All data are informative and useful, but not necessarily for the important questions your company faces. As you build dashboards, reports, or data visualizations, it’s tempting to pack as much information as possible, or to add extra pieces that coworkers request. Avoid all-in-one analytics that try to please everyone or solve every problem. Instead, focus each dashboard/page/project on one question, decision, or strategic goal. Below are several common strategic goals and the metrics that best support their success.
Optimizing a Sales Pipeline
Where do most of your leads drop out? That’s the big question, and it’s the same for most companies. What’s different is what your sales pipeline looks like: a simple call-to-action page, a multi-step funnel, or maybe a complex diagram with many interactions. No matter what shape it takes, map out your typical sales journey and find points where leads make choices or take actions. Your metrics will be a series of percentages of desired actions divided by total leads at that point. Group them by stage or arrange by sequence, and then use colors or other visual indicators to highlight especially low or high behaviors. To make this happen, you may have to invest in a CRM that tracks offline interactions with leads.
Creating a consistent experience of your product or service helps support your brand, which is the promise of meeting customer expectations every single time. Start with qualities your company wishes to be known for, track them over time, and choose benchmarks that let you monitor for consistency. For example, your company wants to be known for quick delivery time. Track average delivery time for each day (week, hour, minute, or whatever scale best suits your business). Overlay benchmarks and use color or other visuals to indicate acceptable and unacceptable levels. For selecting benchmarks, ask product, marketing, and sales professionals at your company what customer expectations are and how you compare with competitors. Choose an acceptable range and make an ongoing goal to reduce that range over time.
Better Targeting Customers
Not all metrics are data visualizations like the two examples above. Some metrics, instead, are used to sort large lists of people by their likelihood of making a particular choice (such as responding to a funding letter). This requires a large subset of data about your customers and their previous behavior. These data teach a statistical software package such as R predictors of the desired behavior, which are used to create a model that can then give every customer a score between 0 and 1. You can then target customers who have scores above 0.5 instead of engaging with every lead in your list.
Your company’s strategy may not be listed above. If you’re interested in learning how data can be used to solve your company’s big questions, reach out to Corbae Creative today.