In continuation to our earlier discussion on Business Analytics Interview Questions, in this post we will discuss some other topics on which questions are frequently asked in business analytics interviews.
Historical lifetime value simply sums up revenue or profit per customer. Predictive lifetime value projects what new customers will spend over their entire lifetime. Which of the above will be more important for you as an analytics manager? Under what circumstances you will prefer one over the other?
Under new approaches of modeling CLV, all customers are considered different – and it is this recognition of what academics call “customer heterogeneity” that makes all the difference.How will you approach the calculation of CLV considering “customer heterogeneity”?
Suggest a comparative analysis of below two approaches to calculate CLV:
i. A historical, average revenue per customer per month metric (ARPU) is calculated for 2 years and multiplied by 24
ii. A linear regression model that latches onto how cohorts of users are changing over time and extrapolates to project CLV. (here is a guide to regression analysis)
Suggest an approach to compare CLV across digital (web based) and traditional (agent based) marketing channels for an insurance company. Once you calculate the CLV what approach would you suggest as an analytics manager to increase the ROI of marketing expenditure.
Most principles used in B2B selling today have been in place since the late nineteenth century and coincide with the rise of large mass manufacturing firms. National Cash Register, Westinghouse Electric and others created large, organized sales forces and with them, standardized sales and sales management techniques. How do you think the rise of predictive sales analytics shall drive genuine innovation in these areas?
Predictive analytics is an estimated $5 billion market that has seen $1.2 billion in VC funding specific to sales analytics. This shows there’s a strong economic case for deploying predictive sales analytics.
Even with a CRM in place, companies lose significant time creating and rolling-up company-wide sales forecasts. Compounding the problem is that forecasts are based on human judgment leading to inaccurate forecasts. How do you think Predictive Forecasting can be used to better manage this situation?
One benefit of sales analytics technology is that it can facilitate the alignment of sales and marketing and fulfill the promise of Sales Enablement. The end result is that marketing has the insight needed to produce content that drives engagement and revenue. How do you think this idea can be applied at a pharma company to boost sales.
By predictive lead mining, companies aim to identify high-quality, hidden prospects—those that share characteristics of your best customers. This is done by of analyzing a combination of CRM data, campaign results, opportunities won and lost, and Internet data mining.How do you think you can leverage that analytics to identify prospects at your firm. (for example, using lead information, teams can prioritize sales calls based on which leads are most likely to close and improve conversion rates throughout the entire customer lifecycle)
How would you use predictive sales analytics to lose fewer deals due to uncompetitive pricing and win more deals at profitable pricing?
Behavioral analytics is a recent advancement in business analytics that reveals new insights into the behavior of consumers on eCommerce platforms, online games, web and mobile applications, and IoT.
How will you formulate a strategy to capture Real-time vast volumes of raw event data across all relevant digital devices and applications used during sessions for an e-commerce company?
How will you plan for an automatic aggregation of raw event data into relevant data sets for rapid access, filtering and analysis?
Ability to query data in a number of ways, enabling users to ask any business question is important for behavioral analytics. What is the tool that you would prefer for a start up in order to build such capability?
Give few examples of built-in analysis functions such as cohort, path and funnel analysis.
Give examples of some actionable metrics. (An actionable metric is one that ties specific and repeatable actions to observed results [like user registration, or checkout].)
Give examples of few vanity metrics. (The opposite of actionable metrics are vanity metrics like web hits or number of downloads) which only serve to document the current state of the product but offer no insight into how we got here or what to do next.