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How AI Is Behind The Planning & Execution Of Mega Sales And Campaigns


How AI Is Behind The Planning & Execution Of Mega Sales And Campaigns


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We’ve seen, heard of, and even participated in some of the biggest online sales like Black Friday/ Cyber Monday, Amazon’s Great Indian Festival, or Flipkart’s Big Billion Day Sale. These are among the most lucrative sales seasons for both e-commerce companies as well as brands since they are timed during peak festive periods each year when consumers are most willing to spend. Thus, capitalising on unique selling opportunities and seasonal consumption trends effectively is imperative to driving the success of such mega sales campaigns.

E-commerce platforms plan their campaigns meticulously by choosing the right messaging, and spending a lot of time creating and pushing timely content to make sure it reaches the maximum possible number of consumers and garners visibility for various brands and products. E-commerce platforms use data – and lots of it – along with marketing automation tools when planning as well as executing such campaigns. These marketing tools or platforms, powered by Artificial Intelligence (AI) and advanced analytics, can deliver phenomenal results by streamlining the entire marketing and advertising process, right from the discovery stage till after the sale has been closed. Let’s see how.



Big Data, Mega Impact: How AI-powered Marketing automation drives e-commerce sales campaigns

A consumer typically spends a significant amount of time interacting with multiple apps, websites, and pages on any given day, and more so before they have to take a purchase decision. On the other hand, e-commerce platforms may get millions of visitors and page views on average, each day. The amount of data generated across each touch point is massive. Moreover, this data is often rich in context, since it is created by customers at various stages of their purchase journey when visiting a platform for product or price discovery. Brands can tap into this data when designing campaigns and promotions in a way that allows them to extensively leverage the insights to optimise outcomes to meet pre-defined sales targets.

Technologies like artificial intelligence and machine learning bring massive computational capabilities to the table. With their ability to process a large amount of data created across multiple touch points, they are quickly becoming highly sought-after tools for brands today, and especially for a vast and rapidly growing industry like e-commerce. Using AI-driven automated marketing platforms, brands can record consumer information and apply predictive analytics models to the data sets and extract insights such as their preferences, the products they have purchased in the past, their motivation behind a particular purchase, etc. Intuitive and self-learning tools can leverage this knowledge to bundle the right combination of products and deals for consumers to ensure a higher degree of personalisation of content, delivered with a seamless experience.

A few essential elements to driving such an experience will usually include the following

1) Crafting sales messages or content to drive engagement:

By examining a large amount of data, AI uncovers a vast trove of information which would hitherto remain unused by brands in the absence of the right tools to help them make sense of it. However, with access to intuitive tools such as machine learning and predictive analytics, marketers can study customer behavior before, during, and after a purchase. Along with this, they can also view real-time information pertaining to a specific transaction such as the buyers’ demographic details, their feedback on the process and the product, etc. The results thus generated are integrated to identify patterns and clusters and patterns, allowing sellers to target the right consumers across different geographic and demographic segments, and accordingly devise their communication in real time for those who are likely to acquire a particular product.

2) Customer-centric and accurate searches:

AI can distinguish between the keywords and search terms used by a consumer on the basis of their past searches. With the help of this information, machine learning models can help marketers and sellers decide what sort of content they want to show to a specific customer when they want to show it, the kind of deals they will be most interested in, etc. This further allows the e-commerce retailers to have adequate and relevant knowledge of the customer’s preferences and package their offerings in a way that appeals to their target audience.

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3) Reaping the power of data and self-learning technology for mega success:

The greater the amount of data available to brands and marketers, the more detailed are the insights which the AI and ML algorithms will be able to supply. Chatbots are an extremely efficient tool to gather meaningful data directly from customers. Simply put, chatbots are conversational, AI-powered messaging interfaces that are capable of providing not only the right answers to consumers’ queries but also accurate recommendations with the help of historical data, thus enhancing the overall quality and impact of customer engagement. This is precisely why e-commerce brands need to deploy interactive and intuitive communication tools on customer-facing touch points to carry out highly contextual and insight-rich conversations with prospective buyers. At the same time, they can access real-time analysis of these conversations, and then enhance the interactions to drive better outcomes.

The insights generated by marketing platforms powered by AI and ML enable e-commerce companies to select the right product, deals and offers for each customer at the right time. The ML algorithms build, learn and enhance the outcomes after processing behavioral data at scale and distilling it to actionable insights. All of these tools work together like the parts of a well-oiled, intricately designed machine to deliver unprecedented businesses outcomes and growth opportunities to brands across the industrial spectrum. 


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