It’s no doubt that artificial intelligence has taken its firm position in every industry, making processes efficient and convenient than ever before; ecommerce of course is no exception. Especially during these pandemic times, buying preference of consumers have witnessed a sharp spike towards digital shopping thereby triggering a tectonic shift in the contours of the age old customer service operating model.

What has worked in the past for customer service teams will not work in the future and several ecommerce brands learnt this lesson the hard way in the recent past. Why?

4X-5X increase in online orders for ecommerce businesses during the global pandemic also means similar increase in call load at the call centers of these businesses. The age old model of call centers manned with trained agents to take incoming calls of customers just cannot support this exponential increase in call volumes and many ecommerce businesses had to look at re-inventing their processes using meaningful technology interventions. Enter AI to the rescue.

AI in ecommerce sector is nothing new though. How many of us actually relied on buying online before the automations, smart recommendations, virtual assistants etc.? Today though we enjoy online shopping more than before, most of us do not realize that we are interacting with an ‘intelligent’ system until we really try to notice it. The pandemic situation has just accelerated the pace in which AI gets adopted by businesses and especially for customer service, AI provides an assortment of benefits that can no longer be ignored or postponed.

Below are some top use cases of AI for customer service and how brands are able to deliver exceptional service quality to their customers anytime and anywhere

Customer needs translated to product suggestions within a second 

Suppose a customer is looking for a specific type of product and is finding it difficult to find the right one from the vast assortment available in the catalog. Instead of having to spend endless hours trying to locate the right product, an intelligent AI that is knowledgeable on the products, can make the customer’s product discovery journey much easier and faster.

Helps customer connect to the brand emotionally and strengthens the relationship

AI’s greatest benefit is that it helps build customer loyalty and hence strengthens the customer-brand relationship. The information provided is more objective and the customer has far greater control over the interaction experience. A study conducted by PwC revealed nearly 80% of online consumers feel that speed, convenience, knowledgeable help, and friendly service are the most important elements of a positive customer experience – something that was grossly lacking in yesterday’s customer service processes but no made a reality with AI based intelligent systems.

Intelligent QnA system to answer customer queries

Question Answering, one of the major Natural Language Processing tasks, has helped us build virtual assistants which can provide answers to user queries/ concerns, generated from all the data it possesses. Best part, this service does not sleep! It is always on, available across any channel and any device.

Automation of Order related Processes

An intelligent AI can be trained to understand the issues of the customer and based on the intentions of the customer it can guide them through the processes such as cancelling an order, changing shipping address, getting refunds all within a few clicks.

Artificial intelligence analysis to understand market demand and customer needs

When consumers interact with the AI, they provide a lot of their context information which can help the ecommerce business understand the needs of the customer better. It is impossible to manually go through these queries to find insights out of these. However AI based analytics made on user queries, can predict consumer demands, forecast sales and also help in competitor analysis as well.

Study of customers’ needs to make relevant decisions

According to Narrative Science, 61% of those who are using technology to implement innovative strategies, said they are using AI to identify opportunities in data that would otherwise be missed. Customer behavior has been changing expeditiously and companies, who care about their customers’ demands, know that they need to provide an amazing experience. A great advantage that AI offers is the ability to constantly learn from customers’ behavior and using that learning to take decisions cutting across diverse areas such as merchandizing, assortment, price match, new product ideas, geography expansion, inventory and price optimization and many more.

Conclusion

AI is the future and in order to survive this challenging and innovating environment, companies that haven’t implemented Artificial Intelligence yet, should consider conversational commerce in their roadmap. Ultimately, making an investment for the sake of great customer service and personalized marketing strategies is not at all a bad idea.

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Many retail stores remain closed due to COVID-19 restrictions, forcing countless outlets to move their operation online, many for the first time. With eCommerce retail becoming an increasingly competitive space, data science is playing a key role in giving retailers a competitive advantage, particularly for those businesses hoping to make a longer-term investment in their online presence.

Data-driven decision making uses facts, metrics and data to inform strategic business decisions that align with a company’s goals, objectives and initiatives. It subsequently enables companies to create new business opportunities, generate more revenue, predict future trends, optimise current operational efforts and produce actionable insights.

