Omnichannel Strategies And Digital Transformation In Retail

Meenal Pathak and Lalit Jagtiani

When was the last time you remember buying a product online and visiting the store to return it? How about buying it in the store and returning it online?

The second scenario is highly unlikely, and customer service offered by today’s retailers has yet to reach this level. Customer service generally follows a multichannel approach to engage with customers, but the focus should be to provide a single unified and seamless customer experience across all channels.

This is called the omnichannel approach: Service is provided in a single channel, offering customer experiences across all touchpoints. Omnichannel can be defined as “a synchronized operating model in which all the company’s channels are aligned and present a single face to the customer, along with one consistent way of doing business.”

Omnichannel delivers a consistent and personalized experience via digital channels like social media, enabling customer interactions whenever, wherever, and however customers want. It works across all devices and methods, from laptops, tablets, and mobile phones to in-person and call center, where users can interact through websites, email, social media, and more.

According to IDC FutureScape: Worldwide Retail 2018 Predictions, 50% of retailers will adopt a retail omnichannel platform by 2019. This will have a direct impact on retailers’ top line as TCO, inventory costs, operational costs, and promotion pressure will increase profitability by 30%. IDC also predicts that by 2021, retailers will have made better use of the geospatial data to drive efficiency in omnichannel orchestration, reducing inventory costs by 25% across distribution centers and stores.

Adoption of artificial intelligence, augmented reality, and IoT are also increasing and are expected to boost customer engagement and customer satisfaction and adoption by 20%, employee productivity by 15%, and inventory turns by 25%.

Image source: SAP Hybris

Retailers must listen to and adopt their customers’ requirements. This will help them not only adapt innovation into their business chain but also give them a competitive edge. Most customers have access to a plethora of shopping options, and they can best judge the services and features of a website or mobile application compared to those of competitors.

In the future, retailers that adopt an omnichannel experience in their business will gain customer satisfaction. As customer service and engagement are a top priority for retailers, here are some examples of how digital transformation offers a clear advantage:

1. Digital transformation drives organizational shifts, with self-service leading

We’ve all experienced the frustration of listening to automated recordings, pressing option numbers, and waiting to be connected with a telephone customer service agent.

Those days are all but gone. Today’s tech-savvy customers have less time and patience for that process and are moving toward self-service — web and mobile self-service, communities, virtual agents, automated chat dialogs, or chatbots — as the first point of contact. Why call the toll-free number if you get your return request approved on chat or your questions answered with just a few clicks on the app? Customers receive service with less friction, and companies contain costs by providing agent-assisted conversational interactions. According to IDC 2019 Retail predictions, by 2019, the top 30% of retailers will be actively using technologies like augmented and virtual reality for many customer interactions and processes.

Future Outlook

Per Forrester Research, “service will become more ubiquitous, via speech interfaces, devices with embedded knowledge and wearables for service technicians.” Emerging cognitive engagement solutions will take input, learn from that input with human assistance, put the content into context, and make relevant evidence-based recommendations.


Robotics and Data

2. Automated conversations improve customer engagement, and AI will lead the change

Have you ever used chat to return an item to an online grocery store? Suppose you want a refund for the returned item, but the chat option misunderstands your request and orders a replacement instead. You would probably be annoyed.

Customers ask questions in natural language, and we want the ability to sustain a conversation.  Chatbots must be intelligent enough to interpret natural language and provide meaningful responses. Emerging intelligent customer service agents span a range of capabilities, from single-purpose chatbots to virtual agents that embed deep learning and AI that make them smarter over time.

Real-time interaction capabilities open new opportunities for customer engagement. However, execution challenges (involving process and organizational changes, for example) and integration across multiple touchpoints—websites, mobile, contact center, and face-to-face interactions—must be addressed before they can be considered a  competitive advantage.

According to IDC 2019 Retail Predictions, by 2021, 10% of retail sales will be created and managed by voice-enabled digital assistants, which will drastically change the way goods are purchased. In fact, Siri and Google Assistant have already been adopted by millions of users. Amazon Alexa and Google Home are two popular electronic home assistants and by 2021, 50% of households across the globe will use home assistants.

Future Outlook

Companies, will continue to explore the power of intelligent agents to add conversational interfaces to static self-service content. Technology enables the customer service organization anticipate needs by context, preferences, and prior queries to deliver proactive alerts, relevant offers, or content. With machine learning, these capabilities will get smarter over time as the algorithms learn from the new data generated and the actions taken to achieve the desired solution to the customer. Companies will also increasingly use linguistic expertise. The customers informal and unstructured speech would then be contextualized, analyzed, and used to execute actions and present the customer with options and other relevant data in a simple and conversational way. These will integrate agents into existing agent-assisted channels so that conversations can escalate without loss of context.


