The wine merchant’s data and insights checklist

A practical reference guide to help you understand what data you have, what it tells you, and how to use it.

Why data and insight now matter

The wine merchants that are winning today are more data led. Not because data is fashionable, but because the economics of wine retail have changed. Customer acquisition is harder, margins are tighter, and sustainable growth depends on making better decisions with the customers you already have. Data and insight are no longer optional. They are foundational.

Successful merchants are using data to:

• Acquire, grow and retain customers

• Increase customer lifetime value and loyalty

• Improve efficiency and protect margin

• Build predictable recurring revenue through innovative offerings

• Bring to market new products and services

Increasingly, this is supported by AI and advanced analytics. Used well, AI helps merchants move beyond hindsight reporting and towards clear actionable recommendations such as which wines to promote, which customers to target, and where social and group buying opportunities exist.

But AI only works if the foundations are right. Without accurate connected data, AI simply scales guesswork. With the right data in place, it becomes a powerful decision making tool. That is why the most important question for any wine merchant is not “Should we use AI?” It is “Do we have the data and insights needed to make better decisions?” This guide is designed to help answer that.

How to use this checklist

For each example below, ask yourself:

• Do we have this data?

• Do we actively use it?

• Does it influence a real decision?

If the answer is no or not sure, that box represents untapped value.

The core data types that matter and why

Most wine merchants already have the data needed to unlock many of the insights in this guide. The challenge is not volume. It is knowing which data creates tangible value when connected and used deliberately.

The examples in this checklist are built on a small number of core data types. Each plays a specific role in supporting better decisions.

Customer data

Customer data provides a structured view of who customers are, how demand forms over time, and how purchasing behaviour spreads across individuals and groups, both online and offline. It connects identity, preferences, behaviour, location, and social relationships into a single source of truth. This moves the business from reacting to transactions to anticipating demand and shaping outcomes.

Tangible value created:

• More predictable and resilient revenue growth

• Consistently relevant customer engagement at scale

• Higher lifetime value driven by long-term loyalty

• Expansion of demand through customer influence and social buying dynamics

Why this matters: Without customer data, organisations cannot build durable personalisation, loyalty, or social commerce strategies. Growth remains campaign-led and short-term, rather than insight-led and compounding.

Product data

Product data provides a structured understanding of how each wine, service, or experience contributes to growth and profitability. It connects attributes such as region, style, price band, margin, availability, and suitability for cases, gifting, or clubs into a single, actionable view. This allows merchants to move from managing products individually to optimising the portfolio as a system.

Tangible value created:

• More effective recommendations, bundles, and cases

• Stronger margin performance through informed pricing and promotion

• Better stock and range decisions based on real demand signals

• Clear visibility into which products drive growth versus volume

Why this matters: Without product data, merchants can see revenue, but they cannot see what is creating it, what is diluting it, or where to invest next.

Payments data

Payments data shows how intent is converted into revenue. It captures transaction value, payment methods, split payments, failures, timing, and completion rates, revealing where value is secured, delayed, or lost. This turns checkout from a black box into a measurable, optimisable revenue engine.

Tangible value created:

• Higher conversion through optimised payment journeys

• Increased average order value by enabling higher-value purchases

• Reduced friction and abandonment for complex or shared payments

• Clear visibility into cost to serve, payment efficiency and leakage

Why this matters: Without payments data, demand and intent may be visible, but revenue loss, inefficiency, and friction remain invisible.

Geo location data

Geo-location data reveals where customers are concentrated, how purchasing behaviour varies by location, and where fulfilment and partnerships make economic sense. It connects customer activity to place, enabling merchants to act locally with precision. This turns geography from a constraint into a growth and efficiency advantage.

Tangible value created:

• More efficient customer acquisition through targeted local activation

• Identification of high-potential locations for tastings, events, and pop-ups

• Insight-led partnerships with restaurants, bars, and local venues

• Lower delivery and fulfilment costs through smarter routing and proximity

Why this matters: Without geo-location data, marketing, partnerships, and fulfilment decisions rely heavily on intuition rather than evidence, limiting scale and repeatability.

Inventory data

Inventory data provides real-time visibility into what is available, in what quantity, and how stock is moving across wines and other products. It connects supply to demand, ensuring commercial decisions can actually be fulfilled. This prevents growth strategies from breaking down at the point of execution.