There are many ways in which data science can revolutionise eCommerce businesses and, in this article, we take a look at some of the most important.

Shopping Cart Abandonment

The shopping cart abandonment rate is an important metric for eCommerce sites to track because a high abandonment rate could signal a poor user experience or broken sales funnel. A sales funnel should run seamlessly from marketing to product selection to checkout, bringing potential customers to a purchase through a series of marketing actions such as automated emails, videos, articles and landing pages. Today, the average cart abandonment rate in online retail is 69.57%, which is $18bn lost every year.¹

There are many possible causes of cart abandonment, making it a complex problem to tackle. Beyond simply improving and optimising the shopping experience through A/B testing, a key strategy for dealing with cart abandonment is shopping cart recovery.

The following methods can be used to entice shoppers to recover items in their cart.

1. Abandoned cart emails or text messages – If the user entered their email address or phone number during the checkout process before leaving the website, then there is the opportunity to send them an abandonment message. This usually takes the form of an offer or discount code to entice the user to return to the site and complete the purchase.
2. Abandoned Cart Retargeting – Ad retargeting is another powerful tactic in cart recovery. With retargeting, retailers place an ad pixel on their checkout page and then can remarket to those users on platforms such as social media and Google. The advantage of retargeting is that it works even in the absence of personal information such an email address.

Sentiment Analysis

Sentiment analysis tools help eCommerce retailers derive valuable insights mined from unstructured customer comments on feedback forms and social media platforms about a given product or brand.  A customer experience strategy that does not integrate sentiment analysis as a core functionality will not capture the overall customer journey in a holistic manner.

Using sophisticated text mining techniques, eCommerce businesses can identify and resolve issues in products or services and enhance the overall user experience. Natural language processing techniques can identify words bearing a negative or positive attitude towards the brand and this feedback helps retailers to improve their products and services in direct response to consumer needs.

Loyalty Cards

Customer loyalty cards, while rewarding shoppers with discounts and deals, are an effective way for retailers to collect data on a large scale.

Customer loyalty cards extend beyond the obvious function of purchase tracking by establishing potential links between online and in store customer behaviour. This helps retailers to understand and shape purchase decisions by targeting advertising and organising products to encourage more sales.

Predictive Forecasting

Enabling personalised product recommendation is one way in which data science is transforming eCommerce businesses. Predictive forecasting uses different data sources to make predictions of a customer’s budget and preferences, including the history of previous sales, economic indicators, customer searches and demographic data. Predictive intelligence technology is used serve what online shoppers need even before they look for a product.

A predictive model can be trained using a historical dataset which classifies customers according to their possession of various characteristics, and the degree to which these characteristics tend to indicate certain product purchases. We would then customise our product suggestions to new customers based on the combination of price and product characteristics the model suggests will be most likely to lead to a purchase. As a further extension of this idea, we can also create metrics such as customer lifetime value (CLV), or incorporate a marketing mix model to understand how exactly we should target each customer.

Pricing Optimisation

Selling a product at the optimal price for each customer can be done with the help of machine learning algorithms. The algorithm analyses a number of parameters from the data at a highly granular level, such as flexibility of prices, location of the customer, the buying attitude of an individual customer and competitor pricing. The resulting price point is optimised to benefit all parties. This is a powerful and important tool for retailers to market their product using customer-specific and location-specific parameters.

Upselling and Cross-Selling

Ecommerce is a particularly rich environment for upselling and cross-selling. Retailers can make offers and recommendations that are truly personalised through insights gained from data science. In doing so, retailers not only increase revenue and profit, but also strengthen customer relationships.

With the help of customer data and product performance analytics, retailers can see what products a person is buying and track the different products they frequently purchase to learn how to optimise their marketing for each customer based on their previous purchases. For example, if a customer frequently buys apple juice and bottled water separately, it may be advantageous for the retailer to market these products together as a bundle, to increase the purchase frequency.

