Kundecentreret AI: Hvordan AI kan forbedre mersalg og krydssalg

Posted 3 måneder ago ved Mark Xavier Quadros

I dag, at opfylde kundernes forventninger er ikke længere bare nok. At trives, virksomheder skal overgå disse forventninger, og udnyttelse af kundecentreret AI er nøglen til at nå dette mål.

Integrering af kunstig intelligens i styring af kunderelationer (CRM) forbedrer mersalg og krydssalgsstrategier, giver virksomheder mulighed for at analysere omfattende kundedata for personlige anbefalinger.

Fortsæt med at læse for at opdage, hvordan kundecentreret AI løfter CRM-strategier, tilbyder personlig indsigt og beslutningstagning i realtid, og i sidste ende leverer mere tilfredsstillende kunderejser.

Udnyttelse af AI til kundeindsigt

AI kan afsløre uvurderlige mønstre og tendenser ved at analysere enorme mængder data. Det gør dig i stand til at forstå kundetendenser, vaner, og præferencer.

Before we discuss how AI can enhance customer relationship management, let’s dive into how AI algorithms analyze customer behavior and data.

How AI Algorithms Analyze Customer Behavior

AI is transforming how businesses analyze consumer behavior and changing how consumers engage with companies.

There are various tools business owners can use to process customer data with AI, men generelt, here’s how the process works:

Dataindsamling: The ecommerce platform collects extensive data on customer interactions, including browsing history, purchase behavior, products viewed, product surveys, time spent on pages, and demographic information. Incorporating kunde feedback into this data collection enriches AI’s understanding of customer satisfaction and service expectations.
AI algorithms implementation: AI algorithms process and analyze this wealth of data. Machine learning in sales, such as collaborative filtering or content-based recommendation systems, is used to identify patterns and correlations among customer behaviors.
Pattern recognition: AI-algoritmerne identificerer mønstre, almindelige produktkombinationer, der ofte købes sammen (krydssalgsmønstre) eller produkter, der ofte er set af kunder før køb (tegn på præferencer).
Personlige anbefalinger: AI-drevne anbefalingsmotorer udnytter denne indsigt. Når en kunde besøger platformen, personlige produktanbefalinger genereres i realtid baseret på browserhistorik, tidligere køb, og lignende brugeradfærd.
Kontinuerlig læring og forbedring: AI-algoritmerne lærer løbende af nye datainput og kundeinteraktioner. Efterhånden som der indsamles mere data, modellerne udvikler sig og forfiner deres anbefalinger, sikre, at de forbliver relevante og nøjagtige.

Sofistikerede prædiktive analyseværktøjer såsom IBMs SPSS-statistik, Alteryx, og Microsofts Azure Machine Learning behandler disse data, identificere mønstre, sammenhænge, og tendenser, der indikerer potentiel fremtidig adfærd eller behov.

Baseret på analysen, prædiktive modeller er udviklet til at forudsige sandsynlig kundeadfærd eller behov. Disse modeller bruger statistiske algoritmer til at forudsige resultater, såsom sandsynligheden for, at en kunde foretager et bestemt køb, churn sandsynlighed, eller foretrukne produktkategorier.

AI-infunderet mersalg & Krydssalgsstrategier

AI-infunderede mersalgsstrategier udnytter kunstig intelligens til at øge salget ved at tilskynde kunder til at købe yderligere eller opgraderede produkter eller tjenester.

Here’s an overview of key AI-driven upselling tactics:

AI-Powered Product Recommendations and Customization

AI-driven customer profiling is a cornerstone of modern marketing strategies, using advanced algorithms to create detailed and dynamic profiles of individual customers.

By collecting and analyzing a wide range of customer data—such as purchase history, browsing behavior, demografi, and interactions with the business—AI pinpoints distinct behavioral patterns, præferencer, and individual traits.

This enables sellers to offer tailored product recommendations based on individual customer behaviors and preferences to suggest complementary or upgraded products.

For eksempel, Amazon’s AI algorithms analyze extensive customer data, including browsing history, items viewed, items purchased, and search queries.

“Customers Who Bought This Also Bought” recommendations on Amazon

Based on this analysis, Amazon’s recommendation engine employs machine learning models to predict and suggest products that align with each customer’s interests and preferences.

When a customer explores a specific product, Amazon’s AI generates “Frequently Bought Together” or “Customers Who Bought This Also Bought” recommendations, showcasing complementary or upgraded products. These suggestions encourage customers to consider additional purchases beyond their initial choice—and suggest items they may be interested in.

