You already have a feel for your busiest days or which products are popular, but with the help of a data scientist, you can take this understanding to a whole new level. Here’s how:
Imagine you’re running a service-based business, and you already know your revenue varies by the day of the week and season. What if you could predict which customers are most likely to return and, more importantly, identify those who are on the verge of leaving for a competitor? Through customer segmentation, I can help you group your customers not just by basic metrics like age or location but by behaviour—how often they visit, what they buy, or how much they spend. This allows you to tailor your marketing and service efforts to each group, sending personalised offers to your most valuable customers and re-engaging those who might be drifting away.
Another powerful tool is predictive modelling. For example, I could analyse your sales and market data to forecast demand more accurately. Instead of guessing when to stock up on inventory or hire seasonal staff, you’ll have concrete data-backed predictions. Imagine knowing that sales for a particular product will surge two months from now because it aligns with an upcoming trend or event. This ensures you avoid overstocking low-demand products and understocking high-demand ones, directly reducing waste and increasing sales.
One real-world example: Let’s say I discover that customers who buy certain low-margin products are also more likely to make high-margin purchases within a few weeks. With this insight, you could implement a targeted upselling strategy, bundling low- and high-margin items, or offering special incentives, leading to an increase in your average customer lifetime value—an improvement that directly impacts ROI.