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How Artificial Intelligence Is Changing The Way We Sell Things

Companies often feel reluctant to give emerging technologies a try unless they feel extremely confident in the eventual payoff. But since retail industry has stirred up in recent years because of the advent of e-commerce, the businesses are desperately looking towards emerging technologies to stay in the run.

Image courtesy: Forbes (https://www.forbes.com/sites/louiscolumbus/2019/07/07/10-charts-that-will-change-your-perspective-of-ai-in-marketing/#d6b2f72d0373)

As they realized their business decisions are being driven by data, they also realized that harnessing data on a large scale is the only way to sustainably gain competitive advantage for the businesses. Juniper Research predicts retailers will spend $7.3 billion on AI by 2022, compared with the approximately $2 billion spent in 2018.

But what is AI and how is it going to revolutionize the way we sell the stuffs? Simply put, AI learns from the data and learns how to predict stuffs from the new incoming data based on the data it has learnt from. An AI algorithm requires massive amount of data which might be company’s internal as well as external data to learn from. In a fast growing and ever changing tech-market, AI is here to stay as it enables humans to make best use of machines to do their chores.

Since AI learns from data and learns to predict in fact, these predictions can be used in applications which further can be integrated into business systems or processes and can harness the power of AI. AI holds the potential to transform the retail industry in below ways.

Pattern Recognition

Based on the previously accumulated data, AI systems can predict if a similar pattern of selling is being observed. For example, a certain brand of cloth is being returned mostly or a particular sales quarter remains sluggish for previous many years. Based on the patters, it becomes easy for the sellers to focus on key parameters to keep a consistent sales or boost them in the best case.  Consider Otto, a German e-commerce company that uses AI to predict consumer purchasing patterns. Otto cuts down on returns by more than 2 million items annually by making more accurate suggestions to shoppers.

Propensity scoring

AI is also able to make informed predictions about how a consumer will behave. For example, an AI system can predict if a customer will purchase a pack of items or only one out of them. This technique is known as propensity scoring, which is an important insight for a seller to understand the customer behaviour of purchasing. This helps the business in personalized or recommendation selling.

Personalized/Recommendation selling

Personalised selling or product recommendation is a very common thing we all come across in our lives. When we see advertisements of relevant products on various websites after browsing an e-commerce website, we must understand that AI is at play. Product recommendation algorithms, as a part of prescriptive analytics, based on the past behavioural data of a buyer, recommend the buyer the things he may like.

Sales forecast

Sales forecasting is one of the most important area for a manager in any business. This helps them in holding good stock as per the upcoming demand and employ or engage more resources as and when demand rises. To predict this, AI algorithms can come to a great help. By combining historical selling, pricing and buying data in a single machine learning model the accuracy of sales forecast can be increased.

Lead scoring and Customer loyalty

AI can help retailers to target new business opportunities efficiently thus helping in lead scoring as well. To predict the value of the prospect and chances of winning a business, AI systems can make use of data available for previous pitches and combine with available sales information to generate the required information. In the similar manner, based on pattern, AI can be useful to find out the customers that may be thinking to exit the relationship with a company in near future. An early detection can help the business to provide offers to such customers and win their loyalty within time.

Price Optimization

Pricing remains to be a crucial area for any seller, upon which the growth of the business depends a lot. But it is also a field where trial and error method is used to define the optimised price. However, if AI is used, this problem can be addressed more efficiently. Based on the historical pricing data along with other factors like size of the deal, amount spent per deal etc. an AI algorithm can recommend the best price, optimum discounts as well as attractive promotions for the customers of a business.

Optimizing advertising spend

Sellers spend a significant part of their revenue on advertisements and sometimes the expense doesn’t produce expected results. You are at wrong foot if you send an advertisement email of luxury car to a customer who can only afford a two-wheeler. If a customer’s taste and his value related data is available, AI can be used to predict the amount as well the strategy that can be used on advertisement targeting that customer.

Smart customer service

Can’t find a product? Ask the robot. Looking to order a meal? Tell the bot what you want. We all have come across the bots that pop up on several popular websites to help the browsing customer buying the right thing. Over the time, customers too, have become more specific about what they want from a business after they buy a certain product or a service. At such a time, an intelligent customer service bot, developed through NLP algorithms of AI can enhance the customer experience. For instance, Delta Airlines recently integrated mobile into customer service and in June 2018 it launched support via Apple Business Chat. Still in the testing phase, Delta’s AI-based virtual assistant starts conversations with customers and answers simple questions or guides users to solutions. And in what is becoming a call centre best practice, the virtual assistant hands the customer over to a human agent when an issue is too complex. So far, approximately one-third of all customer interactions are currently handled by the bot.

Below chart shows the reasons for implementation of AI in marketing.

Image courtesy: Forbes (https://www.forbes.com/sites/louiscolumbus/2019/07/07/10-charts-that-will-change-your-perspective-of-ai-in-marketing/#284cdc812d03)

The era of AI in sales has arrived, there is no question about it. Companies that see the potential are investing in new tools and processes. Sooner or later, every company will embrace AI. According to Salesforce’s State of Marketing Study, 22% of marketers are using AI-based apps with an additional 57% planning to use in the upcoming couple of years.

Having said that, also, retail industry has seen slowdown in recent years with several brick and mortar stores as well as malls shutting down. There will be such hard times in the future inevitably. Nevertheless, AI integrated with conventional marketing systems, is bound to play an ever-increasing role in the retail sector. Traditional businesses are slowly achieving heights they could never think of sometime back, using an AI-driven strategy. For other businesses yet to embrace AI, it is far from too late.

Contact for further details

Madhav Srimohan
Data Scientist
madhavs.in@mouritech.com
MOURI Tech

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