例中，係以PHP動態網頁開發語言(PHP：Hypertext Preprocessor)方式建置，目標客群消費偏好行為觀察系統1之功能介面。 It should be noted that, in addition to being configured as hardware devices, software programs, firmware, or a combination thereof, each of the above modules can also be configured by circuit loops or other appropriate types; and, in addition to being configured as separate modules, each module can also be configured in a separate form. , and can also be configured in combination. A preferred embodiment is that each module is a software program stored in the memory, and each module is executed by a processor (not shown) in the target customer group consumption preference behavior observation system 1 to achieve the functions of the present invention. . In addition, this embodiment only illustrates the preferred embodiments of the present invention. To avoid redundancy, all possible combinations of changes are not described in detail. In addition, in a specific embodiment of the present invention, the functional interface of the target customer group consumption preference behavior observation system 1 is constructed using PHP dynamic web development language (PHP: Hypertext Preprocessor).
以下請繼續參考圖1、圖2、圖3A至圖3C，並一起參考圖4關於本發明之目標客群消費偏好行為觀察方法之一實施例之步驟流程圖。如圖4所示，本發明之目標客群消費偏好行為觀察方法包括步驟S1至步驟S3，以下詳細說明各步驟。 Please continue to refer to Figures 1, 2, 3A to 3C below, and refer to Figure 4 for a step flow chart of one embodiment of the method for observing the consumption preference behavior of a target customer group according to the present invention. As shown in Figure 4, the method for observing the consumption preference behavior of the target customer group of the present invention includes steps S1 to S3. Each step will be described in detail below.
步驟S1：提供以複數人物誌型態為分類基礎之複數虛實融合行為偏好常模。 Step S1: Provide a plurality of virtual and real behavior preference norms based on the classification of plural character types.
本發明之複數虛實融合行為偏好常模12係以複數人物誌型態為分類基礎，本實施例之複數人物誌型態包括精算管家型、享樂翻糖型、知性陀飛輪型、神秘暹羅貓型、刻苦力爭型、積極開創型、決策苦手型及生活從眾型。複數虛實融合行為偏好常模12以前述8種人物誌型態為分類基礎，並可進一步包括如年齡區段、複數性別、複數消費領域及/或複數就業狀態等分類指標。具體來說，本發明將所蒐集的Y世代、X世代與嬰兒潮世代消費者在不同消費領域量化的調查資料，分析其平均數、標準差、百分等級等數據，建立標準分數常模、建構組間常模、組內常模，並依據複數人物誌型態以產生複數人物誌型態對應之複數虛實融合行為偏好常模12。在本實施例中，複數虛實融合行為偏好常模12包括複數虛實融合資源分配常模124及複數虛實融合行為常模123，複數虛實融合行為偏好常模12利用12,000份樣本建立兩種不同的實務常模服務方案：複數虛實融合資源分配常模124及複數虛實融合行為常模123，本實施例之複數虛實融合行為偏好常模12一共有65,952個行為常模，可用來解釋Y世代、X世代與嬰兒潮世代消費者在不同消費偏好行為的程度，未來只需透過產品之目標客群特徵22及人物誌型態23，即可顯示目標客群特徵22及人物誌型態23所屬分類中的各類虛實融合消費行為相對可能偏好程度。根據本發明之一具體實施例，各該虛實融合行為偏好常模12包括複數觀察指標與對應各該觀察指標之行為偏好統計數據，其中該複數觀察指標包括複數消費領域、複數人物誌類 型、複數性別資料(如：男、女)、複數年齡段資料(如：1955年前生、1965前後生)、或世代別資料(如：Y世代、X世代)、或/及複數就業資料(是否就業)。 The plural virtual and real fusion behavior preference norm 12 of the present invention is based on the plural character types. In this embodiment, the plural character types include the actuarial housekeeper type, the hedonic fondant type, the intellectual tourbillon type, and the mysterious Siamese cat type. , hard-working type, proactive and pioneering type, hard-working decision-making type and conformity type in life. The plural virtual and real integration behavioral preference norm 12 is based on the above-mentioned eight personality types, and can further include classification indicators such as age range, plural gender, plural consumption fields and/or plural employment status. Specifically, the present invention collects quantitative survey data on consumers of Generation Y, Generation Construct the between-group norm and the within-group norm, and generate a plurality of virtual and real fusion behavioral preference norms corresponding to the plurality of character types based on the plurality of character types12. In this embodiment, the plural virtual-real fusion behavior preference norm 12 includes the plural virtual-real fusion resource allocation norm 124 and the plural virtual-real fusion behavior norm 123. The plural virtual-real fusion behavior preference norm 12 uses 12,000 samples to establish two different practices. Norm service plan: plural virtual and real integrated resource allocation norm 124 and plural virtual and real integrated behavioral norm 123. In this embodiment, the plural virtual and real integrated behavioral preference norm 12 has a total of 65,952 behavioral norms, which can be used to explain Generation Y and Generation X. Consumers of the Baby Boomer generation have different consumer preferences and behaviors. In the future, only through the target customer group characteristics 22 and persona type 23 of the product, the target customer group characteristics 22 and persona type 23 can be displayed in the category to which they belong. The relative possible preference for various virtual and real consumption behaviors. According to a specific embodiment of the present invention, each virtual and real fusion behavior preference norm 12 includes a plurality of observation indicators and behavioral preference statistical data corresponding to each observation indicator, wherein the plurality of observation indicators include a plurality of consumption fields and a plurality of demographic categories. type, plural gender data (such as male and female), plural age group data (such as those born before 1955, those born before and after 1965), or generation data (such as: Generation Y, Generation X), or/and plural employment data (whether employed or not).
