技术颠覆、需求升级与战略博弈下的共生路径探索

Tradingfor Tomorrow mission statement. This MHP Trendreport gets to the epicenter of the developments that willrevolutionize our B2B sector in the coming years—in terms of both everyday life and the role oftechnology. It looks into the effects of digitalization and examines the rise of AI and machinelearning, including their snowball effect on the industry, production and the whole world of B2B. “ In the future, we will onlyhave e-commerce. Offline,online, and logistics will be The Future of B2BCommerce, or “theGreat Merger” can be significantly expanded: The production, configuration,transport and analysis of products, the supply chain and thecustomer journey are becoming increasingly intertwined.The lines between “product” and “commerce” are becoming Content WOULD YOU LIKE A LITTLE PURCHASING PROS AT LAST WHEN THE ONE-OFF ENTERS NAVIGATING TOMORROW “I JUST WANT TO PLAY” How AI enables new,personalized customer Sales of Tomorrow Are Notabout What, but about Who, Marketplaces—from “digitaldisplay windows” to value Using gamification togenerate customer engage- The new market by and formachine customers ment and higher revenues drivers along the entire supplyp.16 experiencesp.5 Where and Why 01 When the One-Offenters SeriesProduction customer experiences When the CustomerJourney becomes on your briefcase. A designer vase in a customized color. Manu-facturing has come a long way in terms of individualization sincethe Ford Model T and the assembly line. Products manufacturedin batch size of 1—meaning only one example is produced—arebecoming increasingly common. But it’s not just the product— “ Any customer can have a carpainted any color that hewants so long as it’s black.” What the person does: behavioral data What they feel: sentiment data The person: the alpha andomega of each buyer journey •Ratings and reviews: When and for whathas positive, neutral or negative feedbackbeen provided?•Social media interactions: What is thetonality of posts and comments on social •Click behavior: Which products wereviewed? Which sites were visited? •Duration of use: How long do peoplespend on certain pages or viewing certainproducts?•Search behavior: Which search terms are The (purchasing) person is at the center of the model andprovides data to supply the input for personalization. Thisdata is no longer limited to purchase history and name.Whether consciously or subconsciously, customers provide a used? features used (e.g. filters, ratings)? psychographic information•Age, gender, location, income, marital historical data status and level of education•Hobbies, preferred use of free time, ethicalviews (environmental awareness, social been purchased previously? been placed in the shopping cart but werenot purchased?•Repeat purchases: Which products orservices have been frequently purchased? •Return history: How often have productsbeen returned and why? The technology: mediatorsand matchmakers of supply and The personalized experience: everydisplay window becomes one of a kind The outcomes of AI analyses: a personalized buying experience withindividualized products, services, prices and offers, the likes of which havenever been seen. AI-assisted technologies and systems are now capable of gathering,processing and analyzing the legion of data in order to deriveactionable insights. personalized negotiation and communi-cation strategies via digital platforms,optimized on the basis of previous buying products and services tailored exactly tothe company’s operational requirementsbased on previous orders and sector- unification of customer data on a customerdata platform (CDP) learning and predictive analytics to analyzepatterns and predictions simulation models to virtually test andadapt complex machines or equipmentin their production environment real-time adaptation of payment anddelivery conditions depending on thebusiness partnership, order volume andcurrent market conditions make personalized product recommendationsbased on the data analyzed products, upgrades or services based oncurrent business processes and predictiveanalytics and predictive maintenance orthat highlight possibilities for scaling and of customer interactions with chatbots orlanguage assistants even video data From “Model T” to “Model Me”:the evolution of personalizationfrom the past and into the present. one-size-fits-all color of black. And the evolution does not yet seem to becomplete. The first phase, which is still ongoing but is slowly coming to anend, could be described as “semi-personal”. This refers to the era in whichmarket research and an increasingly broad palette of products can simulate a access to the process of product design and presentation. This is one way inwhich each customer journey and subsequent purchase becomes a uniqueexperience. and services. A further, meta-personal scenario with digital intelligence thatcan articulate our desires better than we can is also conceivable. We wi