Financial Planning & Analysis

Financial Data Analytics: Changes Your Enterprise May Need

While most web-heavy companies have implemented some form of analytics program, many are failing to leverage their financial data analytics to the full potential. Measuring the right metrics, organizing data and creating a system for analysis and testing are key.

Q: What types of companies can benefit from implementing digital financial data analytics or improving their current analytics initiatives?

A: Anybody that has an online presence and is trying to engage a large customer base can benefit from better analytics. Analytics aren’t industry-specific, either. If you believe there’s a large potential audience for your product or service, a better analytics program is going to be very useful.

Q: What are most companies doing wrong with regards to financial data analytics? What are they failing to do that could improve their results?

A: Several things. First, a lot of companies have pre-determined what they expect their outcomes to be, and they go on to interpret their data in a way that reinforces those expectations. Instead of objectively analyzing data, they confirm their biases and stop testing. You need to measure and test a hypothesis as you’re trying to implement new data, and you should always be re-testing and tweaking as you go. It should be an iterative process centered on an operational review program.

Next, many enterprises expect that current trends will continue indefinitely, and that’s not always the case. For instance, a company might see a high week-to-week increase in website visitors for a few months, and their assumption that that trend will continue will lead to unrealistic growth projections. As important as data is, you need to take it with a grain of salt, especially in the early days of an enterprise. Positive trends should influence steps to continue those trends; they shouldn’t lead companies to become complacent.

Another major mistake is to put too much weight on the metrics and not enough on the customer’s online experience. People often focus on a metric such as page views, assuming that it will translate to a certain conversion rate or monthly sales figure. If you never talk about the entire sales funnel, however, you won’t know how or why customers are progressing from a landing page to product and information pages to the pages where they make a purchase or request information.

That obsession with page views also leads marketers to skip over one of the most important considerations: how potential customers arrive at their website in the first place. Far too many companies only look at the data on customers who are already on their websites, instead of investigating search engine traffic, referral sources and other metrics that might tell them where their traffic is coming from. In general, most companies hesitate to allocate more resources to marketing and customer acquisition, even though that’s what they need the most.

Finally, poor data storage is a surefire way to hamstring an analytics program. One of my clients in particular did a great job of measuring metrics and collecting data, but when they wanted to produce a report – how many people had purchased a specific item, in this case – they couldn’t create a simple email list of everyone who had bought it. “Garbage in, garbage out” rang true as always, and their undefined fields made it impossible to quickly and thoroughly sift through their unstructured data.

Overall, you need a robust methodology to store and retrieve data and observations, and this usually means defining as many fields as specifically as possible. You can change the personnel collecting data and observations, and you can change the teams making hypothesis and conducting tests. But, when you can’t extract historical data to isolate trends or target specific messaging, you’ll have to revisit and reimport everything from scratch.

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Q: Is inadequate analytics software a common reason for poor results?

A: For many companies, yes. Trying to build everything in house is one of the biggest mistakes – in general and certainly with regards to financial data analytics programs. Companies often think they’re so unique that they can’t take something off the shelf. The reality is that for many companies, an off-the-shelf solution can provide great functionality for a fraction of the costs of a custom build. Take Google Analytics, for instance. It’s cheap, it works well for many enterprises, and it tracks the user experience across many interfaces: mobile, PC, tablet and more. A quick, efficient start with Google Analytics or a similar solution may be a much better choice than spending months implementing a custom platform.

Q: To conclude, what are a few best practices most companies should put into place to improve their digital analytics initiatives?

A: First, determine what it is you’re trying to measure. You’re ultimately analyzing customers – not devices, browsing software or IP addresses – and you really need to understand their behavior. You can collect reams of data on page views, bounce rates and unique visits, but if you don’t learn why customers come to your site, why they return and why they make their buying decisions, you’re not going to know how to improve your outreach and sales.

Second, establish a consistent cycle for reporting, analyzing and hypothesizing. Instead of trying to confirm or refute your expectations, look at each round of reports as a chance to learn something new about your customers. The whole point of an analytics initiative should be to discover truths about your business that you wouldn’t have otherwise known.

Last but not least, involve the right personnel in your program – in-house, third-party or both. Your IT department will need to be involved in collecting data, but many successful organizations also have dedicated analytics teams. Large enterprises often need several people just to comb through data and generate reports. Just as importantly, marketing executives, financial officers and other key personnel need to be reading those reports and generating the hypotheses that will guide future data collection.

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