The ideal ecommerce analytics solution offers lots of information and insights about your audience, brand offerings, personalization tactics, and other facets of your retail marketing.
More specifically, a best-in-class ecommerce analytics platform provides marketers 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 buyers — two major deterrents to ecommerce marketing success.
In other words, your audience insights in solutions like these will only ever be as good as the data available to them. That means choosing the right ecommerce software matters. (A lot.)
Without unified (and accurate) customer data compiled in a single customer view, you and your team can’t effectively move the needle for both your offline and online business.
What’s more, you’re more likely to lose out to competitors with more modern and advanced ecommerce analytics software and setups to guide their marketing efforts.
This means an upgrade from your current ecommerce analytics tools — and investment in a single source of truth where all shopper data can live — is essential for your brand.
Why standalone ecommerce analytics in siloed tools fall short for retail marketers
If you’re like most ecommerce providers, you’ve got a legacy marketing technology stack that includes “analytics” as part of its core capabilities.
The reality of this solution setup, though, is it doesn’t provide complete customer profiles for potential and existing buyers, including offline shoppers, if your brand also has a brick-and-mortar presence in addition to your online store.
Without unified profiles, your audience and customer shopping behaviors are limited by channel. You can’t stitch together a thorough picture of the entirety of your audience, let alone an individual shoppers’ behaviors, transactions, and other critical data points related to their customer journeys.
What’s more, this patchwork ecommerce analytics configuration can also lead to data latency issues. You need the most recent prospect and customer data to deliver accurate, relevant messaging across marketing channels that continually increases conversions:
- On-open email recommendations that update personalized and individualized offers
- Timely social media ads based on landing pages visited and products or services viewed
- On-site messages that compel your current visitors to complete the checkout process
Without this up-to-the-minute customer data, implementing a real-time marketing strategy that accounts for the micro touch points your shopper audience experiences becomes either extremely difficult (or impossible) for your ecommerce business.
Using martech like a customer data platform (CDP) not only helps you create unified profiles with data from across your main marketing systems (CRM, date lake, etc.), but it also offers in-depth analytics capabilities (e.g., lifetime value calculations based on real-time data, transaction data synced from popular ecommerce platforms like Shopify).
With a CDP, you have the bandwidth to implement far more efficient lifecycle orchestration.
Advanced customer analytics: Your path to enhancing ecommerce conversion rates
Enterprise e-tail organizations. Up-and-coming DTC companies. Regional bricks-and-clicks brands. The leaders in these retail niches all 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 shopper audience — one-time buyers, repeat site visitors, app users, etc. — the more capable you are of delivering the right message to the right people at the right times and frequencies and advancing your ecommerce marketing strategy accordingly. (Not exactly news to you and your your team, right?)
And yet, it’s nonetheless crucial for ecommerce marketer such as yourself 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 your ecommerce analytics solution needs unified, up-to-date customer data
As is the case when researching any kind of marketing technology, it’s critical to consider the core functionality you need in an ecommerce analytics solution.
Having said that, there are certain features any martech that deems itself an “advanced” ecommerce data solution must have in order to make it on your short list.
Let’s use BlueConic’s customer analytics capabilities as an example of what kind of martech can help your brand capitalize on your wealth of ecommerce data.
There are many data types you can capture in BlueConic’s pure-play customer data platform. Let’s start with time-based event data based on shoppers’ buying journeys.
Timeline Events gives retail marketers a historical view of prospects’ and customers’ brand interactions: pageviews, call-to-action clicks, form submissions, product orders, and the like.
This first-party data is organized by date and time and provides BlueConic customers (a.k.a. ecommerce marketers like you) the ability to choose which events to store as a profile properties that reside in persistent customer profiles within our CDP.
Unlike event-stream databases, Timeline Events in BlueConic’s 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.
For retail marketers such as yourself, 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 retail customers can get the now-essential single customer view — one that can enable ecommerce marketing messaging that routinely hits the mark, leads to reduced waste, and gives them a sizable edge over the competition.
Machine learning and customers insights: Two critical ecommerce marketing assets
Once your audience data is unified in persistent profiles, you have the ability to 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 and your brand from potential liability.
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 those in our CDP.
RFM and CLV Models
The use of recency, frequency, and monetary value (RFM) and customer lifetime value analyses has been prevalent in the retail marketing space for some time.
But remember what we said earlier? Great analytics rely on great data. With the right technology, ecommerce marketing professionals can conduct analyses based on real-time behaviors that increases the accuracy and reliability of machine learning outcomes.
From within BlueConic, retailers can use our out-of-the-box RFM analysis model — one OOTB model within our AI Workbench feature — to create segmentations based on RFM scores. Or they could 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 using insights to get a glimpse into how segments perform against one another based on certain profile properties.
For instance, our customers can use the Segment Comparison feature to determine if their loyalty members actually spend more than non-loyalty members with a high CLV score.
This could lead an ecommerce business to examine customers with high CLV scores who are not loyalty members to understand why they don’t find the loyalty program valuable.
With unified data, retailers can also compare their audiences using the Profile Distribution graph. This shows how many customers within a certain 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.