Inventory Management

In a supply chain, the warehousing function is critical to link the material flows between the supplier and customer. It is important for retailers to stock the right goods, in the right quantities and the right locations to meet customer demand for products. To achieve this, the stock and supply chain must be analysed thoroughly.

Powerful machine learning algorithms can analyse the data between supply and demand in great detail to detect patterns and correlations among purchases. This data is then analysed and informs a strategy to increase sales, confirm timely delivery and manage the inventory stock. This can be used to predict ahead of time whether periods of very low or no demand for a product are indicators of mistakes in the data, for example miss-stored or misclassified items, or genuine low demand.

Warehouse management software can also dictate how and where stock should be stored to optimise picking routes. Ultimately, by applying intelligence to big data, these systems can recommend stock movements within the warehouse so the flow of goods is constantly optimised.

Reducing Churn Rate

For subscription-based digital products, machine learning models can be used to predict whether a customer may churn. Such models are usually discriminative classifiers, using deep neural networks, tree-based methods or logistic regression. Generative models or recurrent neural networks can also be used. Both kinds of models can provide a probabilistic assessment of whether a customer is likely to take an action and is appropriate for targeting.

The digital world is in a constant state of flux, and to keep up with the competition and move with the ever-changing landscape, retailers must leverage data to make more informed and powerful data-driven business decisions. Data-driven decision-making can help retailers to improve and personalise user experience, predict purchases, optimise inventory management and, ultimately, drive profits. Data-driven insights can enable retailers to increase their agility, compete more effectively and gain a serious competitive advantage over other eCommerce businesses.

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BENGALURU: Flipkart is working with Microsoft to deploy artificial intelligence and machine learning-based solutions that will making it easier to run future sales, the ecommerce company’s head of engineering told ET.

Flipkart, which recently concluded its Big Billion Day sales, saw three-times more users and as much as 30 times its usual amount of traffic during the sale. It saw no major glitches and is already looking at improving its technology systems for the next sales.

“AI and ML are becoming focus for us now. We think there is a lot of opportunity to optimise how we do things like merchandising and offer placement. So we are putting those systems in place and looking to take away any kind of manual tuning and optimisation,” Vinay YS, vice-president of engineering at Flipkart, told ET.

The company is working with Microsoft, which has invested $200 million, to build those capabilities. “We mostly look at Microsoft from the point of view of AI and ML. There are a lot of those capabilities on Microsoft side that we would like to leverage. On aspects like voice recognition we want to partner with them deeply,” Vinay said.

In February, Flipkart announced it had partnered with Microsoft to make Azure its exclusive cloud-computing platform. Currently, it does not use public cloud, having built a large private cloud infrastructure. The last major investment in its own data centre was in 2015. “On the pure cloud front, we are still evaluating – what they have in India and what we need,” said Vinay.
Amazon not only created vast data centre infrastructure but also the public cloud concept, a $12-billion a year business. Flipkart has no immediate plans to follow suit, but Vinay did not rule it out. “We don’t have any plans we can make public at this time,” he said.

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The creation and consumption of data continues to rapidly grow around the globe with large investment in big data analytics hardware, software, and services. The availability of large data sets is one of the core reasons that Deep Learning, a sub-set of artificial intelligence (AI), has recently emerged as the hottest tech trend. Huge giants such Google, Facebook, Baidu, Amazon, IBM, Intel, and Microsoft are heavily investing in big data, with the acquisition of talent hot on their agenda.

Big data is continuously creating new challenges and opportunities, all of which have been forged by the information revolution. This infographic takes a look at how those in the ecommerce industry are already using data sets to introduce a new level of strategic marketing and provide better customer service experiences.

Predicting trends, optimising pricing and forecasting demand, are just some of the ways that ecommerce businesses are using data to gain a competitive advantage. The guesswork has been removed, and now ecommerce businesses can accurately make strategic decisions on how to operate their online empires.

Big data is proving to be a game-changer when it comes to retail and ecommerce. If businesses can successfully implement effective big data strategies then they will reap the rewards of better customer experiences and bigger profits. This infographic explores practical ways to introduce data solutions with simple implementation.

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