Council Post: How businesses can use AI to be more proactive than reactive
According to Gartner, 90 percent of corporate strategies explicitly mention information as a crucial enterprise asset and analytics as an essential competency by 2022. Modern companies have realized the power of big data. However, stockpiling data is one thing, and extracting actionable insights from it is a whole different ball game. Instead of parsing data manually to derive insights, the companies should put an AI system in place to automate the whole process. Technologies like machine learning, computer vision, deep learning, natural language generation and natural language processing can be leveraged to tease out relevant information from both structured and unstructured data. The benefits of using AI are immense. Let’s explore how brands can leverage AI to become more proactive than reactive. Reduce go-to-market time: AI-generated insights let you take action immediately instead of waiting for one team to gather insights manually and then hand them over to another team and then forwarded to another team to run relevant campaigns. AI can streamline and automate the pipeline and improve the turnaround time by many folds. Get insights proactively: The AI system can proactively sift through the growing corpus of data to surface actionable insights. Prioritise automatically Intelligent algorithms can be used to triage insights that focus on key business metrics like LTV, revenue, churn, or engagement. Achieve higher accuracy AI reduces error margins and can work round the clock. AI can proactively create customer segments and cohorts that either need immediate attention before they churn or have the highest probability of repeating a transaction/resubscribing on your platform with close to 100 percent accuracy. Bring in more predictability AI helps in predicting outliers even before it happens. The AI systems can consistently keep track of patterns or gradual changes by analysing the data to help stakeholders take proactive steps to deal with anomalies. Examples of how brands in different industry verticals use AI to be more proactive: Shopping: A fashion retail store can get alerts when there is a sudden increase in orders and leverage the information to reduce acquisition costs and increase the lifetime value (LTV). An e-commerce platform can learn which product categories have a higher organic conversion rate so growth teams can add these categories to recommendation campaigns and drive the average order value. A food delivery platform can proactively learn which customers are prone to churn and set up reactivation campaigns by sending out personalised discounts or offers. Media and entertainment An audio streaming platform can get an alert when a particular album or song was played 3x more than others in the past 24 hours, so it can be used to run viral campaigns on social media. As soon as the Push Notification delivery rate for video streaming apps falls below 20 percent, AI can flag it and inform marketers that a non-active customer segment was added to the campaign so it can be avoided in the next campaign.A digital news publishing platform can learn which customers were interested in updates from the previous FIFA tournament and send a special two-month subscription offer. Online banking and fintech A bank with a mobile app can proactively learn which of the two test onboarding journeys led to lesser conversion rates to allow marketers to make optimisations. An online insurance platform can check why open email rates for a campaign were below the average benchmark so campaign managers can identify a better time to send emails or try a different communication channel like SMS. A fintech payment app can instantly get an alert when there is an increase in payment failures to set up an in-app message to inform their customers. Businesses that want to be more proactive than reactive, they need to take full advantage of AI systems. It helps you discover new insights, predict business and marketing outcomes, unify analytics and customer data, and forecast demands, among other things. This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.
AI can streamline and automate the pipeline and improve the turnaround time by many folds.
Instead of parsing data manually to derive insights, the companies should put an AI system in place to automate the whole process.
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["data science", "machine learning", "AI", "ML", "computer vision", "RAG", "Aim", "deep learning", "analytics", "R"]
["AI", "machine learning", "ML", "deep learning", "computer vision", "data science", "analytics", "Aim", "RAG", "R"]
Nalin Goel is the Senior Vice President, Product at MoEngage Inc. Nalin established the vision for Machine Intelligence at MoEngage and has been instrumental in setting up the Customer Insights and Data Sciences team. He led the launch of MoEngage's Analytics Suite.