Define the “minimum viable data” required for your initial customer data platform (CDP) use cases. Execute those use cases following CDP implementation. Evaluate the return on investment in terms of revenue generated and operational efficiencies gained.
That’s the optimal starting point for companies’ CDP journeys. But it’s just that: the start. They must also adjust their use case ‘roadmaps’ as business goals and conditions change.
Here’s how retailers can expand their distinct CDP use cases over time — and continue to accelerate business growth.
CDP use case #1: Connect your ecommerce platform and Google Analytics account to your customer data platform to improve reporting and multi-dimensional segmentation.
Pluggable connections in BlueConic enable our retail customers to sync first-party data from their ecommerce platform (Shopify, Magento, BigCommerce, etc.) and analytics and big-data tools (Google Analytics, Decibel, Power BI, etc.) in our CDP.
These out-of-the-box (OOTB) connections help retailers merge online order event data with in-store purchase data and other digitally collected data (website visits, email and ad engagement, etc.), which is then stored in persistent customer profiles.
Retailers can then build multi-dimensional segments based on customer attributes (collected via ecommerce platforms, other technologies, and BlueConic ‘listeners’ — more on these shortly) as well as scores, like customer lifetime value (CLV), stored in profiles.
Examples of multi-dimensional segments retailers can develop in BlueConic include:
Shoppers with CLV scores greater than A who’ve visited the site in the last three days
Subscribers with engagement scores below B with renewal dates less than 30 days away
Repeat visitors who haven’t purchased before but have C site sessions in the last week
Prospective shoppers who’ve abandoned an item in their shopping cart in the last day and have a recency, frequency, and monetary value (RFM) score greater than D
‘Combining’ different segments with common characteristics allows BlueConic customers to analyze trends and behaviors in their audience. Many retailers then use these segment insights to execute real-time, cross-channel lifecycle marketing.
Retailers also use our Segment Overlap Insight to pinpoint commonalities and differences among segments, which informs their engagement efforts to high-value customers and helps them suppress messaging to low-value customers.
For example, Franklin Sports compares three distinct segments of customers in BlueConic:
‘Cold’ customers: Haven’t visited the website or bought anything in two months
‘Hot’ customers: Visited the site in the last 30 days, but haven’t bought anything
‘Very Hot’ customers: Visit in the last week, no purchase, but high ‘session quality’
This helps the sporting-goods retailer determine how to modify its personalized marketing accordingly (e.g., which segments to add to acquisition, upsell, or cross-sell Lifecycles).
CDP use case #2: Configure ‘listeners’ to capture visitors’ interests, then use that data to refine your product recommendations, on-site personalization, and retargeting.
Listeners are critical to retail companies’ multi-dimensional segmentation success.
Using BlueConic, retailers set up listeners to capture key data for site visitors and app users (form-field content, site and app behavior, product interests) in a privacy-centric way.
Our Interest Ranker 2.0 Listener, in particular, is ideal for retail businesses to implement:
Retailers with BlueConic configure custom rules for this listener that identify (or exclude) certain keywords, meta tags, page URLs, and/or portions of a given page shoppers visit.
Points are then assigned to these page elements, as well as page events and actions (e.g., scrolling, link clicks, form submissions), based on the existing scoring system set up in BlueConic or whatever custom scoring system these retailers prefer.
Interest scores are assigned for visitors and dynamically updated in their BlueConic profiles based on their activity (or inactivity). Retailers can set up decay periods that lower interest scores for visitors who don’t engage on-site over a given period.
Retailers then use this interest data to create new customer segments to whom they can then deliver individualized messaging: on-site dialogues promoting previously viewed products, open-time emails with real-time product recommendations, and retargeting ads to visitors as they browse the web, among other types.
For example, BlueConic customer Belgian Cycling Factory collects visitors’ interest data and combines it with geographic data to inform its Facebook and Instagram advertising.
Belgian Cycling Factory serves targeted ads to visitors who have expressed interest in particular bike models and promotes the specific dealership where they can buy it.
CDP use case #3: Use predictive analytics data to better understand which specific products and brands sell most and least often and adjust your inventory management.
BlueConic’s OOTB predictive models (propensity-to-buy- and -churn, CLV, RFM) help traditional and digitally native retailers forecast shopper behavior and act on that data accordingly to execute growth initiatives and improve key programs’ performance.
These models also help these retailers better understand which products and brands are top-sellers and worth stocking up on more online and in their brick-and-mortar stores.
One bricks-and-clicks retailer uses BlueConic to accelerate its omni-channel customer engagement strategy. But the company also uses predictive analytics generated in BlueConic to discern which items and brands sell most and least often.
The retailer then uses sales data to negotiate better terms with poor-performing brands when deciding whether to stock up on inventory of their clothes and accessories. The company can also stock up on best-sellers its customers buy often.
This savvy, data-driven approach to managing inventory — in addition to factoring customer scores in their omni-channel lifecycle marketing programs — helps retailers with BlueConic reduce costs, unlock revenue, and increase operational efficiency.