3. Robotic process automation using machine learning

Have you ever had to struggle to learn the status of an order you placed? With RPA, customers get updates on their mobile phones throughout the process. From the time you place your order, real-time updates are sent 24×7, making customer service quick and convenient.

Robotic process automation uses software to perform routine business processes and make simple decisions by mimicking the way agents interact with applications. This solution boosts efficiency, reduces processing time, and increases accuracy, resulting in better customer experience. Customer service centers can automate entire end-to-end processes, such as account onboarding or claims awards, using human agents only to handle exceptions.

Future Outlook

Customer service teams will explore the nascent world of cognitive RPA, where machine learning will drive real business value by improving nonroutine tasks that require judgment. These include natural language processing and building machine learning into RPA trigger points so the bot can reprogram itself through feedback.


4. Journey analytics will enable a consistent service experience

Most of us have experienced the frustration of explaining a problem multiple times to different customer service agents online or on the phone with no resolution. Imagine how frustrated you’d be if you then visited a physical store and had to explain the problem all over again. Customers expect consistent service experiences across all touchpoints. They expect to be able to start an interaction in one channel and complete it in another without having to repeat themselves.

Future Outlook

Customer service organizations will need to actively analyses customer feedback across departmental silos to find and fix cross-channel issues. They will also need to design journeys to guide customers to the right channels by their inquiry and use journey analytics to understand customers’ behaviors across channels, enabling them to contextually engage with customers. The outcomes of this analysis will allow them to pinpoint common customer paths, estimate the frequency of operational bottlenecks, and determine which combinations of interactions will lead to desired business results, such as better customer experiences.


5. Automated advice on customer queries

Wouldn’t it be easier to try on a small or larger clothing item within the dressing room, without having to get dressed and find the item? With machine learning and tablets, many retailers make this possible. Customers use a tablet to get the new size they want, and a robot fetches it.

Decisioning—automatically deciding a customer’s or system’s next action—exists in shared service organizations. Rules route interactions to the appropriate resource or recommend answers to customers’ questions. Many organizations use a combination of rules and analytics to offer customers personalized options. Value creation happens when customer service centers leverage the power of analytics to better understand customers, customer journeys, events that trigger service interactions, and best resolutions.

Future Outlook

In 2019, organizations will continue to extend the power of machine learning to prescribe the right set of steps for customers or agents to more effectively service customers. The machines will learn how to better rout a service ticket or a customer to the customer service center resource who can most effectively answer a question, which is based on past success. Machine learning can also push the right next steps using customers’ current behavior to help pre-empt future calls.

Mobile and Data


6. Visual engagement

Customers expect service at every point in their engagement journey, and they expect customer service centers to value their time. Co-browsing with annotation can help customers navigate complex forms, and use of video can help explain complex situations to support customers remotely.

Future Outlook

In 2019, customer service centers will leverage visual engagement to strengthen customer relationships in a digital world. This boosts success in enabling customers to onboard the services they provide more effectively. Customer service centers in the future will use video to read customers’ facial expressions and react to signs of frustration or anger, displaying this information to the customer service center agent for better engagement.


digital boardroom

7. Machine learning and advanced analytics will empower customer service agents

Customer service centers use text analytics to extract themes in data from digital interactions and surveys that would be impossible to retrieve manually. Using speech analytics, they will be able to measure call quality in real time. They also use technologies like machine learning to extract emotional insights by mining keywords, volume, pitch, speed, and other language patterns to detect changes in sentiment and intensity during customer conversations.

Keep this in mind next time you lost patience on a customer service call, and imagine how much calmer your life will be with digital transformation being adopted in every industry.

Future Outlook

Companies will use a variety of emerging technical approaches to assist agents in providing more empathetic and compassionate experiences to customers. They will leverage behavioral analytics to match a caller’s psychographic profile to the best-skilled agent for serving.

For more on digital transformation in the retail industry, see For Retailers, It’s Back To The Future.

Meenal Pathak

About Meenal Pathak

Meenal Pathak is business consultant in the Business Transformation Services group at SAP. She has experience in helping clients realize value in intelligent technologies, thus helping the customers in their digital transformation journey. She has more than 5 years of industry experience with Retail, Consumer Products and Goods.

Lalit Jagtiani

About Lalit Jagtiani

Lalit is a Thought Leader in Digital Strategy at SAP. His edge lies in his ability to connect the dots, to provide insights on systems, processes & practices for the clients he is consulted by. He works with the top accounts of SAP to develop new business models and leverage technology to drive business strategy and value. Lalit’s book, When Change Happens…is created at the intersection of creativity and construct and features interactions that he has personally experienced while implementing change in the organizations. A Design Thinking Champion, he also is a certified Master Coach in Organization Transformation and a Certified Executive Coach to CXOs.