Actionable Data: Need to Know Metrics and Tactics

While analytics can uncover trends in products and customer behaviors that lead to greater sales and increased profitability, data and analytics do absolutely nothing without action. However, generating a return on investment (ROI) from your analytics means you’ll need to set up success metrics. Many of the larger retail chains and small businesses use the following metrics below to drive their analytical decision-making to help their business’ grow:

  • Average Order Value (AOV) - (Revenue / Orders): This is the amount of revenue you can expect for each new order that comes in.
  • Cost Per Impressions (CPM) - (Ad Spend/(Impressions/1000): This is most commonly used to measure the rate you would pay for a online banner ad campaign or other online advertising campaign. An Impression refers to the times the ad is served on a webpage (how many times the ad is seen).
  • Cost Per Click (CPC) - (Ad Spend / Clicks): This is most commonly used to measure the rate you would pay for a search engine ad campaign. CPC can also be referred to as PPC (Pay Per Click).
  • Cost per Acquisition (CPA) - (Ad Spend / Orders): This is most commonly used to measure the rate you would pay for a new order or customer. CPA is often used when measuring the rate you would pay an affiliate for referring an order to you.
  • Cost of Sale (COS) % - (Ad Spend / Revenue): This is the portion of your revenue that goes to your ad spend and is measured in %.
  • Conversion Rate - (Clicks / Orders): This is the rate at which customers convert from your landing pages. You can look at this metric for each individual advertising campaign or for your site as a whole.
  • Revenue Per Click (RPC) - (Revenue / Clicks): This is the amount of revenue you can expect for each click you receive to your site.
  • Customer Lifetime Value (LTV): This is the projected revenue that a customer will generate during their lifetime. For a full explanation of how to calculate LTV for your online store, check out this infographic.

Actionable Data: Case Studies

Once you understand the metrics above, it’s time to start using analytics like the big retailers by:

  • evaluating your acquisition channels with an LTV/CAC analysis
  • going beyond funnels to paths (path analysis)
  • slicing and segment your shoppers according to cohorts
  • predicting potential churn by identifying changes in purchase and retention patterns
  • using actionable insights to react quickly to dynamic business scenarios
  • unifying data from social networks, blogs, ad networks, and billing platforms to make evaluating your performance a snap and day-today management a breeze
 

Completing these types of data analysis can help you uncover your business’s strengths and opportunities for improvement. Although Amazon may be your direct competition or perhaps they are one of your sales channels, Fortune magazine provided details on how Amazon leveraged customer data to increase the sales by implementing customized product recommendations. Below, you will find ideas around putting such a program into place, as well more information and case studies on how to use data to increase your online store’s revenue.

A solid ecommerce strategy is no longer about making decisions off a single data point. You need to use a collection of data to create a 360-degree view of your buyers that can drive upsell, cross-sell and repeat buying behavior. In fact, one of the reasons Amazon continues to dominate is because they use data-driven information to suggest related items and bundles based on what the customer has previously purchased, what they’ve looked at, and what similar shoppers have also purchased.

In a 2012 Fortune magazine article, an Amazon spokesperson explained the strategy, “Our mission is to delight our customers by allowing them to serendipitously discover great products.” And the customer-focused strategy is winning. While Amazon has never publicly released metrics around the effectiveness of recommendations, shortly after rolling out sitewide integrations, the company saw a 29% increase in sales.

Using the analytics platform, then, track each transaction and interaction to collect insights about your shoppers so you can determine where they’re coming from, what they’re looking at, what they’re buying and what’s compelling them to actually put an item in their shopping cart and check out. By better understanding your visitors’ behavior, you can make smarter decisions to grow your business and build deeper relationships with your customers.

Instead of looking at sales history to forecast inventory, robust analytics allow you to see what’s happening immediately so you can make stocking and pricing decisions and improve your margins on the fly. You don’t over purchase and you know exactly when to launch sales and markdowns to get rid of underperformers. In fact, BigCommerce customers have seen a 10% reduction in abandoned cart rates simply by changing the price of a product. Instead of waiting monthly or weekly to crunch numbers and make business decisions, now all merchants can optimize down to the minute, just like the big retailers.

