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Customer-Centric AI: Kaip AI gali pagerinti papildomą pardavimą ir kryžminį pardavimą

14 min read

Šiais laikais, patenkinti klientų lūkesčius nebepakanka. Klestėti, įmonės turi viršyti šiuos lūkesčius, ir į klientą orientuoto AI panaudojimas yra labai svarbus norint pasiekti šį tikslą.

AI integravimas į santykių su klientais valdymą (CRM) pagerina papildomo pardavimo ir kryžminio pardavimo strategijas, leidžia įmonėms analizuoti išsamius klientų duomenis, kad gautų asmenines rekomendacijas.

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, ir pageidavimus.

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) arba produktai, kuriuos klientai dažnai peržiūri prieš pirkdami (rodo pirmenybes).
  • Individualizuotos rekomendacijos: Dirbtinio intelekto varomi rekomendacijų varikliai panaudoja šias įžvalgas. Kai klientas apsilanko platformoje, suasmenintos produktų rekomendacijos generuojamos realiu laiku, remiantis naršymo istorija, praeities pirkiniai, ir panašus vartotojo elgesys.
  • Nuolatinis mokymasis ir tobulėjimas: AI algoritmai nuolat mokosi iš naujų duomenų įvesties ir klientų sąveikos. Surinkus daugiau duomenų, 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, 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, demografiniai rodikliai, and interactions with the business—AI pinpoints distinct behavioral patterns, pirmenybės, and individual traits.

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

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

Customers Who Bought This Also Boughtrecommendations 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 generatesFrequently Bought Together” arba “Customers Who Bought This Also Boughtrecommendations, showcasing complementary or upgraded products. Šie pasiūlymai skatina klientus apsvarstyti galimybę įsigyti papildomų pirkinių ir pasiūlyti prekių, kurios juos gali sudominti.

Kai klientai bendrauja su platforma, AI nuolat mokosi iš jų elgesio ir tobulina savo rekomendacijas. Sistema prisitaiko prie individualių pageidavimų, užtikrinant vis tikslesnius ir aktualesnius pasiūlymus.

Pavyzdys, kaip „Amazon“ naudoja vartotojo nuostatų duomenis kurdama produktų rekomendacijas. (Šaltinis: Prisijunk)

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.

Beje, jei tu sell online with Ecwid by Lightspeed, tu gali show related products with theYou May Also Likesection that appears on a product details page and at checkout.

Dynamic Pricing Strategies and Offer Optimization

AI enables dynamic pricing strategies by analyzing market trends, competitor pricing, and customer behavior in real time. This allows businesses to optimize pricing strategies for upselling, offering personalized discounts, or bundled deals that resonate with individual customers.

Uber, the ride-hailing service, uses AI-driven dynamic pricing, known assurge pricing,” to optimize pricing strategies based on real-time demand, supply, and other factors.

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, 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.

Papildomai, Uber may offer personalized discounts or promotions to individual riders based on their ride history, frequency of use, arba konkrečiomis progomis. Pavyzdžiui, Tikslinės reklamos gali būti siūlomos dažniems naudotojams arba mažos paklausos laikotarpiais, siekiant paskatinti daugiau važiuoti.

Šios strategijos padidina vairuotojų pajamas ir skatina motociklininkus toliau jomis naudotis.

Klientų patirties gerinimas

Naudojant AI CRM, įmonės gali pagerinti klientų patirtį teikdamos individualizuotas paslaugas.

Pavyzdžiui, „Spotify“ naudoja AI algoritmus, kad analizuotų vartotojų nuostatas, klausymosi įpročiai, ir istorinius duomenis, kad sukurtumėte suasmenintus grojaraščius, recommendations, ir kasdienius mišinius kiekvienam vartotojui.

Suasmeninto „Spotify“ grojaraščio pavyzdys

Šis suasmenintas požiūris pagerina bendrą vartotojo patirtį, pritaikydamas muziką pagal unikalius kiekvieno klausytojo pageidavimus, kad laikas, praleistas klausantis ir atrasti naujos muzikos pagal savo skonį, būtų malonesnis.

Kryžminio pardavimo taktika

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.

Pavyzdžiui, 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

Integruojant papildomo pardavimo taktiką į AI patobulintas CRM sistemas, reikia panaudoti nuspėjamąją analizę, siekiant nustatyti idealias papildomo pardavimo galimybes.. Dirbtinio intelekto valdomos CRM sistemos ragina pardavimų atstovus pateikti atitinkamus papildomo pardavimo pasiūlymus bendraujant su klientais, padidina sėkmingo papildomo pardavimo tikimybę.

