Even the best run companies that know their business and monitor their operations closely are often amazed at what they can gain in terms of insights from analytics. Many derive 10x and even 100x ROI on their investments in data science. All this working with the data they already have or can easily get from their own business. Typically we can use predictive analytics to model and analyze data patterns that lead to certain outcomes. We can then predict these same outcomes as data is collected and even reverse or reinforce these outcomes with additional reinforcement.
Employee retention and turnaround. Behavioral data capture from access cards, call management queues for services industries or other measures that can highlight dissatisfaction are captured. Tardiness, absenteeism, longer breaks, longer phone answer windows, customer complaints and a combination of factors specific to your business or operations. At this point a list of ‘at risk’ staff can be determined and a determination made as to whether or not to take proactive measures such as training, positive reinforcement or implementing feedback loops to help weed out bad sites, managers, or staff.
Customer churn reduction. Often characterized by complains and support calls, by data driven science and seeing how customers are using your products or interacting with your website, subtle changes in behaviors and product use can be an early detection of possible dissatisfaction and churn. A reduced level of product use can mean they are experimenting with competitive solutions or even abandoning or developing in house alternatives. Proactive measures like providing additional support, customer dedicated account reps, discounts, upgrades or other preferential treatment may provide a way of reversing these elements or at least a good customer touch point to understand and have a dialogue around them.
Deterministic product usage based roadmap investments. Are your roadmaps built on data based on how people actually use your products? Data from analytics related to screen accesses and time spent in functional areas of your platform can and should strongly influence how you spend your product development dollars and where your R&D group should invest in the roadmap. Knowing how your product is used can help you determine how to optimize customer adoption and improve satisfaction. Areas that are frequently used, areas that take the longest to complete, areas that require the most keystrokes, even subtle pauses between operations or users having to back out of transaction to correct errors are all tell tale signs of product design improvement opportunities.
Real time analytics and historical data. When the customer is on the line, on the website, or in the store is the time when real time analytics can really make you shine. It’s like the salesperson that knows your name, your spouse and kids names, and even what you like to eat and drink. Imagine that when a customer is in your store or on your shopping portal and you can say ‘ those shirts you have bought three times are now on sale. Look what we have in your size and in your favorite colors for 50% off today …
Real time response measures. When a ship or truck is coming into the warehouse or port you have a slew of preparatory measures planned in advance with highly paid staff, expensive machinery and transport crews lines up. What if there is a delay or a change in plans? Reacting in real time by reallocating resources can be a huge endeavor with the need to have data driven decision making at the ready. Rather than lose the expense of the resources reserved for the activity how can you allocate them to other tasks, how could you utilize the equipment. Again, access to your operational data in real time helps you reallocate and redistribute resources efficiently and economically.