Imagine you’ve just launched a new marketing campaign on Pinterest and minutes later you get your first visitor. Is this a customer who has bought from you before? When was their last purchase and how much was it for? Are they viewing your site on a mobile device, and if so, do they respond better to promotions that are sent via text, email or on social media? Should you send them a text message right now with a special promotion tailored to them based on their average order value?

Actionable Data: Tactical Ideas

Here are several ideas to help you generate a return on investment with the data you have available via your BigCommerce in-store analytics suite.

Here is an example of how you can encourage full price purchasers:

  • Data: Pinpoint your best full-price customers, i.e. those customers that buy often without discount incentive.
  • Action: Gather their emails addresses, days since last purchase, order number and lifetime spend. Then configure how many of your repeat customers are full-price customers.
  • Tactic: Send timed loyalty emails showing them new products before other customers (without discounts) or set up special programs for these extremely loyal consumers, encouraging word-of-mouth promotion that will help you pull in look-alike consumers. In all, increase customer lifetime value for the highest loyal spenders.

Here is an example of how you can launch win-back campaigns:

  • Data: Pinpoint inactive customers, i.e. those customers with higher than average lifetime spend, but who haven’t purchased in a while.
  • Action: Gather customer emails, days since last purchase, order number and lifetime spend. Determine how many of your customers are inactive and use you in-store analytics to determine common characteristics between these customers.
  • Tactic: Set up nurture flow emails to reactivate inactive customers, and A/B test site design, ads and more based upon common drop-off points between inactive consumers. In all, you will likely increase click-through rates and repeat customer purchases, while decreasing bounce rate and abandoned cart.

Here is an example of how you can use offers to increase conversion rate:

  • Data: Pinpoint customers with low average order value (AOV), i.e. those customers that purchase based on discount.
  • Action: Gather customer emails, days since last purchase, order number and AOV. Configure how many of your customers, percentage-wise, are discount driven buyers.
  • Tactic: Send discount emails and loyalty program information to these customers on a more regular basis to increase number of orders while maintaining average order value. Also, encourage customers to spread word-of-mouth promotion via discounts for sharing the site with friends. In other words, give them offers to save more as they buy more.

Here is an example of how you can enable varying site experiences based on visitor frequency:

  • Data: Pinpoint customer location in a sales funnel.
  • Action: Gather purchase and repeat purchase rates to configure the timeline for for new visitors to conversion and repeat customers to conversion.
  • Tactic: Target new site visitors differently, via A/B testing, to continuously push them down a purchase funnel. Set up nurture flow emails for customers who have already purchased on those days they are most likely to convert again. In all, provide next steps for customers stuck in a purchase funnel, eventually leading them to conversion and increased sales.

Actionable Data: Advanced Options

BigCommerce Insights is a premium report that automatically analyzes store data and delivers valuable, actionable advice. This report has helped BigCommerce merchants increase revenue by up to 25% within the first three months of using it as they were empowered to find answers about customers, marketing, and merchandising data so you may take action within seconds.

BigCommerce Insights, provides merchants like you the ability to drill down on information such as what products should get top placement on your site, and which marketing channels bring you customers that spend the most over the lifetime of their relationship with your brand with minimal legwork on your part.

BigCommerce Insights includes a number of features that enables online store owners to analyze data in mere moments by simply viewing pre-formatted, intuitive depictions of your data. In a few clicks, users can view information on repeat purchase rates, cohort performance, and product performance without having to sift through data, create reports, deal with Excel, or any cloud-based reporting solutions.

The BigCommerce Analytics Insights report.

Get Started:
  • To learn more about BigCommerce Insights, see BigCommerce Insights Report.
  • To begin using BigCommerce Insights for your online store, go to AnalyticsInsights and follow the prompts to purchase.

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