Data Analysis is Retail’s Crystal Ball to Increase Holiday Sales

Every year the National Retail Federation (NRF) publishes its annual “Holiday Planning Playbook,” which is the retail sector’s guide to understanding key lessons from the previous holiday season and how it will impact consumers in the upcoming run up to the holidays. The NRF surveys over 2,000 holiday shoppers to understand their experiences and behavior during the previous holiday season and what they expect from retailers moving forward. Along with data highlighting consumer expectations, the report provides suggestions for retailers to consider when planning for the season.

One of the key findings from the 2017 NRF Survey was that: “Nine in 10 holiday shoppers said something convinced them to make a purchase they may have been hesitant about; more than half purchased an item that was recommended to them by the retailer online.”

What was that “something that convinced them?” How can retailers determine what will convince them again?  While there isn’t any magic crystal ball, data analysis can predict and support retailers’ efforts to influence purchasing behavior, both in-store and online.

Bloom Intelligence mixes the digital and in-store experience to give retailers the essential analytic tools they need to act on consumer behavior in real time and encourage purchases.

The goal is to get ahead of customer sentiment when they are on the premises and immediately send offers and suggestions to influence purchasing decisions.

The key is not to totally rely on previous data to drive future sales.  The “crystal ball” for retailers is using real time intelligence to actually personalize the customer experience and deliver dynamic marketing content.  This means engaging with customers via their mobile phones because ninety percent of consumers keep their mobile phones with them at all times.  A marketing automation platform that can trigger direct communication to mobile phones grabs customers’ attention while they’re in the store, drives social media interaction and promotes retention after they leave.

Managing the process is simple.  Customers are encouraged to register for in-store Wi-Fi, which is both a value-add service for customers and an asset to retailers.  Data is collected whether the consumer logs in or not, and Bloom Intelligence is able to interact with a customer who does not have the store’s mobile app installed. The platform is also able to automate customers’ onboarding to third party applications, such as loyalty programs and mobile apps.

Bloom Intelligence triggered offers utilize advanced segmentation based upon demographics, presence analytics, and purchasing behavior. Acting on actual customer behavior, Bloom Intelligence automates one-time, repeating and triggered promotions that align with individuals’ shopping or dining behavior. Specific triggered promotions can be built around several metrics, such as:

• At Risk – sends an offer to people who Bloom Intelligence has marked as having broken their regular visit pattern.
• Milestone – used to send an offer when customers reach predetermined milestones such as their 10th, 25th, or 100th visit. Offers can also be generated annually, for example, on the anniversary of a customer’s first visit to the store or restaurant.
• Loyalty – creates a virtual loyalty program that works like good old-fashioned “coffee club” punch cards. Customers will get an offer every time they reach the number of visits specified by the retailer or restaurant.
• Rolling – rewards frequency and consistency based on number of visits and time period set by the retailer (for example, 3 visits in 10 days); an offer is sent when customers meet these criteria.

In addition to triggered marketing, customer data is collected to measure customer sentiment in real time with Bloom Intelligence ratings and NPS score capabilities. Metrics are made available to the retailer via the proprietary Bloom Business Intelligence Platform, an easy-to-read dashboard that simplifies interpreting and acting on the data to:

• Measure customer behavior and the actual ROI of specific deals that drive customers back into the store.
• Use special offers to drive social interaction, encourage customer reviews and rescue at-risk customers.
• Track and project retail traffic flow by week, day and hourly increments.
• Decrease customer churn rate by creating campaigns for first-time customers that encourage return visits.
• Identify most frequent customers through built-in segmentation for easy targeting.