There's been a lot of talk over the past couple of years about big data. First introduced nearly two decades ago, big data is the collection of large data sets that were once difficult to process. Improvements in batch computing have now made the processing of big data an easier task, and marketers have begun to utilize the new granular information to customize, personalize and perfect their messaging to consumers. The rise and widespread use of mobile computing, social media platforms and now what is dubbed the Internet of Things -- i.e. connected cars, homes and even egg crates and toothbrushes -- have contributed heavily to the surplus of data now available and it's likely you've been reaping its benefits for quite some time. Indeed, most consumers have gotten used to data-driven advertisements, despite security concerns. For instance, banner ad click-thru rates are higher when that ad is served to a consumer who views that information as relevant. It's why when you browse shoes on Zappos or books on Amazon, ads containing those exact products you looked at will begin filling your social media feeds and search engine results. This is called retargeting, and though many criticize these ads for showing them products they already bought, this practice is becoming more and more commonplace.
As it turns out, people are more likely to click on content if what they are clicking on is personally relevant. For marketers, big data solves one of the industry's biggest conundrums: getting the right message to the right consumer. Soon, big data will even allow for marketers to gain insights into the final piece of the trifecta: when and via which platform to send that message. For retailers, big data is changing all of the rules. Now, online stores can function similarly to brick-and-mortar boutiques, offering customized experiences based on consumer browsing and purchasing habits. Big box retailers including Amazon, Apple and Walmart have already begun using big data to personalize their consumers' shopping experiences, resulting in increased revenue and brand loyalty. But, big data isn't a tool merely for those with the finances to heavily invest. Plenty of big data analytics tools have hit the market in the past few years, some going public with IPOs upward of $115 million. Knowing the growing significance data plays in the success of an online store,
it's vital to understand exactly how ecommerce analytics can provide the data that uncovers insights with actionable next steps to improve your YoY revenue. This is what the big box retailers are doing, and there's no reason your emerging brand can't join their ranks. Here are 4 ideas to increase repeat customers, average order value and, overall, your online store revenue.
Encourage Full Price Purchasers
- Data: Pinpoint your best full-price customers, i.e. those customers that buy often without discount incentive.
- Insight: Gather customer emails, days since last purchase, order number and lifetime spend. Configure how many of your repeat customers are full-price customers.
- Action: 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 visitors. In all, increase customer lifetime value for the highest loyal spenders.
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.
- Insight: Gather customer emails, days since last purchase, order number and lifetime spend. Configure how many of your customers are inactive. Use analytics to determine common characteristics between these customers.
- Action: 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 in-active consumers. In all, increase click-thru rates and repeat customer purchases, while decreasing bounce rate and abandoned cart.
Use Offers to Increase Conversion Rate
- Data: Pinpoint customers with low average order value (AOV), i.e. those customers that purchase based on discount.
- Insight: Gather customer emails, days since last purchase, order number and AOV. Configure how many of your customers, percentage-wise, are discount driven buyers.
- Action: 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.
Enable Varying Site Experiences Based on Visitor Frequency
- Data: Pinpoint customer location in a sales funnel.
- Insight: Gather purchase and repeat purchase rates to configure the timeline for new visitors to conversion and repeat customers to conversion.
- Action: 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 -- and reach out to them on the days they are most likely to convert again (i.e. two weeks after their first conversion, for instance). In all, provide next steps for customers stuck in a purchase funnel, eventually leading them to conversion and increased sales.
Take an objective look at your business today and get off to a great start in 2015 using data-driven strategies that take the guesswork out of marketing and customer retention.