Nå for tiden, møte kundenes forventninger er ikke lenger bare nok. Å trives, bedrifter må overgå disse forventningene, og å utnytte kundesentrert AI er nøkkelen til å nå dette målet.
Integrering av AI i administrasjon av kunderelasjoner (CRM) forbedrer mer- og krysssalgsstrategier, slik at bedrifter kan analysere omfattende kundedata for personlig tilpassede anbefalinger.
Keep reading to discover how customer-centric AI elevates CRM strategies, offers personalized insights and real-time decision-making, and ultimately delivers more satisfying customer journeys.
Leveraging AI for Customer Insights
AI can reveal invaluable patterns and trends by analyzing huge amounts of data. It enables you to understand customer tendencies, habits, og preferanser.
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, but in general, here’s how the process works:
- Data collection: 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 customer 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: The AI algorithms identify patterns, such as common product combinations frequently purchased together (cross-selling patterns) or products often viewed by customers before purchasing (indicative of preferences).
- Personalized recommendations: AI-driven recommendation engines leverage these insights. When a customer visits the platform, personalized product recommendations are generated in real time based on browsing history, past purchases, and similar user behaviors.
- Continuous learning and improvement: The AI algorithms continuously learn from new data inputs and customer interactions. As more data is collected, the models evolve and refine their recommendations, ensuring they remain relevant and accurate.
Sophisticated predictive analytics tools such as IBM’s SPSS Statistics, Alteryx, and Microsoft’s Azure Machine Learning process this data, identifying patterns, correlations, and trends that indicate potential future behaviors or needs.
Based on the analysis, predictive models are developed to forecast probable customer behaviors or needs. These models use statistical algorithms to predict outcomes, such as the likelihood of a customer making a certain purchase, churn probability, or preferred product categories.
AI-Infused Upselling & Cross-Selling Strategies
AI-infused upselling strategies leverage artificial intelligence to enhance sales by encouraging customers to purchase additional or upgraded products or services.
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, ved hjelp av avanserte algoritmer for å lage detaljerte og dynamiske profiler av individuelle kunder.
Ved å samle inn og analysere et bredt spekter av kundedata – for eksempel kjøpshistorikk, nettleseratferd, demografi, og interaksjoner med virksomheten – AI identifiserer distinkte atferdsmønstre, preferanser, og individuelle egenskaper.
Dette gjør det mulig for selgere å tilby skreddersydde produktanbefalinger basert på individuell kundeatferd og preferanser for å foreslå komplementære eller oppgraderte produkter.
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” eller “Customers Who Bought This Also Bought” recommendations, showcasing complementary or upgraded products. Disse forslagene oppfordrer kunder til å vurdere ytterligere kjøp utover det opprinnelige valget – og foreslå varer de kan være interessert i.
Når kunder samhandler med plattformen, AI lærer kontinuerlig av deres atferd og avgrenser sine anbefalinger. Systemet tilpasser seg individuelle preferanser, sikre stadig mer nøyaktige og relevante forslag.
Et eksempel på hvordan Amazon utnytter brukerpreferansedata for å lage produktanbefalinger. (Kilde: Bli med)
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.
Forresten, if you sell online with Ecwid by Lightspeed, du kan show related products with the “You May Also Like” section that appears on a product details page and at checkout.
Dynamic Pricing Strategies and Offer Optimization
AI muliggjør dynamiske prisstrategier ved å analysere markedstrender, konkurrentens prissetting, og kundeadferd i sanntid. Dette lar bedrifter optimalisere prisstrategier for mersalg, tilbyr personlige rabatter, eller pakketilbud som faller i smak hos individuelle kunder.
Uber, skysstjenesten, bruker AI-drevet dynamisk prissetting, kjent som “høyprising,” å optimalisere prisstrategier basert på sanntidsetterspørsel, forsyning, og andre faktorer.
Her er hvordan Uber implementerte deres dynamiske prisstrategi ved hjelp av AI.
Uber’s AI algorithms continuously analyze data in real-time, including factors like ride demand, traffic conditions, weather, 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, increasing the fare to incentivize more drivers to be available, ensuring quicker pickups and meeting the increased demand.
I tillegg, Uber may offer personalized discounts or promotions to individual riders based on their ride history, frequency of use, eller spesifikke anledninger. For eksempel, målrettede kampanjer kan tilbys til hyppige brukere eller i perioder med lav etterspørsel for å oppmuntre til flere turer.
