A lot has changed in the retail industry. Two decades ago, when smartphones were in their infancy and the internet was not democratized, Customer Experience included limited channels of communication. A typical retail chain’s Customer Experience strategy would have:
- Centralized phone number and address where customers can call or write letters.
- Sales staff available in store.
- Handwritten notes about the in-store experience.
- Traditional and offline advertising.
Then with the advent of the internet, there were new additions to the Customer Experience plan:
- Website and mobile applications.
- A mix of cold emails and offline advertising.
- Centralized emails and customer service call centers.
- Traditional offline and online surveys.
- In-store customer service agents.
Retailers kept on adding new communication modes to provide a sophisticated experience to customers.
And then came 2020, with a global pandemic everything became digital, and retail became fiercely competitive. Retailers saw firsthand how Customer Experience can make or break their brands. While several trends popped up in the new normal, there is an extraordinary shift in Customer Experience. AI, Automation, Big Data, Rapid Application Development, and Agile became the new normal. This means it’s time for retailers to undergo a major upgrade in terms of building an integrated Customer Experience plan.
This blog post discusses how retailers can build a truly proactive, responsive, and customer-centric experience, that, ultimately, contributes to the bottom line.
Agile and DevSecOps Principles for CX
Imagine a retailer with over 100 physical stores and an excellent product mix but no website or application. Or a retailer with a multichannel presence but delivery time over 15 days. Hard to imagine, right? This means retailers cannot miss any channel of customer communication.
In the age of instant shopping, customers expect new features for a better shopping experience. Features like Image Search, Personal assistants, Voice Commands, QR code scanners, VR and AR no longer stir excitement, but their absence disappoints customers. Two decades ago, including these features will look like a multi-year product plan and customers were ready to wait; but not now. With DevOps and Agile, it’s a matter of a few days or weeks. But still, retailers experience major application glitches like downtime, unable to checkout, UI bugs, security flaws, and many more.
To ensure, smooth customer journey from entry to exit, retailers need to overlap DevOps with Customer Experience. So how does CX-focused DevOps work in practice?
There’s no single formula but there are fundamentals to set the right stage for success. A CX-driven DevOps plan must plug the gaps in Customer journey by closing the feedback loop using software monitoring and quality. Retailers need to link and integrate the DevOps tools and workflows to achieve efficiencies from a technical and business perspective. At Qentelli, we approach Customer Experience using DevOps principles:
- Deliver quality, not features
- Quality is everyone's responsibility
- Maximize performance, not uptime
- Secure your data
- Achieve continuous monitoring
- Build a continuous feedback loop
- Automate the right processes
The Big Data Revolution
Data is often feared…mostly for good reasons. Retailers have a huge amount of data via different channels, this data lies in siloed systems. Supply Chain data is separated from marketing data, marketing data is separated from product design data, and product design works separately from business analysts and leaders. With this, retailers are constantly trying to figure out how they are going to collate all these data and use it to improve Customer Experience. The answer lies in moving beyond basic data collection and traditional surveys to real-time, aggregated, and actionable feedback about customers and tying it back to business outcomes.
As part of Big Data initiatives, there are five distinct areas where applying big data analytics can improve customer experience:
- eCommerce Optimization
- Demand Forecasting
- In-Store Shopping Experience
- Dynamic Pricing
AI and Automation
When I wrote a couple of articles five years back, I loved writing AI and Automation as the future of retail, but now it’s regressive. AI and Automation are already here. They provide a distinct cost advantage to retailers with stressed margins, higher costs to manage supply chains, growing customers’ demands, and cyber-security threats.
Retailers can use data to build intelligent automation systems (classify data, identify patterns, highlight anomalies, and build recommendation engines). Here are few examples of how AI and Automation can help retailers:
- Product insights about overall product experience and areas of improvement.
- Service insights can help in identifying gaps in the overall customer journey by collecting data from various customer touchpoints.
- Audience insights to understand audience demographics, interests, preferences, and opinions.
- Anomaly detection by leveraging an inbuilt AI engine and identifying any deviation in regular patterns.
- Product recommendations based on user search, past purchases, preferred brands, etc.
- Content recommendations to target potential content with the right messaging and a content piece based on their customer journey lifecycle.
- Smart Alerts to detect and prevent a crisis. This can be in application development or customer service or even internal deliveries.
A sample Customer Experience Plan
Consumers go through a series of steps before making a purchase, giving multiple opportunities to retailers to surprise and delight them at each stage. The below example provides suggestions about compelling customer experience and how to build an integrated approach.
The Context: The retailer is a high-end fashion chain carrying a selection of luxury brands and private label products. Their manufacturing units are based out of Asia and major retail outlets are in Europe and the USA. The supply chain is complex with multiple manufacturing units and various retail outlets. Every geography has its version of product mixes, designs, and audience demographics.
The type of Customer: The elite class & upper-middle-class and celebrity influencers. The customer seeks hyper-personalized emails, assisted touchpoints, and premium products.
Tech Context: Their loyal customer base has access to new fashion editions on a priority basis using their website or mobile applications. Based on past purchases, they get notifications, reward points, and discounts. Engineering teams are pressed with time to add new products, discounts, loyalty programs, and other features. The globally distributed operations and supply chain produce a constant stream of data. The in-house engineering team is responsible for all tech and innovation programs. Some of their technology initiatives contributing to Customer Experience are:
Adopting Agile and DevSecOps With new issues coming every season, release teams must be prepared for new feature releases, product additions, AI-powered product recommendations.
Value Proposition – Ease of shopping, access to priority collections, secured shopping experience.
Manufacturing, POS, and marketing channels must communicate regularly with each other to avoid any gaps in advertising, campaigns, and product availability. Big Data was used to achieve this.
Value Proposition – Receives useful and highly targeted emails, no delays in delivery, real-time data for tracking.
AI-powered personal bot assistant for providing product recommendations, assessing their current wardrobe collection to mix and match products. This saves time in selecting and buying products.
Value Proposition – Assisted shopping experience and ease of deciding on the right product.
Automated demand planning/ sales forecasting using data from all distribution points globally using a single IT solution to all manufacturing facilities and outlets.Value Proposition – All POSs have real-time inventory information creating transparent shopping experience.
The path to purchase: The shopper browses the website and calls up their assistants to explore products for the Autumn issue. Based on past purchases and transactions, the shopper receives a personalized catalog of the autumn issue. At the store, she tries various items and selects few pieces. She pays using her in-store wallet and leaves the store. The order is placed for fresh merchandise at the manufacturing unit in Asia and delivered at the location. The personal bot adds loyalty points and gets the package delivered at home. With the 360 Customer Data Platform, the personal assistant knows the preferred freebies to be added.
These tech-led innovations help in creating a delightful experience for customers. Retailers benefit from lower customer acquisition costs and repeat purchases. The demand and supply planning helps in avoiding stockpiles or stock-outs. This is just a sample plan that shows how various technologies can contribute to the overall Customer Experience plan. Based on business objectives and target market, organizations can mix and match various technologies for building a better Customer Experience.
Before we conclude this, I would like to emphasize the importance of building a Quality and Customer Obsessed culture in the organization. To build a great brand, every customer interaction must be delightful and memorable. While human presence can do it naturally, in the digital era it gets tricky. Hence the software design and those who are developing it must be obsessed with the quality delivered and customer needs.