Part of our expertise in developing ecommerce solutions is rooted in identifying advantages companies might not realize they have, and then helping them leverage those hidden advantages. There’s no better example than customer behavior, a wide-ranging spectrum: Some customers will only buy from you once, others become lifetime loyalists of the brand. As you develop a clear view of segments within your customer base, you can determine the customer lifetime value (CLV) of different types of buyers. By calculating your CLV, you can project how much each customer will spend over the length of their relationship with your business — therefore how much you should spend on attracting, engaging, and retaining them.

Calculating CLV is straightforward:

(Avg. product purchase value) x (Avg. rate of purchase) x (Avg. customer lifespan) = CLV

CLV underpins more accurate revenue projections using your existing customer base, tracking performance for campaigns aimed at repeat customers or recapturing lapsed customers, and improving your segmentation of customer groups to better inform your behavioral data.

Digital analytics can improve marketing efforts, but if you don’t leverage payment analytics as well, you lose a vital resource to inform future efforts: how much of your traffic converts to purchases.

The behavioral data that payment analytics provide means you can see how customers are reacting to marketing initiatives beyond engagement, it can show what’s driving actual sales. This can be as narrow in scope as seeing how an individual product is performing, or as far-reaching as seeing how competitor releases affect your overall conversion rates for a given time period.

The average rate of abandonment for ecommerce purchases sits at just below 70 percent. The good news? There’s plenty of room to grow the rate of sales capture through the use of ecommerce payment analytics.

A major contributor to this abandonment rate is a clunky or counter-intuitive checkout process. “Quick and easy” is the mantra when it comes to checkout. Payment analytics allow you to field-test what actually works with customers.

Here’s an easy way to implement payment analytics operationally: Take the total number of successfully converted purchases in a given timeframe, cross-reference them with the total number of shopping carts created in that same timeframe, and then contrast your abandonment rate as you fine-tune your checkout process. Implementing simple but tangible changes like this can make a meaningful difference in the overall completion rate.

Chargebacks — when a credit card network refunds a customer claim of fraudulent behavior or a disputed transaction — are an irritating reality in retail. Payment analytics are the tonic that reduces the frequency at which they occur, which is a huge bonus given that chargebacks are expensive: With a single chargeback, a business loses the cost of the refund and the value of product itself, plus indirect costs like shipping expenses, time spent processing the transaction end-to-end, and the cost of acquiring the customer in the first place.

The answer to this pricey problem lies in analyzing what attributes of transactions most commonly show up in those ending in a chargeback. By identifying these elements, you can implement stronger fraud prevention safeguards to deter high-risk transactions.

An example: You run chargeback analytics as part of your payment analytics solution and find a high correlation between chargeback transactions made with a billing address from a different continent than the shipping address. Payment analytics solutions can set up automatic alerts for you or your loss prevention team to be notified, flagging the purchase for further investigation.

Unparalleled accuracy in modeling future sales performance is a major competitive advantage companies gain by adopting payment analytics. By examining purchases YTD and YoY with an overlay of payment analysis, you can stop projecting based solely on past performance and start using data as it occurs in real time.

By adding in-the-moment behavioral data, your business and sales strategies can incorporate data-backed changes faster and allow for projections to not only include expected increases from those adjustments but create a better idea of what those increases will be.

The ease with which payment data can be cataloged also makes gathering the data for projections easier and quicker. That means you can plan and adjust with additional data without delaying or disrupting your entire sales strategy.

The benefits laid out here are applicable to any eCommerce business, but they’re not the only ones. By utilizing a payment analytics solution, you can also decide which elements of the solution bring the most value to your business and invest in customizing it accordingly.

In the same way that ecommerce is fundamental to modern retail and wholesale operations, data-backed solutions are part and parcel of a successful venture in the online retail landscape. Insights gleaned from analytics offer a rare benefit: enhancements both to the broader business and in specific functions. That rising tide of improved customer experience, higher conversion rates, and data-backed insights lifts all boats.

The only risk? Failing to catch the wave to begin with.