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If retail businesses want to keep up with their customers' wants and needs, they need to know how to collect customer data.
While retailers revamped several aspects of their shopping experience to accommodate customers during the COVID-19 pandemic, the biggest difference is the significant increase in online shopping. Retailers need to adjust the methods and technologies they use to collect and analyze customer experience (CX) data to accommodate this change in shopping behavior.
But first, businesses need to consider how they will benefit from collecting customer data.
Benefits of collecting CX data
Customer experience analysis is the process of gathering customer shopping behavioral data to analyze and predict future customer wants and needs. By analyzing customer data, businesses can modify the retail experience to increase the likelihood that customers will spend more money. Providing the right customer experience for a large portion of a company's customer base is the precursor to brand trust.
Competition within retail is high, so the ability to continuously monitor positive and negative experiences can go a long way toward retaining customers while also marketing properly to prospective ones. This requires rapid data collection and analysis. The easiest way to quickly collect customer data is with a digital CX tool, such as a customer relationship management system, contact center software or customer engagement management software.
Types of customer data to collect
While most retailers offer some form of online shopping, many companies considered in-store shopping as their primary revenue stream prior to the pandemic. Therefore, the bulk of CX data collection and analytics efforts for these retailers commonly focused on experiences that customers had while visiting physical store locations. Customer service desires, in-store promotions and product display preferences are examples of how retailers use data to make adjustments to improve the quality of shopping experiences.
As more and more customers choose to shop online, much of the in-store CX data that businesses collect becomes irrelevant. Companies must have a customer experience strategy that they can quickly adjust to meet customers' changing shopping preferences.
Organizations can use two types of data to decipher customer experiences: quantitative and qualitative.
- Quantitative data is something that businesses can easily count or measure. This data is concrete and does not contain emotional bias. Typically, quantitative data is numeric-based and companies report and analyze it using statistical analysis. From an online shopping perspective, quantitative data includes metrics such as counting the number of site visits, customer clicks on a product page, website bounce rate and shopping cart abandonment rates.
- Qualitative data is something that businesses can collect through customer feedback. This is direct feedback from the customer about their overall shopping experience. Examples of qualitative feedback methods include open-ended online surveys, focus groups, digital suggestion boxes and questionnaires.
While qualitative data is not nearly as easy to analyze as quantitative data, it has better potential to provide true insights into customer likes and dislikes. Thus, collection of both quantitative and qualitative data is necessary for proper CX analysis that results in actionable steps meant to increase revenue.
CX data can be a mix of both first-party data (information businesses collect themselves), second-party data (first-party data that businesses can share or sell to other businesses) and third-party data (information that businesses purchase from another party that is generally aggregated through a number of sources).
How to collect customer data
Digital CX software platforms, such as Adobe Experience Manager and Clarabridge, help retailers collect and analyze customer experience data. These platforms are also useful from a management perspective, as they can see all aspects of CX on a single management interface. Businesses can configure CX platforms to predict different outcomes based on various customer behavior factors, including whether the customer is more likely to shop online as opposed to in-store. Having the ability to segment customers into groups based on shopping preference is one way to cater to all shoppers' needs and personalize their experiences.
Quantitative data is the easiest to obtain using a digital CX platform because collection largely occurs in the background of the customer's shopping experience. Thus, businesses collect quantitative shopping metric data without requiring the customer to do anything. This often includes collecting website shopping statistics such as what customers choose to click on and, eventually, place into their virtual shopping carts for purchase. Digital CX platforms excel in this type of data collection, as businesses can configure the platform to automate CX data collection and perform analysis using built-in AI.
A digital CX platform can collect qualitative data in various ways. This includes surveys, questionnaires and other feedback mechanisms delivered through company webpages, email lists, SMS marketing, smartphone apps and social media engagements. Companies can collect qualitative customer feedback from these types of sources and then place it into a centralized database where the business can cure and analyze the data, regardless of the initial data collection method. This makes the qualitative data analysis process more efficient, which, in turn, can result in much faster adjustments to customer experience wants.