The ideal ecommerce analytics solution offers lots of information and insights about your audience, brand offerings, personalization tactics, and other facets of your marketing.
More specifically, a best-in-class ecommerce analytics platform provides growth-focused teams (ecommerce, marketing, customer experience, etc.) with:
- Detailed transaction data (e.g., number of units ordered by an individual or segment)
- Reporting that highlights which visitors and users abandon online store shopping carts
- Intricate item data, like average order value and total revenue earned for products
There are certainly countless ecommerce analytics tools and capabilities that exist today:
- Google Analytics: Ecommerce tracking for your website and app
- Email service providers: Email marketing campaign metrics
- Point-of-sale (POS) systems: In-store buyer transaction data
But many of these tools operate in silos and don’t work off a unified set of first-party data for prospects and customers— two deterrents to ecommerce marketing success.
Without unified (and accurate) customer data compiled in a single customer view, you and your growth teams can’t effectively move the needle for both your ecommerce business.
This means upgrading your current ecommerce analytics setup — and investing in a single source of truth in which all shopper data can live — is essential for success.
Advanced customer analytics: Your path to enhancing ecommerce conversion rates
Many online retail and bricks-and-clicks companies rely on advanced ecommerce analytics tools today to achieve their principal business objectives and marketing KPIs.
And it’s evident the ecommerce marketing teams for these various retail entities are looking to better leverage unified shopper data from their primary analytics solution:
- 75% of ecommerce brands have dedicated analytics professionals who spend 100% of their time analyzing shopper data. — 2018 Cleveland Research Company “eCommerce Team Benchmark” report
- Improving “data-driven marketing that focuses on the individual” was deemed the most exciting opportunity for B2C retail marketing professionals in 2019. — Econsultancy Digital Trends 2019 report
- 63% of retail marketers stated their efforts to become more ‘customer-obsessed,’ including through the use of modern martech, helped increase their marketing efficiency. — 2018 Listrak and Forrester survey
The more knowledge you have about your shoppers — one-time buyers, repeat visitors, app users, etc. — the more capable you are of delivering the right message at the right places, times, and frequencies and improving your ecommerce marketing strategy.
And yet, it’s nonetheless crucial for ecommerce marketers to a) onboard an easy-to-navigate analytics solution that unearths actionable customer insights and b) utilize that shopper data accordingly to boost your conversion rates and customer engagement.
Why standalone ecommerce analytics in siloed tools fall short for retail marketers
If you’re like most ecommerce companies today, you have a legacy database (CRM, campaign management) that includes “analytics” as part of its core capabilities.
The reality is these solutions don’t provide complete profiles for potential and existing buyers. Without unified, persistently updated profiles, your audience insights (e.g., customer shopping behaviors) are limited by channel.
A patchwork ecommerce analytics configuration can also lead to data latency issues. You need the most recent prospect and customer data to deliver accurate, relevant experiences across channels: from email recommendations to on-site messaging.
Using technology like a customer data platform (CDP) helps you create unified profiles with data from across your main marketing and business systems.
Moreover, a CDP offers in-depth analytics capabilities (e.g., lifetime value scores based on real-time data, transaction data synced from popular ecommerce platforms like Shopify).
The importance of up-to-date shopper data for your ecommerce marketing
As is the case when researching any kind of business technology, it’s critical to consider the core functionality you need in an ecommerce analytics solution.
Having said that, there are certain features any solution that deems itself an “advanced” ecommerce data solution must have in order to make it on your short list.
Here’s how BlueConic’s customer analytics capabilities can help you and your growth-focused teams capitalize on your wealth of ecommerce data in real time.
Timeline Events gives ecommerce companies a historical view of customers’ interactions: pageviews, call-to-action clicks, form submissions, product orders, and the like.
This first-party data is organized by date and time and gives BlueConic customers the ability to choose which events to store as profile properties in customers’ profiles. Our CDP allows you to capture and store defined events that you might need in the future.
So, if a furniture company that sells baby furniture sold a crib to a particular customer two years ago, they could use Timeline data to target said customer with a toddler bed today.
If you’re like most ecommerce companies, unifying data from across systems into a CDP will probably include the aforementioned POS systems, web analytics tools, CRM, and ESP and even transaction data from ecommerce platforms like Shopify or Magento.
BlueConic can import order data from platforms like Shopify, thanks to our unique connection with the ecommerce platform, to help you target your highest-value customers with the right message — or, in certain instances, suppress the wrong ones.
Regarding this latter point, ask yourself: Why spend money targeting someone on Facebook with an ad for a relatively high-priced product they just bought and aren’t likely to buy again soon when you can target net-new prospects who’ve express interest in said product?
With constantly updated customer data from Shopify ingested to BlueConic, our customers get the now-essential single customer view — one that can lead to more effective messaging, reduce ad waste, and increase conversion rates.
Machine learning and customers insights: Two critical ecommerce marketing assets
Once your audience data is unified in persistent profiles, you can create multi-dimensional, dynamic segments that update with customer behaviors as they occur (see: real time) and gain next-level insights about them that can inform your decision-making.
For example, knowing someone is a premier loyalty customer and has low recency of purchase might prompt you to send an individualized email that reminds them how much you value their loyalty and offers them a special, limited-time discount.
You can also use segmentation to suppress messaging to those who haven’t given consent (a la CCPA or GDPR requirements), saving you from non-compliance.
If you really want to take your segmentation (and the ROI from your ecommerce analytics) to the next level, you can implement machine learning models — like the out-of-the-box (OOTB) models offered in our customer data platform.
RFM and CLV Models
Within BlueConic, retailers can use our OOTB RFM analysis model to create segmentations based on RFM scores. Or, they can analyze trends or changes to RFM scores for specific segments based on location or after a new product launch.
Similarly, our easy-to-deploy CLV models can help build segments of premier customers that you can offer premium services to and ones with lower CLV you might opt to spend less on.
Using CLV in conjunction with other profile data can help optimize your marketing spend.
Segment Comparison and Profile Distribution
Speaking of creating quality customer experiences using segmentation and scoring, you can also use insights to compare one segment to another.
For instance, ecommerce companies can use the Segment Comparison feature to see if loyalty program members spend more than non-loyalty members and determine what profile properties are in segments for high- and low-CLV shoppers.
With unified data, retailers can also compare audiences with the Profile Distribution graph.
This shows how many customers within a certain customer segment are distributed across specific profile properties, such as average order value across a customer segment and the relationship of this data to other profile properties.
You might know that the “average spend” of recent purchases is $450. But do the majority of your customers spend between $0 and $100, around $450, or over $700? With the profile distribution graph, there won’t be any need to guess.
So, if you have a segment called “Recent Purchases,” you could set this graph up to show the average order value and then see the percentage of buyers and total number of buyers across that have an average order value within a specific range.
Having the ability to break these kinds of ecommerce analytics down by segments and look see this profile distribution gives you a better sense of the distribution of your customers.
Learn how our pure-play CDP can help you enhance your ecommerce marketing strategy and accelerate business growth. Request your BlueConic demo today.