Are offline and online retail channels different from each other? Both channels aim to optimize the customer experience, drive traffic & frequency, increase basket sizes and improve sales & margin. Yes, the goals of each channel are the same, but the access to data in the e-commerce world is a major difference. It ultimately improves the channel’s ability to deliver the aforementioned objectives.
As part of the Rubikloud team, I’ve had a front row seat to some jaw dropping big data and machine learning projects. (Yes, we are more than a software company!) Luckily, the fixes to retail’s big data and e-commerce challenges are not too difficult, when you have the right tools. Below are four ways we’ve helped global retailers adapt to the data-rich e-commerce world to sell more products.
Many e-commerce sites have become too difficult to navigate. When was the last time you purchased a product online by clicking through the navigation bar’s links? I cannot remember the last time I completed an online purchase this way.
Site complexity is precisely why it is critical to have a prominent, well functioning, search capability on your e-commerce site. Site search is a key interaction point for customers and can make or break a shopping experience. It appeals to visitors with strong purchase intent. They know what they want and are on a mission to find and purchase these items.
In our client engagements, we have found that on average, 33% of all e-commerce sessions use the search function. Almost 66% of those search sessions result in a purchase. Clearly, a better search can lead to higher conversion rates.
Rubikloud has developed a proprietary search query classifier algorithm to identify distinct search behaviour trends for each product category. It optimizes site searches and improves conversion rates. Perhaps now is a good time to revisit your site’s search capabilities to determine if it best suits your customers’ needs.
A great deal of planning is put into creating the ideal customer journey before a new or updated e-commerce site is launched. However, over time, the site changes. New categories or products are added, promotional campaigns are initiated, and before you know it, the e-commerce site is supporting unintended customer journeys that do not enable easy conversions.
Since 88% of US consumers research products online before buying, neglecting to manage customer journeys on your site can seriously hurt your conversion rate. As an e-Commerce leader, you must know your statistics. What are the typical customer journeys on your site? Which ones yield the highest and lowest sales? When do journeys result in bottlenecks or worse yet, an abandoned cart?
We help our retail clients answer these questions. To get to the root of the vast array of customer journeys, Rubikloud built an interactive starburst path analysis tool. It quickly allows an e-commerce retailer to pick certain paths that were taken on their site as determined by specific metrics (raw number, conversions $s, frequency, etc.).
With this information in hand, e-commerce retailers are able to identify and eliminate non-converting pages, dead links, and design new paths that lead to increased conversions.
Mobile technology is changing traditional retail, but perhaps not in the way that you’d think. Although mobile-based transactions are on the rise, these purchases only account for a small share of US retail sales. According to IBM, “smart phones browse and tablets buy.” Yet, desktops are still king when it comes to online spending. The 2014 holiday season numbers indicate that desktop traffic represented 55% of online traffic and 77% of online sales. Research from Mobify shows that conversion rates in 2014 on smartphones range from .63%-1.37% and conversions on tablets were between 1.67%-3.65%. These stats mirror our internal findings quite closely.
So how are consumers using their mobile devices? In our experience it depends on retail vertical and region. For some of our multi-channel retail clients, their customers are primarily browsing and researching products on mobile devices (phones & tablets). Perhaps they do this while waiting in a queue or on a break. These consumers also tend to look at higher value products on their mobile device versus on their desktop.
Clearly, consumer behaviours are different between channels and retailers must understand these differences before investing heavily in technology.
Many multi-channel retailers are challenged to bring their data together from across a variety of online, offline, and mobile sources. A recent Forester and Retail Council of Canada survey found that nearly half of retailers struggled to connect data across devices/channels. As a result, e-commerce teams spend countless hours manually collecting data to analyze using Microsoft Excel, in the hopes of finding a powerful consumer insight to improve the business.
This is an ineffective process that is based on a 25-year-old retail paradigm. It slows down a retailer’s ability to take revenue-generating actions. According to an Econsultancy survey, only a third of client side marketers reported that they were doing a good or excellent job at taking actions based on insights derived from customer data. Many are taking actions on inaccurate or poor data inputs.
Suppose you could reduce insight discovery time by 50% and generate three times the consumer insights you currently find? What would that be worth to you? Could you use that time and accuracy savings to create and execute more data driven campaigns to improve your financial performance?
Rubikloud’s solution to these important retail challenges is RUDI. RUDI brings data from organizational silos into one location. It then runs its insight-tracking algorithms on retail data 24/7. When, and only when, a genuine insight is detected will RUDI alert an eCommerce VP, Digital Director, Category Manager or Digital Marketer.
The advantage of automated insights is that it frees up significant time and improves accuracy when analyzing different data sources. It informs teams with relevant insights so that they can take direct actions quickly to improve KPIs. That’s value added work. Drop me a line at email@example.com if you would like to learn more about RUDI.
So, there you have it. Four ways eCommerce retailers are adapting to a data rich world. Multi-channel retail organizations store a tonne of data. Data science and machine learning tools are a practical and cost-effective way to make sense of this data.
Did you find yourself nodding in agreement at some of the examples above? If so, I’d love to hear your stories or other ways you’ve been able to improve conversion rates at a large multi-channel retailer by leveraging data. Let me know by leaving some feedback below, or by sending me an email at firstname.lastname@example.org. If you liked this article, please give it a ‘thumbs’ up and share it or my slideshare version with your network.