As customers interact with the platform, the AI continuously learns from their behaviors and refines its recommendations. The system adapts to individual preferences, ensuring increasingly accurate and relevant suggestions.

An example of how Amazon leverages user preferences data to create product recommendations. (Kilde: Rejoiner)

Amazon’s AI-driven product recommendations contribute significantly to the platform’s success in upselling. Customers are more inclined to explore and potentially purchase additional products, increasing sales and improving customer satisfaction.

I øvrigt, hvis du sælge online with Ecwid by Lightspeed, du kan vise relaterede produkter with the “You May Also Like” section that appears on a product details page and at checkout.

Dynamiske prisstrategier og tilbudsoptimering

AI muliggør dynamiske prisstrategier ved at analysere markedstendenser, konkurrentens prissætning, og kundeadfærd i realtid. Dette giver virksomheder mulighed for at optimere prisstrategier for mersalg, tilbyder personlige rabatter, eller bundtede aftaler, der vækker genklang hos individuelle kunder.

Uber, the ride-hailing service, uses AI-driven dynamic pricing, known as “surge pricing,” to optimize pricing strategies based on real-time demand, levere, og andre faktorer.

Here’s how Uber implemented their dynamic pricing strategy with the help of AI.

Uber’s AI algorithms continuously analyze data in real-time, including factors like ride demand, traffic conditions, vejr, time of day, and historical rider behavior.

Based on this analysis, Uber’s AI adjusts fares dynamically. During peak times or high demand, surge pricing is activated, at øge billetprisen for at tilskynde flere chauffører til at være tilgængelige, sikrer hurtigere afhentning og imødekommer den øgede efterspørgsel.

Derudover, Uber kan tilbyde personlige rabatter eller kampagner til individuelle ryttere baseret på deres turhistorik, hyppigheden af brug, eller særlige lejligheder. For eksempel, målrettede kampagner kan tilbydes til hyppige brugere eller i perioder med lav efterspørgsel for at tilskynde til flere ture.

Disse strategier maksimerer indtjeningen for chauffører og opmuntrer ryttere til at fortsætte med at bruge dem.

Forbedring af kundeoplevelsen

Ved at udnytte AI i CRM, virksomheder kan forbedre kundeoplevelsen gennem personlige tjenester.

For eksempel, Spotify bruger AI-algoritmer til at analysere brugerpræferencer, lyttevaner, og historiske data til at skabe personlige afspilningslister, anbefalinger, og daglige blandinger for hver bruger.

Et eksempel på en personlig afspilningsliste fra Spotify

Denne personlige tilgang forbedrer den overordnede brugeroplevelse ved at skræddersy musik til hver enkelt lytters unikke præferencer, gør tiden brugt på at lytte og opdage ny musik efter deres smag mere behagelig.

Krydssalgstaktik

Cross-selling tactics integrated into AI-enhanced CRM systems leverage artificial intelligence to identify and capitalize on opportunities to offer complementary products or services to customers aligned with customer buying behaviors.

For eksempel, Netflix effectively tailors its marketing campaigns for cross-selling by recommending TV series or movies to users based on their viewing history.

Netflix makes recommendations based on a user’s viewing history

If a user likes to watch science fiction shows, Netflix’s algorithm suggests similar content or promotes a newly released series within that genre, encouraging the user to explore and watch more content.

Further enhancing these personalized marketing efforts, AI chatbots provide immediate, personalized recommendations to customers. This not only improves the shopping experience but also significantly increases sales opportunities by making every customer interaction an opportunity for targeted marketing and upselling.

Examples of AI-Enhanced CRM Systems

Integrering af mersalgstaktikker i AI-forstærkede CRM-systemer involverer udnyttelse af forudsigende analyser til at identificere ideelle mersalgsmuligheder. AI-drevne CRM-systemer tilskynder salgsrepræsentanter med relevante mersalgsforslag under kundeinteraktioner, øger chancerne for vellykkede mersalg.

Einstein Analytics fra Salesforce

Salgsstyrke, en førende CRM-platform, incorporates AI-powered tools like Einstein Analytics to assist sales representatives in identifying and capitalizing on upselling opportunities during customer interactions.

Salesforce’s Einstein Analytics leverages predictive analytics to analyze vast datasets within the CRM. It evaluates customer data, købshistorie, interaktioner, and other relevant information to predict potential upselling opportunities.

Einstein Analytics spots patterns hinting at upselling opportunities. For eksempel, detecting increased product usage may signal interest in upgrades or add-ons.

Salesforce’s AI system also provides sales reps with actionable insights. It offers upselling suggestions and talking points based on opportunities identified.

