The Ultimate Guide to Shopper Data 2021
What is Shopper Data?
Shopper data is defined as the information your consumers provide while communicating with your business via your website, mobile apps, reviews, social media, marketing campaigns, and other online and offline media. Shopper data is the foundation of a successful business strategy. Organizations realize the value of shopper data and take action to ensure that they get the necessary customer data points that would allow them to improve customer experience and fine-tune marketing strategies over time.
How is Shopper Data collected?
Marketers can gather data from every source that the customer interacts with the brand on. Although there are a range of ways to collect shopper data, some of them are:
- Website Analytics: You can collect shopper data such as demographic and geographic features along with engagement and behavioral data on your website.
- Social Media: You can know a lot about your consumers based on how they communicate with you on social media. Apart from using basic engagement tools (such as likes, comments, and shares), you can get to know a lot about your consumers through the local analytics/insights section of each social media channel.
- Tracking Pixels: Tracking pixels can show the IP addresses, operating systems, browsers, etc. which in turn helps advertisers run complex re-marketing campaigns.
- Customer Feedback and Reviews: Customer feedback and surveys are effective to collect interests, tastes, and preferences of your customers.
What are the attributes of Shopper Data?
An organization collects shopper data points throughout the buyer journey. The amount of these data points is large, and for ease of understanding, we have listed here some of the typical attributes of shopper data:
- Personal Data: Personal data can be classified into two categories, personally identifiable data and non-personally identifiable data.
- Personally-identifiable data: Information that can be used to understand an individual’s identity. It is further divided into two categories:
Linked Information: Linked information is data that can be used to recognise an individual without requiring additional information/data point. For example, full name, physical address, email address etc.
Linkable Information: Linkable information is any data that can’t recognise a person on its own, but it can do so when it’s combined with another piece of information. For example, first or last name, location, country code etc.
- Non-personally identifiable data: It is the opposite of personally-identifiable data, which is anonymous and aggregated data and can’t be used to recognise any one person. For example, IP address, cookies etc.
Engagement Data: This data tells you how your clients interact with your product/brand via multiple marketing channels. This data covers information such as the customer’s behavior on the website, as well as their communication with you on social channels.
Behavioral Data: This helps you reveal underlying patterns that your customers reveal during their buying journey.
Attitudinal Data: This data is driven by the moods and emotions of your customers. It’s how they perceive your brand and products.
What is Shopper Data used for?
Shopper data is useful for understanding the customers and their user experience with your goods and services. Here are the main uses of shopper data:
- It is useful in designing better products.
- It lets you improve your conversions.
- You can draw more audience knowing their expectations.
- It’s useful to ensure proper communication with the customers.
- Helps sales teams provide excellent customer service.
- It’s used to predict customers’ response so that you can make decisions accordingly.
- It’s used to understand a customer’s purchase habits and lifestyle choices.
- It is used to reduce advertising costs by targeting only those customers who will respond to your product.
How can a user assess the quality of Shopper Data?
Here is how you can go about verifying your shopper data:
- Having a system for data validation helps you set the right expectations from the beginning. The plan should lay out your milestones to measure growth. It should also examine the impact it might have on the current operations and make sure that there’s enough time to fix any potential hurdles that may arise.
- Next, check the size of the data and whether the data is available as a complete product. Also, estimate the number of customer records, size of data, and unique IDs.
- Data enrichment helps marketers verify and refine shopper data by verifying its in-house/first-party data against trusted third-party data platforms. Data enrichment also helps you reduce data redundancies and update existing records.
Who are the best Shopper Data providers?
Finding the right Shopper Data provider for you really depends on your unique use case and data requirements, including budget and geographical coverage. Popular Shopper Data providers that you might want to buy Shopper Data from are BIGDBM, Scanlife by Scanbuy, Datonics, Allfactor, and Connexity.
Where can I buy Shopper Data?
Data providers and vendors listed on Datarade sell Shopper Data products and samples. Popular Shopper Data products and datasets available on our platform are BIGDBM Online & Offline Intender Data for USA - 100% Opt-In by BIGDBM, Scanbuy CPG (Consumer Packaged Goods) and DG (Durable Goods) Brand-level Purchase Data (multi-source validated, US-based, National Scale) by Scanlife by Scanbuy, and Allfactor Global E-commerce Product Pricing Data - Millions of Product Listings by Allfactor.
How can I get Shopper Data?
You can get Shopper Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Shopper Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Shopper Data APIs, feeds and streams to download the most up-to-date intelligence.
What are similar data types to Shopper Data?
Shopper Data is similar to Consumer Review Data , Product Data, Ecommerce Data, Brand Data, and In-store Data. These data categories are commonly used for Footfall Attribution and Shopper Data analytics.