[The Chinese version follows the English one. 中文版在底部]
Here we are in 2023, we are all getting familiar with the terms “Digital Transformation”. It is not only about digitally transforming the customer experience, but it is also about the operation and data management of an enterprise. In this era of big data, enterprises may find out about their potential management issues and hidden customer opinion via collecting, integrating, analyzing, and monitoring the users’ data. Data is an indispensable element of the decision-making process of a firm, employees need to report and convince their supervisor and colleagues with the proof of data. Data can also be used to evaluate decision performance, it is slowly becoming a language used between colleagues in a firm.
Data is especially important for digital marketing, with a marketing strategy that matches the enterprise’s goals, marketers may acquire and process data more efficiently with the use of technology, analyzing their target customers, gaining insights from the scattered data, and finally coming up with an important strategy that may drive sales and profit to the firm.
Read More: Why loyalty matters | McKinsey
However, as we have mentioned the last time, people are more aware of data privacy nowadays, hindering enterprises from collecting users’ digital footprint, that is why we have to develop our own first-hand data acquisition channel. Having a first-hand data acquisition channel may lower the dependence on external data and third parties services, as well as follow the movement of the target group constantly, and optimize the collected data in different aspects of a firm. (Read more: Own your own platform, from O2O to OMO)
What data can be acquired with a first-hand data acquisition platform?
Customer behavioral data may provide insights to marketers in every step of the customer journey. Let’s take e-commerce as an example and see how can a brand gather useful insights!
Registering for membership
After attracting customers to download the app, they will most likely open an account there, brands may then collect their demographic data and preferences based on the registration and questionnaire, and then categorize different customers.
Searching and browsing products
After triggering the purchasing demand, customers will use the search function within the app, and insert keywords or product names to navigate their desired products. Marketers may collect their keyword search records, advertising conversion rates, and so on, enhancing the navigation function such as optimizing keywords, search result ranking, and personalized advertisements.
Shopping cart and purchase records
Shopping cart and purchase records can reflect the product reference of customers and help marketers to understand every customer. If customers put the product into the shopping cart, marketers may based on their current on-hold products and the shopping records, display responding product ads for the customers so as to perform cross-selling; They can also remind customers who put the products in the shopping cart but have not purchased. (Some might also provide time-limited offers to motivate their purchase.)
Aftersale behavior
Marketers may learn about the satisfaction of customers based on their aftersale behaviors such as repeated purchases. Marketers may analyze the review attitude and the most mentioned keywords of the customers via comment analytic tools, knowing the product features that the customers like or dislike to make improvements in the future.
Marketing behaviors based on data analysis are called data-driven marketing. Microcosmically, marketers may know about the preferences of the customers and provide personalized products and services to them. Macrocosmically, marketers may compare the historical data of the overall customer data and make bigger and more important strategies for the brand, relocate the company position, and respond to the market trend swiftly.
If you would like to know more about the application of data-driven marketing, don’t miss the next article, and let’s see how we can combine new technology and data-driven marketing together.
還在猜想顧客的喜好？數據驅動行銷 Think about Data-Driven Marketing
科技發展到 2023 年，相信大家都不會對「數碼轉型 (Digital Transformation)」這個單詞感到陌生。要數碼化的不只有供給顧客的服務，還有企業內部的資料和工作模式，在大數據年代，透過收集、整合、分析及監察用戶數據，企業能及早發現隱藏著的管理問題、發掘到顧客不曾明說的意見等。數據是企業在決策過程中不可或缺的要素，員工將會根據數據基礎來提出決策以說服同僚和上司，用數據來證實見解，也能透過決策的迴響數據來評估績效，數據將成為企業員工之間的溝通語言。
數據對數位行銷來說亦尤為重要，營銷策略當然也會緊貼企業轉型的步伐，善用科技收集和使用數據，進行更高效準確的營銷。數據所表達的正是顧客不曾明說的聲音，分析數據有助加深對顧客的了解。行銷人員可觀察那些數字背後隱藏的訊息，抓住更多銷售機會，甚至影響品牌的重大營銷策略，將數據轉化成為利潤。
閱讀更多: Why loyalty matters | McKinsey
然而，正如我們上次提到，保障用戶私隱的意識提高，將會增加追蹤用戶數碼足跡 (Digital Footprint) 的難度和成本，促使企業有必要盡快發展自身第一方的數據收集渠道。擁有第一方數據收集平台後，除了可以避免企業對外部數據及第三方服務的依賴，亦能進行持續追蹤，最大化地將這些收集到的數據運用到企業的不同層面。(閱讀更多：Own your own platform，由 O2O 到 OMO)
平台可以收集到哪些數據？
顧客在購買過程 (Customer Journey) 內每一個步驟的行為數據都具參考價值。讓我們以 E-commerce 的購買流程為例，看看品牌可以收集多少有用的數據吧！
開設會員帳號
在吸引顧客下載應用程式後，他們很大機會會註冊平台帳戶，品牌便可在此時透過開戶程序中的註冊登記資料及喜好詢問，獲取他們基本的人口背景資料及偏好，協助顧客分組。
搜尋及瀏覽商品
在顧客觸發購物需求後，他們會透過應用程式內部的搜索功能，按關鍵字或產品名稱，搜尋及瀏覽符合他們需要的商品。商家可收集他們所輸入的關鍵字、點擊的搜尋結果、廣告的轉化率等行為數據，完善程式內的商品搜索功能如優化關鍵字、搜尋結果排位、個人化廣告等。
購物車及購買紀錄
購物車及購買紀錄都能顯示客戶的產品偏好，幫助品牌了解每一位顧客。如顧客會將打算購買的商品加入購物車以方便結帳，品牌可以根據他們過往購買紀錄及現時購物車內的物品，推送合適該顧客的推薦，進行交叉銷售；亦可以就停留在購物車內但仍未購買的商品作出提醒 (一些品牌更會附上限時優惠券增加結帳誘因)。
購買後行為
品牌可以透過顧客在購買後對商品的評價及有否重複購買等購買後行為來評估顧客對是次購物體驗的滿意程度。如營銷人員透過語言態度分析工具，快速總結客戶對某項產品的商品評價態度(review attitude) 及較多被人提起的商品關鍵字，可更了解顧客對商品的滿意及不滿之處，持續改善顧客來臨購物的體驗。
根據數據及分析數據後得來的觀點進行市場營銷，便是所謂的數據驅動行銷。在微觀角度，品牌可以收集用家的產品偏好，進而為他們提供個人化的服務與營銷方式；在宏觀角度，品牌可以透過觀察和比較不同時段內全體用戶的行為變化趨勢，並跟據這些趨勢制定重大營銷計畫、決定營銷策略的大方向、及時調整及回應市場上的新趨勢等。
如果你想知道更多數據驅動行銷的應用，緊記不要錯過下一篇文章，讓我們一起來看看數據驅動行銷與科技工具如何更進一步結合。