Tangible value created:

• Promotion of wines that are available, viable, and profitable

• Reduced cash tied up in slow-moving or misaligned stock

• Better alignment between demand signals and purchasing decisions

• More reliable fulfilment for clubs, cases, and group buying

Why this matters: Without inventory data, even good recommendations and strong demand can translate into stock-outs, substitutions, and poor customer experiences.

Illustrative examples

Example 1 Identifying ready to buy customers

A wine merchant has a strong customer base and a broad product range, but growth has plateaued. Marketing activity is regular, yet results vary and loyalty is inconsistent. By connecting customer data, product data, and geo-location data, the merchant gains a clearer picture of who creates value, what resonates with them, and where demand clusters geographically. Rather than running generic campaigns, the merchant uses these insights to:

• Identify high-value customer segments based on purchasing behaviour, preferences, and lifetime value

• Align wine recommendations, cases, and experiences with the products that drive margin and repeat purchasing

• Focus local marketing on areas with high customer concentration and proven product affinity

• Activate partnerships with nearby restaurants and bars to host tastings aligned to local buying patterns

This approach attracts new customers through relevant, local experiences while reinforcing loyalty among existing customers through consistently personalised engagement.

The result: More efficient customer acquisition, higher retention, and compounding growth driven by insight rather than intuition without increasing overall marketing spend.

Example 2 Geo location acquisition and partnerships

By analysing customer location alongside purchasing behaviour, a merchant identifies clusters where certain wines consistently perform well. This insight is used to:

• Focus local marketing where demand already exists

• Tailor campaigns around wines popular in each area

• Identify restaurants and bars that match local taste

• Partner with those venues to host tastings featuring wines proven to sell locally

The outcome is more efficient acquisition, stronger partnerships, and higher impact events.

Customer insights

☐ New versus Returning Customers

Question: Do we know whether revenue growth is driven by acquiring new customers or by repeat purchasing from existing customers?

Metric: Percentage of revenue and orders from new customers compared with returning customers

Data required: Customer data + Payments data

Decision supported: Whether growth investment should prioritize customer acquisition, retention, or a rebalancing of both

☐ Purchase Frequency

Question: Do we understand how often customers naturally buy?

Question: Do we understand the natural repeat purchasing cycle of our customers?

Metric: Average number of purchases per customer per year

Data required: Customer data + Payments data

Decision supported: How to increase repeat purchasing through reminders, wine clubs, group buying, or timed offers

☐ Recency of Last Purchase

Question: Can we identify customers at risk of lapsing before they churn?

Metric: Time since last completed purchase per customer

Data required: Customer data + Payments data

Decision supported: When and how to re engage customers to prevent churn

☐ Average Order Value (AOV)

Question: Do we actively work to increase order value without more traffic?

Metric: Average value of a completed purchase

Data required: Customer data + Product data + Payments data

Decision supported: Whether to promote cases, bundles, shared purchases, or higher value wines

☐ Customer Lifetime Value (CLTV)

Question: Do we know which customers matter most long term?

Metric: Total revenue generated per customer over their lifetime

Data required: Customer data + Payments data

Decision supported: How much to invest in acquisition, retention, and loyalty

☐ Wine Preference Patterns

Question: Do we tailor recommendations by taste, region, and price band?

Metric: Frequency of Purchases by wine type, region and price band

Data required: Customer data + Product data + Payments data

Decision supported: Which wines to recommend to each customer to increase conversion

☐ Customer Social Network Mapping

Question: Do we understand the social networks between customers and how group buying reveals these connections?

Metric: Number, size, and frequency of group purchases involving the same customers

Data required: Customer data + Payments data + Location data

Decision supported: How to activate social networks through group buying to increase acquisition, retention, and order value

☐ Group vs Individual Buying Behavior

Question: Do we understand which customers prefer to buy socially rather than alone?

Metric: Percentage of purchases made individually compared with group purchases

Data required: Customer data + Payments data + Product data

Decision supported: When to encourage shared purchases rather than individual checkouts

☐ Acquisition via Shared Purchases

Question: Do existing customers bring in new customers by recommending and buying together?

Metric: Number of new customers acquired through shared purchases

Data required: Customer data + Payments data + Product data

Decision supported: How much customer acquisition occurs organically without additional marketing spend

☐ Retention by Purchase Type

Question: Do different purchasing behaviours lead to different levels of long term loyalty?

Metric: Repeat purchase rate by purchase behaviour segment

Data required: Customer data + Payments data

Decision supported: Which purchasing behaviors to encourage to drive stronger long term retention

Product and inventory aware insights

☐ Product Sales Velocity

Question: Do we understand which wines sell consistently over time?