虛實融合資源分配常模124係由單一消費領域四類虛實融合行為(媒體偏好、通路偏好、導流偏好及會員經營偏好)，其中媒體偏好有10種行為、通路偏好有6種行為、導流偏好有6種行為、及會員經營偏好有6種行為，共計28種虛實融合偏好行為。透過複數人物誌型態、性別資料、年齡資料、就業狀況資料等基本屬性，定義出64種不同之消費者特徵，由7大消費領域(餐飲與食品領域、健康與保健領域、個人與居家用品領域、教育與學習類領域、運動領域、娛樂領域、旅遊領域)、28種虛實融合偏好行為、64種消費者特徵等，建構出12,544種虛實融合資源分配常模124。虛實融合資源分配常模124可讓使用者只需輸入目標客群之人物誌型態及性別資料、年齡資料、就業狀況資料等資訊，運用虛實融合資源分配常模124，就能夠顯示目標客群與其他所有類型比較的相對地位。有助於產業業者進行廣告投放、營銷經營資源配置之參考。使用虛實融合行為常模123，則協助政府相關單位及企業，從行為面的角度，瞭解特定消費者虛實融合行為類型的涉入程度，以及消費者同一種偏好行為在不同產業領域之間是否有差異，以觀察台灣特定消費者目前虛實融合行為成熟度。使用者只要輸入目標客群目標客群之人物誌型態及性別資料、年齡資料、就業狀況資料等資訊，即能夠瞭解該種類型的消費者在虛實融合偏好行為的滲透度與跨業產度，作為擬訂未來行銷發展策略的重點依據。虛實融合行為常模123係由7種消費領域、4類OMO行為偏好(媒體、通路、導流、會員經營)，分別計算個別消費者得分情形；將消費者特徵依人物誌型態、性別、年齡、就業 狀況，分別標記64種消費特徵；在將64種消費特徵、7種消費領域、4種OMO行為滲透度，組成巢狀樣態，分別計算OMO行為滲透度之平均數、標準差、前25%(第3四分位數)、中位數、後25%(第1四分位數)等數值，建構1,792組虛實融合行為常模123。 The virtual and real integration resource allocation norm 124 consists of four types of virtual and real integration behaviors in a single consumption field (media preference, channel preference, diversion preference and member management preference), of which there are 10 behaviors for media preference, 6 behaviors for channel preference, and diversion preference. There are 6 kinds of behaviors for preferences, and 6 kinds of behaviors for member management preferences, for a total of 28 kinds of virtual and real preference behaviors. Through basic attributes such as multiple demographic types, gender data, age data, and employment status data, 64 different consumer characteristics are defined, consisting of 7 major consumer fields (catering and food fields, health and wellness fields, and personal and household products). fields, education and learning fields, sports fields, entertainment fields, tourism fields), 28 types of virtual-real integration preference behaviors, 64 types of consumer characteristics, etc., 12,544 virtual-real integration resource allocation norms were constructed124. The virtual and real resource allocation norm 124 allows users to simply input the target customer group's demographic profile, gender data, age data, employment status data and other information. Using the virtual and real resource allocation norm 124, the target customer group can be displayed. Relative status compared to all other types. It is helpful for industry players to use as a reference for advertising and marketing and operation resource allocation. The use of virtual and real integration behavior norm 123 will assist relevant government units and enterprises to understand the degree of involvement of specific consumers in virtual and real integration behavior types from a behavioral perspective, and whether consumers have the same preference behavior in different industrial fields. Differences in order to observe the current maturity of virtual and real integration behavior of specific consumers in Taiwan. Users only need to input the target customer group's demographic profile, gender data, age data, employment status data and other information to understand the penetration and cross-industry extent of this type of consumer's virtual and real preference behavior. , as a key basis for formulating future marketing development strategies. The virtual and real integration behavioral norm 123 is composed of 7 consumption areas and 4 types of OMO behavioral preferences (media, channels, diversion, member management), and the scores of individual consumers are calculated respectively; consumer characteristics are calculated according to demographic type, gender, age, employment status, respectively marking 64 types of consumption characteristics; after combining 64 types of consumption characteristics, 7 types of consumption areas, and 4 types of OMO behavior penetration into a nested state, the average, standard deviation, and top 25% of OMO behavior penetration were calculated respectively. (3rd quartile), median, bottom 25% (1st quartile) and other values to construct 1,792 sets of virtual and real fusion behavioral norms 123.
