3 Ways Retailers Can Personalize Customer Experience Using Technology

The modern consumer expects personalization as a standard of service. And even more, they expect to have a seamless transition between their online shopping experience and in-store interactions. Today, with much focus on the omni-channel experience, recognizing customers on a personal level still garners results, but it requires a new twist: using technology to augment and improve the shopping experience wherever and whenever the customer chooses to engage.

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The truth is that our grandparents actually did enjoy personalization in customer experience. Shopkeepers came to know them, have relationships with them, and service them accordingly, carrying just the right merchandise and addressing them by name. In today’s digital world, the same methods still work. Only today, our customers expect technology integration because it is how we all live – with technology seamless to so many aspects of life – and retail should be no different.

Though it can seem overwhelming, as a retailer, to consider so many variables, the benefits of doing so cannot be overstated. Enhancing technology-driven personalization throughout customer experience creates exponential dividends for retailers while assuring customer loyalty for the long-term. This drives an increase in customer acquisition and retention, translating into success over competition.

So how can retailers not only keep up with these expectations but also exceed them?

1. Personalization Through Recommendations

For customer acquisition and development, offering truly individual recommendations is key to personalization. Using advanced data analytics, retailers can gain deeper insights on each individual consumer and predict several personalized recommendations. The options are multi-faceted and include: next product to offer cross-selling/up-selling opportunities, customized promotion and incentive offerings, product feature descriptions, tailored marketing and outreach campaigns, and win-back offers.

To dive even deeper into personalization capabilities, consolidating and automating data source integrations into a data lake allows for real-time data streams. Artificial Intelligence/machine learning (ML) then speeds up delivery of the processes above, offering live processing and actionable insights to the retailer, which is a significant advantage. Advanced cybersecurity measures allow retailers to maintain the integrity and privacy of customers’ data.

2. Personalization Through Customer Service

For client retention, constantly improving customer service is essential in the current, competitive marketplace. Google Cloud’s Dialogflow automates conversational customer service interactions, potentially handling frequently asked questions, basic requests, and transactions across platforms. Dialogflow can be used to create interfaces such as chatbots and voicebots that provide the quick, efficient, personalized customer service consumers are demanding. Powered by Cloud Natural Language, Google Cloud Speech-to-Text, and other ML technologies, Dialogflow offers a new level of customer personalization and experience. Incorporating this technology can also help decipher user sentiment to determine when unsatisfied callers should be transferred to a live agent. All of this improves call center outcomes: forecasting volume, optimizing agent utilization, and minimizing customer wait times.

3. Personalization Through Mobile Experience

Providing better mobile experiences is also critical to optimizing customer experience, and personalization is a key way to break away from the pack. For a personal touch on-the-go, Google Cloud Vision Product Search empowers users with more ways to find what they are looking for with ease. Tools such as AutoML Vision facilitate this, allowing teams to build search engines that find products, not just with words but also with images.

And for increased personalization, in-store apps give customers an interface to answer their questions while they’re in the store, if onsite representatives are busy. With this technology, teams can create CI/CD pipelines to deploy PWA or mobile solutions, reducing the amount of effort to keep an app running with the latest information. Cloud Backend builds resilient, secure and scalable backends to allow apps to quickly scale up to meet demand at peak times and scale down to avoid charges when traffic is low. Teams can develop backends as micro-services deployed on Kubernetes or as serverless solutions.

The ultimate customer experience achieves personalization that grows over time. A multi-pronged, fully integrated approach using Google Cloud is ideal for retailers seeking seamless, personalized customer experiences.

If you’re attending NRF 2020 in New York City this week, stop by Booth #1229 on Level 1 to meet the Maven Wave team and learn how AI, ML, and data analytics can help you personalize the customer experience. If you aren’t at the conference but would like to learn more, contact Maven Wave to set up a time to connect with our experts.

January 13th, 2020
DATA ANALYTICS & MACHINE LEARNING

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2020-07-21T08:09:22-05:00