Metric: Units sold per product over a defined period

Data required: Product data + Payments data

Decision supported: Which wines to promote, prioritize, or restock

☐ Product Affinity

Question: Do we understand which wines are most often purchased together?

Metric: Frequency of wines purchased together in the same order

Data required: Product data + Payments data + Customer data

Decision supported: Which wines to bundle, recommend together, or include in mixed cases

☐ Case vs Bottle Purchasing

Question: Do we understand which customers prefer higher commitment purchases?

Metric: Percentage of case purchases versus single bottle purchases

Data required: Product data + Payments data

Decision supported: When and how to encourage case purchasing

☐ Price Band Performance

Question: Do we understand where demand is concentrated across price bands?

Metric: Revenue by price band over a defined period

Data required: Product data + Payments data

Decision supported: Which price points to prioritize, invest in, or expand

☐ Trade Up Behavior

Question: Are customers moving to higher price points over time?

Metric: Movement of customers between price bands over time

Data required: Customer data + Product data + Payments data

Decision supported: Whether to introduce or expand premium ranges

☐ Margin by Product

Question: Do we know which wines drive profitable growth?

Metric: Gross margin per product

Data required: Product data + Payments data + Cost of goods data

Decision supported: Which wines to prioritize commercially

☐ Seasonal Performance

Question: Do we promote wines at the right time?

Metric: Sales by season over time

Data required: Product data + Payments data

Decision supported: When to promote specific wines or ranges

☐ Gifting Product Mix

Question: Do we understand which wines are most often purchased as gifts?

Metric: Percentage of orders identified as gifts by product

Data required: Product data + Customer data

Decision supported: Which wines to position and promote for gifting

☐ Slow Moving Stock

Question: Do we identify wines that are tying up cash?

Metric: Products with low sales velocity over time

Data required: Product data + Payments data

Decision supported: Which wines to discount, reposition, or remove

☐ Stock led Promotion Opportunities

Question: Do we promote wines based on availability as well as demand?

Metric: Inventory levels relative to sales velocity

Data required: Product data + Inventory data

Decision supported: Where targeted promotion can reduce excess stock

Geo-location insights

☐ Customer Concentration by Location

Question: Do we understand where our customers are geographically clustered?

Metric: Number of customers by area

Data required: Customer data + Geo location data

Decision supported: Where to focus local marketing and engagement activity

☐ Revenue by Location

Question: Do we understand which areas generate, or should generate, the most revenue?

Metric: Revenue by area over a defined period

Data required: Payments data + Geo location data + Customer data

Decision supported: Where to prioritize marketing and local activation spend

☐ Local Social Buying Density

Question: Do customers buy together more frequently in specific locations?

Metric: Frequency of group purchases by location

Data required: Customer data + Geo location data + Payments data + Product data

Decision supported: Where social buying is strongest or where it can be activated

☐ Partner Opportunity Mapping

Question: Do we use data to identify restaurants, bars, or venues for tastings and events?

Metric: Customer concentration and wine preferences by location

Data required: Customer data + Product data + Geo location data

Decision supported: Which partners to approach and where to activate them

☐ Event driven Demand Hotspots

Questions: Do local events correlate with spikes in demand?

Metric: Sales uplift before, during, and after events by location

Data required: Payments data + Geo location data

Decision supported: When and where to run tastings or pop ups

☐ Targeted Local Marketing Efficiency

Question: Do we focus marketing spend where demand already exists?

Metric: Conversion rate by location (and ROMI)

Data required: Conversion rate and return on marketing investment by location

Decision supported: How to reduce wasted spend and improve marketing efficiency

☐ Online to Offline Influence

Question: Do online interactions lead to in store purchases?

Metric: Cross channel purchase behavior by customer and location

Data required: Customer data + Payments data + Geo location data + Website data

Decision supported: How online and physical experiences should work together to drive sales

☐ Delivery Cost by Location

Question; Do some areas erode margin more than others?

Metric: Average delivery cost per area

Data required: Payments data + Geo location data

Decision supported: Where pricing, delivery fees, or fulfillment policies should change

Payments insights

☐ Checkout Abandonment

Question: Do we understand where and why customers drop out online?

Metric: Percentage of abandoned checkouts by step

Data required: Payments data + Customer data + Website data

Decision supported: Where to remove friction and improve conversion

☐ Payment Method Performance

Question: Are we offering the payment methods customers prefer?

Metric: Conversion rate by payment method

Data required: Payments data + Customer data + Website data

Decision supported: Which payment methods to support, prioritize, or expand

☐ Split Payment Usage

Question: Does payment flexibility increase conversion?

