Advanced analytics goes beyond traditional business intelligence reporting and dashboards to discover deeper insights, make predictions, and generate recommendations.
Why is advanced analytics important?
Businesses operate in competitive markets where the ability to make decisions fast is valuable. Analysis paralysis is not an option if you want to stay relevant. A business needs to be able to foresee potential outcomes when making decisions. Advanced analytics uses available data to make data-driven decisions considering complex market dynamics that align with emerging trends without undue risk and bias.
What technology does advanced analytics use?
Advanced analytics takes advantage of sophisticated technology to predict the future. Artificial Intelligence (AI) can be used to create knowledge from data. Below is a list of some of the ways AI can help:
- AI can connect the dots and deduce an outcome using multiple data sources.
- Machine Learning (ML) models consume vast amounts of historical data to provide guidance based on an iterative refinement approach that assesses every outcome to predict the future.
- Deep learning can use the outputs from advanced analytics engines to find hidden patterns and trends. Deep learning can look for clusters of predictions to develop even deeper insights.
- Natural Language Processing (NLP) engines can be fed unstructured data such as audio transcripts and recordings to look for buying signals or customers who are likely to cancel services. Such engines can also automatically deduce consumer sentiment. For example, was a webchat positive or negative?
Businesses can use advanced analytics to guide interactions. A great use case for advanced analytics is to advise a sales team on the subsequent actions they should take based on historical customer interactions. Imagine you are a mobile phone plan sales representative who has just taken over a territory and is unfamiliar with the status of prospects or customer relationships. Advanced analytics can quickly analyze every interaction in business applications such as Salesforce and ServiceNow to gauge the conversation the renewal rep should have. Is the customer about to leave? There needs to be a retention-based conversation. Is the customer pleased? This is an upsell opportunity.
Usage of advanced analytics
A UK-based automotive insurance provider uses the Actian Data Data Platform to provide risk-adjusted insurance quotes in around 20 milliseconds to ensure their offer appears close to the top of the list for potential clients. The AI-driven advanced analytics consider the individual’s demographics and their address for the base premium calculation. The key risk reduction feature for the provider is that they can review any incidences of breakdowns that they also offer coverage for. If a prospective policyholder has too many breakdown calls, that is a red flag that results in a costly offer versus someone with no breakdown to get a compelling quote.
Fraud detection and prevention
Credit card issuers use advanced analytics to limit losses and protect customers from fraud. Every transaction is monitored for potential fraud. The location of the transaction, an unusually high amount, a high frequency of transactions and the purchase of out-of-the-ordinary items are all potential clues. When the risk exceeds thresholds, the transaction is prevented until verified with the customer.
Mobile carriers can use advanced analytics to look for signs that their customers are shopping for a new provider. When a potential customer loss looks likely, they can proactively be offered incentives to renew.
AI-driven advanced analytics can analyze incoming web traffic to look for signs of an attack on servers. When potential threats such as Denial of Service (DDoS) occur, the company can proactively block the offending IP addresses associated with the source of the attack before servers are overwhelmed. Alerts sent to network admins allow them to take action to implement policies that de-prioritize traffic from networks that have previously hosted threats.
Improving marketing campaigns
Advanced analytics can be used to improve the return on marketing campaign spending by studying the performance of each outreach campaign via email. Email lists can be adjusted based on who opens emails and what links and offers get clicks. Each click shows what product or service prospects find most compelling and can be used to prescribe what the following email or call should be about. This way, marketing can automate the buyer’s journey to the point where a live person can successfully convert a prospect into a customer.
Gaming and retail platforms use advanced behavioral analytics to recommend to customers what game or product to buy next. If a player uses in-game chat to discuss a particular team or event with others, the platform will use that knowledge to increase engagement to drive repeat visits.
Digital customer-facing systems increasingly use AI-based chatbots to engage visitors to do more than connect them to users. Broadband providers can use AI to diagnose faults and step customers through reset procedures to restore service without engaging live agents. This reduces service costs and reduces resolution times.
Advanced analytics looking ahead to the future
Intel, IBM, Nvidia and Tesla are developing hardware optimized for running neural networks that can use advanced analytics to make judgements about piloting vehicles, for example. Interactions with machines are becoming more gesture-based, and personal assistant software predicts where to set navigation systems based on your historical behavior patterns. Advanced analytics enables computers to evolve from needing to be told what to do, as in procedural programming days, to a level where they can now suggest what we should do.
Advanced analytics with Actian
The Actian Data Platform is the ideal cross-cloud solution for integrating, structuring and storing multiple data sources for advanced analytic processing. Get started with a free 30-day trial by signing up on the website.