虛實融合行為常模123更包括虛實融合行為滲透度指標與虛實融合行為跨業廣度指標，其中虛實融合行為滲透度指標為檢視4類虛實融合偏好行為中，消費者涉入/喜好程度，以觀察該特徵之消費者虛實融合行為深度，其中虛實融合偏好行為為媒體偏好、通路偏好、虛實融合(OMO)導流偏好、及會員經營偏好。虛實融合行為滲透度指標的計算方式為分別計算4類虛實融合偏好行為消費者涉入/喜好的行為數。在本實施例中，媒體偏好共有10種行為，具體包括社群/粉絲團/部落格、入口網站/關字搜尋、電子郵件/電子報/DM、YouTube、Clubhouse/podcast、論壇、電視、平面媒體、各品牌/店家APP、及內容網站。通路偏好共有6種行為，具體包括品牌門市/餐廳、連鎖通路、品牌官網、品牌/餐廳APP、購物網站/APP、及電話購買(電銷)。導流偏好共有6種行為，具體包括線上下單到店取貨、使用門市優惠券、參與門市活動、註冊會員、下載APP、及門市購買線上積點。會員經營偏好共有6種行為，具體包括會員紅利積點/里程回饋、會員限量/限時搶購、會員獨家折扣、會員專屬現金回饋、會員獨家贈品、及專屬你個人的推薦服務。前述各類虛實融合偏好行為，計算勾選項目數即為該行為滲透度分數，勾選項目越多，代表該領域消費時，該項虛實融合行為滲透度越高。 The Virtual and Real Integration Behavior Norm 123 also includes the Virtual and Real Integration Behavior Penetration Index and the Virtual and Real Integration Behavior Cross-Industry Breadth Index. The Virtual and Real Integration Behavior Penetration Index examines the degree of consumer involvement/preference in the four types of virtual and real integration preference behaviors to observe This characteristic represents the depth of consumers’ virtual-real integration behavior, in which virtual-real integration preference behaviors include media preference, channel preference, virtual-real integration (OMO) diversion preference, and member management preference. The calculation method for the penetration index of virtual and real fusion behavior is to calculate the number of behaviors that consumers are involved in/like in the four types of virtual and real fusion preference behaviors. In this example, there are 10 types of media preferences, including community/fan club/blog, portal/keyword search, email/email/DM, YouTube, Clubhouse/podcast, forum, TV, print Media, various brand/store APPs, and content websites. There are 6 types of behaviors in channel preference, including brand stores/restaurants, chain channels, brand official websites, brand/restaurant APPs, shopping websites/APPs, and telephone purchases (telemarketing). There are 6 types of behaviors for diversion preferences, including placing an order online and picking up the goods in a store, using store coupons, participating in store activities, registering as a member, downloading an APP, and purchasing online points in a store. There are 6 types of behaviors for member management preferences, including member bonus points/mileage feedback, member limited/limited time sales, member exclusive discounts, member exclusive cash rewards, member exclusive gifts, and your own personal recommendation service. For the aforementioned various virtual and real fusion preference behaviors, the penetration score of the behavior is calculated by calculating the number of checked items. The more checked items, the higher the penetration of the virtual and real fusion behavior when consuming in this field.