Metric: Percentage of orders using split payments

Data required: Payments data + Customer data + Website data

Decision supported: Whether and where to promote shared payment options

☐ Payment Friction by Order Value

Question: Do higher order values increase checkout drop off?

Metric: Drop off rate by order value

Data required: Payments data + Customer data

Decision supported: Where payment flexibility matters most to protect conversion

☐ Payment Failures and Retries

Question: Are we losing revenue unnecessarily due to payment failures?

Metric: Payment failure rate and recovery rate

Data required: Payments data + Website data

Decision supported: Where technical or process improvements deliver the greatest return

☐ Cost Efficiency of Group vs Individual Orders

Question: Do different order types reduce the cost per order or cost of sale?

Metric: Average processing cost by order type

Data required: Payments data

Decision supported: Whether shared purchasing models improve operational efficiency

☐ Payment Timing Lag

Question: How long does purchase intent take to convert into payment?

Metric: Time from payment initiation to completion

Data required: Payments data + Customer data + Website data

Decision supported: When to prompt customers to complete their purchase

Profitability, efficiency and recurring revenue

☐ Revenue Per Employee

Question: Are we scaling efficiently as the business grows?

Metric: Total revenue divided by employee count

Data required: Payments data + Employee data

Decision supported: Where automation, tooling, or process changes are needed to support growth

☐ Cost to Serve per Order

Question: Do some orders cost significantly more to fulfill than others?

Metric: Average cost to serve per order by order type or customer segment

Data required: Payments data + Customer data

Decision supported: Which orders or behaviors erode margin and where to adjust pricing, fulfillment or policies

☐ Profitability by Customer Segment

Question: Are some customer segments unprofitable despite high spending?

Metric: Margin by customer segment

Data required: Customer data + Payments data

Decision supported: Which customer segments to prioritize, invest in, or deprioritize

☐ Discount Reliance

Question: How much revenue depends on discounting?

Metric: Percentage of revenue generated through discounted sales

Data required: Payments data + Customer data + Product data

Decision supported: Whether current discounting levels are sustainable or need to be reduced

Wine clubs and recurring revenue

☐ Recurring Revenue Contribution

Question: What percentage of our revenue is predictable and repeatable?

Metric: Recurring revenue as a percentage of total revenue

Data required: Customer data + Product data + Payments data

Decision supported: How stable and predictable overall revenue is

☐ Average Recurring Revenue per Member

Question: How valuable is each wine club member over time?

Metric: Average recurring subscription revenue per member

Data required: Customer data + Payments data

Decision supported: How much to invest in acquiring and retaining members

☐ Wine Club Churn Rate

Question: How quickly is recurring revenue being lost?

Metric: Percentage of members canceling over a defined period

Data required: Customer data + Payments data

Decision supported: Where to improve retention and reduce churn

☐ Wine Club Member Lifetime Value

Question: Are wine club members significantly more valuable than non members?

Metric: Lifetime revenue per member

Data required: Customer data + Payments data

Decision supported: How wine club economics compare to one off buyers and where to invest for long term value

☐ Inventory alignment with club demand

Question: Do wine club commitments actively guide purchasing decisions?

Metric: Stock levels relative to forecasted club demand

Data required: Product data + Inventory data

Decision supported: How to plan purchasing to meet club demand without overstocking

☐ Upsell from Club Members

Question: Do members purchase beyond their subscription?

Metric: Incremental spend by members outside their subscription

Data required: Customer data + Payments data + Product data

Decision supported: How to grow member value beyond recurring revenue

Bringing it together

Across this guide, one truth is consistent. Real value is created when data is connected, not when it is reported in isolation. Growth does not come from separate views of customers, products, payments, or locations. It comes from understanding how they interact as a system.

Cobuyr was built around this reality. By unifying customer, product, payment, behavioural, and location data and applying AI, Cobuyr enables merchants to:

• Understand who creates value and how purchasing behaviour forms over time

• Match wines, cases, and experiences to customers and groups with precision

• Reveal social and group buying dynamics that traditional commerce misses

• Support repeatable revenue models such as wine clubs and shared purchasing

Crucially, Cobuyr does not stop at insight. It turns connected data into better buying experiences, embedded directly into how customers discover, buy, and buy together, not more dashboards to interpret later.

For wine merchants seeking connected, repeatable, and profitable growth, this shift matters more than any single metric. It is the difference between analysing the past and actively shaping future demand.