Sales reps leverage AI-driven suggestions to customize conversations, addressing customers’ needs with relevant upselling offers. For eksempel, they may suggest an upgraded subscription or additional features based on usage patterns.

I øvrigt, if you sell online with Ecwid, du kan forbinde din onlinebutik med Salesforce via Zapier. Denne måde, nye kunder oprettes automatisk i Salesforce fra nye Ecwid-ordrer.

Amazon personliggør

Amazon personliggør, en maskinlæringstjeneste, der tilbydes af Amazon, er designet til at imødegå de udfordringer, man ofte støder på i at skabe personlige anbefalinger, herunder problemer med nye brugerdata, popularitetsforstyrrelser, og udviklende brugerhensigt.

I modsætning til traditionelle anbefalingsmotorer, Amazon personliggør udmærker sig i scenarier med begrænsede eller udviklende brugerdata. This proves especially beneficial for identifying upselling opportunities, even with new users or when user preferences change over time.

Several well-known companies, such as Domino’s, Subway, and Yamaha, have recognized the significance of AI in understanding and catering to customer needs.

How to Tailor Marketing Campaigns for Upselling and Cross-Selling

You can tailor marketing campaigns for upselling and cross-selling with the help of strategic approaches even if you don’t use AI-powered tools.

For the best outcomes, you need customer data and targeted messaging. Here’s a breakdown of the process:

Perform Customer Segmentation

Use CRM data to segment customers based on their purchase history, præferencer, og adfærd. Categorize them into groups with similar buying patterns or interests.

Hvis du sælger online med Ecwid, du kan se, finde, and edit all the customer information you need on the kunder side. Derfra, you can filter your customer base using various parameters and export the segment to work with it in a different service (for eksempel, to send targeted emails via an email service of your choice.)

Siden Kunder i Ecwid giver også adgang til kundeordrehistorik, lette segmenteringsprocessen. By understanding your customers’ buying habits and preferences, du kan skræddersy din besked til hvert segment mere effektivt.

Kunder-siden i Ecwid admin

Identificer muligheder

Analyser købshistorik og adfærdsdata for at lokalisere muligheder for mersalg og krydssalg. Determine which products or services complement previous purchases or align with customers’ interests.

For eksempel, når du sælger online gennem Ecwid, du har mulighed for at konfigurere automatiserede marketing -e -mails fremvisning af relaterede produkter eller topsælgere.

Relaterede produkter i en ordrebekræftelse e-mail

Lav personlige anbefalinger

Opret personlige anbefalinger baseret på kundesegmenter. Brug AI-algoritmer til at foreslå relaterede eller opgraderede produkter i marketingmateriale, e-mail-nyhedsbreve, eller på en hjemmeside. For eksempel, Amazon’s “Frequently Bought Together” or “You May Also Like” sections.

Stræb efter målrettet meddelelser

Lav målrettede budskaber, der fremhæver værdien af komplementære produkter eller tjenester. Vis, hvordan det ekstra tilbud forbedrer kundeoplevelsen eller løser et specifikt problem.

For en virkelig optimeret besked, overveje oversætte indhold at resonere effektivt med forskellige målgrupper og sprog.

Tilbyd incitamenter eller bundter

Giv incitamenter som rabatter, bundte tilbud, eller loyalitetsbelønninger for at opmuntre kunder til at udforske yderligere tilbud. Gør værdiforslaget attraktivt og klart.

Med Ecwid fra Lightspeed, du kan sælge produktbundter ved hjælp af opsalg & Krydssalg produktpakker, Produktpakker, og Bogø apps.

Anvend Multichannel Approach

Implement a multichannel markedsstrategi to reach customers through various touchpoints. Use emails, indhold på sociale medier, hjemmeside pop-ups, og personlige platformanbefalinger.

Afslør styrken af personlige anbefalinger

I det dynamiske landskab af kunderelationer, personlige anbefalinger og målrettet markedsføring står som søjler for succes. Ved at udnytte CRM-data, du kan frigøre potentialet for skræddersyede mersalg og krydssalgskampagner.

Når den er finjusteret, these strategies resonate with individual customers, driving engagement, stigende salg, og nurturing brand loyalty.

Embrace insights from your CRM system, create custom campaigns, and see how meeting your customers’ unique preferences and needs can work wonders.

About The Author

Mark Quadros is a SaaS content marketer that helps brands create and distribute rad content. On a similar note, Mark loves content and contributes to several authoritative blogs like HubSpot, CoSchedule, Foundr, etc. Connect with him via LinkedIn eller Twitter.