虛實融合行為跨業廣度指標為檢視前述28種虛實融合偏好行為中，消費者在7種消費領域涉入/喜好的情況。用以觀察目標客群消費者虛實融合行為廣度。企業能夠使用虛實融合行為跨業廣度判斷特定消費者的偏好行為強度，並檢視該偏好行為是只侷限在少數領域，或是在各種領域都會使用或喜好。虛實融合行為跨業廣度指標計算方式為分別計算28種虛實融合行為(媒體10種、通路6種、導流6種、會員經營6種)，消費者在7大消費領域(餐飲與食品領域、健康與保健領域、個人與居家用品領域、教育與學習類領域、運動領域、娛樂領域、旅遊領域)涉入/喜好的行為數。以虛實融合偏好下單一行為，在不同消費領域出現之數量，即為該行為廣度分數，單一行為出現在不同領域數越多，代表該項虛實融合行為廣度越高。 The cross-industry breadth index of virtual and real integration behaviors examines consumers’ involvement/preferences in 7 consumption areas among the 28 aforementioned virtual and real integration preference behaviors. It is used to observe the breadth of virtual and real behaviors of consumers in the target customer group. Enterprises can use virtual and real integration behaviors to judge the strength of specific consumers’ preference behaviors across industries and examine whether the preference behavior is limited to a few areas, or is used or preferred in various areas. The calculation method for the cross-industry breadth index of virtual and real integration behaviors is to calculate 28 types of virtual and real integration behaviors (10 types of media, 6 types of channels, 6 types of diversion, and 6 types of membership management). Consumers in 7 major consumption fields (catering and food fields, The number of behaviors involved/favored in the fields of health and wellness, personal and household products, education and learning, sports, entertainment, and travel. The number of a single behavior appearing in different consumption fields based on virtual and real fusion preferences is the behavioral breadth score. The more a single behavior appears in different fields, the higher the breadth of the virtual and real fusion behavior.
步驟S2：接收由該複數人物誌型態指定其中之一之該人物誌型態。 Step S2: Receive the persona type specified by one of the plurality of persona types.
如圖1與圖2所示，觀察條件接收模組20於使用者裝置8之顯示螢幕產生觀察條件介面21，以供使用者於對應的欄位中填入觀察條件。在本實施例中，觀察條件介面21可供使用者填入的觀察條件包括消費領域80、人物誌型態23及目標客群特徵22、22a、22b，其中消費領域80包括餐飲與食品領域、健康與保健領域、個人與居家用品領域、教育與學習類領域、運動領域、娛樂領域、及/或旅遊領域；人物誌型態23為8種人物誌型態；目標客群特徵22為性別、目標客群特徵22a為世代別、目標客群特徵22b為就業狀態。使用者可分別於每個欄位中指定目標客群的目標客群特徵22及人物誌型態23。以圖2的例子來說，使用者指定之消費領域80為餐飲 與食品領域；人物誌型態23為享樂翻糖型之人物誌型態；目標客群特徵22為女性、目標客群特徵22a為Y世代、目標客群特徵22b為有就業。 As shown in FIGS. 1 and 2 , the observation condition receiving module 20 generates an observation condition interface 21 on the display screen of the user device 8 for the user to fill in the observation conditions in the corresponding fields. In this embodiment, the observation conditions that the user can fill in in the observation condition interface 21 include the consumption field 80, the demographic profile 23, and the target customer group characteristics 22, 22a, and 22b. The consumption field 80 includes the catering and food field, Health and wellness field, personal and home supplies field, education and learning field, sports field, entertainment field, and/or tourism field; Persona type 23 is 8 persona types; Target customer group characteristics 22 are gender, The target customer group characteristic 22a is generation, and the target customer group characteristic 22b is employment status. The user can specify the target customer group characteristics 22 and persona type 23 of the target customer group in each field respectively. Taking the example in Figure 2 as an example, the consumption area 80 specified by the user is catering. and the food field; personality type 23 is a hedonic fondant type; target customer group characteristics 22 are women, target customer group characteristics 22a are Generation Y, and target customer group characteristics 22b are employed.
步驟S3：根據該指定之該人物誌型態由該複數虛實融合行為偏好常模產生對應指定之該人物誌型態之一目標客群消費偏好行為結果。 Step S3: Generate a consumption preference behavior result of the target customer group corresponding to the specified persona type from the plural virtual and real fusion behavioral preference norm according to the specified persona type.