„Salesforce“ sukurta „Einstein Analytics“.

Pardavimų galia, pirmaujanti CRM platforma, 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. Pavyzdžiui, 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 customersneeds with relevant upselling offers. Pavyzdžiui, they may suggest an upgraded subscription or additional features based on usage patterns.

Beje, if you sell online with Ecwid, tu gali prijunkite savo internetinę parduotuvę prie „Salesforce“. per Zapier. Tokiu būdu, nauji klientai bus sukurti Salesforce automatiškai iš naujų Ecwid užsakymų.

„Amazon“ personalizavimas

„Amazon“ personalizavimas, mašininio mokymosi paslauga, kurią siūlo „Amazon“., skirta spręsti problemas, su kuriomis dažniausiai susiduriama kuriant asmenines rekomendacijas, įskaitant problemas, susijusias su naujais naudotojo duomenimis, populiarumo šališkumo, ir besikeičiantys naudotojo ketinimai.

Skirtingai nuo tradicinių rekomendacinių variklių, „Amazon“ personalizavimas puikiai tinka scenarijuose su ribotais arba besikeičiančiais vartotojų duomenimis. 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, pirmenybės, and behavior. Categorize them into groups with similar buying patterns or interests.

If you sell online with Ecwid, you can view, find, and edit all the customer information you need on the Customers puslapį. Iš ten, you can filter your customer base using various parameters and export the segment to work with it in a different service (pavyzdžiui, siųsti tikslinius el. laiškus per pasirinktą el. pašto paslaugą.)

Ecwid puslapyje Klientai taip pat galima pasiekti klientų užsakymų istoriją, palengvinantis segmentavimo procesą. Suprasdami savo klientus’ pirkimo įpročius ir pageidavimus, galite efektyviau pritaikyti pranešimus kiekvienam segmentui.

Ecwid administratoriaus puslapis Klientai

Nustatykite galimybes

Išanalizuokite pirkimo istorijas ir elgsenos duomenis, kad tiksliai nustatytumėte papildomo pardavimo ir kryžminio pardavimo galimybes. Determine which products or services complement previous purchases or align with customers’ interesus.

Pavyzdžiui, when selling online through Ecwid, you have the option to configure automatizuotos rinkodaros el showcasing related products or top sellers.

Related products in automated marketing emails

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. Pavyzdžiui, Amazon’sFrequently Bought Together” arba “You May Also Like” skyriuose.

Siekite tikslinių pranešimų

Kurkite tikslinius pranešimus, pabrėžiančius papildomų produktų ar paslaugų vertę. Parodykite, kaip papildomas pasiūlymas pagerina klientų patirtį arba išsprendžia konkrečią problemą.

Norėdami gauti tikrai optimizuotą pranešimą, apsvarstyti verčiant turinį veiksmingai rezonuoti su įvairiomis auditorijomis ir kalbomis.

Siūlykite paskatas arba paketus

Suteikite paskatų, pavyzdžiui, nuolaidas, paketų pasiūlymai, arba lojalumo premijos, skatinančios klientus ieškoti papildomų pasiūlymų. Padarykite vertės pasiūlymą patrauklų ir aiškų.

Su Lightspeed Ecwid, Galite parduoti produktų paketus naudodami Perparduoti & Kryžminio pardavimo produktų paketai, Produktų paketai, ir BOGO programėlės.

Taikykite kelių kanalų metodą

Implement a multichannel marketing strategy to reach customers through various touchpoints. Naudokite el, socialinės žiniasklaidos turinį, svetainės iššokantieji langai, ir asmenines platformos rekomendacijas.

Atskleiskite suasmenintų rekomendacijų galią

Dinamiškame santykių su klientais kraštovaizdyje, individualizuotos rekomendacijos ir tikslinga rinkodara yra sėkmės ramsčiai. Naudojant CRM duomenis, galite atskleisti pritaikytų papildomo pardavimo ir kryžminio pardavimo kampanijų potencialą.

Kai tiksliai sureguliuotas, šios strategijos rezonuoja su individualiais klientais, vairavimo užsiėmimas, didinant pardavimus, ir nurturing brand loyalty.

Pasinaudokite savo CRM sistemos įžvalgomis, sukurti pasirinktines kampanijas, ir pamatysite, kaip susitikti su savo klientais’ unikalūs pageidavimai ir poreikiai gali padaryti stebuklus.

 

Turinys

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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, ir tt. Connect with him via LinkedIn arba Twitter.

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