Picture this, you’ve huddled with your Marketing team and agree that your next advertising campaign should target the customers you are at risk for losing. After rummaging through your spreadsheets, talking to IT about their recent financial dashboard and qualifying your own gut feeling, you come to realize that you can’t tell whether those “at-risk” customers are shopping in another department of your store, are just using a different medium like online for their purchases, or as many retailers are experiencing in the current state of the economy, not shopping at all.
It sounds all too familiar. You have plenty of data about your customers, but it is sourced in various systems and only gives you a portion of the picture you need to adapt to the changing economic environment caused by the pandemic.
By enhancing your customer intelligence maturity, your organization can better understand the patterns in the makeup of your customer base, purchasing habits, and frequency of visits. These concepts allow your retail business the opportunity to detect patterns to predict where time and effort should be spent strategically. Below are three scenarios that can provide immediate impact to your business.
One of the most fundamental concepts within customer intelligence is segmentation. Customer Segmentation clusters behaviorally-distinct groups to use as a basis for identifying opportunities and gaps within the customer base for most spending. The main concepts of segmentation are geographic, demographic, behavioral, and psychographic. Within a customer intelligence platform, you can use customer segmentation when looking at purchasing data.
Customer Health looks at ways that a customer base is attracting, retaining, and growing. For most retailers, this incorporates both marketing and customer services initiatives. This has become increasingly more important with retailers needing to navigate the current state of their industries. The idea of Customer Churn is turnover, or when a customer stops using your products or services for some time. Right now companies are experiencing increases in customer churn and a disruption to the normalcy of customers lives has caused what seems like unpredictable situations. However, with sales data and knowledge on buyer behavior, this concept can be predicted. An example of a churn model, which uses predictive analytics, is a One-Time Purchase Propensity. This model looks at a one-time customer’s likelihood to return to your business.
Understanding predictive churn can ultimately save your retail business both time and money. Here is a scenario: Plank Furniture sells home décor to customers at rates of every four months to a year. Initially, Plank Furniture set up their discounts and promotions to their entire customer base and new markets. By using a blanket approach to their customer base, however, there wasn’t much of an impact. Instead, by using Churn Propensity they were able to differentiate discounts across customers. By evaluating different customer segments, Plank Furniture was able to determine how to offer minimal discounts through digital advertisements to customers who were already deemed likely to buy. Then, they offered more substantial discounts through email to customers who were at a high risk of churning. In the end, Plank Furniture received higher sales and retention without increasing costs.
Through the above customer intelligence scenarios, you were able to identify your customer groups and understand who you are attracting, growing, and retaining. Now it’s time to figure out what channel would best allow for us to acquire said customer groups.
Channel Affinity allows retailers to develop a sense of each customer’s engagement within each channel. Examples of channels are direct mail, apps, email, and social media. A customer intelligence platform can enable you to combine analytics from social media and web analysis to see from the first touch to last touch attribution of a sale. Understanding your different customer touch points can enable you to see that the same person who bought online last week then used a direct-mail coupon in-store. These analytics can be leveraged using credit card data, CRM data, and membership/loyalty data.
By better knowing what channels attract which customers, you can analyze the Propensity to Convert. This analytics scenario leverages historical data on conversion ratios and success metrics from your buyers. Then, with third party data, create a lookalike audience with similar segmentation qualities. With conversion propensity, it’s assumed this lookalike audience will have the same conversion rates to the originally placed customer segments.
This concept allows you to predict customer likelihood to convert based on their favorable response to a marketing stimulus (email, direct mail, etc.) to create repeatable and successful processes.
Segmenting customers, monitoring retention, and identifying channel preferences can lend to immediate results on your business. By using concepts like segmentation analysis coupled with category affinity, for example, you can immediately see what types of units need more attention in both Minneapolis and Manhattan. Or, see what offer would work best through digital channels by using channel affinity and propensity to convert analysis. By leveraging these concepts, you can drive your research and development teams with better guidance and make use of the data that you’ve been retaining for years.
Let CCG help you prepare for changes across churn, channel preferences, and customer expectations. Visit our ADAPT with Analytics page by clicking here, or contact an expert at info@ccganalytics.com.