觀察條件接收模組20接收前述之觀察條件(人物誌型態23及目標客群特徵22、22a、22b)後，觀察結果產生模組30所接收之觀察條件利用複數虛實融合行為偏好常模12中之與觀察條件接收模組20所接收之觀察條件相對應之複數觀察指標與對應各該觀察指標之行為偏好統計數據產生如圖3A至圖3C所示之一目標客群消費偏好行為結果31、31a、31b。如圖3A至圖3C所示之對應指定之該人物誌型態23、女性、Y世代、有就業之一目標客群消費偏好行為結果31、31a、31b。本實施例之目標客群消費偏好行為結果31、31a、31b包括根據觀察規則70與目標客群特徵22及人物誌型態23產生之一虛實融合策略佈局建議311如圖3A所示、一虛實融合資源佈局建議312如圖3B所示、一虛實融合異業合作方式建議313如圖3C所示。且如圖3A至圖3C所示，虛實融合策略佈局建議311、虛實融合資源佈局建議312、虛實融合異業合作方式建議313皆包括一媒體偏好結果314、314a、314b、一通路偏好結果315、315a、315b、一虛實融合導流偏好結果316、316a、316b、及/或一會員經營偏好結果317、317a、317b。在此須注意的是，目標客群消費偏好行為結果31、31a、31b的內容已於前段詳細說明，故不再贅述，請參考相關段落。 After the observation condition receiving module 20 receives the aforementioned observation conditions (character profile 23 and target customer group characteristics 22, 22a, 22b), the observation result generation module 30 uses the complex virtual and real fusion behavior preference norm 12 to receive the observation conditions. The plural observation indicators corresponding to the observation conditions received by the observation condition receiving module 20 and the behavioral preference statistical data corresponding to each observation indicator produce a target customer group consumption preference behavior result 31 as shown in Figures 3A to 3C. , 31a, 31b. As shown in Figures 3A to 3C, the consumption preference behavior results 31, 31a, and 31b of the target customer group corresponding to the designated demographic type 23, female, Y generation, and employed are shown. The consumption preference behavior results 31, 31a, and 31b of the target customer group in this embodiment include a virtual and real fusion strategy layout suggestion 311 generated based on the observation rule 70, the target customer group characteristics 22, and the character profile 23. As shown in Figure 3A, a virtual and real The integrated resource layout suggestion 312 is shown in Figure 3B, and the virtual-real integration cross-industry cooperation method suggestion 313 is shown in Figure 3C. As shown in Figures 3A to 3C, the virtual-real integration strategy layout suggestion 311, the virtual-real integration resource layout suggestion 312, and the virtual-real integration cross-industry cooperation method suggestion 313 all include a media preference result 314, 314a, 314b, a channel preference result 315, 315a, 315b, a virtual and real fusion diversion preference result 316, 316a, 316b, and/or a member operation preference result 317, 317a, 317b. It should be noted here that the contents of the target customer group’s consumption preference behavior results 31, 31a, and 31b have been explained in detail in the previous paragraph, so they will not be described again. Please refer to the relevant paragraphs.
本發明之目標客群消費偏好行為觀察系統1及方法依據複數虛實融合行為偏好常模12結果顯示，分別出現指定目標客群特徵22及人物 誌型態23零售消費偏好行為，並考提供三項觀察指標：虛實融合策略佈局建議311、虛實融合資源佈局建議312、虛實融合異業合作方式建議313。 The target customer group consumption preference behavior observation system 1 and method of the present invention display the results based on the complex virtual and real fusion behavior preference norm 12, and the specified target customer group characteristics 22 and characters appear respectively. ZhiTai 23 retail consumption preference behavior, and three observation indicators are provided: virtual and real integration strategic layout suggestions 311, virtual and real integration resource layout suggestions 312, virtual and real integration cross-industry cooperation methods suggestions 313.
應注意的是，上述諸多實施例僅係為了便於說明而舉例而已，本發明所主張之權利範圍自應以申請專利範圍所述為準，而非僅限於上述實施例。 It should be noted that the above-mentioned embodiments are only examples for convenience of explanation, and the scope of rights claimed by the present invention should be subject to the scope of the patent application and is not limited to the above-mentioned embodiments.
1:目標客群消費偏好行為觀察系統 1: Target customer group consumption preference behavior observation system
10:儲存裝置 10:Storage device
12:虛實融合行為偏好常模 12: Virtual and real integration behavior preference norm
123:虛實融合行為常模 123: Behavioral norm of integration of virtual and real
124:虛實融合資源分配常模 124: Virtual and real integration resource allocation norm
20:觀察條件接收模組 20: Observe conditional receiving module
31:目標客群消費偏好行為結果 31: Target customer group consumption preference behavior results
8:使用者裝置 8: User device
13:人物誌資料庫 13:Character database
22:目標客群特徵 22: Characteristics of target customer group
23:人物誌型態 23:Character type
30:觀察結果產生模組 30: Observation result generation module
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