Disse strategiene maksimerer inntektene for sjåførene og oppmuntrer ryttere til å fortsette å bruke dem.
Forbedre kundeopplevelsen
Ved å utnytte AI i CRM, bedrifter kan forbedre kundeopplevelsene gjennom personlige tjenester.
For eksempel, Spotify bruker AI-algoritmer for å analysere brukerpreferanser, lyttevaner, og historiske data for å lage personlige spillelister, recommendations, og daglige mikser for hver bruker.
Et eksempel på en personlig tilpasset spilleliste fra Spotify
Denne personlige tilnærmingen forbedrer den generelle brukeropplevelsen ved å skreddersy musikken til hver enkelt lytters unike preferanser, gjør tiden brukt til å lytte og oppdage ny musikk etter deres smak morsommere.
Krysssalgstaktikker
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 av oppsalgstaktikker i AI-forbedrede CRM-systemer innebærer å utnytte prediktiv analyse for å identifisere ideelle oppsalgsmuligheter. AI-drevne CRM-systemer ber salgsrepresentanter om relevante mersalgsforslag under kundeinteraksjoner, øker sjansene for vellykkede oppsalg.
Einstein Analytics fra Salesforce
Salesforce, en ledende CRM-plattform, 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, purchase history, interactions, 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.
Forresten, if you sell online with Ecwid, du kan koble nettbutikken din til Salesforce via Zapier. Denne måten, nye kunder vil bli opprettet i Salesforce automatisk fra nye Ecwid-bestillinger.
Tilpass Amazon
Tilpass Amazon, en maskinlæringstjeneste som tilbys av Amazon, er designet for å møte utfordringer som ofte oppstår ved å lage personlige anbefalinger, inkludert problemer med nye brukerdata, popularitetsskjevheter, og utviklende brukerintensjon.
I motsetning til tradisjonelle anbefalingsmotorer, Tilpass Amazon utmerker seg i scenarier med begrensede eller utviklende brukerdata. 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, preferanser, and behavior. Categorize them into groups with similar buying patterns or interests.
Hvis du selger online med Ecwid, you can view, find, and edit all the customer information you need on the Customers 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, å sende målrettede e-poster via en e-posttjeneste du velger.)
Kunder-siden i Ecwid tilbyr også tilgang til kundeordrehistorikk, lette segmenteringsprosessen. Ved å forstå kundene dine’ kjøpsvaner og preferanser, du kan skreddersy meldingene dine til hvert segment mer effektivt.
Kunder-siden i Ecwid admin
Identifiser muligheter
Analyser kjøpshistorikk og atferdsdata for å finne muligheter for mersalg og krysssalg. Determine which products or services complement previous purchases or align with customers’ interesser.
For eksempel, when selling online through Ecwid, you have the option to configure automatiserte markedsførings-e-poster showcasing related products or top sellers.
Related products in an order confirmation email
Make Personalized Recommendations
Create personalized recommendations based on customer segments. Use AI algorithms to suggest related or upgraded products in marketing materials, email newsletters, or on a website. For eksempel, Amazon’s “Frequently Bought Together” eller “You May Also Like” seksjoner.
Strebe for målrettet meldinger
Lag målrettede meldinger som fremhever verdien av komplementære produkter eller tjenester. Vis frem hvordan tilleggstilbudet forbedrer kundeopplevelsen eller løser et spesifikt problem.
For en virkelig optimalisert melding, ta i betraktning oversette innhold å resonere effektivt med ulike målgrupper og språk.
Tilby insentiver eller pakker
Gi insentiver som rabatter, pakketilbud, eller lojalitetsbelønninger for å oppmuntre kunder til å utforske flere tilbud. Gjør verdiforslaget attraktivt og tydelig.
Med Ecwid av Lightspeed, du kan selge produktbunter ved hjelp av Mersalg & Krysssalg produktpakker, Produktpakker, og BOGO apper.
Bruk Multichannel Approach
Implementer en flerkanalsmarkedsføringsstrategi for å nå kunder gjennom ulike kontaktpunkter. Bruk e-post, innhold på sosiale medier, popup-vinduer på nettstedet, og personlige plattformanbefalinger.
Avslør kraften til personlig tilpassede anbefalinger
I det dynamiske landskapet av kunderelasjoner, personlig tilpassede anbefalinger og målrettet markedsføring står som bærebjelker for suksess. By leveraging CRM data, you can unlock the potential for tailored upselling and cross-selling campaigns.
When finely tuned, these strategies resonate with individual customers, driving engagement, increasing sales, and 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.
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